A Novel Human Microbe-Disease Association Prediction Method Based on the Bidirectional Weighted Network

The survival of human beings is inseparable from microbes. More and more studies have proved that microbes can affect human physiological processes in various aspects and are closely related to some human diseases. In this paper, based on known microbe-disease associations, a bidirectional weighted network was constructed by integrating the schemes of normalized Gaussian interactions and bidirectional recommendations firstly. And then, based on the newly constructed bidirectional network, a computational model called BWNMHMDA was developed to predict potential relationships between microbes and diseases. Finally, in order to evaluate the superiority of the new prediction model BWNMHMDA, the framework of LOOCV and 5-fold cross validation were implemented, and simulation results indicated that BWNMHMDA could achieve reliable AUCs of 0.9127 and 0.8967 ± 0.0027 in these two different frameworks respectively, which is outperformed some state-of-the-art methods. Moreover, case studies of asthma, colorectal carcinoma, and chronic obstructive pulmonary disease were implemented to further estimate the performance of BWNMHMDA. Experimental results showed that there are 10, 9, and 8 out of the top 10 predicted microbes having been confirmed by related literature in these three kinds of case studies separately, which also demonstrated that our new model BWNMHMDA could achieve satisfying prediction performance

Application of Machine Learning in Microbiology

Microorganisms are ubiquitous and closely related to people’s daily lives. Since they were first discovered in the 19th century, researchers have shown great interest in microorganisms. People studied microorganisms through cultivation, but this method is expensive and time consuming. However, the cultivation method cannot keep a pace with the development of high-throughput sequencing technology. To deal with this problem, machine learning (ML) methods have been widely applied to the field of microbiology. Literature reviews have shown that ML can be used in many aspects of microbiology research, especially classification problems, and for exploring the interaction between microorganisms and the surrounding environment. In this study, we summarize the application of ML in microbiology

Human Microbe-Disease Association Prediction Based on Adaptive Boosting

There are countless microbes in the human body, and they play various roles in the physiological process. There is growing evidence that microbes are closely associated with human diseases. Researching disease-related microbes helps us understand the mechanisms of diseases and provides new strategies for diseases diagnosis and treatment. Many computational models have been proposed to predict disease-related microbes, in this paper, we developed a model of Adaptive Boosting for Human Microbe-Disease Association prediction (ABHMDA) to reveal the associations between diseases and microbes by calculating the relation probability of disease-microbe pair using a strong classifier. Our model could be applied to new diseases without any known related microbes. In order to assess the prediction power of the model, global and local leave-one-out cross validation (LOOCV) were implemented. As shown in the results, the global and local LOOCV values reached 0.8869 and 0.7910, respectively. What’s more, 10, 10, and 8 out of the top 10 microbes predicted to be most likely to be associated with Asthma, Colorectal carcinoma and Type 1 diabetes were all verified by relevant literatures or database HMDAD, respectively. The above results verify the superior predictive performance of ABHMDA

Exiting the Anthropocene : Achieving personal and planetary health in the 21st century

Planetary health provides a perspective of ecological interdependence that connects the health and vitality of individuals, communities, and Earth's natural systems. It includes the social, political, and economic ecosystems that influence both individuals and whole societies. In an era of interconnected grand challenges threatening health of all systems at all scales, planetary health provides a framework for cross-sectoral collaboration and unified systems approaches to solutions. The field of allergy is at the forefront of these efforts. Allergic conditions are a sentinel measure of environmental impact on human health in early life-illuminating how ecological changes affect immune development and predispose to a wider range of inflammatory noncommunicable diseases (NCDs). This shows how adverse macroscale ecology in the Anthropocene penetrates to the molecular level of personal and microscale ecology, including the microbial systems at the foundations of all ecosystems. It provides the basis for more integrated efforts to address widespread environmental degradation and adverse effects of maladaptive urbanization, food systems, lifestyle behaviors, and socioeconomic disadvantage. Nature-based solutions and efforts to improve nature-relatedness are crucial for restoring symbiosis, balance, and mutualism in every sense, recognizing that both personal lifestyle choices and collective structural actions are needed in tandem. Ultimately, meaningful ecological approaches will depend on placing greater emphasis on psychological and cultural dimensions such as mindfulness, values, and moral wisdom to ensure a sustainable and resilient future.Peer reviewe

A Bidirectional Label Propagation Based Computational Model for Potential Microbe-Disease Association Prediction

A growing number of clinical observations have indicated that microbes are involved in a variety of important human diseases. It is obvious that in-depth investigation of correlations between microbes and diseases will benefit the prevention, early diagnosis, and prognosis of diseases greatly. Hence, in this paper, based on known microbe-disease associations, a prediction model called NBLPIHMDA was proposed to infer potential microbe-disease associations. Specifically, two kinds of networks including the disease similarity network and the microbe similarity network were first constructed based on the Gaussian interaction profile kernel similarity. The bidirectional label propagation was then applied on these two kinds of networks to predict potential microbe-disease associations. We applied NBLPIHMDA on Human Microbe-Disease Association database (HMDAD), and compared it with 3 other recent published methods including LRLSHMDA, BiRWMP, and KATZHMDA based on the leave-one-out cross validation and 5-fold cross validation, respectively. As a result, the area under the receiver operating characteristic curves (AUCs) achieved by NBLPIHMDA were 0.8777 and 0.8958 ± 0.0027, respectively, outperforming the compared methods. In addition, in case studies of asthma, colorectal carcinoma, and Chronic obstructive pulmonary disease, simulation results illustrated that there are 10, 10, and 8 out of the top 10 predicted microbes having been confirmed by published documentary evidences, which further demonstrated that NBLPIHMDA is promising in predicting novel associations between diseases and microbes as well

Human Microbe-Disease Association Prediction With Graph Regularized Non-Negative Matrix Factorization

A microbe is a microscopic organism which may exists in its single-celled form or in a colony of cells. In recent years, accumulating researchers have been engaged in the field of uncovering microbe-disease associations since microbes are found to be closely related to the prevention, diagnosis, and treatment of many complex human diseases. As an effective supplement to the traditional experiment, more and more computational models based on various algorithms have been proposed for microbe-disease association prediction to improve efficiency and cost savings. In this work, we developed a novel predictive model of Graph Regularized Non-negative Matrix Factorization for Human Microbe-Disease Association prediction (GRNMFHMDA). Initially, microbe similarity and disease similarity were constructed on the basis of the symptom-based disease similarity and Gaussian interaction profile kernel similarity for microbes and diseases. Subsequently, it is worth noting that we utilized a preprocessing step in which unknown microbe-disease pairs were assigned associated likelihood scores to avoid the possible negative impact on the prediction performance. Finally, we implemented a graph regularized non-negative matrix factorization framework to identify potential associations for all diseases simultaneously. To assess the performance of our model, cross validations including global leave-one-out cross validation (LOOCV) and local LOOCV were implemented. The AUCs of 0.8715 (global LOOCV) and 0.7898 (local LOOCV) proved the reliable performance of our computational model. In addition, we carried out two types of case studies on three different human diseases to further analyze the prediction performance of GRNMFHMDA, in which most of the top 10 predicted disease-related microbes were verified by database HMDAD or experimental literatures

MDAD: A Special Resource for Microbe-Drug Associations

The human-associated microbiota is diverse and complex. It takes an essential role in human health and behavior and is closely related to the occurrence and development of disease. Although the diversity and distribution of microbial communities have been widely studied, little is known about the function and dynamics of microbes in the human body or the complex mechanisms of interaction between them and drugs, which are important for drug discovery and design. A high-quality comprehensive microbe and drug association database will be extremely beneficial to explore the relationship between them. In this article, we developed the Microbe-Drug Association Database (MDAD), a collection of clinically or experimentally supported associations between microbes and drugs, collecting 5,055 entries that include 1,388 drugs and 180 microbes from multiple drug databases and related publications. Moreover, we provided detailed annotations for each record, including the molecular form of drugs or hyperlinks from DrugBank, microbe target information from Uniprot and the original reference links. We hope MDAD will be a useful resource for deeper understanding of microbe and drug interactions and will also be beneficial to drug design, disease therapy and human health

Eighth Annual Conference of inVIVO Planetary Health: From Challenges to Opportunities

inVIVO Planetary Health (inVIVO) is a progressive scientific movement providing evidence, advocacy, and inspiration to align the interests and vitality of people, place, and planet. Our goal is to transform personal and planetary health through awareness, attitudes, and actions, and a deeper understanding of how all systems are interconnected and interdependent. Here, we present the abstracts and proceedings of our 8th annual conference, held in Detroit, Michigan in May 2019, themed “From Challenges, to Opportunities”. Our far-ranging discussions addressed the complex interdependent ecological challenges of advancing global urbanization, including the biopsychosocial interactions in our living environment on physical, mental, and spiritual wellbeing, together with the wider community and societal factors that govern these. We had a strong solutions focus, with diverse strategies spanning from urban-greening and renewal, nature-relatedness, nutritional ecology, planetary diets, and microbiome rewilding, through to initiatives for promoting resilience, positive emotional assets, traditional cultural narratives, creativity, art projects for personal and community health, and exploring ways of positively shifting mindsets and value systems. Our cross-sectoral agenda underscored the importance and global impact of local initiatives everywhere by contributing to new normative values as part of a global interconnected grass-roots movement for planetary health

Gut Microbes, Enteropathy and Child Growth: The Role of the Microbiota in the Cycle of Diarrhea and Undernutrition in Peru

Background. The cycle between diarrhea and undernutrition continues to underlie a vast proportion of under-five mortality and is the primary driver of long-term disability among children living in lower and middle-income countries (LMIC). Interventions aimed at reducing childhood stunting have not achieved desired results, highlighting the need for novel research and strategies to target this problem. There is increasing evidence that the gut microbiota are implicated in growth acquisition and sustaining intestinal barrier integrity in a manner that impacts immunity to and consequences of disease; however, these relationships have not yet been examined in large-scale studies of children living in LMIC. Objective. To evaluate relationships between the gut microbial community, child growth, diarrhea and enteric infections (Campylobacter spp) in a birth cohort of 271 children aged 0-24 months in Iquitos, Peru. Methods. Analyses were conducted on children participating in the multi-site cohort study entitled ‘The Interactions of Malnutrition & Enteric Infections: Consequences for Child Health and Development (MAL-ED).’ Data were contributed over two years by mothers and children living in a peri-urban riverine community in Amazonian Peru. Regular home-visits were conducted to ascertain anthropometric indices, illness history, and dietary habits. Length-for-age (LAZ) and weight-for-length (WLZ) Z-scores below - 2 were used to classify stunting and wasting, respectively. Fecal specimens were collected during routine surveillance visits at monthly intervals (N=6004) and additionally during each maternal report of diarrheal symptoms (N=2436). Culture methods, immunoassays and amplification methods were employed according to a unified MAL-ED protocol to identify a panel of over 40 protozoa, bacteria and viruses of public health importance. Microbiota in fecal samples contributed at 6, 12, 18 and 24 months were analyzed by polymerase chain reactions using primers to identify variable regions of bacterial 16S ribosomal RNA genes at the Gordon Laboratory at Washington University. Members were binned into operational taxonomic units (OTU) sharing ≥97% nucleotide sequence identity, producing bacterial communities differentiated at the species level which were then used to generate metrics of maturity (microbiota-for-age Z score; MAZ), diversity (Shannon, Simpson indices) and richness (CHAO1, Faith’s Phylogenetic Diversity). Multivariable regression was used to detect and describe population-averaged associations between microbial metrics, growth acquisition, illness and infection with a generalized estimating equations approach to adjust for within-child correlations over time. Indicator species analysis (ISA) was employed to identify particular gut taxa whose presence and abundance was statistically indicative of phenotypes of interest. Results. Two-thirds of children (67%) were stunted and 9% of children experienced wasting before their 2nd birthday. Microbial diversity and richness increased significantly with age and weaning, and were suppressed by breastmilk exposure. In the first two years of life, we detected a suggestive relationship between microbial maturity and WLZ, but did not observe evidence of associations between microbial maturity, diversity or richness with LAZ in the full cohort. LAZ at birth was significantly associated with MAZ score throughout follow-up (β=0.10, p=0.012) and children born stunted had significantly lower gut microbial diversity and richness (Shannonβ=-0.19, CHAO1 =- 9.75; p-values <0.05) from birth to two years of age. In this subgroup, we additionally observed that children weaned before 24m of age experienced significantly pronounced deficits in microbial diversity and richness acquisition relative to those with continued breastfeeding. Nearly all children (96%) experienced diarrhea during follow-up. Odds of being severely stunted increased by 8% with each additional diarrheal episode throughout the first two years of life (OR=1.08; p<0.001). Cumulative diarrheal frequency, duration and severity were associated with significant reductions in microbial indices (p<0.05), and we observed evidence of enduring deficits beyond 1m after exposure. Children who were born stunted experienced greater insults to microbial diversity per diarrheal episode than those children who were not (Interaction terms: Shannon β =-0.04, p=0.037; Simpson β =-0.01, p=0.032). Time elapsed since last diarrheal episode was associated with recovery of Shannon (β =0.02, p=0.03) and phylogenetic diversity (β =0.11, p<0.01) and we detected evidence that this regeneration process was significantly slower among severely stunted children. Lower diversity and richness were associated with increased subsequent diarrheal incidence; a 1-unit increase in the Shannon and Simpson’s Diversity scales at 6m corresponding to a mean reduction of 1.3 and 3.4 diarrheal episodes from 6-24m of age, respectively. By two years of age, 251 (93%) of all children in the cohort had Campylobacter present in asymptomatic stools, and 221 (82%) experienced infection with diarrhea. Asymptomatic infection was associated with reduced LAZ concurrently and at 3, 6, and 9m thereafter (β=0.02, p<0.01 across all time points). Frequency of Campylobacter- positive diarrhea was associated with a concurrent reduction in -0.03 LAZ (p=0.002), independently from all-cause diarrhea. Asymptomatic Campylobacter infections were associated with changes to the gut microbial environment. Infection was associated with increased microbial diversity and richness metrics, and we identified 21 taxa indicative of being in the highest or lowest quartile of infection from birth to two years of age. Of these, seven indicator species showed suggestive evidence of a link with LAZ concurrently and 1m thereafter. Conclusions. This study provided evidence of associations between the gut microbial community, anthropometric indices, and enteric infections in a population of children experiencing the classical cycle of diarrhea and undernutrition. This is the first study to our knowledge to interrogate these pathways longitudinally in a large, representative sample of infants in LMIC. Our findings generate questions regarding the precise causal mechanisms underlying the observed associations, and should inform subsequent efforts to identify specific and actionable targets to interrupt pathways compounding childhood morbidity and mortality in LMIC