Clin.iobio: A Collaborative Diagnostic Workflow to Enable Team-Based Precision Genomics

The primary goal of precision genomics is the identification of causative genetic variants in targeted or whole-genome sequencing data. The ultimate clinical hope is that these findings lead to an efficacious change in treatment for the patient. In current clinical practice, these findings are typically returned by expert analysts as static, text-based reports. Ideally, these reports summarize the quality of the data obtained, integrate known gene-phenotype associations, follow allele segregation and affected status within the sequenced samples, and weigh computational evidence of pathogenicity. These findings are used to prioritize the variant(s) most likely to cause the given patient's phenotypes. In most diagnostic settings, a team of experts contribute to these reports, including bioinformaticians, clinicians, and genetic counselors, among others. However, these experts often do not have the necessary tools to review genomic findings, test genetic hypotheses, or query specific gene and variant information. Additionally, team members often rely on different tools and methods based on their given expertise, resulting in further difficulties in communicating and discussing genomic findings. Here, we present clin.iobio-a web-based solution to collaborative genomic analysis that enables diagnostic team members to focus on their area of expertise within the diagnostic process, while allowing them to easily review and contribute to all steps of the diagnostic process. Clin.iobio integrates tools from the popular iobio genomic visualization suite into a comprehensive diagnostic workflow, encompassing (1) genomic data quality review, (2) dynamic phenotype-driven gene prioritization, (3) variant prioritization using a comprehensive set of knowledge bases and annotations, (4) and an exportable findings summary. In conclusion, clin.iobio is a comprehensive solution to team-based precision genomics, the findings of which stand to inform genomic considerations in clinical practice.

Characterization of SARS-CoV-2 infection clusters based on integrated genomic surveillance, outbreak analysis and contact tracing in an urban setting

Background

Tracing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission chains is still a major challenge for public health authorities, when incidental contacts are not recalled or are not perceived as potential risk contacts. Viral sequencing can address key questions about SARS-CoV-2 evolution and may support reconstruction of viral transmission networks by integration of molecular epidemiology into classical contact tracing.

Methods

In collaboration with local public health authorities, we set up an integrated system of genomic surveillance in an urban setting, combining a) viral surveillance sequencing, b) genetically based identification of infection clusters in the population, c) integration of public health authority contact tracing data, and d) a user-friendly dashboard application as a central data analysis platform.

Results

Application of the integrated system from August to December 2020 enabled a characterization of viral population structure, analysis of 4 outbreaks at a maximum care hospital, and genetically based identification of 5 putative population infection clusters, all of which were confirmed by contact tracing. The system contributed to the development of improved hospital infection control and prevention measures and enabled the identification of previously unrecognized transmission chains, involving a martial arts gym and establishing a link between the hospital to the local population.

Conclusions

Integrated systems of genomic surveillance could contribute to the monitoring and, potentially, improved management of SARS-CoV-2 transmission in the population.

Real-time, MinION-based, amplicon sequencing for lineage typing of infectious bronchitis virus from upper respiratory samples

Infectious bronchitis (IB) causes significant economic losses in the global poultry industry. Control of IB is hindered by the genetic diversity of the causative agent, infectious bronchitis virus (IBV), which has led to the emergence of several serotypes that lack complete serologic cross-protection. Although serotyping requires immunologic characterization, genotyping is an efficient means to identify IBVs detected in samples. Sanger sequencing of the S1 subunit of the spike gene is currently used to genotype IBV; however, the universal S1 PCR was created to work from cultured IBV, and it is inefficient at detecting multiple viruses in a single sample. We describe herein a MinION-based, amplicon-based sequencing (AmpSeq) method that genetically categorized IBV from clinical samples, including samples with multiple IBVs. Total RNA was extracted from 15 tracheal scrapings and choanal cleft swab samples, randomly reverse transcribed, and PCR amplified using modified S1-targeted primers. Amplicons were barcoded to allow for pooling of samples, processed per manufacturer’s instructions into a 1D MinION sequencing library, and then sequenced on the MinION. The AmpSeq method detected IBV in 13 of 14 IBV-positive samples. AmpSeq accurately detected and genotyped both IBV lineages in 3 of 5 samples containing 2 IBV lineages. Additionally, 1 sample contained 3 IBV lineages, and AmpSeq accurately detected 2 of the 3 lineages. Strain identification, including detection of different IBVs from the same lineage, was also possible with this AmpSeq method. Our results demonstrate the feasibility of using MinION-based AmpSeq for rapid and accurate identification and lineage typing of IBV from oral swab samples.

Targeted gene panel sequencing for the rapid diagnosis of acutely ill infants

Background

Exome/genome sequencing (ES/GS) have been recently used in neonatal and pediatric/cardiac intensive care units (NICU and PICU/CICU) to diagnose and care for acutely ill infants, but the effectiveness of targeted gene panels for these purposes remains unknown.

Methods

RapSeq, a newly developed panel targeting 4,503 disease‐causing genes, was employed on selected patients in our NICU/PICU/CICU. Twenty trios were sequenced from October 2015 to March 2017. We assessed diagnostic yield, turnaround times, and clinical consequences.

Results

A diagnosis was made in 10/20 neonates (50%); eight had de novo variants (ASXL1, CHD, FBN1, KMT2D, FANCB, FLNA, PAX3), one was a compound heterozygote for CHAT, and one had a maternally inherited GNAS variant. Preliminary reports were generated by 9.6 days (mean); final reports after Sanger sequencing at 16.3 days (mean). In all positive infants, the diagnosis changed management. In a case with congenital myasthenia, diagnosis and treatment occurred at 17 days versus 7 months in a historical control.

Conclusions

This study shows that a gene panel that includes the majority of known disease‐causing genes can rapidly identify a diagnosis in a large number of tested infants. Due to simpler deployment and interpretation and lower costs, this approach might represent an alternative to ES/GS in the NICU/PICU/CICU.

Rapid Metagenomic Next-Generation Sequencing during an Investigation of Hospital-Acquired Human Parainfluenza Virus 3 Infections

Metagenomic next-generation sequencing (mNGS) is increasingly used for the unbiased detection of viruses, bacteria, fungi, and eukaryotic parasites in clinical samples. Whole-genome sequencing (WGS) of clinical bacterial isolates has been shown to inform hospital infection prevention practices, but this technology has not been utilized during potential respiratory virus outbreaks. Here, we report on the use of mNGS to inform the real-time infection prevention response to a cluster of hospital-acquired human parainfluenza 3 virus (HPIV3) infections at a children's hospital. Samples from 3 patients with hospital-acquired HPIV3 identified over a 12-day period on a general medical unit and 10 temporally associated samples from patients with community-acquired HPIV3 were analyzed. Our sample-to-sequencer time was <24 h, while our sample-to-answer turnaround time was <60 h with a hands-on time of approximately 6 h. Eight (2 cases and 6 controls) of 13 samples had sufficient sequencing coverage to yield the whole genome for HPIV3, while 10 (2 cases and 8 controls) of 13 samples gave partial genomes and all 13 samples had >1 read for HPIV3. Phylogenetic clustering revealed the presence of identical HPIV3 genomic sequence in the two of the cases with hospital-acquired infection, consistent with the concern for recent transmission within the medical unit. Adequate sequence coverage was not recovered for the third case. This work demonstrates the promise of mNGS for providing rapid information for infection prevention in addition to microbial detection.