MicroPhenoDB Associates Metagenomic Data with Pathogenic Microbes, Microbial Core Genes, and Human Disease Phenotypes

Microbes play important roles in human health and disease.The interaction between microbes and hosts is a reciprocal relationship,which remains largely under-explored.Current computational resources lack manually and consistently curated data to connect metagenomic data to pathogenic microbes,microbial core genes,and disease phenotypes.We developed the MicroPhenoDB database by manually curating and consistently integrating microbe-disease association data.MicroPhenoDB provides 5677 non-redundant associations between 1781 microbes and 542 human disease phenotypes across more than 22 human body sites.MicroPhenoDB also provides 696,934 relationships between 27,277 unique clade-specific core genes and 685 microbes.Disease phenotypes are classified and described using the Experimental Factor Ontology(EFO).A refined score model was developed to prioritize the associations based on evidential metrics.The sequence search option in MicroPhenoDB enables rapid identification of existing pathogenic microbes in samples without running the usual metagenomic data processing and assembly.MicroPhenoDB offers data browsing,searching,and visualization through user-friendly web interfaces and web service application programming interfaces.MicroPhenoDB is the first database platform to detail the relationships between pathogenic microbes,core genes,and disease phenotypes.It will accelerate metagenomic data analysis and assist studies in decoding microbes related to human diseases.MicroPhenoDB is available through http://www.liwzlab.cn/microphenodb and http://lilab2.sysu.edu.cn/microphenodb.

Phenome-Wide Association Analysis Reveals Novel Links Between Genetically Determined Levels of Liver Enzymes and Disease Phenotypes

Serum liver enzymes(alanine aminotransferase[ALT],aspartate aminotransferase[AST],λ-glutamyl transferase[GGT]and alkaline phosphatase[ALP])are the leading biomarkers to measure liver injury,and they have been reported to be associated with several intrahepatic and extrahepatic diseases in observational studies.We conducted a phenome-wide association study(PheWAS)to identify disease phenotypes associated with genetically predicted liver enzymes based on the UK Biobank cohort.Univariable and multivariable Mendelian randomization(MR)analyses were performed to obtain the causal esti-mates of associations that detected in PheWAS.Our PheWAS identified 40 out of 1,376 pairs(16,17,three and four pairs for ALT,AST,GGT and ALP,respectively)of genotype-phenotype associations reaching statistical significance at the 5%false discovery rate threshold.A total of 34 links were further validated in Mendelian randomization analyses.Most of the disease phenotypes that associated with genetically determined ALT level were liver-related,including primary liver cancer and alcoholic liver damage.The disease outcomes associated with genetically determined AST involved a wide range of phenotypic categories including endocrine/metabolic diseases,digestive diseases,and neurological disorder.Genetically predicted GGT level was associated with the risk of other chronic non-alcoholic liver disease,abnormal results of function study of liver,and cholelithiasis.Genetically determined ALP level was associated with pulmonary heart disease,phlebitis and thrombophlebitis of lower extremities,and hypercholesterolemia.Our findings reveal novel links between liver enzymes and disease phenotypes providing insights into the full understanding of the biological roles of liver enzymes.

Coexistent Charcot-Marie-Tooth type 1A and type 2 diabetes mellitus neuropathies in a Chinese family

Charcot-Marie-Tooth disease type 1A（CMT1A） is caused by duplication of the peripheral myelin protein 22（PMP22） gene on chromosome 17. It is the most common inherited demyelinating neuropathy. Type 2 diabetes mellitus is a common metabolic disorder that frequently causes predominantly sensory neuropathy. In this study, we report the occurrence of CMT1 A in a Chinese family affected by type 2 diabetes mellitus. In this family, seven individuals had duplication of the PMP22 gene, although only four had clinical features of polyneuropathy. All CMT1 A patients with a clinical phenotype also presented with type 2 diabetes mellitus. The other three individuals had no signs of CMT1 A or type 2 diabetes mellitus. We believe that there may be a genetic link between these two diseases.

APOLIPOPROTEIN (a) PHENOTYPES IN CARDIOCEREBROVASCULAR DISEASES

Apolipoprotein (a) [Lp(a)] phenotypes of 69 myocardial infarction survivor and 56 stroke patients were reported and compared to those of 190 healthy Chinese. The results revealed that the distributions of apo(a) phcnotype frequency in patients with cardio-cerebrovascular disease (CCVD) were different from those of controls. The frequency of the phenotypes S1 and S2 were remarkably higher in patients than in controls within the same single-band apo(a) phcnotype. Moreover, the Lp (a) serum concentrations in CCVD patients were significantly higher than in controls within the same single-band apo (a) phenotype. The apo (a) phenotype analysis of two pedigrees were shown as a typical autosmal dominant inheritance.

Machine learning-based spectral and spatial analysis of hyper-and multi-spectral leaf images for Dutch elm disease detection and resistance screening

Diseases caused by invasive pathogens are an increasing threat to forest health,and early and accurate disease detection is essential for timely and precision forest management.The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees.In this study,Dutch elm disease(DED;caused by Ophiostoma novo-ulmi,)and American elm(Ulmus americana)was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper-and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection.Hyper-and multi-spectral images were collected from leaves of American elm geno-types with varied disease susceptibilities after mock-inoculation and inoculation with O.novo-ulmi under green-house conditions.Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes.Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED.Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees.In addition,spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes.Though further studies are needed to assess applications in other pathosystems,hyper-and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees.

Comparative study of network-based prioritization of protein domains associated with human complex diseases

Domains are basic structural and functional unit of proteins,and,thus,exploring associations between protein domains and human inherited diseases will greatly improve our understanding of the pathogenesis of human complex diseases and further benefit the medical prevention,diagnosis and treatment of these diseases.Based on the assumption that deleterious nonsynonymous single nucleotide polymorphisms(nsSNPs)underlying human complex diseases may actually change structures of protein domains,affect functions of corresponding proteins,and finally result in these diseases,we compile a dataset that contains 1174 associations between 433 protein domains and 848 human disease phenotypes.With this dataset,we compare two approaches(guilt-by-association and correlation coefficient)that use a domain-domain interaction network and a phenotype similarity network to prioritize associations between candidate domains and human disease phenotypes.We implement these methods with three distance measures(direct neighbor,shortest path with Gaussian kernel,and diffusion kernel),demonstrate the effectiveness of these methods using three large-scale leave-one-out cross-validation experiments(random control,simulated linkage interval,and whole-genome scan),and evaluate the performance of these methods in terms of three criteria(mean rank ratio,precision,and AUC score).Results show that both methods can effectively prioritize domains that are associated with human diseases at the top of the candidate list,while the correlation coefficient approach can achieve slightly higher performance in most cases.Finally,taking the advantage that the correlation coefficient method does not require known disease-domain associations,we calculate a genome-wide landscape of associations between 4036 protein domains and 5080 human disease phenotypes using this method and offer a freely accessible web interface for this landscape.