Towards multi-omics synthetic data integration.

Across many scientific disciplines, the development of computational models and algorithms for generating artificial or synthetic data is gaining momentum. In biology, there is a great opportunity to explore this further as more and more big data at multi-omics level are generated recently. In this opinion, we discuss the latest trends in biological applications based on process-driven and data-driven aspects. Moving ahead, we believe these methodologies can help shape novel multi-omics-scale cellular inferences.

Identification and semi-quantification of protein allergens in complex mixtures using proteomic and AllerCatPro 2.0 bioinformatic analyses: a proof-of-concept investigation.

The demand for botanicals and natural substances in consumer products has increased in recent years. These substances usually contain proteins and these, in turn, can pose a risk for immunoglobulin E (IgE)-mediated sensitization and allergy. However, no method has yet been accepted or validated for assessment of potential allergenic hazards in such materials. In the studies here, a dual proteomic-bioinformatic approach is proposed to evaluate holistically allergenic hazards in complex mixtures of plants, insects, or animal proteins. Twelve commercial preparations of source materials (plant products, dust mite extract, and preparations of animal dander) known to contain allergenic proteins were analyzed by label-free proteomic analyses to identify and semi-quantify proteins. These were then evaluated by bioinformatics using AllerCatPro 2.0 (https://allercatpro.bii.a-star.edu.sg/) to predict no, weak, or strong evidence for allergenicity and similarity to source-specific allergens. In total, 4,586 protein sequences were identified in the 12 source materials combined. Of these, 1,665 sequences were predicted with weak or strong evidence for allergenic potential. This first-tier approach provided top-level information about the occurrence and abundance of proteins and potential allergens. With regards to source-specific allergens, 129 allergens were identified. The sum of the relative abundance of these allergens ranged from 0.8% (lamb's quarters) to 63% (olive pollen). It is proposed here that this dual proteomic-bioinformatic approach has the potential to provide detailed information on the presence and relative abundance of allergens, and can play an important role in identifying potential allergenic hazards in complex protein mixtures for the purposes of safety assessments.

The Allergen Profile of Two Edible Insect Species-Acheta domesticus and Hermetia illucens.

SCOPE: Edible insect proteins are increasingly introduced as an alternative sustainable food source to address the world's need to feed the growing population. Tropomyosin is the main insect allergen; however, additional potential allergens are not well characterized and the impact of extraction procedures on immunological reactivity is unknown. METHODS AND RESULTS: Proteins from different commercial food products derived from cricket (Acheta domesticus) and black soldier fly (BSF) (Hermetia illucens) are extracted using five different extraction buffers. The proteins are analyzed by SDS-PAGE and immunoblotting using allergen-specific antibodies and crustacean allergic patient sera. IgE binding bands are analyzed by mass spectrometry as well as the complete allergen profile of all 30 extracts. Urea-based buffers are most efficient in extracting insect allergens. Shrimp-specific antibody cross-reactivity to tropomyosin from cricket and BSF indicates high sequence and structural similarity between shrimp and insects. Additional unique allergens are identified in both species, including hemocyanin, vitellogenin, HSP20, apolipophorin-III, and chitin-binding protein. CONCLUSIONS: Identifying potential allergenic proteins and their isoforms in cricket and BSF requires specific extraction approaches using urea-based methods. While tropomyosin is the most abundant and immunoreactive allergen, seven unique allergens are identified, highlighting the need for insect species-specific allergen detection in food products.

Imprinting of IgA responses in previously infected individuals receiving bivalent mRNA vaccines (WT and BA.4/BA.5 or WT and BA.1).

OBJECTIVES: The emergence of new SARS-CoV-2 variants has led to the development of Omicron-targeting bivalent mRNA vaccines. It is crucial to understand how bivalent vaccines may improve antibody responses against new variants. METHODS: A total of 107 participants, who had three COVID-19 WT mRNA vaccine doses, were recruited, and given either a monovalent (WT) or a bivalent mRNA vaccination (Pfizer/BioNTech Bivalent (WT and BA.4/BA.5) or Moderna Bivalent (WT and BA.1). Blood samples were taken before booster and at 28 days post-booster. RESULTS: We found significantly lower fold change in serum binding IgA responses against BA.1, BA.5 and EG.5.1 spike in the bivalent booster group, compared with the monovalent (WT) booster group, following vaccination. However, this was only observed in individuals with prior infection. The relative fold change in serum binding IgA response was more skewed towards WT over variant (BA.1, BA.5 or EG.5.1) spike in previously infected bivalent-booster-vaccinees, as compared with previously infected monovalent-(WT)-booster-vaccinees. CONCLUSION: The findings suggest imprinting of antibody responses that is shaped by the first vaccination (WT spike). Previous infection also affects the boosting effect of follow-up vaccination. Studies are needed to understand how to induce a robust and long-lasting IgA immunity for protection against COVID-19 infection.

Artificial intelligence innovation in healthcare: Relevance of reporting guidelines for clinical translation from bench to bedside.

Artificial intelligence (AI) and digital innovation are transforming healthcare. Technologies such as machine learning in image analysis, natural language processing in medical chatbots and electronic medical record extraction have the potential to improve screening, diagnostics and prognostication, leading to precision medicine and preventive health. However, it is crucial to ensure that AI research is conducted with scientific rigour to facilitate clinical implementation. Therefore, reporting guidelines have been developed to standardise and streamline the development and validation of AI technologies in health. This commentary proposes a structured approach to utilise these reporting guidelines for the translation of promising AI techniques from research and development into clinical translation, and eventual widespread implementation from bench to bedside.