LmFKBP24 interacts with LmEaster to inhibit the antifungal immunity of Locusta migratoria.

Locusta migratoria is one of the most destructive pests that threaten crop growth and food production security in China. Metarhizium anisopliae has been widely used to control locusts around the world. Previous laboratory studies have revealed that LmFKBP24 is significantly upregulated after M. anisopliae infection, suggesting that it may play a role in immune regulation, yet the mechanism remains largely unknown. To gain further insight, we conducted an RNA interference (RNAi) study to investigate the function of LmFKBP24 in the regulation of antifungal immunity and analyzed the expression patterns of immune-induced genes. Our research revealed that LmFKBP24 is activated and upregulated when locusts are infected by M. anisopliae, and it inhibits the expression of antimicrobial peptide (AMP) defensin in the downstream of Toll pathway by combining with LmEaster rather than LmCyPA, thus exerting an immunosuppressive effect. To further investigate this, we conducted yeast two-hybrid (Y2H) and pull down assays to identify the proteins interacting with LmFKBP24. Our results provided compelling evidence for revealing the immune mechanism of L. migratoria and uncovered an innovative target for the development of new biological pesticides. Furthermore, our research indicates that LmFKBP24 interacts with LmEaster through its intact structure, providing a strong foundation for further exploration.

TIPD: A Probability Distribution-Based Method for Trajectory Inference from Single-Cell RNA-Seq Data.

Single-cell RNA-seq technology provides an unprecedented opportunity to allow researchers to study the biological heterogeneity during cell differentiation and development with higher resolution. Although many computational methods have been proposed to infer cell lineages from single-cell RNA-seq data, constructing accurate cell trajectories remains a challenge. We develop a novel trajectory inference method-based probability distribution (TIPD) to describe the heterogeneity of cell population. TIPD combines signalling entropy and clustering results of the gene expression profile to describe the probability distributions of heterogeneous states in a cell population. It does not require external knowledge to determine the direction of the differentiation trajectories, so its application is not limited by the annotations of the data set. We also propose a new distance metric to measure the distance of the probability distributions of the identified heterogeneous states. On this distance matrix, a minimum spanning tree (MST) is built to reorganize the order of cell clusters. The constructed MST is calculated based on systems-level information, so it is consistent with the real biological process. We validated our method on four previously published single-cell RNA-seq data sets including the linear structure and branch structure. The results showed that TIPD successfully reconstructed the differentiation trajectories that are highly consistent with the known differentiation trajectories and outperformed the other four state-of-the-art methods under different assessment criteria.

Differential Network Analysis Reveals Regulatory Patterns in Neural Stem Cell Fate Decision.

Deciphering regulatory patterns of neural stem cell (NSC) differentiation with multiple stages is essential to understand NSC differentiation mechanisms. Recent single-cell transcriptome datasets became available at individual differentiation. However, a systematic and integrative analysis of multiple datasets at multiple temporal stages of NSC differentiation is lacking. In this study, we propose a new method integrating prior information to construct three gene regulatory networks at pair-wise stages of transcriptome and apply this method to investigate five NSC differentiation paths on four different single-cell transcriptome datasets. By constructing gene regulatory networks for each path, we delineate their regulatory patterns via differential topology and network diffusion analyses. We find 12 common differentially expressed genes among the five NSC differentiation paths, with one common regulatory pattern (Gsk3b_App_Cdk5) shared by all paths. The identified regulatory pattern, partly supported by previous experimental evidence, is essential to all differentiation paths, but it plays a different role in each path when regulating other genes. Together, our integrative analysis provides both common and specific regulatory mechanisms for each of the five NSC differentiation paths.

DNF: A differential network flow method to identify rewiring drivers for gene regulatory networks.

Differential network analysis has become an important approach in identifying driver genes in development and disease. However, most studies capture only local features of the underlying gene-regulatory network topology. These approaches are vulnerable to noise and other changes which mask driver-gene activity. Therefore, methods are urgently needed which can separate the impact of true regulatory elements from stochastic changes and downstream effects. We propose the differential network flow (DNF) method to identify key regulators of progression in development or disease. Given the network representation of consecutive biological states, DNF quantifies the essentiality of each node by differences in the distribution of network flow, which are capable of capturing comprehensive topological differences from local to global feature domains. DNF achieves more accurate driver-gene identification than other state-of-the-art methods when applied to four human datasets from The Cancer Genome Atlas and three single-cell RNA-seq datasets of murine neural and hematopoietic differentiation. Furthermore, we predict key regulators of crosstalk between separate networks underlying both neuronal differentiation and the progression of neurodegenerative disease, among which APP is predicted as a driver gene of neural stem cell differentiation. Our method is a new approach for quantifying the essentiality of genes across networks of different biological states.

The Association of Neuronal Stress with Activating Transcription Factor 3 in Dorsal Root Ganglion of in vivo and in vitro Models of Bortezomib- Induced Neuropathy.

BACKGROUND: The notion that proteasome inhibitor bortezomib (BTZ) induced intracellular oxidative stress resulting in peripheral neuropathy has been generally accepted. The association of mitochondrial dysfunction, cell apoptosis, and endoplasmic reticulum (ER) stress with intracellular oxidative stress is ambiguous and still needs to be investigated. The activation of activating transcription factor 3 (ATF3) is a stress-hub gene which was upregulated in dorsal root ganglion (DRG) neurons after different kinds of peripheral nerve injuries. OBJECTIVE: To investigate a mechanism underlying the action of BTZ-induced intracellular oxidative stress, mitochondrial dysfunction, cell apoptosis, and ER stress via activation of ATF3. METHODS: Primary cultured DRG neurons with BTZ induced neurotoxicity and DRG from BTZ induced painful peripheral neuropathic rats were used to approach these questions. RESULTS: BTZ administration caused the upregulation of ATF3 paralleled with intracellular oxidative stress, mitochondrial dysfunction, cell apoptosis, and ER stress in DRG neurons both in vitro and in vivo. Blocking ATF3 signaling by small interfering RNA (siRNA) gene silencing technology resulted in decreased intracellular oxidative stress, mitochondrial dysfunction, cell apoptosis, and ER stress in DRG neurons after BTZ treatment. CONCLUSION: This study exhibited important mechanistic insight into how BTZ induces neurotoxicity through the activation of ATF3 resulting in intracellular oxidative stress, mitochondrial dysfunction, cell apoptosis, and ER stress and provided a novel potential therapeutic target by blocking ATF3 signaling.

Interactions of Notch1 and TLR4 signaling pathways in DRG neurons of in vivo and in vitro models of diabetic neuropathy.

Understanding the interactions between Notch1 and toll-like receptor 4 (TLR4) signaling pathways in the development of diabetic peripheral neuropathy may lead to interpretation of the mechanisms and novel approaches for preventing diabetic neuropathic pain. In the present study, the interactions between Notch1 and TLR4 signaling pathways were investigated by using dorsal root ganglion (DRG) from diabetic neuropathic pain rats and cultured DRG neurons under high glucose challenge. The results showed that high glucose induced not only Notch1 mRNA, HES1 mRNA, and TLR4 mRNA expression, but also Notch1 intracellular domain (NICD1) and TLR4 protein expression in DRG neurons. The proportion of NICD1-immunoreactive (IR) and TLR4-IR neurons in DRG cultures was also increased after high glucose challenge. The above alterations could be partially reversed by inhibition of either Notch1 or TLR4 signaling pathway. Inhibition of either Notch1 or TLR4 signaling pathway could improve mechanical allodynia and thermal hyperalgesia thresholds. Inhibition of Notch1 or TLR4 signaling also decreased tumor necrosis factor-α (TNF-α) levels in DRG from diabetic neuropathic rats. These data imply that the interaction between Notch1 and TLR4 signaling pathways is one of the important mechanisms in the development or progression of diabetic neuropathy.