FRCD: A comprehensive food risk component database with molecular scaffold, chemical diversity, toxicity, and biodegradability analysis

Theranostic Applications of Scaffolds in Current Biomedical Research

Theranostics, a remarkable combination of diagnostics and therapeutics, has given rise to tissue/organ-format theranostic scaffolds that integrate targeted therapy and real-time disease monitoring. The scaffold is a 3D structuring template for cell or tissue attachment and growth. These scaffolds offer unprecedented opportunities for personalized medicine and hold great potential for revolutionizing healthcare. Recent advancements in fabrication techniques have enabled the creation of highly intricate and precisely engineered scaffolds with controllable physical and chemical properties, enhancing their therapeutic potential for tissue engineering and regenerative medicine. This paper proposes a new categorization method for scaffolds in tissue engineering based on the relativity of scaffold design-independent parameters. Five types of scaffolds are defined at different levels, highlighting the importance of understanding and analyzing scaffold types. It possesses the ability to seamlessly integrate diagnostics and therapeutics within a single platform, enhancing the efficacy and precision of personalized medicine. Natural scaffolds derived from biomaterials and synthetic scaffolds fabricated by human intervention are discussed, with synthetic scaffolds offering advantages such as tunable mechanical properties and controlled drug delivery, while natural scaffolds provide inherent biocompatibility and bioactivity, making them ideal for promoting cellular responses. The use of synthetic scaffolds shows great promise in advancing regenerative medicine and improving patient outcomes. The transfer of new technologies and changes in society have accelerated the evolution of health monitoring into the era of personal health monitoring. Using emerging health data, cost-effective analytics, wireless sensor networks, mobile smartphones, and easy internet access, the combination of these technologies is expected to accelerate the transition to personal health monitoring outside of traditional healthcare settings. The main objective of this review article is to provide a comprehensive overview of the theranostic applications of scaffolds in current biomedical research, highlighting their dual role in therapy and diagnostics. The review aims to explore the latest advancements in scaffold design, fabrication, and functionalization, emphasizing how these innovations contribute to improved therapeutic efficacy, targeted drug delivery, and the real-time monitoring of disease progression across various medical fields.

Sourdough process and spirulina-enrichment can mitigate the limitations of colon fermentation performances of gluten-free breads in non-celiac gut model

In this work, the impact of gluten free (GF) breads enriched with spirulina on the ecology of the colon microbiota of non-celiac volunteers was investigated. Simulation of digestion of GF breads was conducted with an in vitro gut model. Microbiomics and metabolomics analyses were done during colon fermentations to study the modulation of the microbiota. From the results, a general increase in Proteobacteria and no reduction of detrimental microbial metabolites were observed in any conditions. Notwithstanding, algae enriched sourdough breads showed potential functionalities, as the improvement of some health-related ecological indicators, like i) microbiota eubiosis; ii) production of bioactive volatile organic fatty acids; iii) production of bioactives terpenes. Our results indicate that a sourdough fermentation and algae enrichment can mitigate the negative effect of GF breads on gut microbiota of non-celiac consumers.

Chemical Multiverse and Diversity of Food Chemicals

Food chemicals have a fundamental role in our lives, with an extended impact on nutrition, disease prevention, and marked economic implications in the food industry. The number of food chemical compounds in public databases has substantially increased in the past few years, which can be characterized using chemoinformatics approaches. We and other groups explored public food chemical libraries containing up to 26,500 compounds. This study aimed to analyze the chemical contents, diversity, and coverage in the chemical space of food chemicals and additives and, from here on, food components. The approach to food components addressed in this study is a public database with more than 70,000 compounds, including those predicted via omics techniques. It was concluded that food components have distinctive physicochemical properties and constitutional descriptors despite sharing many chemical structures with natural products. Food components, on average, have large molecular weights and several apolar structures with saturated hydrocarbons. Compared to reference databases, food component structures have low scaffold and fingerprint-based diversity and high structural complexity, as measured by the fraction of sp3 carbons. These structural features are associated with a large fraction of macronutrients as lipids. Lipids in food components were decompiled by an analysis of the maximum common substructures. The chemical multiverse representation of food chemicals showed a larger coverage of chemical space than natural products and FDA-approved drugs by using different sets of representations.

Data-Driven Elucidation of Flavor Chemistry

Flavor molecules are commonly used in the food industry to enhance product quality and consumer experiences but are associated with potential human health risks, highlighting the need for safer alternatives. To address these health-associated challenges and promote reasonable application, several databases for flavor molecules have been constructed. However, no existing studies have comprehensively summarized these data resources according to quality, focused fields, and potential gaps. Here, we systematically summarized 25 flavor molecule databases published within the last 20 years and revealed that data inaccessibility, untimely updates, and nonstandard flavor descriptions are the main limitations of current studies. We examined the development of computational approaches (e.g., machine learning and molecular simulation) for the identification of novel flavor molecules and discussed their major challenges regarding throughput, model interpretability, and the lack of gold-standard data sets for equitable model evaluation. Additionally, we discussed future strategies for the mining and designing of novel flavor molecules based on multi-omics and artificial intelligence to provide a new foundation for flavor science research.

MycotoxinDB: A Data-Driven Platform for Investigating Masked Forms of Mycotoxins

Mycotoxins are likely to be converted into masked forms when subjected to plant metabolism or food processing. These masked forms of mycotoxins together with their prototypes may cause mixture toxicity effects, causing adverse effects on animal welfare and productivity. The structure elucidation of masked mycotoxins is the most challenging task in mycotoxin research due to the limitations of traditional analysis methods. To assist in the rapid identification of masked mycotoxins, we developed a data-driven online prediction tool, MycotoxinDB, based on reaction rules. Using MycotoxinDB, we identified seven masked DONs from wheat samples. Given its widespread applications, we expect that MycotoxinDB will become an indispensable tool in future mycotoxin research. MycotoxinDB is freely available at: http://www.mycotoxin-db.com/.

Beneficial metabolic transformations and prebiotic potential of hemp bran and its alcalase hydrolysate, after colonic fermentation in a gut model

Hemp seed bran (HB) is an industrial food byproduct that is generally discarded. Knowledge on the functional capabilities of HB is limited and it is not known the impact of HB on human colon microbiota, where vegetable fibers are metabolized. In this work, we investigated in depth the prebiotic potential of HB and HB protein extract hydrolyzed by alcalase (HBPA) in comparison to fructooligosaccharides (FOS) after human distal colonic fermentation using MICODE (multi-unit in vitro colon gut model). During the 24 h of fermentation, metabolomics (SPME GC/MS) and microbiomics (MiSeq and qPCR) analyses were performed. The results indicated that HBPA on a colonic fermentation had a higher prebiotic index than HB (p < 0.05), and slightly lower to that of FOS (p > 0.05). This feature was described and explained as HBPA colonic fermentation produces beneficial organic fatty acids (e.g. Pentanoic and Hexanoic acids); reduces detrimental phenol derivates (e.g. p-Cresol); produces bioactives VOCs (e.g. Acetophenone or 4-Terpineol); increases beneficial bacteria (e.g. 1.76 fold and 2.07 fold more of Bifidobacterium bifidum and Bacteroides fragilis, respectively) and limits opportunistic bacteria (e.g. 3.04 fold and 2.07 fold less of Bilophila wadsworthia and Desulfovibrio, respectively). Our study evidenced the prebiotic role of HB and HBPA, and within the principles of OneHealth it valorizes a byproduct from the queen plant of sustainable crops as a food supplement.

Analysis of public opinion on food safety in Greater China with big data and machine learning

The Internet contains a wealth of public opinion on food safety, including views on food adulteration, food-borne diseases, agricultural pollution, irregular food distribution, and food production issues. To systematically collect and analyze public opinion on food safety in Greater China, we developed IFoodCloud, which automatically collects data from more than 3,100 public sources. Meanwhile, we constructed sentiment classification models using multiple lexicon-based and machine learning-based algorithms integrated with IFoodCloud that provide an unprecedented rapid means of understanding the public sentiment toward specific food safety incidents. Our best model's F1 score achieved 0.9737, demonstrating its great predictive ability and robustness. Using IFoodCloud, we analyzed public sentiment on food safety in Greater China and the changing trend of public opinion at the early stage of the 2019 Coronavirus Disease pandemic, demonstrating the potential of big data and machine learning for promoting risk communication and decision-making.

Unveiling toxicity profile for food risk components: A manually curated toxicological databank of food-relevant chemicals

Rigorous risk assessment of chemicals in food and feed is essential to address the growing worldwide concerns about food safety. High-quality toxicological data on food-relevant chemicals are fundamental for risk modeling and assessment in the food safety area. The organization and analysis of substantial toxicity information can positively support decision-making by providing insight into toxicity trends. However, it remains challenging to systematically obtain fragmented toxicity data, and related toxicological resources are required to meet the current demands. In this study, we collected 221,439 experimental toxicity records for 5,657 food-relevant chemicals identified from extensive databases and literature, along with their information on chemical identification, physicochemical properties, environmental fates, and biological targets. Based on the aggregated data, a freely available web-based databank, Food-Relevant Available Chemicals Toxicology Databank (FRAC-TD) is presented, which supports multiple browsing ways and search criterions. Applying FRAC-TD for data-driven analysis, we revealed the underlying toxicity profiles of food-relevant chemicals in humans, mammals, and other species in the food chain. Expectantly, FRAC-TD could positively facilitate toxicological studies, toxicity prediction, and risk assessments in the food industry.

Recent advances in assessment methods and mechanism of microbe-mediated chlorpyrifos remediation

Chlorpyrifos (CP) is one of the Organophosphorus pesticides (OPs) primarily used in agriculture to safeguard crops from pests and diseases. The pervasive use of chlorpyrifos is hazardous to humans and the environment as it inhibits the receptor for acetylcholinesterase activity, leading to abnormalities linked to the central nervous system. Hence, there is an ardent need to develop an effective and sustainable approach to the on-site degradation of chlorpyrifos. The role of microbes in the remediation of pesticides is considered the most effective and eco-friendly approach, as they have strong degradative potential due to their gene and enzymes naturally adapted to these sites. Several reports have previously been published on exploring the role of microbes in the degradation of CP. However, detection of CP as an environmental contaminant is an essential prerequisite for developing an efficient microbial-mediated biodegradation method with less harmful intermediates. Most of the articles published to date discuss the fate and impact of CP in the environment along with its degradation mechanism but still fail to discuss the analytical portion. This review is focused on the latest developments in the field of bioremediation of CP along with its physicochemical properties, toxicity, fate, and conventional (UV-Visible spectrophotometer, FTIR, NMR, GC-MS, etc) and advanced detection methods (Biosensors and immunochromatography-based methods) from different environmental samples. Apart from it, this review explores the role of metagenomics, system biology, in-silico tools, and genetic engineering in facilitating the bioremediation of CP. One of the objectives of this review is to educate policymakers with scientific data that will enable the development of appropriate strategies to reduce pesticide exposure and the harmful health impacts on both Human and other environmental components. Moreover, this review provides up-to-date developments related to the sustainable remediation of CP.

Maternal amoxicillin affects piglets colon microbiota: microbial ecology and metabolomics in a gut model

Abstract

The first weeks of life represent a crucial stage for microbial colonization of the piglets’ gastrointestinal tract. Newborns’ microbiota is unstable and easily subject to changes under stimuli or insults. Nonetheless, the administration of antibiotics to the sow is still considered as common practice in intensive farming for pathological conditions in the postpartum. Therefore, transfer of antibiotic residues through milk may occurs, affecting the piglets’ colon microbiota. In this study, we aimed to extend the knowledge on antibiotic transfer through milk, employing an in vitro dedicated piglet colon model (MICODE—Multi Unit In vitro Colon Model). The authors’ focus was set on the shifts of the piglets’ microbiota composition microbiomics (16S r-DNA MiSeq and qPCR—quantitative polymerase chain reaction) and on the production of microbial metabolites (SPME GC/MS—solid phase micro-extraction gas chromatography/mass spectrometry) in response to milk with different concentrations of amoxicillin. The results showed an effective influence of amoxicillin in piglets’ microbiota and metabolites production; however, without altering the overall biodiversity. The scenario is that of a limitation of pathogens and opportunistic taxa, e.g., Staphylococcaceae and Enterobacteriaceae, but also a limitation of commensal dominant Lactobacillaceae, a reduction in commensal Ruminococcaceae and a depletion in beneficial Bifidobactericeae. Lastly, an incremental growth of resistant species, such as Enterococcaceae or Clostridiaceae, was observed. To the authors’ knowledge, this study is the first evaluating the impact of antibiotic residues towards the piglets’ colon microbiota in an in vitro model, opening the way to include such approach in a pipeline of experiments where a reduced number of animals for testing is employed.

Key points

• Piglet colon model to study antibiotic transfer through milk.

• MICODE resulted a robust and versatile in vitro gut model.

• Towards the “3Rs” Principles to replace, reduce and refine the use of animals used for scientific purposes (Directive 2010/63/UE).

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s00253-022-12223-3.

A data-driven integrative platform for computational prediction of toxin biotransformation with a case study

Recently, biogenic toxins have received increasing attention owing to their high contamination levels in feed and food as well as in the environment. However, there is a lack of an integrative platform for seamless linking of data-driven computational methods with 'wet' experimental validations. To this end, we constructed a novel platform that integrates the technical aspects of toxin biotransformation methods. First, a biogenic toxin database termed ToxinDB (http://www.rxnfinder.org/toxindb/), containing multifaceted data on more than 4836 toxins, was built. Next, more than 8000 biotransformation reaction rules were extracted from over 300,000 biochemical reactions extracted from ~580,000 literature reports curated by more than 100 people over the past decade. Based on these reaction rules, a toxin biotransformation prediction model was constructed. Finally, the global chemical space of biogenic toxins was constructed, comprising ~550,000 toxins and putative toxin metabolites, of which 94.7% of the metabolites have not been previously reported. Additionally, we performed a case study to investigate citrinin metabolism in Trichoderma, and a novel metabolite was identified with the assistance of the biotransformation prediction tool of ToxinDB. This unique integrative platform will assist exploration of the 'dark matter' of a toxin's metabolome and promote the discovery of detoxification enzymes.

Alternative model organisms for toxicological fingerprinting of relevant parameters in food and nutrition

In the field of (food) toxicology, there is a strong trend of replacing animal trials with alternative methods for the assessment of adverse health effects in humans. The replacement of animal trials is not only driven by ethical concerns but also by the number of potential testing substances (food additives, packaging material, contaminants, and toxicants), which is steadily increasing. In vitro 2D cell culture applications in combination with in silico modeling might provide an applicable first response. However, those systems lack accurate predictions of metabolic actions. Thus, alternative in vivo models could fill the gap between cell culture and animal trials. In this review, we highlight relevant studies in the field and spotlight the applicability of alternative models, including C. elegans, D. rerio, Drosophila, HET-CAM and Lab-on-a-chip.

Pyrazoline analogs as potential anticancer agents and their apoptosis, molecular docking, MD simulation, DNA binding and antioxidant studies

N-formyl pyrazoline derivatives (3a-3l) were designed and synthesized via Michael addition reaction through cyclization of chalcones with hydrazine hydrate in presence of formic acid. The structural elucidation of N-formyl pyrazoline derivatives was carried out by various spectroscopic techniques such as ¹H, ¹³C NMR, FT-IR, UV-visible spectroscopy, mass spectrometry and elemental analysis. Anticancer activity of the pyrazoline derivatives (3a-3l) was evaluated against human lung cancer (A549), fibrosarcoma cell lines (HT1080) and human primary normal lung cells (HFL-1) by MTT assay. The results of anticancer activity showed that potent analogs 3b and 3d exhibited promising activity against A549 (IC₅₀ = 12.47 ± 1.08 and 14.46 ± 2.76 µM) and HT1080 (IC₅₀ = 11.40 ± 0.66 and 23.74 ± 13.30 µM) but low toxic against the HFL-1 (IC₅₀ = 116.47 ± 43.38 and 152.36 ± 22.18 µM). The anticancer activity of potent derivatives (3b and 3d) against A549 cancer cell line was further confirmed by flow cytometry based approach. DNA binding interactions of the pyrazoline derivatives 3b and 3d have been carried out with calf thymus DNA (Ct-DNA) using absorption, fluorescence and viscosity measurements, circular dichroism and cyclic voltammetry. Antioxidant potential of N-formyl pyrazoline derivatives (3a-3l) has been also estimated through DPPH (2,2-diphenyl-1-picrylhydrazyl) free radical and H₂O_2. Results revealed that all the compounds exhibited significant antioxidant activity. In silico molecular modelling and ADMET properties of pyrazoline derivatives were also studied using PyRx software against topoisomerase II receptor with PDB ID: 1ZXM to explore their best hits. MD simulation of 3b and 3d was also carried out with topoisomerase II for structure-function correlation in a protein. HuTopoII inhibitory activity of the analogs (3a-3l) was examined by relaxation assay at varying concentrations 100-1000 µM.

FOCUS-DB: An Online Comprehensive Database on Food Additive Safety

Processing and packaging food has greatly exaggerated the use of food additives in different types of food products. Safety assessment to determine the pharmacokinetic and toxicological properties of food additives is imperative and experimentally challenging. Several resources of food additives properties have been collated; however, information remains partial, scattered, and not readily accessible, particularly for food safety. To address the concern related to the potential health hazard of food additives, we have developed the Food-Additive-Consumption-Safety Database (FOCUS-DB). Presently, the database comprises 2885 food additives, distributed into 18 categories with 40,800 collected data points, 89,435 predicted data points, and 14,425 external links. The dynamic web interface of the FOCUS-DB resource enables a risk assessment of additives, their approval status in various regulatory authorities, physicochemical properties, acceptable daily intake, GHS signals, biological pathways, predicted pharmacokinetic parameters, and various toxicity endpoint values. FOCUS-DB supports the exploration of food additives; it is beneficial for both the regulatory authorities and industries to optimize the usage limits of the additives and formulations. This database is a promising tool that helps understand the relationship between food additives and toxicity, which could be used to develop a future food safety framework.