Preclinical Evaluation of a Food-Derived Functional Ingredient to Address Skeletal Muscle Atrophy

Smart systems in producing algae-based protein to improve functional food ingredients industries

Production and extraction systems of algal protein and handling process of functional food ingredients need to control several parameters such as temperature, pH, intensity, and turbidity. Many researchers have investigated the Internet of Things (IoT) approach for enhancing the yield of microalgae biomass and machine learning for identifying and classifying microalgae. However, there have been few specific studies on using IoT and artificial intelligence (AI) for production and extraction of algal protein as well as functional food ingredients processing. In order to improve the production of algal protein and functional food ingredients, the implementation of smart system is a must to have real-time monitoring, remote control system, quick response to sudden events, prediction and characterisation. Techniques of IoT and AI are expected to help functional food industries to have a big breakthrough in the future. Manufacturing and implementation of beneficial smart systems are important to provide convenience and to increase the efficiency of work by using the interconnectivity of IoT devices to have good capturing, processing, archiving, analyzing, and automation. This review investigates the possibilities of implementation of IoT and AI in production and extraction of algal protein and processing of functional food ingredients.

Improved Strength Recovery and Reduced Fatigue with Suppressed Plasma Myostatin Following Supplementation of a Vicia faba Hydrolysate, in a Healthy Male Population

Delayed onset muscle soreness (DOMS) due to intense physical exertion can negatively impact contractility and performance. Previously, NPN_1 (PeptiStrong™), a Vicia faba hydrolysate derived from a protein concentrate discovered through artificial intelligence (AI), was preclinically shown to help maintain muscle health, indicating the potential to mediate the effect of DOMS and alter molecular markers of muscle damage to improve recovery and performance. A randomised double-blind placebo-controlled trial was conducted on 30 healthy male (30-45 years old) volunteers (NCT05159375). Following initial strength testing on day 0, subjects were administered either placebo or NPN_1 (2.4 g/day). On day 14, DOMS was induced using resistance exercise. Strength recovery and fatigue were measured after 48 and 72 h. Biomarker analysis was performed on blood samples collected prior to DOMS induction and 0, 2, 48 and 72 h post-DOMS induction. NPN_1 supplementation significantly improved strength recovery compared to placebo over the 72 h period post-resistance exercise (p = 0.027), measured by peak torque per bodyweight, but not at individual timepoints. Muscle fatigue was significantly reduced over the same 72 h period (p = 0.041), as was myostatin expression (p = 0.006). A concomitant increase in other acute markers regulating muscle protein synthesis, regeneration and myoblast differentiation was also observed. NPN_1 significantly improves strength recovery and restoration, reduces fatigue and positively modulates alterations in markers related to muscle homeostasis.

Faba Bean: An Untapped Source of Quality Plant Proteins and Bioactives

Faba beans are emerging as sustainable quality plant protein sources, with the potential to help meet the growing global demand for more nutritious and healthy foods. The faba bean, in addition to its high protein content and well-balanced amino acid profile, contains bioactive constituents with health-enhancing properties, including bioactive peptides, phenolic compounds, GABA, and L-DOPA. Faba bean peptides released after gastrointestinal digestion have shown antioxidant, antidiabetic, antihypertensive, cholesterol-lowering, and anti-inflammatory effects, indicating a strong potential for this legume crop to be used as a functional food to help face the increasing incidences of non-communicable diseases. This paper provides a comprehensive review of the current body of knowledge on the nutritional and biofunctional qualities of faba beans, with a particular focus on protein-derived bioactive peptides and how they are affected by food processing. It further covers the adverse health effects of faba beans associated with the presence of anti-nutrients and potential allergens, and it outlines research gaps and needs.

Artificial Intelligence in Functional Food Ingredient Discovery and Characterisation: A Focus on Bioactive Plant and Food Peptides

Scientific research consistently demonstrates that diseases may be delayed, treated, or even prevented and, thereby, health may be maintained with health-promoting functional food ingredients (FFIs). Consumers are increasingly demanding sound information about food, nutrition, nutrients, and their associated health benefits. Consequently, a nutrition industry is being formed around natural foods and FFIs, the economic growth of which is increasingly driven by consumer decisions. Information technology, in particular artificial intelligence (AI), is primed to vastly expand the pool of characterised and annotated FFIs available to consumers, by systematically discovering and characterising natural, efficacious, and safe bioactive ingredients (bioactives) that address specific health needs. However, FFI-producing companies are lagging in adopting AI technology for their ingredient development pipelines for several reasons, resulting in a lack of efficient means for large-scale and high-throughput molecular and functional ingredient characterisation. The arrival of the AI-led technological revolution allows for the comprehensive characterisation and understanding of the universe of FFI molecules, enabling the mining of the food and natural product space in an unprecedented manner. In turn, this expansion of bioactives dramatically increases the repertoire of FFIs available to the consumer, ultimately resulting in bioactives being specifically developed to target unmet health needs.

Measuring the oral bioavailability of protein hydrolysates derived from food sources: A critical review of current bioassays

BACKGROUND

Food proteins are a source of hydrolysates with potentially useful biological attributes. Bioactive peptides from food-derived proteins are released from hydrolysates using exogenous industrial processes or endogenous intestinal enzymes. Current in vitro permeability assays have limitations in predicting the oral bioavailability (BA) of bioactive peptides in humans. There are also difficulties in relating the low blood levels of food-derived bioactive peptides detected in preclinical in vivo models to pharmacodynamic read-outs relevant for humans.

SCOPE AND APPROACH

In this review, we describe in vitro assays of digestion, permeation, and metabolism as indirect predictors of the potential oral BA of hydrolysates and their constituent bioactive peptides. We discuss the relationship between industrial hydrolysis processes and the oral BA of hydrolysates and their peptide by-products.

KEY FINDINGS

Hydrolysates are challenging for analytical detection methods due to capacity for enzymatic generation of peptides with novel sequences and also new modifications of these peptides during digestion. Mass spectrometry and peptidomics can improve the capacity to detect individual peptides released from complex hydrolysates in biological milieu.

Fish and Shellfish-Derived Anti-Inflammatory Protein Products: Properties and Mechanisms

The interest in utilizing food-derived compounds therapeutically has been rising. With the growing prevalence of systematic chronic inflammation (SCI), efforts to find treatments that do not result in the side effects of current anti-inflammatory drugs are underway. Bioactive peptides (BAPs) are a particularly promising class of compounds for the treatment of SCI, and the abundance of high-quality seafood processing byproducts (SPB) makes it a favorable material to derive anti-inflammatory BAPs. Recent research into the structural properties of anti-inflammatory BAPs has found a few key tendencies including they tend to be short and of low molecular weight (LMW), have an overall positive charge, contain hydrophobic amino acids (AAs), and be rich in radical scavenging AAs. SPB-derived anti-inflammatory BAPs have been observed to work via inhibition of the NF-κB and MAPK pathways by disrupting the phosphorylation of IκBα and one or more kinases (ERK, JNK, and p38), respectively. Radical scavenging capacity has also been shown to play a significant role in the efficacy of SPB-derived anti-inflammatory BAPs. To determine if SPB-derived BAPs can serve as an effective treatment for SCI it will be important to understand their properties and mechanisms of action, and this review highlights such findings in recent research.

An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population

The prevalence of prediabetes is rapidly increasing, and this can lead to an increased risk for individuals to develop type 2 diabetes and associated diseases. Therefore, it is necessary to develop nutritional strategies to maintain healthy glucose levels and prevent glucose metabolism dysregulation in the general population. Functional ingredients offer great potential for the prevention of various health conditions, including blood glucose regulation, in a cost-effective manner. Using an artificial intelligence (AI) approach, a functional ingredient, NRT_N0G5IJ, was predicted and produced from Pisum sativum (pea) protein by hydrolysis and then validated. Treatment of human skeletal muscle cells with NRT_N0G5IJ significantly increased glucose uptake, indicating efficacy of this ingredient in vitro. When db/db diabetic mice were treated with NRT_N0G5IJ, we observed a significant reduction in glycated haemoglobin (HbA1c) levels and a concomitant benefit on fasting glucose. A pilot double-blinded, placebo controlled human trial in a population of healthy individuals with elevated HbA1c (5.6% to 6.4%) showed that HbA1c percentage was significantly reduced when NRT_N0G5IJ was supplemented in the diet over a 12-week period. Here, we provide evidence of an AI approach to discovery and demonstrate that a functional ingredient identified using this technology could be used as a supplement to maintain healthy glucose regulation.

Characterising the efficacy and bioavailability of bioactive peptides identified for attenuating muscle atrophy within a Vicia faba-derived functional ingredient

Characterising key components within functional ingredients as well as assessing efficacy and bioavailability is an important step in validating nutritional interventions. Machine learning can assess large and complex data sets, such as proteomic data from plants sources, and so offers a prime opportunity to predict key bioactive components within a larger matrix. Using machine learning, we identified two potentially bioactive peptides within a Vicia faba derived hydrolysate, NPN_1, an ingredient which was previously identified for preventing muscle loss in a murine disuse model. We investigated the predicted efficacy of these peptides in vitro and observed that HLPSYSPSPQ and TIKIPAGT were capable of increasing protein synthesis and reducing TNF-α secretion, respectively. Following confirmation of efficacy, we assessed bioavailability and stability of these predicted peptides and found that as part of NPN_1, both HLPSYSPSPQ and TIKIPAGT survived upper gut digestion, were transported across the intestinal barrier and exhibited notable stability in human plasma. This work is a first step in utilising machine learning to untangle the complex nature of functional ingredients to predict active components, followed by subsequent assessment of their efficacy, bioavailability and human plasma stability in an effort to assist in the characterisation of nutritional interventions.

Using Peptidomics and Machine Learning to Assess Effects of Drying Processes on the Peptide Profile within a Functional Ingredient

Bioactive peptides are known to have many health benefits beyond nutrition; yet the peptide profile of high protein ingredients has been largely overlooked when considering the effects of different processing techniques. Therefore, to investigate whether drying conditions could affect the peptide profile and bioactivity within a functional ingredient, we examined the effects of spray (SD) and freeze (FD) drying on rice natural peptide network (NPN), a characterised functional ingredient sourced from the Oryza sativa proteome, which has previously been shown to effectively modulate circulating cytokines and improve physical performance in humans. In the manufacturing process, rice NPN was either FD or SD. Employing a peptidomic approach, we investigated the physicochemical characteristics of peptides common and unique to FD and SD preparations. We observed similar peptide profiles regarding peptide count, amino acid distribution, weight, charge, and hydrophobicity in each sample. Additionally, to evaluate the effects of drying processes on functionality, using machine learning, we examined constituent peptides with predicted anti-inflammatory activity within both groups and identified that the majority of anti-inflammatory peptides were common to both. Of note, key bioactive peptides validated within rice NPN were recorded in both SD and FD samples. The present study provides an important insight into the overall stability of the peptide profile and the use of machine learning in assessing predicted retention of bioactive peptides contributing to functionality during different types of processing.