Fecal microbiota profiles of growing pigs and their relation to growth performance.

The early gut microbiota composition is fundamentally important for piglet health, affecting long-term microbiome development and immunity. In this study, the gut microbiota of postparturient dams was compared with that of their offspring in three Finnish pig farms at three growth phases. The differences in fecal microbiota of three study development groups (Good, Poorly, and PrematureDeath) were analyzed at birth (initial exposure phase), weaning (transitional phase), and before slaughter (stable phase). Dam Lactobacillaceae abundance was lower than in piglets at birth. Limosilactobacillus reuteri and Lactobacillus amylovorus were dominantly expressed in dams and their offspring. Altogether 17 piglets (68%) were identified with Lactobacillaceae at the initial exposure phase, divided unevenly among the development groups: 85% of Good, 37.5% of Poorly, and 75% of PrematureDeath pigs. The development group Good was identified with the highest microbial diversity, whereas the development group PrematureDeath had the lowest diversity. After weaning, the abundance and versatility of Lactobacillaceae in piglets diminished, shifting towards the microbiome of the dam. In conclusion, the fecal microbiota of pigs tends to develop towards a similar alpha and beta diversity despite development group and rearing environment.

A concept for international societally relevant microbiology education and microbiology knowledge promulgation in society.

EXECUTIVE SUMMARY: Microbes are all pervasive in their distribution and influence on the functioning and well-being of humans, life in general and the planet. Microbially-based technologies contribute hugely to the supply of important goods and services we depend upon, such as the provision of food, medicines and clean water. They also offer mechanisms and strategies to mitigate and solve a wide range of problems and crises facing humanity at all levels, including those encapsulated in the sustainable development goals (SDGs) formulated by the United Nations. For example, microbial technologies can contribute in multiple ways to decarbonisation and hence confronting global warming, provide sanitation and clean water to the billions of people lacking them, improve soil fertility and hence food production and develop vaccines and other medicines to reduce and in some cases eliminate deadly infections. They are the foundation of biotechnology, an increasingly important and growing business sector and source of employment, and the centre of the bioeconomy, Green Deal, etc. But, because microbes are largely invisible, they are not familiar to most people, so opportunities they offer to effectively prevent and solve problems are often missed by decision-makers, with the negative consequences this entrains. To correct this lack of vital knowledge, the International Microbiology Literacy Initiative-the IMiLI-is recruiting from the global microbiology community and making freely available, teaching resources for a curriculum in societally relevant microbiology that can be used at all levels of learning. Its goal is the development of a society that is literate in relevant microbiology and, as a consequence, able to take full advantage of the potential of microbes and minimise the consequences of their negative activities. In addition to teaching about microbes, almost every lesson discusses the influence they have on sustainability and the SDGs and their ability to solve pressing problems of societal inequalities. The curriculum thus teaches about sustainability, societal needs and global citizenship. The lessons also reveal the impacts microbes and their activities have on our daily lives at the personal, family, community, national and global levels and their relevance for decisions at all levels. And, because effective, evidence-based decisions require not only relevant information but also critical and systems thinking, the resources also teach about these key generic aspects of deliberation. The IMiLI teaching resources are learner-centric, not academic microbiology-centric and deal with the microbiology of everyday issues. These span topics as diverse as owning and caring for a companion animal, the vast range of everyday foods that are produced via microbial processes, impressive geological formations created by microbes, childhood illnesses and how they are managed and how to reduce waste and pollution. They also leverage the exceptional excitement of exploration and discovery that typifies much progress in microbiology to capture the interest, inspire and motivate educators and learners alike. The IMiLI is establishing Regional Centres to translate the teaching resources into regional languages and adapt them to regional cultures, and to promote their use and assist educators employing them. Two of these are now operational. The Regional Centres constitute the interface between resource creators and educators-learners. As such, they will collect and analyse feedback from the end-users and transmit this to the resource creators so that teaching materials can be improved and refined, and new resources added in response to demand: educators and learners will thereby be directly involved in evolution of the teaching resources. The interactions between educators-learners and resource creators mediated by the Regional Centres will establish dynamic and synergistic relationships-a global societally relevant microbiology education ecosystem-in which creators also become learners, teaching resources are optimised and all players/stakeholders are empowered and their motivation increased. The IMiLI concept thus embraces the principle of teaching societally relevant microbiology embedded in the wider context of societal, biosphere and planetary needs, inequalities, the range of crises that confront us and the need for improved decisioning, which should ultimately lead to better citizenship and a humanity that is more sustainable and resilient. ABSTRACT: The biosphere of planet Earth is a microbial world: a vast reactor of countless microbially driven chemical transformations and energy transfers that push and pull many planetary geochemical processes, including the cycling of the elements of life, mitigate or amplify climate change (e.g., Nature Reviews Microbiology, 2019, 17, 569) and impact the well-being and activities of all organisms, including humans. Microbes are both our ancestors and creators of the planetary chemistry that allowed us to evolve (e.g., Life's engines: How microbes made earth habitable, 2023). To understand how the biosphere functions, how humans can influence its development and live more sustainably with the other organisms sharing it, we need to understand the microbes. In a recent editorial (Environmental Microbiology, 2019, 21, 1513), we advocated for improved microbiology literacy in society. Our concept of microbiology literacy is not based on knowledge of the academic subject of microbiology, with its multitude of component topics, plus the growing number of additional topics from other disciplines that become vitally important elements of current microbiology. Rather it is focused on microbial activities that impact us-individuals/communities/nations/the human world-and the biosphere and that are key to reaching informed decisions on a multitude of issues that regularly confront us, ranging from personal issues to crises of global importance. In other words, it is knowledge and understanding essential for adulthood and the transition to it, knowledge and understanding that must be acquired early in life in school. The 2019 Editorial marked the launch of the International Microbiology Literacy Initiative, the IMiLI. HERE, WE PRESENT: our concept of how microbiology literacy may be achieved and the rationale underpinning it; the type of teaching resources being created to realise the concept and the framing of microbial activities treated in these resources in the context of sustainability, societal needs and responsibilities and decision-making; and the key role of Regional Centres that will translate the teaching resources into local languages, adapt them according to local cultural needs, interface with regional educators and develop and serve as hubs of microbiology literacy education networks. The topics featuring in teaching resources are learner-centric and have been selected for their inherent relevance, interest and ability to excite and engage. Importantly, the resources coherently integrate and emphasise the overarching issues of sustainability, stewardship and critical thinking and the pervasive interdependencies of processes. More broadly, the concept emphasises how the multifarious applications of microbial activities can be leveraged to promote human/animal, plant, environmental and planetary health, improve social equity, alleviate humanitarian deficits and causes of conflicts among peoples and increase understanding between peoples (Microbial Biotechnology, 2023, 16(6), 1091-1111). Importantly, although the primary target of the freely available (CC BY-NC 4.0) IMiLI teaching resources is schoolchildren and their educators, they and the teaching philosophy are intended for all ages, abilities and cultural spectra of learners worldwide: in university education, lifelong learning, curiosity-driven, web-based knowledge acquisition and public outreach. The IMiLI teaching resources aim to promote development of a global microbiology education ecosystem that democratises microbiology knowledge.

Microbiome confounders and quantitative profiling challenge predicted microbial targets in colorectal cancer development.

Despite substantial progress in cancer microbiome research, recognized confounders and advances in absolute microbiome quantification remain underused; this raises concerns regarding potential spurious associations. Here we study the fecal microbiota of 589 patients at different colorectal cancer (CRC) stages and compare observations with up to 15 published studies (4,439 patients and controls total). Using quantitative microbiome profiling based on 16S ribosomal RNA amplicon sequencing, combined with rigorous confounder control, we identified transit time, fecal calprotectin (intestinal inflammation) and body mass index as primary microbial covariates, superseding variance explained by CRC diagnostic groups. Well-established microbiome CRC targets, such as Fusobacterium nucleatum, did not significantly associate with CRC diagnostic groups (healthy, adenoma and carcinoma) when controlling for these covariates. In contrast, the associations of Anaerococcus vaginalis, Dialister pneumosintes, Parvimonas micra, Peptostreptococcus anaerobius, Porphyromonas asaccharolytica and Prevotella intermedia remained robust, highlighting their future target potential. Finally, control individuals (age 22-80 years, mean 57.7 years, standard deviation 11.3) meeting criteria for colonoscopy (for example, through a positive fecal immunochemical test) but without colonic lesions are enriched for the dysbiotic Bacteroides2 enterotype, emphasizing uncertainties in defining healthy controls in cancer microbiome research. Together, these results indicate the importance of quantitative microbiome profiling and covariate control for biomarker identification in CRC microbiome studies.

Gut microbiome-derived bacterial extracellular vesicles in patients with solid tumours.

INTRODUCTION: Gut microbiome-derived nanoparticles, known as bacterial extracellular vesicles (bEVs), have garnered interest as promising tools for studying the link between the gut microbiome and human health. The diverse composition of bEVs, including their proteins, mRNAs, metabolites, and lipids, makes them useful for investigating diseases such as cancer. However, conventional approaches for studying gut microbiome composition alone may not be accurate in deciphering host-gut microbiome communication. In clinical microbiome research, there is a gap in the knowledge on the role of bEVs in solid tumor patients. OBJECTIVES: Analyzing the functionality of bEVs using (meta)genomics and proteomics could highlight the unique aspects of host-gut microbiome interactions in solid tumor patients. Therefore, we performed a comparative analysis of the proteome and microbiota composition of gut microbiome-derived bEVs isolated from patients with solid tumors and healthy controls. METHODS: After isolating bEVs from the feces of solid tumor patients and healthy controls, we performed spectrometry analysis of their proteomes and next-generation sequencing (NGS) of the 16S gene. We also investigated the gut microbiomes of feces from patients and controls using 16S sequencing and used machine learning to classify the samples into patients and controls based on their bEVs and fecal microbiomes. RESULTS: Solid tumor patients showed decreased microbiota richness and diversity in both the bEVs and feces. However, the bEV proteomes were more diverse in patients than in the controls and were enriched with proteins associated with the metabolism of amino acids and carbohydrates, nucleotide binding, and oxidoreductase activity. Metadata classification of samples was more accurate using fecal bEVs (100%) compared with fecal samples (93%). CONCLUSION: Our findings suggest that bEVs are unique functional entities. There is a need to explore bEVs together with conventional gut microbiome analysis in functional cancer research to decipher the potential of bEVs as cancer diagnostic or therapeutic biomarkers.

Integration of polygenic and gut metagenomic risk prediction for common diseases.

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Shotgun metagenomic analysis of the oral microbiome in gingivitis: a nested case-control study.

Background: Gingivitis, i.e. inflammation of the gums, is often induced by dentalplaque. However, its exact link to the oral microbiota remains unclear. Methods: In a case-control study involving 120 participants, comprising 60 cases and 60 controls (mean age (SD) 36.6 (7.6) years; 50% males), nested within a prospective multicentre cohort study, we examined theoral microbiome composition of gingivitis patients and their controlsusing shotgun metagenomic sequencing of saliva samples. Participants underwent clinical and radiographic oral health examinations, including bleeding on probing (BOP), at six tooth sites. BOP ≥33%was considered 'generalized gingivitis/initial periodontitis'(GG/IP), and BOP <33% as 'healthy and localized gingivitis'(H/LG). Functional potential was inferred using HUMANn3. Results: GG/IP exhibited an increase in the abundance of Actinomyces, Porphyromonas, Aggregatibacter, Corynebacterium, Olsenella, and Treponema, whereas H/LG exhibited an increased abundance of Candidatus Nanosynbacter. Nineteen bacterial species and fourmicrobial functional profiles, including L-methionine, glycogen, andinosine-5'-phosphate biosynthesis, were associated with GG/IP. Constructing models with multiple markers resulted in a strong predictive value for GG/IP, with an area under the curve (ROC) of 0.907 (95% CI: 0.848-0.966). Conclusion: We observed distinct differences in the oral microbiome between the GG/IP and H/LG groups, indicating similar yet unique microbial profiles and emphasizing their potential role in progression of periodontal diseases.

Fewer culturable Lactobacillaceae species identified in faecal samples of pigs performing manipulative behaviour.

Manipulative behaviour that consists of touching or close contact with ears or tails of pen mates is common in pigs and can become damaging. Manipulative behaviour was analysed from video recordings of 45-day-old pigs, and 15 manipulator-control pairs (n = 30) were formed. Controls neither received nor performed manipulative behaviour. Rectal faecal samples of manipulators and controls were compared. 16S PCR was used to identify Lactobacillaceae species and 16S amplicon sequencing to determine faecal microbiota composition. Seven culturable Lactobacillaceae species were identified in control pigs and four in manipulator pigs. Manipulators (p = 0.02) and females (p = 0.005) expressed higher Lactobacillus amylovorus, and a significant interaction was seen (sex * status: p = 0.005) with this sex difference being more marked in controls. Females (p = 0.08) and manipulator pigs (p = 0.07) tended to express higher total Lactobacillaceae. A tendency for an interaction was seen in Limosilactobacillus reuteri (sex * status: p = 0.09). Results suggest a link between observed low diversity in Lactobacillaceae and the development of manipulative behaviour.

Daily skin-to-skin contact alters microbiota development in healthy full-term infants.

The gut microbiota is vital for human body development and function. Its development in early life is influenced by various environmental factors. In this randomized controlled trial, the gut microbiota was obtained as a secondary outcome measure in a study on the effects of one hour of daily skin-to-skin contact (SSC) for five weeks in healthy full-term infants. Specifically, we studied the effects on alpha/beta diversity, volatility, microbiota maturation, and bacterial and gut-brain-axis-related functional abundances in microbiota assessed thrice in the first year. Pregnant Dutch women (n = 116) were randomly assigned to the SSC or care-as-usual groups. The SSC group participants engaged in one hour of daily SSC from birth to five weeks of age. Stool samples were collected at two, five, and 52 weeks and the V4 region was sequenced. We observed significant differences in the microbiota composition, bacterial abundances, and predicted functional pathways between the groups. The SSC group exhibited lower microbiota volatility during early infancy. Microbiota maturation was slower in the SSC group during the first year and our results suggested that breastfeeding duration may have partially mediated this relation. Our findings provide evidence that postpartum SSC may influence microbiota development. Replication is necessary to validate and generalize these results. Future studies should include direct stress measurements and extend microbiota sampling beyond the first year to investigate stress as a mechanism and research SSC's impact on long-term microbiota maturation trajectories.

Association of Long-Term Habitual Dietary Fiber Intake since Infancy with Gut Microbiota Composition in Young Adulthood.

BACKGROUND: Dietary fiber is an important health-promoting component of the diet, which is fermented by the gut microbes that produce metabolites beneficial for the host's health. OBJECTIVES: We studied the associations of habitual long-term fiber intake from infancy with gut microbiota composition in young adulthood by leveraging data from the Special Turku Coronary Risk Factor Intervention Project, an infancy-onset 20-y dietary counseling study. METHODS: Fiber intake was assessed annually using food diaries from infancy ≤ age 20 y. At age 26 y, the first postintervention follow-up study was conducted including food diaries and fecal sample collection (N = 357). Cumulative dietary fiber intake was assessed as the area under the curve for energy-adjusted fiber intake throughout the study (age 0-26 y). Gut microbiota was profiled using 16S ribosomal ribonucleic acid amplicon sequencing. The primary outcomes were 1) α diversity expressed as the observed richness and Shannon index, 2) ß diversity using Bray-Curtis dissimilarity scores, and 3) differential abundance of each microbial taxa with respect to the cumulative energy-adjusted dietary fiber intake. RESULTS: Higher cumulative dietary fiber intake was associated with decreased Shannon index (ß = -0.019 per unit change in cumulative fiber intake, P = 0.008). Overall microbial community composition was related to the amount of fiber consumed (permutational analysis of variation R2 = 0.005, P = 0.024). The only genus that was increased with higher cumulative fiber intake was butyrate-producing Butyrivibrio (log2 fold-change per unit change in cumulative fiber intake 0.40, adjusted P = 0.023), whereas some other known butyrate producers such as Faecalibacterium and Subdoligranulum were decreased with higher cumulative fiber intake. CONCLUSIONS: As early-life nutritional exposures may affect the lifetime microbiota composition and disease risk, this study adds novel information on the associations of long-term dietary fiber intake with the gut microbiota. This trial was registered at clinicaltrials.gov as NCT00223600.

Role of Gut Microbiota in Statin-Associated New-Onset Diabetes-A Cross-Sectional and Prospective Analysis of the FINRISK 2002 Cohort.

BACKGROUND: Dyslipidemia is treated effectively with statins, but treatment has the potential to induce new-onset type-2 diabetes. Gut microbiota may contribute to this outcome variability. We assessed the associations of gut microbiota diversity and composition with statins. Bacterial associations with statin-associated new-onset type-2 diabetes (T2D) risk were also prospectively evaluated. METHODS: We examined shallow-shotgun-sequenced fecal samples from 5755 individuals in the FINRISK-2002 population cohort with a 17+-year-long register-based follow-up. Alpha-diversity was quantified using Shannon index and beta-diversity with Aitchison distance. Species-specific differential abundances were analyzed using general multivariate regression. Prospective associations were assessed with Cox regression. Applicable results were validated using gradient boosting. RESULTS: Statin use associated with differing taxonomic composition (R2, 0.02%; q=0.02) and 13 differentially abundant species in fully adjusted models (MaAsLin; q<0.05). The strongest positive association was with Clostridium sartagoforme (ß=0.37; SE=0.13; q=0.02) and the strongest negative association with Bacteroides cellulosilyticus (ß=-0.31; SE=0.11; q=0.02). Twenty-five microbial features had significant associations with incident T2D in statin users, of which only Bacteroides vulgatus (HR, 1.286 [1.136-1.457]; q=0.03) was consistent regardless of model adjustment. Finally, higher statin-associated T2D risk was seen with [Ruminococcus] torques (ΔHRstatins, +0.11; q=0.03), Blautia obeum (ΔHRstatins, +0.06; q=0.01), Blautia sp. KLE 1732 (ΔHRstatins, +0.05; q=0.01), and beta-diversity principal component 1 (ΔHRstatin, +0.07; q=0.03) but only when adjusting for demographic covariates. CONCLUSIONS: Statin users have compositionally differing microbiotas from nonusers. The human gut microbiota is associated with incident T2D risk in statin users and possibly has additive effects on statin-associated new-onset T2D risk.

Bone marrow metabolism is affected by body weight and response to exercise training varies according to anatomical location.

AIM: High body weight is a protective factor against osteoporosis, but obesity also suppresses bone metabolism and whole-body insulin sensitivity. However, the impact of body weight and regular training on bone marrow (BM) glucose metabolism is unclear. We studied the effects of regular exercise training on bone and BM metabolism in monozygotic twin pairs discordant for body weight. METHODS: We recruited 12 monozygotic twin pairs (mean ± SD age 40.4 ± 4.5 years; body mass index 32.9 ± 7.6, mean difference between co-twins 7.6 kg/m2 ; eight female pairs). Ten pairs completed the 6-month long training intervention. We measured lumbar vertebral and femoral BM insulin-stimulated glucose uptake (GU) using 18 F-FDG positron emission tomography, lumbar spine bone mineral density and bone turnover markers. RESULTS: At baseline, heavier co-twins had higher lumbar vertebral BM GU (p < .001) and lower bone turnover markers (all p < .01) compared with leaner co-twins but there was no significant difference in femoral BM GU, or bone mineral density. Training improved whole-body insulin sensitivity, aerobic capacity (both p < .05) and femoral BM GU (p = .008). The training response in lumbar vertebral BM GU was different between the groups (time × group, p = .02), as GU tended to decrease in heavier co-twins (p = .06) while there was no change in leaner co-twins. CONCLUSIONS: In this study, regular exercise training increases femoral BM GU regardless of weight and genetics. Interestingly, lumbar vertebral BM GU is higher in participants with higher body weight, and training counteracts this effect in heavier co-twins even without reduction in weight. These data suggest that BM metabolism is altered by physical activity.

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.

What are patterns of rise and decline?

The notions of change, such as birth, death, growth, evolution and longevity, extend across reality, including biological, cultural and societal phenomena. Patterns of change describe how success and composition of every entity, from species to societies, vary across time. Languages develop into new languages, music and fashion continuously evolve, economies rise and decline, ecological and societal crises come and go. A common way to perceive and analyse change processes is through patterns of rise and decline, the ubiquitous, often distinctively unimodal trajectories describing life histories of various entities. These patterns come in different shapes and are measured according to varying definitions. Depending on how they are measured, patterns of rise and decline can reveal, emphasize, mask or obscure important dynamics in natural and cultural phenomena. Importantly, the variations of how dynamics are measured can be vast, making it impossible to directly compare patterns of rise and decline across fields of science. Standardized analysis of these patterns has the potential to uncover important but overlooked commonalities across natural phenomena and potentially help us catch the onset of dramatic shifts in entities' state, from catastrophic crashes in success to gradual emergence of new entities. We provide a framework for standardized recognizing, characterizing and comparing patterns of change by combining understanding of dynamics across fields of science. Our toolkit aims at enhancing understanding of the most general tendencies of change, through two complementary perspectives: dynamics of emergence and dynamics of success. We gather comparable cases and data from different research fields and summarize open research questions that can help us understand the universal principles, perception-biases and field-specific tendencies in patterns of rise and decline of entities in nature.

Maternal microbiota communicates with the fetus through microbiota-derived extracellular vesicles.

BACKGROUND: Reports regarding the presence of bacteria in the fetal environment remain limited and controversial. Recently, extracellular vesicles secreted by the human gut microbiota have emerged as a novel mechanism for host-microbiota interaction. We aimed to investigate the presence of bacterial extracellular vesicles in the fetal environment during healthy pregnancies and determine whether extracellular vesicles derived from the gut microbiota can cross biological barriers to reach the fetus. RESULTS: Bacterial extracellular vesicles were detectable in the amniotic fluid of healthy pregnant women, exhibiting similarities to extracellular vesicles found in the maternal gut microbiota. In pregnant mice, extracellular vesicles derived from human maternal gut microbiota were found to reach the intra-amniotic space. CONCLUSIONS: Our findings reveal maternal microbiota-derived extracellular vesicles as an interaction mechanism between the maternal microbiota and fetus, potentially playing a pivotal role in priming the prenatal immune system for gut colonization after birth. Video Abstract.

Infant gut microbiota and negative and fear reactivity.

BACKGROUND: Studies indicate that gut microbiota is related to neurodevelopmental and behavioral outcomes. Accordingly, early gut microbiota composition (GMC) has been linked to child temperament, but research is still scarce. The aim of this study was to examine how early GMC at 2.5 months is associated with child negative and fear reactivity at 8 and 12 months since they are potentially important intermediate phenotypes of later child psychiatric disorders. METHODS: Our study population was 330 infants enrolled in the longitudinal FinnBrain Birth Cohort Study. Gut microbiota composition was analyzed using stool sample 16s rRNA sequencing. Negative and fear reactivity were assessed using the Laboratory Temperament Assessment Battery (Lab-TAB) at child's age of 8 months (n =150) and the Infant Behavior Questionnaire-Revised Short Form (IBQ-R SF) at child's age of 12 months (n = 276). CONCLUSIONS: We found a positive association between alpha diversity and reported fear reactivity and differing microbial community composition based on negative reactivity for boys. Isobutyric acid correlated with observed negative reactivity, however, this association attenuated in the linear model. Several genera were associated with the selected infant temperament traits. This study adds to the growing literature on links between infant gut microbiota and temperament informing future mechanistic studies.

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.

Microbiome-based risk prediction in incident heart failure: a community challenge.

Heart failure (HF) is a major public health problem. Early identification of at-risk individuals could allow for interventions that reduce morbidity or mortality. The community-based FINRISK Microbiome DREAM challenge (synapse.org/finrisk) evaluated the use of machine learning approaches on shotgun metagenomics data obtained from fecal samples to predict incident HF risk over 15 years in a population cohort of 7231 Finnish adults (FINRISK 2002, n=559 incident HF cases). Challenge participants used synthetic data for model training and testing. Final models submitted by seven teams were evaluated in the real data. The two highest-scoring models were both based on Cox regression but used different feature selection approaches. We aggregated their predictions to create an ensemble model. Additionally, we refined the models after the DREAM challenge by eliminating phylum information. Models were also evaluated at intermediate timepoints and they predicted 10-year incident HF more accurately than models for 5- or 15-year incidence. We found that bacterial species, especially those linked to inflammation, are predictive of incident HF. This highlights the role of the gut microbiome as a potential driver of inflammation in HF pathophysiology. Our results provide insights into potential modeling strategies of microbiome data in prospective cohort studies. Overall, this study provides evidence that incorporating microbiome information into incident risk models can provide important biological insights into the pathogenesis of HF.