Lung Microbiome Analysis in Steroid-Naїve Asthma Patients by Using Whole Sputum

Molecular Accounting and Profiling of Human Respiratory Microbial Communities: Toward Precision Medicine by Targeting the Respiratory Microbiome for Disease Diagnosis and Treatment

The wide diversity of microbiota at the genera and species levels across sites and individuals is related to various causes and the observed differences between individuals. Efforts are underway to further understand and characterize the human-associated microbiota and its microbiome. Using 16S rDNA as a genetic marker for bacterial identification improved the detection and profiling of qualitative and quantitative changes within a bacterial population. In this light, this review provides a comprehensive overview of the basic concepts and clinical applications of the respiratory microbiome, alongside an in-depth explanation of the molecular targets and the potential relationship between the respiratory microbiome and respiratory disease pathogenesis. The paucity of robust evidence supporting the correlation between the respiratory microbiome and disease pathogenesis is currently the main challenge for not considering the microbiome as a novel druggable target for therapeutic intervention. Therefore, further studies are needed, especially prospective studies, to identify other drivers of microbiome diversity and to better understand the changes in the lung microbiome along with the potential association with disease and medications. Thus, finding a therapeutic target and unfolding its clinical significance would be crucial.

The salivary and nasopharyngeal microbiomes are associated with SARS-CoV-2 infection and disease severity

Emerging evidence suggests the oral and upper respiratory microbiota may play important roles in modulating host immune responses to viral infection. As the host microbiome may be involved in the pathophysiology of coronavirus disease 2019 (COVID-19), we investigated associations between the oral and nasopharyngeal microbiome and COVID-19 severity. We collected saliva (n = 78) and nasopharyngeal swab (n = 66) samples from a COVID-19 cohort and characterized the microbiomes using 16S ribosomal RNA gene sequencing. We also examined associations between the salivary and nasopharyngeal microbiome and age, COVID-19 symptoms, and blood cytokines. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection status, but not COVID-19 severity, was associated with community-level differences in the oral and nasopharyngeal microbiomes. Salivary and nasopharyngeal microbiome alpha diversity negatively correlated with age and were associated with fever and diarrhea. Oral Bifidobacterium, Lactobacillus, and Solobacterium were depleted in patients with severe COVID-19. Nasopharyngeal Paracoccus was depleted while nasopharyngeal Proteus, Cupravidus, and Lactobacillus were increased in patients with severe COVID-19. Further analysis revealed that the abundance of oral Bifidobacterium was negatively associated with plasma concentrations of known COVID-19 biomarkers interleukin 17F and monocyte chemoattractant protein-1. Our results suggest COVID-19 disease severity is associated with the relative abundance of certain bacterial taxa.

Peripheral blood transcriptomic clusters uncovered immune phenotypes of asthma

BACKGROUND

Transcriptomic analysis has been used to elucidate the complex pathogenesis of heterogeneous disease and may also contribute to identify potential therapeutic targets by delineating the hub genes. This study aimed to investigate whether blood transcriptomic clustering can distinguish clinical and immune phenotypes of asthmatics, and microbiome in asthmatics.

METHODS

Transcriptomic expression of peripheral blood mononuclear cells (PBMCs) from 47 asthmatics and 21 non-asthmatics was measured using RNA sequencing. A hierarchical clustering algorithm was used to classify asthmatics. Differentially expressed genes, clinical phenotypes, immune phenotypes, and microbiome of each transcriptomic cluster were assessed.

RESULTS

In asthmatics, three distinct transcriptomic clusters with numerously different transcriptomic expressions were identified. The proportion of severe asthmatics was highest in cluster 3 as 73.3%, followed by cluster 2 (45.5%) and cluster 1 (28.6%). While cluster 1 represented clinically non-severe T2 asthma, cluster 3 tended to include severe non-T2 asthma. Cluster 2 had features of both T2 and non-T2 asthmatics characterized by the highest serum IgE level and neutrophil-dominant sputum cell population. Compared to non-asthmatics, cluster 1 showed higher CCL23 and IL1RL1 expression while the expression of TREML4 was suppressed in cluster 3. CTSD and ALDH2 showed a significant positive linear relationship across three clusters in the order of cluster 1 to 3. No significant differences in the diversities of lung and gut microbiomes were observed among transcriptomic clusters of asthmatics and non-asthmatics. However, our study has limitations in that small sample size data were analyzed with unmeasured confounding factors and causal relationships or function pathways were not verified.

CONCLUSIONS

Genetic clustering based on the blood transcriptome may provide novel immunological insight, which can be biomarkers of asthma immune phenotypes. Trial registration Retrospectively registered.

The respiratory microbiota alpha-diversity in chronic lung diseases: first systematic review and meta-analysis

Background

While there seems to be a consensus that a decrease in gut microbiome diversity is related to a decline in health status, the associations between respiratory microbiome diversity and chronic lung disease remain a matter of debate. We provide a systematic review and meta-analysis of studies examining lung microbiota alpha-diversity in patients with asthma, chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF) or bronchiectasis (NCFB), in which a control group based on disease status or healthy subjects is provided for comparison.

Results

We reviewed 351 articles on title and abstract, of which 27 met our inclusion criteria for systematic review. Data from 24 of these studies were used in the meta-analysis. We observed a trend that CF patients have a less diverse respiratory microbiota than healthy individuals. However, substantial heterogeneity was present and detailed using random-effects models, which limits the comparison between studies.

Conclusions

Knowledge on respiratory microbiota is under construction, and for the moment, it seems that alpha-diversity measurements are not enough documented to fully understand the link between microbiota and health, excepted in CF context which represents the most studied chronic respiratory disease with consistent published data to link alpha-diversity and lung function. Whether differences in respiratory microbiota profiles have an impact on chronic respiratory disease symptoms and/or evolution deserves further exploration.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02132-4.

Microbiome Research and Multi-Omics Integration for Personalized Medicine in Asthma

Asthma is a multifactorial inflammatory disorder of the respiratory system characterized by high diversity in clinical manifestations, underlying pathological mechanisms and response to treatment. It is generally established that human microbiota plays an essential role in shaping a healthy immune response, while its perturbation can cause chronic inflammation related to a wide range of diseases, including asthma. Systems biology approaches encompassing microbiome analysis can offer valuable platforms towards a global understanding of asthma complexity and improving patients' classification, status monitoring and therapeutic choices. In the present review, we summarize recent studies exploring the contribution of microbiota dysbiosis to asthma pathogenesis and heterogeneity in the context of asthma phenotypes-endotypes and administered medication. We subsequently focus on emerging efforts to gain deeper insights into microbiota-host interactions driving asthma complexity by integrating microbiome and host multi-omics data. One of the most prominent achievements of these research efforts is the association of refractory neutrophilic asthma with certain microbial signatures, including predominant pathogenic bacterial taxa (such as Proteobacteria phyla, Gammaproteobacteria class, especially species from Haemophilus and Moraxella genera). Overall, despite existing challenges, large-scale multi-omics endeavors may provide promising biomarkers and therapeutic targets for future development of novel microbe-based personalized strategies for diagnosis, prevention and/or treatment of uncontrollable asthma.

Microbiota and IPF: hidden and detected relationships

Lung microbiota (LM) is an interesting new way to consider and redesign pathogenesis and possible therapeutic approach to many lung diseases, such as idiopathic pulmonary fibrosis (IPF), which is an interstitial pneumonia with bad prognosis. Chronic inflammation is the basis but probably not the only cause of lung fibrosis and although the risk factors are not completely clear, endogenous factors (e.g. gastroesophageal reflux) and environmental factors like cigarette smoking, industrial dusts, and precisely microbial agents could contribute to the IPF development. It is well demonstrated that many bacteria can cause epithelial cell injuries in the airways through induction of a host immune response or by activating flogosis mediators following a chronic, low-level antigenic stimulus. This persistent host response could influence fibroblast responsiveness suggesting that LM may play a role in repetitive alveolar injury in IPF. We reviewed literature regarding not only bacteria but also the role of virome and mycobiome in IPF. In fact, some viruses such as hepatitis C virus or certain fungi could be etiological agents or co-factors in the IPF progress. We aim to illustrate how the cross-talk between different local microbiotas throughout specific axis and immune modulation governed by microorganisms could be at the basis of lung dysfunctions and IPF development. Finally, since the future direction of medicine will be personalized, we suggest that the analysis of LM could be a goal to research new therapies also in IPF.

Effect of chewing betel nut on the gut microbiota of Hainanese

Betel nut chewing (BNC) is prevalent in South Asia and Southeast Asia. BNC can affect host health by modulating the gut microbiota. The aim of this study is to evaluate the effect of BNC on the gut microbiota of the host. Feces samples were obtained from 34 BNC individuals from Ledong and Lingshui, Hainan, China. The microbiota was analyzed by 16S rRNA gene sequencing. BNC decreased the microbial α-diversity. Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria were the predominant phyla, accounting for 99.35% of the BNC group. The Firmicutes-to-Bacteroidetes ratio was significantly increased in the BNC group compared to a control group. The abundances of the families Aerococcaceae, Neisseriaceae, Moraxellaceae, Porphyromonadaceae, and Planococcaceae were decreased in the BNC/BNC_Male/BNC_Female groups compared to the control group, whereas the abundances of Coriobacteriaceae, Streptococcaceae, Micrococcaceae, Xanthomonadaceae, Coxiellaceae, Nocardioidaceae, Rhodobacteraceae, and Succinivibrionaceae were increased. In general, the gut microbiome profiles suggest that BNC may have positive effects, such as an increase in the abundance of beneficial microbes and a reduction in the abundance of disease-related microbes. However, BNC may also produce an increase in the abundance of disease-related microbes. Therefore, extraction of prebiotic components could increase the beneficial value of betel nut.

Respiratory Microbiota Profiles Associated With the Progression From Airway Inflammation to Remodeling in Mice With OVA-Induced Asthma

Background

The dysbiosis of respiratory microbiota plays an important role in asthma development. However, there is limited information on the changes in the respiratory microbiota and how these affect the host during the progression from acute allergic inflammation to airway remodeling in asthma.

Objective

An ovalbumin (OVA)-induced mouse model of chronic asthma was established to explore the dynamic changes in the respiratory microbiota in the different stages of asthma and their association with chronic asthma progression.

Methods

Hematoxylin and eosin (H&E), periodic acid-schiff (PAS), and Masson staining were performed to observe the pathological changes in the lung tissues of asthmatic mice. The respiratory microbiota was analyzed using 16S rRNA gene sequencing followed by taxonomical analysis. The cytokine levels in bronchoalveolar lavage fluid (BALF) specimens were measured. The matrix metallopeptidase 9 (MMP-9) and vascular endothelial growth factor (VEGF-A) expression levels in lung tissues were measured to detect airway remodeling in OVA-challenged mice.

Results

Acute allergic inflammation was the major manifestation at weeks 1 and 2 after OVA atomization stimulation, whereas at week 6 after the stimulation, airway remodeling was the most prominent observation. In the acute inflammatory stage, Pseudomonas was more abundant, whereas Staphylococcus and Cupriavidus were more abundant at the airway remodeling stage. The microbial compositions of the upper and lower respiratory tracts were similar. However, the dominant respiratory microbiota in the acute inflammatory and airway remodeling phases were different. Metagenomic functional prediction showed that the pathways significantly upregulated in the acute inflammatory phase and airway remodeling phase were different. The cytokine levels in BALF and the expression patterns of proteins associated with airway remodeling in the lung tissue were consistent with the metagenomic function results.

Conclusion

The dynamic changes in respiratory microbiota are closely associated with the progression of chronic asthma. Metagenomic functional prediction indicated the changes associated with acute allergic inflammation and airway remodeling.

The Effects of Corticosteroids on the Respiratory Microbiome: A Systematic Review

Background: Since its discovery, the respiratory microbiome has been implicated in the pathogenesis of multiple pulmonary diseases. Even though corticosteroid treatments are widely prescribed for pulmonary diseases, their effects on the respiratory microbiome are still poorly understood. This systematic review summarizes the current understanding of the effects of corticosteroids on the microbiome of the airways. Research Question: How does treatment with corticosteroids impact the respiratory microbiome? Study Design and Methods: According to the PRISMA guidelines, Embase, Medline, and the Cochrane Central Register of Controlled Trials (CENTRAL) databases were systematically searched for all observational or randomized-controlled studies comparing the microbiome parameters of patients receiving corticosteroids to those of controls. The primary outcomes of interest were changes in the diversity, composition and total burden of the respiratory microbiome as assessed by culture-independent molecular methods. Results: Out of 1,943 identified reports, five studies could be included: two on patients with asthma, two on patients with chronic obstructive pulmonary disease and one on patients with chronic rhinosinusitis. The studies were highly heterogeneous with regards to the methods used and the populations investigated. Microbiome diversity increased with corticosteroids at least transiently in three studies and decreased in one study. The effects of corticosteroids on the composition of the respiratory microbiome were significant but without a clear shared direction. A significant increase in microbial burden after corticosteroids was seen in one study. Interpretation: Data on the effect of corticosteroids on the respiratory microbiome are still limited, with considerable heterogeneity between studies. However, available data suggest that corticosteroid treatment may have significant effects on the composition and possibly the diversity of the respiratory microbiome. Further research is needed to better understand the influence of corticosteroids on the respiratory microbiome and thus better target its widespread therapeutic use.

Sex effects in the association between airway microbiome and asthma

BACKGROUND

Sex differences exist in asthma susceptibility and severity. Accumulating evidence has linked airway microbiome dysbiosis to asthma, and airway microbial communities have been found to differ by sex. However, whether sex modifies the link between airway microbiome and asthma has not been investigated.

OBJECTIVE

To evaluate sex effects in the association between airway microbiome and asthma.

METHODS

We analyzed induced sputum samples from 47 subjects (n = 23 patients with asthma and n = 24 normal controls) using 16S ribosomal RNA gene sequencing methods. The bacterial composition was analyzed for sex differences. Bacterial associations with asthma were assessed for each sex at the core taxa and genus levels.

RESULTS

The microbiome in induced sputum differed in women vs men at the community level. A total of 5 core bacterial taxa were found in all samples. No sex-specific core taxa were detected. The most abundant core taxon, Streptococcus salivarius, was significantly enriched in women than in men (P = .02). Within each sex, individuals with relatively lower abundance of S salivarius were more likely to have asthma (P = .006). For both sexes, increased Lactobacillus species were found in sputum samples of patients with patients compared with normal controls (adjusted P = .01). Haemophilus species were associated with asthma in men and not in women.

CONCLUSION

The airway microbiome differed by sex, and sex effects exist in the association of airway microbial markers and asthma. Future airway microbiome studies may yield better resolution if the context of specific sex is considered. The airway microbiome is a potential mechanism driving sex differences in asthma.

NTSHMDA: Prediction of Human Microbe-Disease Association Based on Random Walk by Integrating Network Topological Similarity

Accumulating clinic evidences have demonstrated that the microbes residing in human bodies play a significantly important role in the formation, development, and progression of various complex human diseases. Identifying latent related microbes for disease could provide insight into human disease mechanisms and promote disease prevention, diagnosis, and treatment. In this paper, we first construct a heterogeneous network by connecting the disease similarity network and the microbe similarity network through known microbe-disease association network, and then develop a novel computational model to predict human microbe-disease associations based on random walk by integrating network topological similarity (NTSHMDA). Specifically, each microbe-disease association pair is regarded as a distinct relationship level and, thus, assigned different weights based on network topological similarity. The experimental results show that NTSHMDA outperforms some state-of-the-art methods with average AUCs of 0.9070, 0.8896 ± 0.0038 in the frameworks of Leave-one-out cross validation and 5-fold cross validation, respectively. In case studies, 9, 18, 38 and 9, 18, 45 out of top-10, 20, 50 candidate microbes are verified by recently published literatures for asthma and inflammatory bowel disease, respectively. In conclusion, NTSHMDA has potential ability to identify novel disease-microbe associations and can also provide valuable information for drug discovery and biological researches.

A Novel Approach Based on Bipartite Network Recommendation and KATZ Model to Predict Potential Micro-Disease Associations

Accumulating evidence indicates that the microbes colonizing human bodies have crucial effects on human health and the discovery of disease-related microbes will promote the discovery of biomarkers and drugs for the prevention, diagnosis, treatment, and prognosis of diseases. However clinical experiments of disease-microbe associations are time-consuming, laborious and expensive, and there are few methods for predicting potential microbe-disease association. Therefore, developing effective computational models utilizing the accumulated public data of clinically validated microbe-disease associations to identify novel disease-microbe associations is of practical importance. We propose a novel method based on the KATZ model and Bipartite Network Recommendation Algorithm (KATZBNRA) to discover potential associations between microbes and diseases. We calculate the Gaussian interaction profile kernel similarity of diseases and microbes based on validated disease-microbe associations. Then, we construct a bipartite graph and execute a bipartite network recommendation algorithm. Finally, we integrate the disease similarity, microbe similarity and bipartite network recommendation score to obtain the final score, which is used to infer whether there are some novel disease-microbe interactions. To evaluate the predictive power of KATZBNRA, we tested it with the walk length 2 using global leave-one-out cross validation (LOOV), two-fold and five-fold cross validations, with AUCs of 0.9098, 0.8463 and 0.8969, respectively. The test results also show that KATZBNRA is more accurate than two recent similar methods KATZHMDA and BNPMDA.

WMGHMDA: a novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network

BACKGROUND

An increasing number of biological and clinical evidences have indicated that the microorganisms significantly get involved in the pathological mechanism of extensive varieties of complex human diseases. Inferring potential related microbes for diseases can not only promote disease prevention, diagnosis and treatment, but also provide valuable information for drug development. Considering that experimental methods are expensive and time-consuming, developing computational methods is an alternative choice. However, most of existing methods are biased towards well-characterized diseases and microbes. Furthermore, existing computational methods are limited in predicting potential microbes for new diseases.

RESULTS

Here, we developed a novel computational model to predict potential human microbe-disease associations (MDAs) based on Weighted Meta-Graph (WMGHMDA). We first constructed a heterogeneous information network (HIN) by combining the integrated microbe similarity network, the integrated disease similarity network and the known microbe-disease bipartite network. And then, we implemented iteratively pre-designed Weighted Meta-Graph search algorithm on the HIN to uncover possible microbe-disease pairs by cumulating the contribution values of weighted meta-graphs to the pairs as their probability scores. Depending on contribution potential, we described the contribution degree of different types of meta-graphs to a microbe-disease pair with bias rating. Meta-graph with higher bias rating will be assigned greater weight value when calculating probability scores.

CONCLUSIONS

The experimental results showed that WMGHMDA outperformed some state-of-the-art methods with average AUCs of 0.9288, 0.9068 ±0.0031 in global leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. In the case studies, 9, 19, 37 and 10, 20, 45 out of top-10, 20, 50 candidate microbes were manually verified by previous reports for asthma and inflammatory bowel disease (IBD), respectively. Furthermore, three common human diseases (Crohn's disease, Liver cirrhosis, Type 1 diabetes) were adopted to demonstrate that WMGHMDA could be efficiently applied to make predictions for new diseases. In summary, WMGHMDA has a high potential in predicting microbe-disease associations.

RWHMDA: Random Walk on Hypergraph for Microbe-Disease Association Prediction

Based on advancements in deep sequencing technology and microbiology, increasing evidence indicates that microbes inhabiting humans modulate various host physiological phenomena, thus participating in various disease pathogeneses. Owing to increasing availability of biological data, further studies on the establishment of efficient computational models for predicting potential associations are required. In particular, computational approaches can also reduce the discovery cycle of novel microbe-disease associations and further facilitate disease treatment, drug design, and other scientific activities. This study aimed to develop a model based on the random walk on hypergraph for microbe-disease association prediction (RWHMDA). As a class of higher-order data representation, hypergraph could effectively recover information loss occurring in the normal graph methodology, thus exclusively illustrating multiple pair-wise associations. Integrating known microbe-disease associations in the Human Microbe-Disease Association Database (HMDAD) and the Gaussian interaction profile kernel similarity for microbes, random walk was then implemented for the constructed hypergraph. Consequently, RWHMDA performed optimally in predicting the underlying disease-associated microbes. More specifically, our model displayed AUC values of 0.8898 and 0.8524 in global and local leave-one-out cross-validation (LOOCV), respectively. Furthermore, three human diseases (asthma, Crohn's disease, and type 2 diabetes) were studied to further illustrate prediction performance. Moreover, 8, 10, and 8 of the 10 highest ranked microbes were confirmed through recent experimental or clinical studies. In conclusion, RWHMDA is expected to display promising potential to predict disease-microbe associations for follow-up experimental studies and facilitate the prevention, diagnosis, treatment, and prognosis of complex human diseases.

The crosstalk between microbiome and asthma: Exploring associations and challenges

With the advancement of high‐throughput DNA/RNA sequencing and computational analysis techniques, commensal bacteria are now considered almost as important as pathological ones. Understanding the interaction between these bacterial microbiota, host and asthma is crucial to reveal their role in asthma pathophysiology. Several airway and/or gut microbiome studies have shown associations between certain bacterial taxa and asthma. However, challenges remain before gained knowledge from these studies can be implemented into clinical practice, such as inconsistency between studies in choosing sampling compartments and/or sequencing approaches, variability of results in asthma studies, and not taking into account medication intake and diet composition especially when investigating gut microbiome. Overcoming those challenges will help to better understand the complex asthma disease process. The therapeutic potential of using pro‐ and prebiotics to prevent or reduce risk of asthma exacerbations requires further investigation. This review will focus on methodological issues regarding setting up a microbiome study, recent developments in asthma bacterial microbiome studies, challenges and future therapeutic potential.

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.

Paradigms of Lung Microbiota Functions in Health and Disease, Particularly, in Asthma

Improvements in our knowledge of the gut microbiota have broadened our vision of the microbes associated with the intestine. These microbes are essential actors and protectors of digestive and extra-digestive health and, by extension, crucial for human physiology. Similar reconsiderations are currently underway concerning the endogenous microbes of the lungs, with a shift in focus away from their involvement in infections toward a role in physiology. The discovery of the lung microbiota was delayed by the long-held view that the lungs of healthy individuals were sterile and by sampling difficulties. The lung microbiota has a low density, and the maintenance of small numbers of bacteria seems to be a critical determinant of good health. This review aims to highlight how knowledge about the lung microbiota can change our conception of lung physiology and respiratory health. We provide support for this point of view with knowledge acquired about the gut microbiota and intestinal physiology. We describe the main characteristics of the lung microbiota and its functional impact on lung physiology, particularly in healthy individuals, after birth, but also in asthma. We describe some of the physiological features of the respiratory tract potentially favoring the installation of a dysbiotic microbiota. The gut microbiota feeds and matures the intestinal epithelium and is involved in immunity, when the principal role of the lung microbiota seems to be the orientation and balance of aspects of immune and epithelial responsiveness. This implies that the local and remote effects of bacterial communities are likely to be determinant in many respiratory diseases caused by viruses, allergens or genetic deficiency. Finally, we discuss the reciprocal connections between the gut and lungs that render these two compartments inseparable.

PRWHMDA: Human Microbe-Disease Association Prediction by Random Walk on the Heterogeneous Network with PSO

Microorganisms resided in human body play a vital role in metabolism, immune defense, nutrition absorption, cancer control and protection against pathogen colonization. The changes of microbial communities can cause human diseases. Based on the known microbe-disease association, we presented a novel computational model employing Random Walking with Restart optimized by Particle Swarm Optimization (PSO) on the heterogeneous interlinked network of Human Microbe-Disease Associations (PRWHMDA) (see Figure 1). Based on the known human microbe-disease associations, we constructed the heterogeneous interlinked network with Cosine similarity. The extended random walk with restart (RWR) method was derived to get the potential microbe-disease associations. PSO was utilized to get the optimal parameters of RWR. To evaluate the prediction effectiveness, we performed leave one out cross validation (LOOCV) and 5-fold cross validation (CV), which got the AUC (The area under ROC curve) of 0.915 (LOOCV) and the average AUCs of 0.8875 ± 0.0046 (5-fold CV). Moreover, we carried out three case studies of asthma, inflammatory bowel disease (IBD) and type 1 diabetes (T1D) for the further evaluation. The result showed that 10, 10 and 9 of top-10 predicted microbes were verified by previously published experimental results, respectively. It is anticipated that PRWHMDA can be effective to identify the disease-related microbes and maybe helpful to disclose the relationship between microorganisms and their human host.

A novel approach for predicting microbe-disease associations by bi-random walk on the heterogeneous network

Since the microbiome has a significant impact on human health and disease, microbe-disease associations can be utilized as a valuable resource for understanding disease pathogenesis and promoting disease diagnosis and prognosis. Accordingly, it is necessary for researchers to achieve a comprehensive and deep understanding of the associations between microbes and diseases. Nevertheless, to date, little work has been achieved in implementing novel human microbe-disease association prediction models. In this paper, we develop a novel computational model to predict potential microbe-disease associations by bi-random walk on the heterogeneous network (BiRWHMDA). The heterogeneous network was constructed by connecting the microbe similarity network and the disease similarity network via known microbe-disease associations. Microbe similarity and disease similarity were calculated by the Gaussian interaction profile kernel similarity measure; moreover, a logistic function was applied to regulate disease similarity. Additionally, leave-one-out cross validation and 5-fold cross validation were implemented to evaluate the predictive performance of our method; both cross validation methods performed well. The leave-one-out cross validation experiment results illustrate that our method outperforms other previously proposed methods. Furthermore, case studies on asthma and inflammatory bowel disease prove the favorable performance of our method. In conclusion, our method can be considered as an effective computational model for predicting novel microbe-disease associations.

A new assay based on terminal restriction fragment length polymorphism of homocitrate synthase gene fragments for Candida species identification

Candida sp. have been responsible for an increasing number of infections, especially in patients with immunodeficiency. Species-specific differentiation of Candida sp. is difficult in routine diagnosis. This identification can have a highly significant association in therapy and prophylaxis. This work has shown a new application of the terminal restriction fragment length polymorphism (t-RFLP) method in the molecular identification of six species of Candida, which are the most common causes of fungal infections. Specific for fungi homocitrate synthase gene was chosen as a molecular target for amplification. The use of three restriction enzymes, DraI, RsaI, and BglII, for amplicon digestion can generate species-specific fluorescence labeled DNA fragment profiles, which can be used to determine the diagnostic algorithm. The designed method can be a cost-efficient high-throughput molecular technique for the identification of six clinically important Candida species.