Primary study on frass fertilizers from mass-reared insects: Species variation, heat treatment effects, and implications for soil application at laboratory scale.

Insect farming has gained popularity as a resource-efficient and eco-friendly method for managing organic wastes by converting them into high-quality protein, fat, and frass. Insect frass is a powerful organic fertilizer that enriches the soil with essential plant nutrients and enhances plant defense mechanisms through chitin stimulation. Given the importance of frass commercialization for many insect farmers and the use of increasingly diverse organic wastes as insect feedstocks, there is a need for legal guidelines to enable clean production practices. The recent introduction of a legal definition for frass and heat treatment requirements by the EU commission marks a significant step towards standardizing its quality; however, little is known about the processes shaping its nutritional profiles and contributing to its maturation. Our study addresses key knowledge gaps in frass composition and production practices. Here, we analyzed the physicochemical, plant-nutritional, and microbiological properties of black soldier fly, yellow mealworm, and Jamaican field cricket frass from mass-rearing operations and assessed the impact of hygienizing heat treatment on fertilizer properties and frass microbiota. The results showed that frass properties varied significantly across insect species, revealing concentrations of plant-available nutrients as high as 7000 µg NH4+-N, 150 µg NO2-NO3--N, and 20 mg available P per g of total solids. Heat treatment reduced microbial activity, biomass, and viable counts of pathogenic Escherichia coli and Salmonella spp. In terms of frass microbiome composition, alpha diversity showed no significant differences between fresh and heat-treated frass samples; however, significant differences in microbial community composition were observed across the three insect species. Despite heat treatment, soil application of frass reactivated and boosted soil microbial activity, inducing up to a 25-fold increase in microbial respiration, suggesting no long-term detrimental effects on microorganisms. These findings not only enhance our understanding of insect frass as a nutrient-rich organic fertilizer but also have implications for regulatory frameworks, underscoring its promising potential for soil health and nutrient cycling. However, it is important to recognize the primary nature of this research, conducted at laboratory scale and over a short term. Future studies should aim to validate these findings in agricultural settings and explore additional factors influencing frass properties and its (long-term) interaction with soil ecosystems.

Microbiota in a cooling-lubrication circuit and an option for controlling triethanolamine biodegradation.

Cooling and lubrication agents like triethanolamine (TEA) are essential for many purposes in industry. Due to biodegradation, they need continuous replacement, and byproducts of degradation may be toxic. This study investigates an industrial (1,200 m³) cooling-lubrication circuit (CLC) that has been in operation for 20 years and is supposedly in an ecological equilibrium, thus offering a unique habitat. Next-generation (Illumina Miseq 16S rRNA amplicon) sequencing was used to profile the CLC-based microbiota and relate it to TEA and bicine dynamics at the sampling sites, influent, machine rooms, biofilms and effluent. Pseudomonas pseudoalcaligenes dominated the effluent and influent sites, while Alcaligenes faecalis dominated biofilms, and both species were identified as the major TEA degrading bacteria. It was shown that a 15 min heat treatment at 50°C was able to slow down the growth of both species, a promising option to control TEA degradation at large scale.

A Comparative Study of Effects of Biodegradable and Non-biodegradable Microplastics on the Growth and Development of Black Soldier Fly Larvae (Hermetia illucens).

Purpose: This study aimed to investigate the digestion process of biodegradable and non-biodegradable microplastics (MPs) within black soldier fly larvae (BSFL) and assess their impact on larval growth and development. The goal was to understand the fate of MPs within BSFL, considering their potential for waste conversion polluted with MPs. Methods: BSFL were exposed to two types of MPs, and their growth, development, potential accumulation and excretion of MPs were monitored. Results: The findings revealed that the MPs accumulated solely in the larval gut and had no adverse effects on the growth and development of BSFL. Larvae efficiently excreted MPs before reaching the pupation stage. Conclusion: This research emphasizes the potential of BSFL as a bioconversion agent for organic waste, even in the presence of MPs. The effective excretion of MPs by BSFL before pupation suggests their ability to mitigate potential harm caused by MP accumulation. The fact that BSFL may excrete MPs before pupation would contribute to their safe use as animal feedstock. A careful evaluation of the effects of using BSFL reared on contaminated substrates especially containing visually non-detectable residuals like nanoplastics, chemicals or toxic metals and further examination of the broader implications for waste management and sustainable livestock farming remains important. Graphical Abstract: Experimental design outlining the workflow for the analyses used to investigate the effect of two types of microplastics, polyamide (PA), and polylactic acid (PLA), on growth and development of black soldier fly larvae.

Machine learning approaches in microbiome research: challenges and best practices.

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action.

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.

A toolbox of machine learning software to support microbiome analysis.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

Black soldier fly larvae for organic manure recycling and its potential for a circular bioeconomy: A review.

Livestock farming and its products provide a diverse range of benefits for our day-to-day life. However, the ever-increasing demand for farmed animals has raised concerns about waste management and its impact on the environment. Worldwide, cattle produce enormous amounts of manure, which is detrimental to soil properties if poorly managed. Waste management with insect larvae is considered one of the most efficient techniques for resource recovery from manure. In recent years, the use of black soldier fly larvae (BSFL) for resource recovery has emerged as an effective method. Using BSFL has several advantages over traditional methods, as the larvae produce a safe compost and extract trace elements like Cu and Zn. This paper is a comprehensive review of the potential of BSFL for recycling organic wastes from livestock farming, manure bioconversion, parameters affecting the BSFL application on organic farming, and process performance of biomolecule degradation. The last part discusses the economic feasibility, lifecycle assessment, and circular bioeconomy of the BSFL in manure recycling. Moreover, it discusses the future perspectives associated with the application of BSFL. Specifically, this review discusses BSFL cultivation and its impact on the larvae's physiology, gut biochemical physiology, gut microbes and metabolic pathways, nutrient conservation and global warming potential, microbial decomposition of organic nutrients, total and pathogenic microbial dynamics, and recycling of rearing residues as fertilizer.

Recent trends and advances in composting and vermicomposting technologies: A review.

Composting technologies have come a long way, developing from static heaps and windrow composting to smart, artificial intelligence-assisted reactor composting. While in previous years, much attention has been paid to identifying ideal organic waste streams and suitable co-composting candidates, more recent efforts tried to determine novel process-enhancing supplements. These include various single and mixed microbial cultures, additives, bulking agents, or combinations thereof. However, there is still ample need to fine-tune the composting process in order to reduce its impact on the environment and streamline it with circular economy goals. In this review, we highlight recent advances in integrating mathematical modelling, novel supplements, and reactor designs with (vermi-) composting practices and provide an outlook for future developments. These results should serve as reference point to target adjusting screws for process improvement and provide a guideline for waste management officials and stakeholders.

Impact of Processed Food (Canteen and Oil Wastes) on the Development of Black Soldier Fly (Hermetia illucens) Larvae and Their Gut Microbiome Functions.

Canteens represent an essential food supply hub for educational institutions, companies, and business parks. Many people in these locations rely on a guaranteed service with consistent quality. It is an ongoing challenge to satisfy the demand for sufficient serving numbers, portion sizes, and menu variations to cover food intolerances and different palates of customers. However, overestimating this demand or fluctuating quality of dishes leads to an inevitable loss of unconsumed food due to leftovers. In this study, the food waste fraction of canteen leftovers was identified as an optimal diet for black soldier fly (Hermetia illucens) larvae based on 50% higher consumption and 15% higher waste reduction indices compared with control chicken feed diet. Although the digestibility of food waste was nearly twice as high, the conversion efficiency of ingested and digested chicken feed remains unparalleled (17.9 ± 0.6 and 37.5 ± 0.9 in CFD and 7.9 ± 0.9 and 9.6 ± 1.0 in FWD, respectively). The oil separator waste fraction, however, inhibited biomass gain by at least 85% and ultimately led to a larval mortality of up to 96%. In addition to monitoring larval development, we characterized physicochemical properties of pre- and post-process food waste substrates. High-throughput amplicon sequencing identified Firmicutes, Proteobacteria, and Bacteroidota as the most abundant phyla, and Morganella, Acinetobacter, and certain Lactobacillales species were identified as indicator species. By using metagenome imputation, we additionally gained insights into the functional spectrum of gut microbial communities. We anticipate that the results will contribute to the development of decentralized waste-management sites that make use of larvae to process food waste as it has become common practice for biogas plants.

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

The Core Gut Microbiome of Black Soldier Fly (Hermetia illucens) Larvae Raised on Low-Bioburden Diets.

An organism's gut microbiome handles most of the metabolic processes associated with food intake and digestion but can also strongly affect health and behavior. A stable microbial core community in the gut provides general metabolic competences for substrate degradation and is robust against extrinsic disturbances like changing diets or pathogens. Black Soldier Fly larvae (BSFL; Hermetia illucens) are well known for their ability to efficiently degrade a wide spectrum of organic materials. The ingested substrates build up the high fat and protein content in their bodies that make the larvae interesting for the animal feedstuff industry. In this study, we subjected BSFL to three distinct types of diets carrying a low bioburden and assessed the diets' impact on larval development and on the composition of the bacterial and archaeal gut community. No significant impact on the gut microbiome across treatments pointed us to the presence of a predominant core community backed by a diverse spectrum of low-abundance taxa. Actinomyces spp., Dysgonomonas spp., and Enterococcus spp. as main members of this community provide various functional and metabolic skills that could be crucial for the thriving of BSFL in various environments. This indicates that the type of diet could play a lesser role in guts of BSFL than previously assumed and that instead a stable autochthonous collection of bacteria provides the tools for degrading of a broad range of substrates. Characterizing the interplay between the core gut microbiome and BSFL helps to understand the involved degradation processes and could contribute to further improving large-scale BSFL rearing.