Gene co-expression network analysis for identifying genetic markers in Parkinson's disease - a three-way comparative approach

Parkinson's disease (PD) is a neurodegenerative disorder involving progressive deterioration of dopaminergic neurons. Although few genetic markers for familial PD are known, the etiology of sporadic PD remains poorly understood. Microarray data was analysed for induced pluripotent stem cells (iPSCs) derived from PD patients and mature neuronal cells (mDA) differentiated from these iPSCs. Combining expression and semantic similarity, a highly-correlated PD interactome was constructed that included interactions of established Parkinson's disease marker genes. A novel three-way comparative approach was employed, delineating topologically and functionally important genes. These genes showed involvement in pathways like Parkin-ubiquitin proteosomal system (UPS), immune associated biological processes and apoptosis. Of interest are three genes, eEF1A1, CASK, and PSMD6 that are linked to PARK2 activity in the cell and thereby form attractive candidate genes for understanding PD. Network biology approach delineated in this study can be applied to other neurodegenerative disorders for identification of important genetic regulators.     

Mechanism of neoagarotetraose protects against intense exercise-induced liver injury based on molecular ecological network analysis

Here we have explored the effect of neoagarotetraose (NAT) on liver injury caused by intense exercise. Our results showed that NAT treatment obviously decreased liver weight (p < 0.01), improved the liver morphological structure, decreased ALT level (p < 0.05) and endotoxin (LPS) (p < 0.01). In addition, NAT could regulate bile acid profiles in feces and serum of mice, which indicated the potential of liver function, suggesting that NAT was effective to relieve intense exercise-induced liver injury. NAT could regulate the expression of colon genes. NAT tended to alter the microbial composition of mice under intense exercise. We uncovered the network interactions between liver traits and microbial communities in NAT treatment mice. Interestingly, our data indicated that intense exercise-induced liver injury may be related to Clostridiales. In summary, these results demonstrated that NAT relieved liver injury induced by intense exercise may be related to gut microbiota.     

Synthetic biology and metabolic engineering of actinomycetes for natural product discovery

Actinomycetes are one of the most valuable sources of natural products with industrial and medicinal importance. After more than half a century of exploitation, it has become increasingly challenging to find novel natural products with useful properties as the same known compounds are often repeatedly re-discovered when using traditional approaches. Modern genome mining approaches have led to the discovery of new biosynthetic gene clusters, thus indicating that actinomycetes still harbor a huge unexploited potential to produce novel natural products. In recent years, innovative synthetic biology and metabolic engineering tools have greatly accelerated the discovery of new natural products and the engineering of actinomycetes. In the first part of this review, we outline the successful application of metabolic engineering to optimize natural product production, focusing on the use of multi-omics data, genome-scale metabolic models, rational approaches to balance precursor pools, and the engineering of regulatory genes and regulatory elements. In the second part, we summarize the recent advances of synthetic biology for actinomycetal metabolic engineering including cluster assembly, cloning and expression, CRISPR/Cas9 technologies, and chassis strain development for natural product overproduction and discovery. Finally, we describe new advances in reprogramming biosynthetic pathways through polyketide synthase and non-ribosomal peptide synthetase engineering. These new developments are expected to revitalize discovery and development of new natural products with medicinal and other industrial applications.     

An evaluation of transferability of ecological niche models

Ecological niche modeling (ENM) is used widely to study species’ geographic distributions. ENM applications frequently involve transferring models calibrated with environmental data from one region to other regions or times that may include novel environmental conditions. When novel conditions are present, transferability implies extrapolation, whereas, in absence of such conditions, transferability is an interpolation step only. We evaluated transferability of models produced using 11 ENM algorithms from the perspective of interpolation and extrapolation in a virtual species framework. We defined fundamental niches and potential distributions of 16 virtual species distributed across Eurasia. To simulate real situations of incomplete understanding of species’ distribution or existing fundamental niche (environmental conditions suitable for the species contained in the study area; N* _F ), we divided Eurasia into six regions and used 1–5 regions for model calibration and the rest for model evaluation. The models produced with the 11 ENM algorithms were evaluated in environmental space, to complement the traditional geographic evaluation of models. None of the algorithms accurately estimated the existing fundamental niche (N* _F ) given one region in calibration, and model evaluation scores decreased as the novelty of the environments in the evaluation regions increased. Thus, we recommend quantifying environmental similarity between calibration and transfer regions prior to model transfer, providing an avenue for assessing uncertainty of model transferability. Different algorithms had different sensitivity to completeness of knowledge of N* _F , with implications for algorithm selection. If the goal is to reconstruct fundamental niches, users should choose algorithms with limited extrapolation when N* _F is well known, or choose algorithms with increased extrapolation when N* _F is poorly known. Our assessment can inform applications of ecological niche modeling transference to anticipate species invasions into novel areas, disease emergence in new regions, and forecasts of species distributions under future climate conditions.     

Integrated mRNA-Seq and miRNA-Seq analysis of PLCγ2-overexpressing hepatocarcinoma cells and identification of the associated miRNA-mRNA network

Our previous study has discovered the positive effect of phospholipase Cγ 2 (PLCγ2) on the growth of hepatocarcinoma cells; however, the underlying mechanism is far from being understood. For this reason, this study attempts to identify the differently expressed microRNAs (miRNAs) and messenger RNAs (mRNAs) in PLCγ2-overexpressing hepatocarcinoma cells. The results showed that totally 596 differently-expressed genes (DEGs) were identified in PLCγ2-expressed cells, including 314 upregulated and 282 downregulated ones; according to gene ontology analysis, these DEGs were involved in different cellular processes. Concurrently, 34 differently-expressed miRNAs (DEMs) were also detected in PLCγ2-expressing hepatocarcinoma cells. Moreover, the integrative analysis of miRNA and mRNA expression profiles identified the potential regulatory network linked to hepatocarcinoma-related biological processes, including metabolic activity, gene expression, cell cycle, cell migration, and so on. To our knowledge, it is the first study on the effect of PLCγ2 on miRNA and mRNA expressions in hepatocarcinoma cells, and the findings provide new insights into the mechanism supporting the growth-promoting effect of PLCγ2 in hepatocarcinoma cells.     

A comprehensive examination of the network position hypothesis across multiple river metacommunities

The hierarchical branching nature of river networks can have a strong influence on the assembly of freshwater communities. This unique structure has spurred the development of the network position hypothesis (NPH), which states that the strength of different assembly processes depends on the community position in the river network. Specifically, it predicts that 1) headwater communities should be exclusively controlled by the local environment given that they are more isolated and environmentally heterogeneous relative to downstream reaches. In contrast, 2) downstream communities should be regulated by both environmental and dispersal processes due to increased connectivity given their central position in the riverscape. Although intuitive, the NPH has only been evaluated on a few catchments and it is not yet clear whether its predictions are generalizable. To fill this gap, we tested the NPH on river dwelling fishes using an extensive dataset from 28 French catchments. Stream and climatic variables were assembled to characterize environmental conditions and graph theory was applied on river networks to create spatial variables. We tested both predictions using variation partitioning analyses separately for headwater and downstream sites in each catchment. Only 10 catchments supported both predictions, 11 failed to support at least one of them, while in 7 the NPH was partially supported given that spatial variables were also significant for headwater communities. We then assembled a dataset at the catchment scale (e.g. topography, environmental heterogeneity, network connectivity) and applied a classification tree analysis (CTA) to determine which regional property could explain these results. The CTA showed that the NPH was not supported in catchments with high heterogeneity in connectivity among sites. In more homogeneously connected catchments, the NPH was only supported when headwaters were more environmentally heterogeneous than downstream sites. We conclude that the NPH is context dependent even for taxa dispersing exclusively within streams.