Harnessing the Potential of Multiomics Studies for Precision Medicine in Infectious Disease

Abstract

The field of infectious diseases currently takes a reactive approach, treating infections as they present in patients. Although certain populations are known to be at greater risk of developing infection (e.g., immunocompromised), we lack a systems approach to define the true risk of future infection for a patient. Guided by impressive gains in -omics technologies, future strategies to infectious diseases should take a precision approach to infection through identification of patients at intermediate and high-risk of infection and deploy targeted preventative measures (i.e., prophylaxis). The advances of high-throughput immune profiling by multiomics approaches (i.e., transcriptomics, epigenomics, metabolomics, proteomics) holds the promise to identify patients at increased risk of infection and enable risk-stratifying approaches to be applied in the clinic. Integration of patient-specific data using machine learning improves the effectiveness of prediction, providing the necessary technologies needed to propel the field of infectious diseases medicine into the era of personalized medicine.

A Formative Study of the Implementation of Whole Genome Sequencing in Northern Ireland

“…Further multiomic functional analyses of rare diseases, conducted collaboratively between clinicians and researchers, following recommendations for application of PS3/BS3 ACMG criteria, is vital to fully explore phenotypically plausible variants which may not always be feasible to explore in a clinical environment [ 44 ]. Several recent studies have highlighted the power of multiomics for improving our understanding of the biological mechanisms of disease where genetics alone is insufficient, for example in common complex diseases such as hypertension and diabetes mellitus [ 45 , 46 ] as well as rare diseases [ 47 , 48 , 49 ]. A multidisciplinary, collaborative approach exploring additional variants based on prior genotype-phenotype relationships helps maximise diagnostic yield from WGS [ 50 ].…”

Opportunities and challenges of 5G network technology toward precision medicine

“…35 Moreover, predicting infectious diseases via integrating patient-specific data offers advantages in transforming infectious disease medicine into the era of precision medicine. 36 One of the key data sources used in infectious disease prediction is surveillance data, which includes information on the number of cases of infection and demographic information about the affected individuals from hospital records with their informed consent. 37 With the usage of 5G network technology, it is now possible to collect and transmit the data in real-time to identify patterns and trends in infection incidence and high-risk populations.…”

“…Generally, infectious prediction aims to identify potential outbreaks early, so that public health interventions can be implemented to reduce the spread of the infection and prevent large‐scale outbreaks 35 . Moreover, predicting infectious diseases via integrating patient‐specific data offers advantages in transforming infectious disease medicine into the era of precision medicine 36 …”

Metabolomics as a powerful tool for diagnostic, pronostic and drug intervention analysis in COVID-19

“…To account for it, the “omics” technologies can help building a suitable description of the individual’s specific characteristics to find personalized treatment for the cure of specific diseases ( Zhang, 2015 ). Genomics, transcriptomic, proteomics and metabolomics investigated on non-invasive fluid samples (i.e., serum, plasma or urine) can provide useful information to understand the disparate response to a given disorder from different subjects ( Ward et al, 2021 ). In this regard, metabolomics is specially suitable because it is more sensitive to any phenotypic alteration ( Nicholson, 2021 ).…”