COVID-19 phenomics

Phenomic studies of coronavirus disease 2019 (COVID-19) attempt to comprehensively describe the range of phenotypes associated with disease-related outcomes, by either breadth or depth of characterization. The primary aims of such studies are the unbiased generation of hypotheses concerning COVID-19 pathophysiology and the empirical determination of effective prognostic indicators. Of particular relevance to COVID-19 are phenome-wide association studies—large-scale, data-driven studies evaluating associations between a multitude of phenotypic traits and COVID-19 severity or other outcomes of interest, often employing bioinformatic and statistical approaches for the analysis of databases of electronic health records. This type of extensive phenotyping, in combination with intensive interrogation of particular aspects of the pathophysiological response, also allows investigators to reconstruct the network of phenomena that underpin disease, of particular significance because of the systemic nature of COVID-19. Because of their ability to detect novel associations, another great utility of extensive phenomic analyses applied to COVID-19 is in the development of prognostic tools and biomarkers that improve the efficacy of patient care. Finally, when applied to those in the convalescent phase, phenomics has helped to elucidate both the nature of postacute sequalae of COVID-19 and the characteristics that predispose an individual toward them. Hence, phenomics provides an additional and unique perspective which is crucial to our understanding of COVID-19 to better equip us against unforeseen adverse outcomes of this pandemic and potential infectious outbreaks in the future. © 2023 Elsevier Inc. All rights reserved.

Systems biology in COVID-19

Coronavirus disease 2019 (COVID-19), the disease cause by the novel severe acute respiratory syndrome coronavirus 2 represents a global, unresolved challenge for researchers and clinicians alike. In the shadow of overwhelmed healthcare systems, the pressure to produce knowledge, standard operating procedures, efficacious treatments, and prophylactic agents has been unlike any other occasion in recent history. Systems biology, an assortment of methods that aim to model biological systems and their properties has risen to meet this multifaceted challenge. In this chapter, we review approaches and breakthroughs of systems biology research in COVID-19, along with the nascent clade of phenomics, a deep-phenotyping systems concept that has enabled the real-time integration of big data and analytical methods in clinical decision making. © 2023 Elsevier Inc. All rights reserved.

A phenomics approach for antiviral drug discovery.

BACKGROUND: The emergence and continued global spread of the current COVID-19 pandemic has highlighted the need for methods to identify novel or repurposed therapeutic drugs in a fast and effective way. Despite the availability of methods for the discovery of antiviral drugs, the majority tend to focus on the effects of such drugs on a given virus, its constituent proteins, or enzymatic activity, often neglecting the consequences on host cells. This may lead to partial assessment of the efficacy of the tested anti-viral compounds, as potential toxicity impacting the overall physiology of host cells may mask the effects of both viral infection and drug candidates. Here we present a method able to assess the general health of host cells based on morphological profiling, for untargeted phenotypic drug screening against viral infections. RESULTS: We combine Cell Painting with antibody-based detection of viral infection in a single assay. We designed an image analysis pipeline for segmentation and classification of virus-infected and non-infected cells, followed by extraction of morphological properties. We show that this methodology can successfully capture virus-induced phenotypic signatures of MRC-5 human lung fibroblasts infected with human coronavirus 229E (CoV-229E). Moreover, we demonstrate that our method can be used in phenotypic drug screening using a panel of nine host- and virus-targeting antivirals. Treatment with effective antiviral compounds reversed the morphological profile of the host cells towards a non-infected state. CONCLUSIONS: The phenomics approach presented here, which makes use of a modified Cell Painting protocol by incorporating an anti-virus antibody stain, can be used for the unbiased morphological profiling of virus infection on host cells. The method can identify antiviral reference compounds, as well as novel antivirals, demonstrating its suitability to be implemented as a strategy for antiviral drug repurposing and drug discovery.

Phenotyping coronavirus disease 2019 during a global health pandemic: Lessons learned from the characterization of an early cohort.

From the start of the coronavirus disease 2019 (COVID-19) pandemic, researchers have looked to electronic health record (EHR) data as a way to study possible risk factors and outcomes. To ensure the validity and accuracy of research using these data, investigators need to be confident that the phenotypes they construct are reliable and accurate, reflecting the healthcare settings from which they are ascertained. We developed a COVID-19 registry at a single academic medical center and used data from March 1 to June 5, 2020 to assess differences in population-level characteristics in pandemic and non-pandemic years respectively. Median EHR length, previously shown to impact phenotype performance in type 2 diabetes, was significantly shorter in the SARS-CoV-2 positive group relative to a 2019 influenza tested group (median 3.1 years vs 8.7; Wilcoxon rank sum P = 1.3e-52). Using three phenotyping methods of increasing complexity (billing codes alone and domain-specific algorithms provided by an EHR vendor and clinical experts), common medical comorbidities were abstracted from COVID-19 EHRs, defined by the presence of a positive laboratory test (positive predictive value 100%, recall 93%). After combining performance data across phenotyping methods, we observed significantly lower false negative rates for those records billed for a comprehensive care visit (p = 4e-11) and those with complete demographics data recorded (p = 7e-5). In an early COVID-19 cohort, we found that phenotyping performance of nine common comorbidities was influenced by median EHR length, consistent with previous studies, as well as by data density, which can be measured using portable metrics including CPT codes. Here we present those challenges and potential solutions to creating deeply phenotyped, acute COVID-19 cohorts.