The Singapore National Precision Medicine Strategy.

Precision medicine promises to transform healthcare for groups and individuals through early disease detection, refining diagnoses and tailoring treatments. Analysis of large-scale genomic-phenotypic databases is a critical enabler of precision medicine. Although Asia is home to 60% of the world's population, many Asian ancestries are under-represented in existing databases, leading to missed opportunities for new discoveries, particularly for diseases most relevant for these populations. The Singapore National Precision Medicine initiative is a whole-of-government 10-year initiative aiming to generate precision medicine data of up to one million individuals, integrating genomic, lifestyle, health, social and environmental data. Beyond technologies, routine adoption of precision medicine in clinical practice requires social, ethical, legal and regulatory barriers to be addressed. Identifying driver use cases in which precision medicine results in standardized changes to clinical workflows or improvements in population health, coupled with health economic analysis to demonstrate value-based healthcare, is a vital prerequisite for responsible health system adoption.

Global disparities in SARS-CoV-2 genomic surveillance.

Genomic sequencing provides critical information to track the evolution and spread of SARS-CoV-2, optimize molecular tests, treatments and vaccines, and guide public health responses. To investigate the spatiotemporal heterogeneity in the global SARS-CoV-2 genomic surveillance, we estimated the impact of sequencing intensity and turnaround times (TAT) on variant detection in 167 countries. Most countries submit genomes >21 days after sample collection, and 77% of low and middle income countries sequenced <0.5% of their cases. We found that sequencing at least 0.5% of the cases, with a TAT <21 days, could be a benchmark for SARS-CoV-2 genomic surveillance efforts. Socioeconomic inequalities substantially impact our ability to quickly detect SARS-CoV-2 variants, and undermine the global pandemic preparedness.

Structure-based virtual screening of CYP1A1 inhibitors: towards rapid tier-one assessment of potential developmental toxicants.

Cytochrome P450 1A1 (CYP1A1) metabolizes estrogens, melatonin, and other key endogenous signaling molecules critical for embryonic/fetal development. The enzyme has increasing expression during pregnancy, and its inhibition or knockout increases embryonic/fetal lethality and/or developmental problems. Here, we present a virtual screening model for CYP1A1 inhibitors based on the orthosteric and predicted allosteric sites of the enzyme. Using 1001 reference compounds with CYP1A1 activity data, we optimized the decision thresholds of our model and classified the training compounds with 68.3% balanced accuracy (91.0% sensitivity and 45.7% specificity). We applied our final model to 11 known CYP1A1 orthosteric binders and related compounds, and found that our ranking of the known orthosteric binders generally agrees with the relative activity of CYP1A1 in metabolizing these compounds. We also applied the model to 22 new test compounds with unknown/unclear CYP1A1 inhibitory activity, and predicted 16 of them are CYP1A1 inhibitors. The CYP1A1 potency and modes of inhibition of these 22 compounds were experimentally determined. We confirmed that most predicted inhibitors, including drugs contraindicated during pregnancy (amiodarone, bicalutamide, cyproterone acetate, ketoconazole, and tamoxifen) and environmental agents suspected to be endocrine disruptors (bisphenol A, diethyl and dibutyl phthalates, and zearalenone), are indeed potent inhibitors of CYP1A1. Our results suggest that virtual screening may be used as a rapid tier-one method to screen for potential CYP1A1 inhibitors, and flag them out for further experimental evaluations.

Protein extraction protocols for optimal proteome measurement and arginine kinase quantitation from cricket Acheta domesticus for food safety assessment.

Insects have been consumed by people for millennia and have recently been proposed as a complementary, sustainable source of protein to feed the world's growing population. Insects and crustaceans both belong to the arthropod family. Crustacean (shellfish) allergies are common and potentially severe; hence, the cross-reactivity of the immune system with insect proteins is a potential health concern. Herein, LC-MS/MS was used to explore the proteome of whole, roasted whole and roasted powdered cricket products. Eight protein extraction protocols were compared using the total number of protein and distinct peptide identifications. Within these data, 20 putative allergens were identified, of which three were arginine kinase (AK) proteoforms. Subsequently, a multiple reaction monitoring MS assay was developed for the AK proteoforms and applied to a subset of extracts. This targeted assay demonstrated that allergen abundance/detectability varies according to the extraction method as well as the food processing method.

Identification and in vivo Efficacy Assessment of Approved Orally Bioavailable Human Host Protein-Targeting Drugs With Broad Anti-influenza A Activity.

The high genetic variability of influenza A viruses poses a continual challenge to seasonal and pandemic vaccine development, leaving antiviral drugs as the first line of defense against antigenically different strains or new subtypes. As resistance against drugs targeting viral proteins emerges rapidly, we assessed the antiviral activity of already approved drugs that target cellular proteins involved in the viral life cycle and were orally bioavailable. Out of 15 candidate compounds, four were able to inhibit infection by 10- to 100-fold without causing toxicity, in vitro. Two of the drugs, dextromethorphan and ketotifen, displayed a 50% effective dose between 5 and 50 µM, not only for the classic H1N1 PR8 strain, but also for a pandemic H1N1 and a seasonal H3N2 strain. Efficacy assessment in mice revealed that dextromethorphan consistently resulted in a significant reduction of viral lung titers and also enhanced the efficacy of oseltamivir. Dextromethorphan treatment of ferrets infected with a pandemic H1N1 strain led to a reduction in clinical disease severity, but no effect on viral titer was observed. In addition to identifying dextromethorphan as a potential influenza treatment option, our study illustrates the feasibility of a bioinformatics-driven rational approach for repurposing approved drugs against infectious diseases.

Proteomic and Bioinformatic Analyses for the Identification of Proteins With Low Allergenic Potential for Hazard Assessment.

Use of botanicals and natural substances in consumer products has increased in recent years. Such extracts can contain protein that may theoretically represent a potential risk of IgE-mediated allergy. No method has yet been generally accepted or validated for assessment of the allergenic potential of proteins. For development of suitable methods datasets of allergenic and nonallergenic (or low allergenic) proteins are required that can serve, respectively, as positive and negative controls. However, data are unavailable on proteins that lack or have low allergenic potential. Here, low allergenic potential proteins are identified based on the assumption that proteins with established human exposure, but with a lack of an association with allergy, possess low allergenic potential. Proteins were extracted from sources considered to have less allergenic potential (corn, potato, spinach, rice, and tomato) as well as higher allergenic potential (wheat) regarding common allergenic foods. Proteins were identified and semi-quantified by label-free proteomic analysis conducted using mass spectrometry. Predicted allergenicity was determined using AllerCatPro (https://allercatpro.bii.a-star.edu.sg/). In summary, 9077 proteins were identified and semi-quantified from 6 protein sources. Within the top 10% of the most abundant proteins identified, 178 characterized proteins were found to have no evidence for allergenicity predicted by AllerCatPro and were considered to have low allergenic potential. This panel of low allergenic potential proteins provides a pragmatic approach to aid the development of alternative methods for robust testing strategies to distinguish between proteins of high and low allergenic potential to assess the risk of proteins from natural or botanical sources.

Viral factors in influenza pandemic risk assessment.

The threat of an influenza A virus pandemic stems from continual virus spillovers from reservoir species, a tiny fraction of which spark sustained transmission in humans. To date, no pandemic emergence of a new influenza strain has been preceded by detection of a closely related precursor in an animal or human. Nonetheless, influenza surveillance efforts are expanding, prompting a need for tools to assess the pandemic risk posed by a detected virus. The goal would be to use genetic sequence and/or biological assays of viral traits to identify those non-human influenza viruses with the greatest risk of evolving into pandemic threats, and/or to understand drivers of such evolution, to prioritize pandemic prevention or response measures. We describe such efforts, identify progress and ongoing challenges, and discuss three specific traits of influenza viruses (hemagglutinin receptor binding specificity, hemagglutinin pH of activation, and polymerase complex efficiency) that contribute to pandemic risk.

Improving pandemic influenza risk assessment.

Assessing the pandemic risk posed by specific non-human influenza A viruses is an important goal in public health research. As influenza virus genome sequencing becomes cheaper, faster, and more readily available, the ability to predict pandemic potential from sequence data could transform pandemic influenza risk assessment capabilities. However, the complexities of the relationships between virus genotype and phenotype make such predictions extremely difficult. The integration of experimental work, computational tool development, and analysis of evolutionary pathways, together with refinements to influenza surveillance, has the potential to transform our ability to assess the risks posed to humans by non-human influenza viruses and lead to improved pandemic preparedness and response.

How bioinformatics influences health informatics: usage of biomolecular sequences, expression profiles and automated microscopic image analyses for clinical needs and public health.

ABSTRACT: The currently hyped expectation of personalized medicine is often associated with just achieving the information technology led integration of biomolecular sequencing, expression and histopathological bioimaging data with clinical records at the individual patients' level as if the significant biomedical conclusions would be its more or less mandatory result. It remains a sad fact that many, if not most biomolecular mechanisms that translate the human genomic information into phenotypes are not known and, thus, most of the molecular and cellular data cannot be interpreted in terms of biomedically relevant conclusions. Whereas the historical trend will certainly be into the general direction of personalized diagnostics and cures, the temperate view suggests that biomedical applications that rely either on the comparison of biomolecular sequences and/or on the already known biomolecular mechanisms have much greater chances to enter clinical practice soon. In addition to considering the general trends, we exemplarily review advances in the area of cancer biomarker discovery, in the clinically relevant characterization of patient-specific viral and bacterial pathogens (with emphasis on drug selection for influenza and enterohemorrhagic E. coli) as well as progress in the automated assessment of histopathological images. As molecular and cellular data analysis will become instrumental for achieving desirable clinical outcomes, the role of bioinformatics and computational biology approaches will dramatically grow. AUTHOR SUMMARY: With DNA sequencing and computers becoming increasingly cheap and accessible to the layman, the idea of integrating biomolecular and clinical patient data seems to become a realistic, short-term option that will lead to patient-specific diagnostics and treatment design for many diseases such as cancer, metabolic disorders, inherited conditions, etc. These hyped expectations will fail since many, if not most biomolecular mechanisms that translate the human genomic information into phenotypes are not known yet and, thus, most of the molecular and cellular data collected will not lead to biomedically relevant conclusions. At the same time, less spectacular biomedical applications based on biomolecular sequence comparison and/or known biomolecular mechanisms have the potential to unfold enormous potential for healthcare and public health. Since the analysis of heterogeneous biomolecular data in context with clinical data will be increasingly critical, the role of bioinformatics and computational biology will grow correspondingly in this process.

The Janus-faced E-values of HMMER2: extreme value distribution or logistic function?

E-value guided extrapolation of protein domain annotation from libraries such as Pfam with the HMMER suite is indispensable for hypothesizing about the function of experimentally uncharacterized protein sequences. Since the recent release of HMMER3 does not supersede all functions of HMMER2, the latter will remain relevant for ongoing research as well as for the evaluation of annotations that reside in databases and in the literature. In HMMER2, the E-value is computed from the score via a logistic function or via a domain model-specific extreme value distribution (EVD); the lower of the two is returned as E-value for the domain hit in the query sequence. We find that, for thousands of domain models, this treatment results in switching from the EVD to the statistical model with the logistic function when scores grow (for Pfam release 23, 99% in the global mode and 75% in the fragment mode). If the score corresponding to the breakpoint results in an E-value above a user-defined threshold (e.g. 0.1), a critical score region with conflicting E-values from the logistic function (below the threshold) and from EVD (above the threshold) does exist. Thus, this switch will affect E-value guided annotation decisions in an automated mode. To emphasize, switching in the fragment mode is of no practical relevance since it occurs only at E-values far below 0.1. Unfortunately, a critical score region does exist for 185 domain models in the hmmpfam and 1,748 domain models in the hmmsearch global-search mode. For 145 out the respective 185 models, the critical score region is indeed populated by actual sequences. In total, 24.4% of their hits have a logistic function-derived E-value < 0.1 when the EVD provides an E-value > 0.1. We provide examples of false annotations and critically discuss the appropriateness of a logistic function as alternative to the EVD.

More than 1,001 problems with protein domain databases: transmembrane regions, signal peptides and the issue of sequence homology.

Large-scale genome sequencing gained general importance for life science because functional annotation of otherwise experimentally uncharacterized sequences is made possible by the theory of biomolecular sequence homology. Historically, the paradigm of similarity of protein sequences implying common structure, function and ancestry was generalized based on studies of globular domains. Having the same fold imposes strict conditions over the packing in the hydrophobic core requiring similarity of hydrophobic patterns. The implications of sequence similarity among non-globular protein segments have not been studied to the same extent; nevertheless, homology considerations are silently extended for them. This appears especially detrimental in the case of transmembrane helices (TMs) and signal peptides (SPs) where sequence similarity is necessarily a consequence of physical requirements rather than common ancestry. Thus, matching of SPs/TMs creates the illusion of matching hydrophobic cores. Therefore, inclusion of SPs/TMs into domain models can give rise to wrong annotations. More than 1001 domains among the 10,340 models of Pfam release 23 and 18 domains of SMART version 6 (out of 809) contain SP/TM regions. As expected, fragment-mode HMM searches generate promiscuous hits limited to solely the SP/TM part among clearly unrelated proteins. More worryingly, we show explicit examples that the scores of clearly false-positive hits, even in global-mode searches, can be elevated into the significance range just by matching the hydrophobic runs. In the PIR iProClass database v3.74 using conservative criteria, we find that at least between 2.1% and 13.6% of its annotated Pfam hits appear unjustified for a set of validated domain models. Thus, false-positive domain hits enforced by SP/TM regions can lead to dramatic annotation errors where the hit has nothing in common with the problematic domain model except the SP/TM region itself. We suggest a workflow of flagging problematic hits arising from SP/TM-containing models for critical reconsideration by annotation users.