Widespread macromolecular interaction perturbations in human genetic disorders.

How disease-associated mutations impair protein activities in the context of biological networks remains mostly undetermined. Although a few renowned alleles are well characterized, functional information is missing for over 100,000 disease-associated variants. Here we functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays. The majority of disease-associated alleles exhibit wild-type chaperone binding profiles, suggesting they preserve protein folding or stability. While common variants from healthy individuals rarely affect interactions, two-thirds of disease-associated alleles perturb protein-protein interactions, with half corresponding to "edgetic" alleles affecting only a subset of interactions while leaving most other interactions unperturbed. With transcription factors, many alleles that leave protein-protein interactions intact affect DNA binding. Different mutations in the same gene leading to different interaction profiles often result in distinct disease phenotypes. Thus disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread.

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.

A statistical physics approach for disease module detection.

Extensive evidence indicates that the pathobiological processes of a complex disease are associated with perturbation in specific neighborhoods of the human protein-protein interaction (PPI) network (also known as the interactome), often referred to as the disease module. Many computational methods have been developed to integrate the interactome and omics profiles to extract context-dependent disease modules. Yet, existing methods all have fundamental limitations in terms of rigor and/or efficiency. Here, we developed a statistical physics approach based on the random-field Ising model (RFIM) for disease module detection, which is both mathematically rigorous and computationally efficient. We applied our RFIM approach to genome-wide association studies (GWAS) of ten complex diseases to examine its performance for disease module detection. We found that our RFIM approach outperforms existing methods in terms of computational efficiency, connectivity of disease modules, and robustness to the interactome incompleteness.

Challenges in benchmarking metagenomic profilers.

Accurate microbial identification and abundance estimation are crucial for metagenomics analysis. Various methods for classification of metagenomic data and estimation of taxonomic profiles, broadly referred to as metagenomic profilers, have been developed. Nevertheless, benchmarking of metagenomic profilers remains challenging because some tools are designed to report relative sequence abundance while others report relative taxonomic abundance. Here we show how misleading conclusions can be drawn by neglecting this distinction between relative abundance types when benchmarking metagenomic profilers. Moreover, we show compelling evidence that interchanging sequence abundance and taxonomic abundance will influence both per-sample summary statistics and cross-sample comparisons. We suggest that the microbiome research community pay attention to potentially misleading biological conclusions arising from this issue when benchmarking metagenomic profilers, by carefully considering the type of abundance data that were analyzed and interpreted and clearly stating the strategy used for metagenomic profiling.

Graph convolutional network-based feature selection for high-dimensional and low-sample size data.

MOTIVATION: Feature selection is a powerful dimension reduction technique which selects a subset of relevant features for model construction. Numerous feature selection methods have been proposed, but most of them fail under the high-dimensional and low-sample size (HDLSS) setting due to the challenge of overfitting. RESULTS: We present a deep learning-based method-GRAph Convolutional nEtwork feature Selector (GRACES)-to select important features for HDLSS data. GRACES exploits latent relations between samples with various overfitting-reducing techniques to iteratively find a set of optimal features which gives rise to the greatest decreases in the optimization loss. We demonstrate that GRACES significantly outperforms other feature selection methods on both synthetic and real-world datasets. AVAILABILITY AND IMPLEMENTATION: The source code is publicly available at https://github.com/canc1993/graces.

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.

Structure-based identification of sensor species for anticipating critical transitions.

Ecological systems can undergo sudden, catastrophic changes known as critical transitions. Anticipating these critical transitions remains challenging in systems with many species because the associated early warning signals can be weakly present or even absent in some species, depending on the system dynamics. Therefore, our limited knowledge of ecological dynamics may suggest that it is hard to identify those species in the system that display early warning signals. Here, we show that, in mutualistic ecological systems, it is possible to identify species that early anticipate critical transitions by knowing only the system structure-that is, the network topology of plant-animal interactions. Specifically, we leverage the mathematical theory of structural observability of dynamical systems to identify a minimum set of "sensor species," whose measurement guarantees that we can infer changes in the abundance of all other species. Importantly, such a minimum set of sensor species can be identified by using the system structure only. We analyzed the performance of such minimum sets of sensor species for detecting early warnings using a large dataset of empirical plant-pollinator and seed-dispersal networks. We found that species that are more likely to be sensors tend to anticipate earlier critical transitions than other species. Our results underscore how knowing the structure of multispecies systems can improve our ability to anticipate critical transitions.

Benchmarking omics-based prediction of asthma development in children.

BACKGROUND: Asthma is a heterogeneous disease with high morbidity. Advancement in high-throughput multi-omics approaches has enabled the collection of molecular assessments at different layers, providing a complementary perspective of complex diseases. Numerous computational methods have been developed for the omics-based patient classification or disease outcome prediction. Yet, a systematic benchmarking of those methods using various combinations of omics data for the prediction of asthma development is still lacking. OBJECTIVE: We aimed to investigate the computational methods in disease status prediction using multi-omics data. METHOD: We systematically benchmarked 18 computational methods using all the 63 combinations of six omics data (GWAS, miRNA, mRNA, microbiome, metabolome, DNA methylation) collected in The Vitamin D Antenatal Asthma Reduction Trial (VDAART) cohort. We evaluated each method using standard performance metrics for each of the 63 omics combinations. RESULTS: Our results indicate that overall Logistic Regression, Multi-Layer Perceptron, and MOGONET display superior performance, and the combination of transcriptional, genomic and microbiome data achieves the best prediction. Moreover, we find that including the clinical data can further improve the prediction performance for some but not all the omics combinations. CONCLUSIONS: Specific omics combinations can reach the optimal prediction of asthma development in children. And certain computational methods showed superior performance than other methods.

Gut feelings: associations of emotions and emotion regulation with the gut microbiome in women.

BACKGROUND: Accumulating evidence suggests that positive and negative emotions, as well as emotion regulation, play key roles in human health and disease. Recent work has shown the gut microbiome is important in modulating mental and physical health through the gut-brain axis. Yet, its association with emotions and emotion regulation are understudied. Here we examined whether positive and negative emotions, as well as two emotion regulation strategies (i.e. cognitive reappraisal and suppression), were associated with the gut microbiome composition and functional pathways in healthy women. METHODS: Participants were from the Mind-Body Study (N = 206, mean age = 61), a sub-study of the Nurses' Health Study II cohort. In 2013, participants completed measures of emotion-related factors. Two pairs of stool samples were collected, 6 months apart, 3 months after emotion-related factors measures were completed. Analyses examined associations of emotion-related factors with gut microbial diversity, overall microbiome structure, and specific species/pathways and adjusted for relevant covariates. RESULTS: Alpha diversity was negatively associated with suppression. In multivariate analysis, positive emotions were inversely associated with the relative abundance of Firmicutes bacterium CAG 94 and Ruminococcaceae bacterium D16, while negative emotions were directly correlated with the relative abundance of these same species. At the metabolic pathway level, negative emotions were inversely related to the biosynthesis of pantothenate, coenzyme A, and adenosine. CONCLUSIONS: These findings offer human evidence supporting linkages of emotions and related regulatory processes with the gut microbiome and highlight the importance of incorporating the gut microbiome in our understanding of emotion-related factors and their associations with physical health.

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.

Identifying chronic obstructive pulmonary disease from integrative omics and clustering in lung tissue.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a highly morbid and heterogenous disease. While COPD is defined by spirometry, many COPD characteristics are seen in cigarette smokers with normal spirometry. The extent to which COPD and COPD heterogeneity is captured in omics of lung tissue is not known. METHODS: We clustered gene expression and methylation data in 78 lung tissue samples from former smokers with normal lung function or severe COPD. We applied two integrative omics clustering methods: (1) Similarity Network Fusion (SNF) and (2) Entropy-Based Consensus Clustering (ECC). RESULTS: SNF clusters were not significantly different by the percentage of COPD cases (48.8% vs. 68.6%, p = 0.13), though were different according to median forced expiratory volume in one second (FEV1) % predicted (82 vs. 31, p = 0.017). In contrast, the ECC clusters showed stronger evidence of separation by COPD case status (48.2% vs. 81.8%, p = 0.013) and similar stratification by median FEV1% predicted (82 vs. 30.5, p = 0.0059). ECC clusters using both gene expression and methylation were identical to the ECC clustering solution generated using methylation data alone. Both methods selected clusters with differentially expressed transcripts enriched for interleukin signaling and immunoregulatory interactions between lymphoid and non-lymphoid cells. CONCLUSIONS: Unsupervised clustering analysis from integrated gene expression and methylation data in lung tissue resulted in clusters with modest concordance with COPD, though were enriched in pathways potentially contributing to COPD-related pathology and heterogeneity.

Association of the gut microbiome and metabolome with wheeze frequency in childhood asthma.

BACKGROUND: While the microbiome has an established role in asthma development, less is known about its contribution to morbidity in children with asthma. OBJECTIVE: In this ancillary study of the Vitamin D Antenatal Asthma Reduction Trial (VDAART), we analyzed the gut microbiome and metabolome of wheeze frequency in children with asthma. METHODS: Bacterial 16S ribosomal RNA microbiome and untargeted metabolomic profiling were performed on fecal samples collected from 3-year-old children with parent-reported physician-diagnosed asthma. We analyzed wheeze frequency by calculating the proportion of quarterly questionnaires administered between ages 3 and 5 years in which parents reported the child had wheezed (wheeze proportion). Taxa and metabolites associated with wheeze were analyzed by identifying log fold changes with respect to wheeze frequency and correlation/linear regression analyses, respectively. Microbe-metabolite and microbe-microbe correlation networks were compared between subjects with high and low wheeze proportion. RESULTS: Specific taxa, including the genus Veillonella and histidine pathway metabolites, were enriched in subjects with high wheeze proportion. Among wheeze-associated taxa, Veillonella and Oscillospiraceae UCG-005, which was inversely associated with wheeze, were correlated with the greatest number of fecal metabolites. Microbial networks were similar between subjects with low versus high wheeze frequency. CONCLUSION: Gut microbiome features are associated with wheeze frequency in children with asthma, suggesting an impact of the gut microbiome on morbidity in childhood asthma.

Fecal Mycobiota Combined With Host Immune Factors Distinguish Clostridioides difficile Infection From Asymptomatic Carriage.

BACKGROUND & AIMS: Although the role of gut microbiota in Clostridioides difficile infection (CDI) has been well established, little is known about the role of mycobiota in CDI. Here, we performed mycobiome data analysis in a well-characterized human cohort to evaluate the potential of using gut mycobiota features for CDI diagnosis. METHODS: Stool samples were collected from 118 hospital patients, divided into 3 groups: CDI (n = 58), asymptomatic carriers (Carrier, n = 28), and Control (n = 32). The nuclear ribosomal DNA internal transcribed spacer 2 was sequenced using the Illumina HiSeq platform to assess the fungal composition. Downstream statistical analyses (including Alpha diversity analysis, ordination analysis, differential abundance analysis, fungal correlation network analysis, and classification analysis) were then performed. RESULTS: Significant differences were observed in alpha and beta diversity between patients with CDI and Carrier (P < .05). Differential abundance analysis identified 2 genera (Cladosporium and Aspergillus) enriched in Carrier. The ratio of Ascomycota to Basidiomycota was dramatically higher in patients with CDI than in Carrier and Control (P < .05). Correlations between host immune factors and mycobiota features were weaker in patients with CDI than in Carrier. Using 4 fungal operational taxonomic units combined with 6 host immune markers in the random forest classifier can achieve very high performance (area under the curve ∼92.38%) in distinguishing patients with CDI from Carrier. CONCLUSIONS: Our study provides specific markers of stool fungi combined with host immune factors to distinguish patients with CDI from Carrier. It highlights the importance of gut mycobiome in CDI, which may have been underestimated. Further studies on the diagnostic applications and therapeutic potentials of these findings are warranted.

Universality of human microbial dynamics.

Human-associated microbial communities have a crucial role in determining our health and well-being, and this has led to the continuing development of microbiome-based therapies such as faecal microbiota transplantation. These microbial communities are very complex, dynamic and highly personalized ecosystems, exhibiting a high degree of inter-individual variability in both species assemblages and abundance profiles. It is not known whether the underlying ecological dynamics of these communities, which can be parameterized by growth rates, and intra- and inter-species interactions in population dynamics models, are largely host-independent (that is, universal) or host-specific. If the inter-individual variability reflects host-specific dynamics due to differences in host lifestyle, physiology or genetics, then generic microbiome manipulations may have unintended consequences, rendering them ineffective or even detrimental. Alternatively, microbial ecosystems of different subjects may exhibit universal dynamics, with the inter-individual variability mainly originating from differences in the sets of colonizing species. Here we develop a new computational method to characterize human microbial dynamics. By applying this method to cross-sectional data from two large-scale metagenomic studies--the Human Microbiome Project and the Student Microbiome Project--we show that gut and mouth microbiomes display pronounced universal dynamics, whereas communities associated with certain skin sites are probably shaped by differences in the host environment. Notably, the universality of gut microbial dynamics is not observed in subjects with recurrent Clostridium difficile infection but is observed in the same set of subjects after faecal microbiota transplantation. These results fundamentally improve our understanding of the processes that shape human microbial ecosystems, and pave the way to designing general microbiome-based therapies.

A tensor-based framework for studying eigenvector multicentrality in multilayer networks.

Centrality is widely recognized as one of the most critical measures to provide insight into the structure and function of complex networks. While various centrality measures have been proposed for single-layer networks, a general framework for studying centrality in multilayer networks (i.e., multicentrality) is still lacking. In this study, a tensor-based framework is introduced to study eigenvector multicentrality, which enables the quantification of the impact of interlayer influence on multicentrality, providing a systematic way to describe how multicentrality propagates across different layers. This framework can leverage prior knowledge about the interplay among layers to better characterize multicentrality for varying scenarios. Two interesting cases are presented to illustrate how to model multilayer influence by choosing appropriate functions of interlayer influence and design algorithms to calculate eigenvector multicentrality. This framework is applied to analyze several empirical multilayer networks, and the results corroborate that it can quantify the influence among layers and multicentrality of nodes effectively.

Revealing Complex Ecological Dynamics via Symbolic Regression.

Understanding the dynamics of complex ecosystems is a necessary step to maintain and control them. Yet, reverse-engineering ecological dynamics remains challenging largely due to the very broad class of dynamics that ecosystems may take. Here, this challenge is tackled through symbolic regression, a machine learning method that automatically reverse-engineers both the model structure and parameters from temporal data. How combining symbolic regression with a "dictionary" of possible ecological functional responses opens the door to correctly reverse-engineering ecosystem dynamics, even in the case of poorly informative data, is shown. This strategy is validated using both synthetic and experimental data, and it is found that this strategy is promising for the systematic modeling of complex ecological systems.

Covering Problems and Core Percolations on Hypergraphs.

We introduce two generalizations of core percolation in graphs to hypergraphs, related to the minimum hyperedge cover problem and the minimum vertex cover problem on hypergraphs, respectively. We offer analytical solutions of these two core percolations for uncorrelated random hypergraphs whose vertex degree and hyperedge cardinality distributions are arbitrary but have nondiverging moments. We find that for several real-world hypergraphs their two cores tend to be much smaller than those of their null models, suggesting that covering problems in those real-world hypergraphs can actually be solved in polynomial time.

Control of fluxes in metabolic networks.

Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism.

Timing for the second fecal microbiota transplantation to maintain the long-term benefit from the first treatment for Crohn's disease.

Increasing evidence has shown that fecal microbiota transplantation (FMT) could be a promising treatment option for Crohn's disease (CD). However, the frequency of FMT for CD treatment remains unclear. This study aimed to evaluate the optimal timing for administering the second course of FMT to maintain the long-term clinical effects from the first FMT for patients with CD. Sixty-nine patients with active CD who underwent FMT twice and benefited from the first FMT were enrolled in this study. Clinical response, stool microbiota, and urine metabolome of patients were assessed during the follow-up. The median time of maintaining clinical response to the first FMT in total 69 patients was 125 days (IQR, 82.5-225.5). The time of maintaining clinical response to the second FMT in 56 of 69 patients was 176.5 days (IQR, 98.5-280). The fecal microbiota composition of each patient post the first FMT was closer to that of his/her donor. Compared to that of the baseline, patients prior to the second course of FMT showed significant differences in urinary metabolic profiles characterized by increased indoxyl sulfate, 4-hydroxyphenylacetate, creatinine, dimethylamine, glycylproline, hippurate, and trimethylamine oxide (TMAO). This study demonstrated that patients with CD could be administered the second course of FMT less than 4 months after the first FMT for maintaining the clinical benefits from the first FMT. This was supported by the host-microbial metabolism changes in patients with active CD. Trial registration: ClinicalTrials.gov , NCT01793831. Registered 18 February 2013. https://clinicaltrials.gov/ct2/show/NCT01793831?term=NCT01793831&rank=1.

Inferring human microbial dynamics from temporal metagenomics data: Pitfalls and lessons.

The human gut microbiota is a very complex and dynamic ecosystem that plays a crucial role in health and well-being. Inferring microbial community structure and dynamics directly from time-resolved metagenomics data is key to understanding the community ecology and predicting its temporal behavior. Many methods have been proposed to perform the inference. Yet, as we point out in this review, there are several pitfalls along the way. Indeed, the uninformative temporal measurements and the compositional nature of the relative abundance data raise serious challenges in inference. Moreover, the inference results can be largely distorted when only focusing on highly abundant species by ignoring or grouping low-abundance species. Finally, the implicit assumptions in various regularization methods may not reflect reality. Those issues have to be seriously considered in ecological modeling of human gut microbiota.