Thomas Klammsteiner

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.

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.

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.

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.