Complex disease interventions from a network model for type 2 diabetes

Diversity and biopotential of Bacillus velezensis strains A6 and P42 against rice blast and bacterial blight of pomegranate

Bacillus velezensis is widely known for its inherent biosynthetic potential to produce a wide range of bio-macromolecules and secondary metabolites, including polyketides (PKs) and siderophores, as well as ribosomally and non-ribosomally synthesized peptides. In the present study, we aimed to investigate the bio-macromolecules, such as proteins and peptides of Bacillus velezensis strains, namely A6 and P42 by whole-cell sequencing and highlighted the potential application in controlling phytopathogens. The bioactive compounds, specifically secondary metabolites, were characterized by whole-cell protein profiling, Thin-Layer Chromatography, Infra-Red Spectroscopy, Nuclear Magnetic Resonance, Gas Chromatograph and Electro Spray Liquid Chromatography. Gas Chromatography analysis revealed that the A6 and P42 strains exert different functional groups of compounds, such as aromatic ring, aliphatic, alkene, ketone, amine groups and carboxylic acid. Whole-cell protein profiling of A6 and P42 strains of B. velezensis by nano-ESI LC-MS/MS revealed the presence of 945 and 5303 proteins, respectively. The in vitro evaluation of crude extracts (10%) of A6 and P42 significantly inhibited the rice pathogen, Magnaporthe oryzae (MG01), whereas the cell-free culture filtrate (75%) of strain P42 showed 58.97% inhibition. Similarly, in vitro evaluation of crude extract (10%) of P42 strain inhibited bacterial blight of pomegranate pathogen, Xanthomonas axonopodis pv. punicae, which eventually resulted in a higher inhibition zone of 3 cm, whereas the cell-free extract (75%) of the same strain significantly suppressed the growth of the pathogen with an inhibition zone of 1.48 cm. From the results obtained, the crude secondary metabolites and cell-free filtrates (containing bio-macromolecules) of the strains A6 and P42 of B. velezensis can be employed for controlling the bacterial and fungal pathogens of crop plants.

Progress in Defining the Genetic Contribution to Type 2 Diabetes in Individuals of East Asian Ancestry

PURPOSE OF REVIEW

Prevalence of type 2 diabetes (T2D) and progression of complications differ between worldwide populations. While obesity is a major contributing risk factor, variations in physiological manifestations, e.g., developing T2D at lower body mass index in some populations, suggest other contributing factors. Early T2D genetic associations were mostly discovered in European ancestry populations. This review describes the progression of genetic discoveries associated with T2D in individuals of East Asian ancestry in the last 10 years and highlights the shared genetic susceptibility between the population groups and additional insights into genetic contributions to T2D.

RECENT FINDINGS

Through increased sample size and power, new genetic associations with T2D were discovered in East Asian ancestry populations, often with higher allele frequencies than European ancestry populations. As we continue to generate maps of T2D-associated variants across diverse populations, there will be a critical need to expand and diversify other omics resources to enable integration for clinical translation.

Predicting diabetes mellitus genes via protein-protein interaction and protein subcellular localization information

BACKGROUND

Diabetes mellitus characterized by hyperglycemia as a result of insufficient production of or reduced sensitivity to insulin poses a growing threat to the health of people. It is a heterogeneous disorder with multiple etiologies consisting of type 1 diabetes, type 2 diabetes, gestational diabetes and so on. Diabetes-associated protein/gene prediction is a key step to understand the cellular mechanisms related to diabetes mellitus. Compared with experimental methods, computational predictions of candidate proteins/genes are cheaper and more effortless. Protein-protein interaction (PPI) data produced by the high-throughput technology have been used to prioritize candidate disease genes/proteins. However, the false interactions in the PPI data seriously hurt computational methods performance. In order to address that particular question, new methods are developed to identify candidate disease genes/proteins via integrating biological data from other sources.

RESULTS

In this study, a new framework called PDMG is proposed to predict candidate disease genes/proteins. First, the weighted networks are building in terms of the combination of the subcellular localization information and PPI data. To form the weighted networks, the importance of each compartment is evaluated based on the number of interacted proteins in this compartment. This is because the very different roles played by different compartments in cell activities. Besides, some compartments are more important than others. Based on the evaluated compartments, the interactions between proteins are scored and the weighted PPI networks are constructed. Second, the known disease genes are extracted from OMIM database as the seed genes to expand disease-specific networks based on the weighted networks. Third, the weighted values between a protein and its neighbors in the disease-related networks are added together and the sum is as the score of the protein. Last but not least, the proteins are ranked based on descending order of their scores. The candidate proteins in the top are considered to be associated with the diseases and are potential disease-related proteins. Various types of data, such as type 2 diabetes-associated genes, subcellular localizations and protein interactions, are used to test PDMG method.

CONCLUSIONS

The results show that the proteins/genes functionally exerting a direct influence over diabetes are consistently placed at the head of the queue. PDMG expands and ranks 445 candidate proteins from the seed set including original 27 type 2 diabetes proteins. Out of the top 27 proteins, 14 proteins are the real type 2 diabetes proteins. The literature extracted from the PubMed database has proved that, out of 13 novel proteins, 8 proteins are associated with diabetes.

Confirming an integrated pathology of diabetes and its complications by molecular biomarker-target network analysis

Despite ongoing research into diabetes and its complications, the underlying molecular associations remain to be elucidated. The systematic identification of molecular interactions in associated diseases may be approached using a network analysis strategy. The biomarker-target interrelated molecules associated with diabetes and its complications were identified via the Comparative Toxicogenomics Database (CTD); the Search Tool for Recurring Instances of Neighboring Genes was utilized for network construction. Functional enrichment analysis was performed with Database for Annotation, Visualization and Integrated Discovery software to investigate connections between diabetes and its complications. A total of 142 (including 122 biomarkers, 10 therapeutic targets and 10 overlapping molecules) biomarker-target interrelated molecules associated with diabetes and its complications were identified via the CTD database, and analysis of the network yielded 1,087 biological processes and fifteen Kyoto Encyclopedia of Genes and Genomes pathways with significant P‑values. Various critical aspects of the networks were examined in the present study: a) Intermolecular horizontal and vertical combinations in biomarkers and therapeutic targets associated with diabetes and its complicationb) network topology properties associated with molecular pathological responsec) contribution of key molecules to integrated regulation; and d) crosstalk between multiple pathways. Based on a multi-dimensional analysis, it was concluded that the integrated molecular pathological development of diabetes and its complications does not proceed randomly, which suggests a requirement for integrated, multi-target intervention.

The role of protein interaction networks in systems biomedicine

The challenging task of studying and modeling complex dynamics of biological systems in order to describe various human diseases has gathered great interest in recent years. Major biological processes are mediated through protein interactions, hence there is a need to understand the chaotic network that forms these processes in pursuance of understanding human diseases. The applications of protein interaction networks to disease datasets allow the identification of genes and proteins associated with diseases, the study of network properties, identification of subnetworks, and network-based disease gene classification. Although various protein interaction network analysis strategies have been employed, grand challenges are still existing. Global understanding of protein interaction networks via integration of high-throughput functional genomics data from different levels will allow researchers to examine the disease pathways and identify strategies to control them. As a result, it seems likely that more personalized, more accurate and more rapid disease gene diagnostic techniques will be devised in the future, as well as novel strategies that are more personalized. This mini-review summarizes the current practice of protein interaction networks in medical research as well as challenges to be overcome.

System-based approaches to decode the molecular links in Parkinson's disease and diabetes

A growing body of evidence indicates an increased risk for developing Parkinson's disease (PD) among people with type 2 diabetes (T2DM). The relationship between the etiology and development of both chronic diseases is beginning to be uncovered and recent studies show that PD and T2DM share remarkably similar dysregulated pathways. It has been proposed that a cascade of events including mitochondrial dysfunction, impaired insulin signaling, and metabolic inflammation trigger neurodegeneration in T2DM models. Network-based approaches have elucidated a potential molecular framework linking both diseases. Further, transcriptional signatures that modulate the neurodegenerative phenotype in T2DM have been identified. Here we contextualize the current experimental approaches to dissect the mechanisms underlying the association between PD and T2DM and discuss the existing challenges toward the understanding of the coexistence of these devastating aging diseases.