Perception of motion salience shapes the emergence of collective motions.

Despite the profound implications of self-organization in animal groups for collective behaviors, understanding the fundamental principles and applying them to swarm robotics remains incomplete. Here we propose a heuristic measure of perception of motion salience (MS) to quantify relative motion changes of neighbors from first-person view. Leveraging three large bird-flocking datasets, we explore how this perception of MS relates to the structure of leader-follower (LF) relations, and further perform an individual-level correlation analysis between past perception of MS and future change rate of velocity consensus. We observe prevalence of the positive correlations in real flocks, which demonstrates that individuals will accelerate the convergence of velocity with neighbors who have higher MS. This empirical finding motivates us to introduce the concept of adaptive MS-based (AMS) interaction in swarm model. Finally, we implement AMS in a swarm of ~102 miniature robots. Swarm experiments show the significant advantage of AMS in enhancing self-organization of the swarm for smooth evacuations from confined environments.

Rational Design of Live Biotherapeutic Products for the Prevention of Clostridioides difficile Infection.

Clostridioides difficile infection (CDI) is one of the leading causes of healthcare- and antibiotic-associated diarrhea. While fecal microbiota transplantation (FMT) has emerged as a promising therapy for recurrent CDI, its exact mechanisms of action and long-term safety are not fully understood. Defined consortia of clonal bacterial isolates, known as live biotherapeutic products (LBPs), have been proposed as an alternative therapeutic option. However, the rational design of LBPs remains challenging. Here, we employ a computational pipeline and three independent metagenomic datasets to systematically identify microbial strains that have the potential to inhibit CDI. We first constructed the CDI-related microbial genome catalog, comprising 3,741 non-redundant metagenome-assembled genomes (nrMAGs) at the strain level. We then identified multiple potential protective nrMAGs that can be candidates for the design of microbial consortia targeting CDI, including strains from Dorea formicigenerans, Oscillibacter welbionis, and Faecalibacterium prausnitzii. Importantly, some of these potential protective nrMAGs were found to play an important role in the success of FMT, and the majority of the top protective nrMAGs can be validated by various previously reported findings. Our results demonstrate a computational framework for the rational selection of microbial strains targeting CDI, paving the way for the computational design of microbial consortia against other enteric infections.

Pairing metagenomics and metaproteomics to characterize ecological niches and metabolic essentiality of gut microbiomes.

The genome of a microorganism encodes its potential functions that can be implemented through expressed proteins. It remains elusive how a protein's selective expression depends on its metabolic essentiality to microbial growth or its ability to claim resources as ecological niches. To reveal a protein's metabolic or ecological role, we developed a computational pipeline, which pairs metagenomics and metaproteomics data to quantify each protein's gene-level and protein-level functional redundancy simultaneously. We first illustrated the idea behind the pipeline using simulated data of a consumer-resource model. We then validated it using real data from human and mouse gut microbiome samples. In particular, we analyzed ABC-type transporters and ribosomal proteins, confirming that the metabolic and ecological roles predicted by our pipeline agree well with prior knowledge. Finally, we performed in vitro cultures of a human gut microbiome sample and investigated how oversupplying various sugars involved in ecological niches influences the community structure and protein abundance. The presented results demonstrate the performance of our pipeline in identifying proteins' metabolic and ecological roles, as well as its potential to help us design nutrient interventions to modulate the human microbiome.

Dynamics of collective cooperation under personalised strategy updates.

Collective cooperation is essential for many social and biological systems, yet understanding how it evolves remains a challenge. Previous investigations report that the ubiquitous heterogeneous individual connections hinder cooperation by assuming individuals update strategies at identical rates. Here we develop a general framework by allowing individuals to update strategies at personalised rates, and provide the precise mathematical condition under which universal cooperation is favoured. Combining analytical and numerical calculations on synthetic and empirical networks, we find that when individuals' update rates vary inversely with their number of connections, heterogeneous connections actually outperform homogeneous ones in promoting cooperation. This surprising property undercuts the conventional wisdom that heterogeneous structure is generally antagonistic to cooperation and, further helps develop an efficient algorithm OptUpRat to optimise collective cooperation by designing individuals' update rates in any population structure. Our findings provide a unifying framework to understand the interplay between structural heterogeneity, behavioural rhythms, and cooperation.

Data-driven prediction of colonization outcomes for complex microbial communities.

Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.

Fulfilled Mind, Healthy Gut? Relationships of Eudaimonic Psychological Well-Being With the Gut Microbiome in Postmenopausal Women.

OBJECTIVE: Eudaimonic facets of psychological well-being (PWB), like purpose in life and sense of mastery, are associated with healthy aging. Variation in the gut microbiome may be one pathway by which mental health influences age-related health outcomes. However, associations between eudaimonic PWB and the gut microbiome are understudied. We examined whether purpose in life and sense of mastery, separately, were associated with features of the gut microbiome in older women. METHODS: Participants were from the Mind-Body Study ( N = 206, mean age = 61 years), a substudy of the Nurses' Health Study II cohort. In 2013, participants completed the Life Engagement Test and the Pearlin Mastery Scale. Three months later, up to two pairs of stool samples were collected, 6 months apart. Covariates included sociodemographics, depression, health status, and health behaviors. Analyses examined associations of PWB with gut microbiome taxonomic diversity, overall community structure, and specific species/pathways. To account for multiple testing, statistical significance was established using Benjamini-Hochberg adjusted p values (i.e., q values ≤0.25). RESULTS: We found no evidence of an association between PWB and gut microbiome alpha diversity. In multivariate analysis, higher purpose levels were significantly associated with lower abundance of species previously linked with poorer health outcomes, notably Blautia hydrogenotrophica and Eubacterium ventriosum ( q values ≤0.25). No significant associations were found between PWB and metabolic pathways. CONCLUSIONS: These findings offer early evidence suggesting that eudaimonic PWB is linked with variation in the gut microbiome, and this might be one pathway by which PWB promotes healthy aging.

Structured community transitions explain the switching capacity of microbial systems.

Microbial systems appear to exhibit a relatively high switching capacity of moving back and forth among few dominant communities (taxon memberships). While this switching behavior has been mainly attributed to random environmental factors, it remains unclear the extent to which internal community dynamics affect the switching capacity of microbial systems. Here, we integrate ecological theory and empirical data to demonstrate that structured community transitions increase the dependency of future communities on the current taxon membership, enhancing the switching capacity of microbial systems. Following a structuralist approach, we propose that each community is feasible within a unique domain in environmental parameter space. Then, structured transitions between any two communities can happen with probability proportional to the size of their feasibility domains and inversely proportional to their distance in environmental parameter space-which can be treated as a special case of the gravity model. We detect two broad classes of systems with structured transitions: one class where switching capacity is high across a wide range of community sizes and another class where switching capacity is high only inside a narrow size range. We corroborate our theory using temporal data of gut and oral microbiota (belonging to class 1) as well as vaginal and ocean microbiota (belonging to class 2). These results reveal that the topology of feasibility domains in environmental parameter space is a relevant property to understand the changing behavior of microbial systems. This knowledge can be potentially used to understand the relevant community size at which internal dynamics can be operating in microbial systems.

Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments.

Since dietary intake is challenging to directly measure in large-scale cohort studies, we often rely on self-reported instruments (e.g., food frequency questionnaires, 24-hour recalls, and diet records) developed in nutritional epidemiology. Those self-reported instruments are prone to measurement errors, which can lead to inaccuracies in the calculation of nutrient profiles. Currently, few computational methods exist to address this problem. In the present study, we introduce a deep-learning approach --- Microbiome-based nutrient profile corrector (METRIC), which leverages gut microbial compositions to correct random errors in self-reported dietary assessments using 24-hour recalls or diet records. We demonstrate the excellent performance of METRIC in minimizing the simulated random errors, particularly for nutrients metabolized by gut bacteria in both synthetic and three real-world datasets. Further research is warranted to examine the utility of METRIC to correct actual measurement errors in self-reported dietary assessment instruments.

Identifying keystone species in microbial communities using deep learning.

Previous studies suggested that microbial communities can harbour keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. Here we propose a data-driven keystone species identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep-learning model using microbiome samples collected from this habitat. The well-trained deep-learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data and applied DKI to analyse real data. We found that those taxa with high median keystoneness across different communities display strong community specificity. The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem in community ecology, paving the way for the data-driven management of complex microbial communities.

Genotype-microbiome-metabolome associations in early childhood, and their link to BMI and childhood obesity.

The influence of genotype on defining the human gut microbiome has been extensively studied, but definite conclusions have not yet been found. To fill this knowledge gap, we leverage data from children enrolled in the Vitamin D Antenatal Asthma Reduction Trial (VDAART) from 6 months to 8 years old. We focus on a pool of 12 genes previously found to be associated with the gut microbiome in independent studies, establishing a Bonferroni corrected significance level of p-value < 2.29 × 10 -6 . We identified significant associations between SNPs in the FHIT gene (known to be associated with obesity and type 2 diabetes) and obesity-related microbiome features, and the children's BMI through their childhood. Based on these associations, we defined a set of SNPs of interest and a set of taxa of interest. Taking a multi-omics approach, we integrated plasma metabolome data into our analysis and found simultaneous associations among children's BMI, the SNPs of interest, and the taxa of interest, involving amino acids, lipids, nucleotides, and xenobiotics. Using our association results, we constructed a quadripartite graph where each disjoint node set represents SNPs in the FHIT gene, microbial taxa, plasma metabolites, or BMI measurements. Network analysis led to the discovery of patterns that identify several genetic variants, microbial taxa and metabolites as new potential markers for obesity, type 2 diabetes, or insulin resistance risk.

Early life gut microbiome in children following spontaneous preterm birth and maternal preeclampsia.

The early life microbiome plays an important role in developmental and long-term health outcomes. However, it is unknown whether adverse pregnancy complications affect the offspring's gut microbiome postnatally and in early years. In a longitudinal cohort with a five-year follow-up of mother-child pairs affected by preeclampsia (PE) or spontaneous preterm birth (sPTB), we evaluated offspring gut alpha and beta diversity as well as taxa abundances considering factors like breastfeeding and mode of delivery. Our study highlights a trend where microbiome diversity exhibits comparable development across adverse and normal pregnancies. However, specific taxa at genus level emerge with distinctive abundances, showing enrichment and/or depletion over time in relation to PE or sPTB. These findings underscore the potential for certain adverse pregnancy complications to induce alterations in the offspring's microbiome over the course of early life. The implications of these findings on the immediate and long-term health of offspring should be investigated in future studies.

Removal of false positives in metagenomics-based taxonomy profiling via targeting Type IIB restriction sites.

Accurate species identification and abundance estimation are critical for the interpretation of whole metagenome sequencing (WMS) data. Yet, existing metagenomic profilers suffer from false-positive identifications, which can account for more than 90% of total identified species. Here, by leveraging species-specific Type IIB restriction endonuclease digestion sites as reference instead of universal markers or whole microbial genomes, we present a metagenomic profiler, MAP2B (MetAgenomic Profiler based on type IIB restriction sites), to resolve those issues. We first illustrate the pitfalls of using relative abundance as the only feature in determining false positives. We then propose a feature set to distinguish false positives from true positives, and using simulated metagenomes from CAMI2, we establish a false-positive recognition model. By benchmarking the performance in metagenomic profiling using a simulation dataset with varying sequencing depth and species richness, we illustrate the superior performance of MAP2B over existing metagenomic profilers in species identification. We further test the performance of MAP2B using real WMS data from an ATCC mock community, confirming its superior precision against sequencing depth. Finally, by leveraging WMS data from an IBD cohort, we demonstrate the taxonomic features generated by MAP2B can better discriminate IBD and predict metabolomic profiles.

The emerging role of the gut microbiome in posttraumatic stress disorder.

Posttraumatic stress disorder (PTSD) occurs in some people following exposure to a terrifying or catastrophic event involving actual/threatened death, serious injury, or sexual violence. PTSD is a common and debilitating mental disorder that imposes a significant burden on individuals, their families, health services, and society. Moreover, PTSD is a risk factor for chronic diseases such as coronary heart disease, stroke, diabetes, as well as premature mortality. Furthermore, PTSD is associated with dysregulated immune function. Despite the high prevalence of PTSD, the mechanisms underlying its etiology and manifestations remain poorly understood. Compelling evidence indicates that the human gut microbiome, a complex community of microorganisms living in the gastrointestinal tract, plays a crucial role in the development and function of the host nervous system, complex behaviors, and brain circuits. The gut microbiome may contribute to PTSD by influencing inflammation, stress responses, and neurotransmitter signaling, while bidirectional communication between the gut and brain involves mechanisms such as microbial metabolites, immune system activation, and the vagus nerve. In this literature review, we summarize recent findings on the role of the gut microbiome in PTSD in both human and animal studies. We discuss the methodological limitations of existing studies and suggest future research directions to further understand the role of the gut microbiome in PTSD.

A computational method to dissect colonization resistance of the gut microbiota against pathogens.

The mammalian gut microbiome protects the host through colonization resistance (CR) against the incursion of exogenous and often harmful microorganisms, but identifying the exact microbes responsible for the gut microbiota-mediated CR against a particular pathogen remains a challenge. To address this limitation, we developed a computational method: generalized microbe-phenotype triangulation (GMPT). We first systematically validated GMPT using a classical population dynamics model in community ecology and demonstrated its superiority over baseline methods. We then tested GMPT on simulated data generated from the ecological network inferred from a real community (GnotoComplex microflora) and real microbiome data on two mouse studies on Clostridioides difficile infection. We demonstrated GMPT's ability to streamline the discovery of microbes that are potentially responsible for microbiota-mediated CR against pathogens. GMPT holds promise to advance our understanding of CR mechanisms and facilitate the rational design of microbiome-based therapies for preventing and treating enteric infections.

Association of Vitamin D Level and Maternal Gut Microbiome during Pregnancy: Findings from a Randomized Controlled Trial of Antenatal Vitamin D Supplementation.

Shifts in the maternal gut microbiome and vitamin D deficiency during pregnancy have been associated, separately, with health problems for both the mother and the child. Yet, they have rarely been studied simultaneously. Here, we analyzed the gut microbiome (from stool samples obtained in late pregnancy) and vitamin D level (from blood samples obtained both in early and late pregnancy) data of pregnant women in the Vitamin D Antenatal Asthma Reduction Trial (VDAART), a randomized controlled trial of vitamin D supplementation during pregnancy, to investigate the association of vitamin D status on the pregnant women's microbiome. To find associations, we ran linear regressions on alpha diversity measures, PERMANOVA tests on beta diversity distances, and used the ANCOM-BC and Maaslin2 algorithms to find differentially abundant taxa. Analyses were deemed significant using a cut-off p-value of 0.05. We found that gut microbiome composition is associated with the vitamin D level in early pregnancy (baseline), the maternal gut microbiome does not show a shift in response to vitamin D supplementation during pregnancy, and that the genus Desulfovibrio is enriched in women without a substantial increase in vitamin D level between the first and the third trimesters of pregnancy. We conclude that increasing the vitamin D level during pregnancy could be protective against the growth of sulfate-reducing bacteria such as Desulfovibrio, which has been associated with chronic intestinal inflammatory disorders. More in-depth investigations are needed to confirm this hypothesis.

Nutritional redundancy in the human diet and its application in phenotype association studies.

Studying human dietary intake may help us identify effective measures to treat or prevent many chronic diseases whose natural histories are influenced by nutritional factors. Here, by examining five cohorts with dietary intake data collected on different time scales, we show that the food intake profile varies substantially across individuals and over time, while the nutritional intake profile appears fairly stable. We refer to this phenomenon as 'nutritional redundancy' and attribute it to the nested structure of the food-nutrient network. This network enables us to quantify the level of nutritional redundancy for each diet assessment of any individual. Interestingly, this nutritional redundancy measure does not strongly correlate with any classical healthy diet scores, but its performance in predicting healthy aging shows comparable strength. Moreover, after adjusting for age, we find that a high nutritional redundancy is associated with lower risks of cardiovascular disease and type 2 diabetes.

Revealing proteome-level functional redundancy in the human gut microbiome using ultra-deep metaproteomics.

Functional redundancy is a key ecosystem property representing the fact that different taxa contribute to an ecosystem in similar ways through the expression of redundant functions. The redundancy of potential functions (or genome-level functional redundancy [Formula: see text]) of human microbiomes has been recently quantified using metagenomics data. Yet, the redundancy of expressed functions in the human microbiome has never been quantitatively explored. Here, we present an approach to quantify the proteome-level functional redundancy [Formula: see text] in the human gut microbiome using metaproteomics. Ultra-deep metaproteomics reveals high proteome-level functional redundancy and high nestedness in the human gut proteomic content networks (i.e., the bipartite graphs connecting taxa to functions). We find that the nested topology of proteomic content networks and relatively small functional distances between proteomes of certain pairs of taxa together contribute to high [Formula: see text] in the human gut microbiome. As a metric comprehensively incorporating the factors of presence/absence of each function, protein abundances of each function and biomass of each taxon, [Formula: see text] outcompetes diversity indices in detecting significant microbiome responses to environmental factors, including individuality, biogeography, xenobiotics, and disease. We show that gut inflammation and exposure to specific xenobiotics can significantly diminish the [Formula: see text] with no significant change in taxonomic diversity.

A Survey on Hyperlink Prediction.

As a natural extension of link prediction on graphs, hyperlink prediction aims for the inference of missing hyperlinks in hypergraphs, where a hyperlink can connect more than two nodes. Hyperlink prediction has applications in a wide range of systems, from chemical reaction networks and social communication networks to protein-protein interaction networks. In this article, we provide a systematic and comprehensive survey on hyperlink prediction. We adopt a classical taxonomy from link prediction to classify the existing hyperlink prediction methods into four categories: similarity-based, probability-based, matrix optimization-based, and deep learning-based methods. To compare the performance of methods from different categories, we perform a benchmark study on various hypergraph applications using representative methods from each category. Notably, deep learning-based methods prevail over other methods in hyperlink prediction.