The Involvement of Let-7 miRNA in the Regulation of Retinal Progenitor Cells

Emerging evidence has shown that miRNA-mediated gene regulation plays an important role in the development of the central nervous system (CNS). One of the developmental mechanisms may involve let-7 miRNAs and their up-stream regulator, Lin28a and Lin28b in regulating neural stem cells (NSCs). Recently, let-7 has been observed to influence the differentiation of NSCs along neuronal versus glial lineage. However, there is also a report suggesting that the neurogliogenic decision is independent of let-7. These paradoxical results might represent contextual roles of let-7 and lin28 during the CNS development. We have tested this premise in the mammalian retina, a reliable CNS model, during late histogenesis when neurons and the sole glia of the retina, Müller glia (MG), are generated by retinal progenitors cells (RPCs). Using the perturbation of let-7 and Lin28 function analyses in in vitro and ex vivo models of late retinal histogenesis, we observed that let-7 did not influence the neurogliogenic decision; it facilitated the differentiation of RPCs into both neurons and MG. We demonstrated that one of the mechanisms by which let-7 promoted RPCs differentiation was by targeting transcripts corresponding to Hmga2, a high mobility group AT-hook 2 protein, which is known to regulate RPC self-renewal. A similar role for Lin28b emerged when we perturbed its function; it facilitated RPC maintenance and thus influenced their differentiation regardless of the neuronal or glial lineages. However, perturbation of function of Lin28a, whose expression persisted during late retinal histogenesis, influenced RPC differentiation into neurons and MG. For example, the gain of function of Lin28a promoted neurogenesis at the expense of gliogenesis. Taken together, our observations suggest an important role for the Lin28-let-7-Hmga2 axis in the regulation of RPCs, where the expression of lin28a versus lin28b may determine the outcome of the differentiation, orchestrated by let-7, along the neuronal and/or glial lineage(s)

Pyrimidine metabolism regulator-mediated molecular subtypes display tumor microenvironmental hallmarks and assist precision treatment in bladder cancer

BackgroundBladder cancer (BLCA) is a common urinary system malignancy with a significant morbidity and death rate worldwide. Non-muscle invasive BLCA accounts for over 75% of all BLCA cases. The imbalance of tumor metabolic pathways is associated with tumor formation and proliferation. Pyrimidine metabolism (PyM) is a complex enzyme network that incorporates nucleoside salvage, de novo nucleotide synthesis, and catalytic pyrimidine degradation. Metabolic reprogramming is linked to clinical prognosis in several types of cancer. However, the role of pyrimidine metabolism Genes (PyMGs) in the BLCA-fighting process remains poorly understood.MethodsPredictive PyMGs were quantified in BLCA samples from the TCGA and GEO datasets. TCGA and GEO provided information on stemness indices (mRNAsi), gene mutations, CNV, TMB, and corresponding clinical features. The prediction model was built using Lasso regression. Co-expression analysis was conducted to investigate the relationship between gene expression and PyM.ResultsPyMGs were overexpressed in the high-risk sample in the absence of other clinical symptoms, demonstrating their predictive potential for BLCA outcome. Immunological and tumor-related pathways were identified in the high-risk group by GSWA. Immune function and m6a gene expression varied significantly between the risk groups. In BLCA patients, DSG1, C6orf15, SOST, SPRR2A, SERPINB7, MYBPH, and KRT1 may participate in the oncology process. Immunological function and m6a gene expression differed significantly between the two groups. The prognostic model, CNVs, single nucleotide polymorphism (SNP), and drug sensitivity all showed significant gene connections.ConclusionsBLCA-associated PyMGs are available to provide guidance in the prognostic and immunological setting and give evidence for the formulation of PyM-related molecularly targeted treatments. PyMGs and their interactions with immune cells in BLCA may serve as therapeutic targets

Human Learning of Hierarchical Graphs

Humans are constantly exposed to sequences of events in the environment. Those sequences frequently evince statistical regularities, such as the probabilities with which one event transitions to another. Collectively, inter-event transition probabilities can be modeled as a graph or network. Many real-world networks are organized hierarchically and understanding how humans learn these networks is an ongoing aim of current investigations. While much is known about how humans learn basic transition graph topology, whether and to what degree humans can learn hierarchical structures in such graphs remains unknown. We investigate how humans learn hierarchical graphs of the Sierpi\'nski family using computer simulations and behavioral laboratory experiments. We probe the mental estimates of transition probabilities via the surprisal effect: a phenomenon in which humans react more slowly to less expected transitions, such as those between communities or modules in the network. Using mean-field predictions and numerical simulations, we show that surprisal effects are stronger for finer-level than coarser-level hierarchical transitions. Surprisal effects at coarser levels of the hierarchy are difficult to detect for limited learning times or in small samples. Using a serial response experiment with human participants (n=$100$), we replicate our predictions by detecting a surprisal effect at the finer-level of the hierarchy but not at the coarser-level of the hierarchy. To further explain our findings, we evaluate the presence of a trade-off in learning, whereby humans who learned the finer-level of the hierarchy better tended to learn the coarser-level worse, and vice versa. Our study elucidates the processes by which humans learn hierarchical sequential events. Our work charts a road map for future investigation of the neural underpinnings and behavioral manifestations of graph learning.Comment: 22 pages, 10 figures, 1 tabl

Direct Conversion of Mouse Astrocytes Into Neural Progenitor Cells and Specific Lineages of Neurons

Background: Cell replacement therapy has been envisioned as a promising treatment for neurodegenerative diseases. Due to the ethical concerns of ESCs-derived neural progenitor cells (NPCs) and tumorigenic potential of iPSCs, reprogramming of somatic cells directly into multipotent NPCs has emerged as a preferred approach for cell transplantation. Methods: Mouse astrocytes were reprogrammed into NPCs by the overexpression of transcription factors (TFs) Foxg1, Sox2, and Brn2. The generation of subtypes of neurons was directed by the force expression of cell-type specific TFs Lhx8 or Foxa2/Lmx1a. Results: Astrocyte-derived induced NPCs (AiNPCs) share high similarities, including the expression of NPC-specific genes, DNA methylation patterns, the ability to proliferate and differentiate, with the wild type NPCs. The AiNPCs are committed to the forebrain identity and predominantly differentiated into glutamatergic and GABAergic neuronal subtypes. Interestingly, additional overexpression of TFs Lhx8 and Foxa2/Lmx1a in AiNPCs promoted cholinergic and dopaminergic neuronal differentiation, respectively. Conclusions: Our studies suggest that astrocytes can be converted into AiNPCs and lineage-committed AiNPCs can acquire differentiation potential of other lineages through forced expression of specific TFs. Understanding the impact of the TF sets on the reprogramming and differentiation into specific lineages of neurons will provide valuable strategies for astrocyte-based cell therapy in neurodegenerative diseases

Internal m7G methylation: A novel epitranscriptomic contributor in brain development and diseases

In recent years, N7-methylguanosine (m7G) methylation, originally considered as messenger RNA (mRNA) 5′ caps modifications, has been identified at defined internal positions within multiple types of RNAs, including transfer RNAs, ribosomal RNAs, miRNA, and mRNAs. Scientists have put substantial efforts to discover m7G methyltransferases and methylated sites in RNAs to unveil the essential roles of m7G modifications in the regulation of gene expression and determine the association of m7G dysregulation in various diseases, including neurological disorders. Here, we review recent findings regarding the distribution, abundance, biogenesis, modifiers, and functions of m7G modifications. We also provide an up-to-date summary of m7G detection and profile mapping techniques, databases for validated and predicted m7G RNA sites, and web servers for m7G methylation prediction. Furthermore, we discuss the pathological roles of METTL1/WDR-driven m7G methylation in neurological disorders. Last, we outline a roadmap for future directions and trends of m7G modification research, particularly in the central nervous system

Emerging roles of extracellular vesicles in mediating RNA virus infection

The sudden outbreak of COVID-19 has once again shrouded people in the enormous threat of RNA virus. Extracellular vesicles (EVs), eukaryotic cells-derived small bi-layer vesicles mainly consisting of exosomes and microvesicles, share many properties with RNA viruses including structure, size, generation, and uptake. Emerging evidence has implicated the involvement of EVs in the pathogenesis of infectious diseases induced by RNA viruses. EVs can transfer viral receptors (e.g., ACE2 and CD9) to recipient cells to facilitate viral infection, directly transport infectious viral particles to adjacent cells for virus spreading, and mask viruses with a host structure to escape immune surveillance. Here, we examine the current status of EVs to summarize their roles in mediating RNA virus infection, together with a comprehensive discussion of the underlying mechanisms

Detecting Stems in Dense and Homogeneous Forest Using Single-Scan TLS

Stem characteristics of plants are of great importance to both ecology study and forest management. Terrestrial laser scanning (TLS) may provide an effective way to characterize the fine-scale structures of vegetation. However, clumping plants, dense foliage and thin structure could intensify the shadowing effect and pose a series of problems in identifying stems, distinguishing neighboring stems, and merging disconnected stem parts in point clouds. This paper presents a new method to automatically detect stems in dense and homogeneous forest using single-scan TLS data. Stem points are first identified with a two-scale classification method. Then a clustering approach is used to group the candidate stem points. Finally, a direction-growing algorithm based on a simple stem curve model is applied to merge stem points. Field experiments were carried out in two different bamboo plots with a stem density of about 7500 stems/ha. Overall accuracy of the stem detection is 88% and the quality of detected stems is mainly affected by the shadowing effect. Results indicate that the proposed method is feasible and effective in detection of bamboo stems using TLS data, and can be applied to other species of single-stem plants in dense forests

Detecting Stems in Dense and Homogeneous Forest Using Single-Scan TLS

This article presents a new method to automatically detect stems in dense and homogeneous forest using single-scan terrestrial laser scanning (TLS) data

Forest above Ground Biomass Inversion by Fusing GLAS with Optical Remote Sensing Data

This article describes a study combining Geoscience Laser Altimeter System (GLAS) data with MODIS/BRDF (Bidirectional Reflectance Distribution Function) and ASTER GDEM data to estimate forest aboveground biomass in Xishuangbanna, Yunnan Province, China