A Cross-Disciplinary View of Testing and Bioinformatic Analysis of SARS-CoV-2 and Other Human Respiratory Viruses in Pandemic Settings

Abstract

The SARS-Coronavirus-2 (SARS-CoV-2) infectious disease, COVID-19, has spread rapidly, resulting in a global pandemic with significant mortality. The combination of early diagnosis via rapid screening, contact tracing, social distancing and quarantine has helped to control the pandemic. The absence of real time response and diagnosis is a crucial technology shortfall and is a key reason why current contact tracing methods are inadequate to control spread. In contrast, current information technology combined with a new generation of near-real time tests offers consumer-engaged smartphone-based "lab-in-a-phone" internet-of-things (IoT) connected devices that provide increased pandemic monitoring. This review brings together key aspects required to create an entire global diagnostic ecosystem. Cross-disciplinary understanding and integration of both mechanisms and technologies for effective detection, incidence mapping and disease containment in near real-time is summarized. Available measures to monitor and/or sterilize surfaces, next-generation laboratory and smartphone-based diagnostic approaches can be brought together and networked for instant global monitoring that informs Public Health policy. Cloud-based analysis enabling real-time mapping will enable future pandemic control, drive the suppression and elimination of disease spread, saving millions of lives globally. A new paradigm is introduced - scaled and multiple diagnostics for mapping and spreading of a pandemic rather than traditional accumulation of individual measurements. This can do away with the need for ultra-precise and ultra-accurate analysis by taking mass measurements that can relax tolerances and build resilience through networked analytics and informatics, the basis for novel swarm diagnostics. These include addressing ethical standards, local, national and international collaborative engagement, multidisciplinary and analytical measurements and standards, and data handling and storage protocols.

Keywords: Antibody test; COVID-19; Internet-of-Things; LAMP test; PCR; SARS-CoV; antigen test; bioinformatics; lab-in-a-phone; point-of-care test.

FIGURE 1.

Schematic of current diagnostic methods for viral nucleic acid detection. (A) Sampling from suspected cases or patients produces the test specimen. (B) Test samples and the structure of the SARS-CoV-2 virus. (C) Mechanisms of the COVID-19 diagnosis using nucleic acid-based tests: RT-PCR, RT-LAMP & the CRISPR (SHERLOCK) systems.

FIGURE 2.

Next generation sequencing technologies applications during the SARS-CoV-2 pandemic. A. Metagenomics approaches used at the beginning of the SARS-CoV-2 epidemic to investigate the causal agent of a cluster of “novel” pneumonia cases, in Wuhan, China. Extraction of nucleic acid, fragmenting and amplifying the nucleic acid fragments, then adding “adaptors” that permit the fragments bind to a flow cell in the NGS platform. All fragments are sequenced in parallel, and detected by different fluorescent signals emitted by the four nucleotides: A, T, G, C. The sequence for all fragments is assembled to generate a contiguous “reference” sequence. The sequences are then queried against existing reference databases. This permits the taxonomic classification and identification of genomic species. B. Whole genome sequence of SARS-CoV-2 also begins with nucleic acid extractions, then amplifying selected targeted genome regions with specific oligonucleotide primers that bind to overlapping regions of the viral genome. This PCR product is sequenced in the flow cell. Because SARS-CoV-2 genomes are similar, the assembly is guided by mapping all the sequenced reads to a reference sequence. A consensus of these references is then obtained as one linear sequence from a patient’s sample.

FIGURE 3.

Uses of viral genetic sequence data. With the thousands of sequences obtained globally, phylogenetic trees are constructed to understand the evolutionary relationship of the viral genomes. When integrating this information with the epidemiological data (for example, the sample’s location of origin and day in which the sample is collected) maps that illustrate the virus spread and evolution can be generated. Tracking new lineages or sequence variants (viruses that are genetically related and that group in a branch of the phylogenetic tree) can help the early detection of a potentially highly transmissible or more virulent form of the virus. For example, the specific changes in a viral spike protein sequence can be monitored - the spike protein is the viral surface protein engages with a host cell receptor molecule. The viral spike is recognized by the host immune system antibodies that confer immunity. Therefore, tracking changes in the spike protein sequence identifies the evolution of the virus with respect to its antigenic properties and even the capacity of vaccines to confer immunity – protection capability. The phylogenetic tree in the image was constructed using 200 random samples from GISAID database, IQ-TREE with the GTR + F + R2 substitution model. The map was created in QGIS and the genome map was created using SnapGene .

FIGURE 4.

Lateral flow-based detection devices. (A), (B) General immunoassay techniques using rapid diagnostic lateral flow assay (Image reproduced from permission is not required). (C) A smartphone analyzer for lateral flow tests reduces a large benchtop instrument into a portable hand carried device that automatically collects data and sends into the cloud. (Images C courtesy of AusSI Systems).

FIGURE 5.

Transmission networks underpin the internet and cloud-based diagnostics and informatics capabilities. Diagnostics at the perception layer feeds into the cloud via the edge using various transmission capabilities such as wireless and optical fibers. Alternatives exist for how data can be transferred. The results are collected at a central server from detection sites by the sensor devices which are used to analyze sequence, predict virus types using AI and develop a suitable vaccine which can be prescribed to the affected sites.