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Liquid–liquid phase separation (LLPS) in biology describes a process by which proteins form membraneless condensates within a cellular compartment when conditions are met, including the concentration and posttranslational modifications of the protein components, the condition of the aqueous solution (pH, ionic strength, pressure, and temperature), and the existence of assisting factors (such as RNAs or other proteins). In these supramolecular liquid droplet-like inclusion bodies, molecules are held together through weak intermolecular and/or intramolecular interactions. With the aid of LLPS, cells can assemble functional sub-units within a given cellular compartment by enriching or excluding specific factors, modulating cellular function, and rapidly responding to environmental or physiological cues. Hence, LLPS is emerging as an important means to regulate biology and physiology. Yet, excessive inclusion body formation by, for instance, higher-than-normal concentrations or mutant forms of the protein components could result in the conversion from dynamic liquid condensates into more rigid gel- or solid-like aggregates, leading to the disruption of the organelle’s function followed by the development of human disorders like neurodegenerative diseases. In summary, well-controlled formation and de-formation of LLPS is critical for normal biology and physiology from single cells to individual organisms, whereas abnormal LLPS is involved in the pathophysiology of human diseases. In turn, targeting these aggregates or their formation represents a promising approach in treating diseases driven by abnormal LLPS including those neurodegenerative diseases that lack effective therapies.

Background Epigenetic alterations are involved in various aspects of colorectal carcinogenesis. N 6 -methyladenosine (m 6 A) modifications of RNAs are emerging as a new layer of epigenetic regulation. As the most abundant chemical modification of eukaryotic mRNA, m 6 A is essential for the regulation of mRNA stability, splicing, and translation. Alterations of m 6 A regulatory genes play important roles in the pathogenesis of a variety of human diseases. However, whether this mRNA modification participates in the glucose metabolism of colorectal cancer (CRC) remains uncharacterized. Methods Transcriptome-sequencing and liquid chromatography-tandem mass spectrometry (LC-MS) were performed to evaluate the correlation between m 6 A modifications and glucose metabolism in CRC. Mass spectrometric metabolomics analysis, in vitro and in vivo experiments were conducted to investigate the effects of METTL3 on CRC glycolysis and tumorigenesis. RNA MeRIP-sequencing, immunoprecipitation and RNA stability assay were used to explore the molecular mechanism of METTL3 in CRC. Results A strong correlation between METTL3 and 18 F-FDG uptake was observed in CRC patients from Xuzhou Central Hospital. METTL3 induced-CRC tumorigenesis depends on cell glycolysis in multiple CRC models. Mechanistically, METTL3 directly interacted with the 5′/3’UTR regions of HK2 , and the 3’UTR region of SLC2A1 (GLUT1), then further stabilized these two genes and activated the glycolysis pathway. M 6 A-mediated HK2 and SLC2A1 (GLUT1) stabilization relied on the m 6 A reader IGF2BP2 or IGF2BP2/3, respectively. Conclusions METTL3 is a functional and clinical oncogene in CRC. METTL3 stabilizes HK2 and SLC2A1 (GLUT1) expression in CRC through an m 6 A-IGF2BP2/3- dependent mechanism. Targeting METTL3 and its pathway offer alternative rational therapeutic targets in CRC patients with high glucose metabolism.

The perturbations of the gut microbiota and metabolites are closely associated with the progression of inflammatory bowel disease (IBD). However, inconsistent findings across studies impede a comprehensive understanding of their roles in IBD and their potential as reliable diagnostic biomarkers. To address this challenge, here we comprehensively analyze 9 metagenomic and 4 metabolomics cohorts of IBD from different populations. Through cross-cohort integrative analysis (CCIA), we identify a consistent characteristic of commensal gut microbiota. Especially, three bacteria, namely Asaccharobacter celatus , Gemmiger formicilis , and Erysipelatoclostridium ramosum , which are rarely reported in IBD. Metagenomic functional analysis reveals that essential gene of Two-component system pathway, linked to fecal calprotectin, are implicated in IBD. Metabolomics analysis shows 36 identified metabolites with significant differences, while the roles of these metabolites in IBD are still unknown. To further elucidate the relationship between gut microbiota and metabolites, we construct multi-omics biological correlation (MOBC) maps, which highlights gut microbial biotransformation deficiencies and significant alterations in aminoacyl-tRNA synthetases. Finally, we identify multi-omics biomarkers for IBD diagnosis, validated across multiple global cohorts (AUROC values ranging from 0.92 to 0.98). Our results offer valuable insights and a significant resource for developing mechanistic hypotheses on host-microbiome interactions in IBD. Gut microbiota play pivotal roles in IBD. Here, Ning et al . use a multi-omics approach to characterize gut microbiota and metabolites alterations, and potential pathogenic bacteria associated with IBD, with the aim to help develop more precise biomarkers for IBD diagnosis and drug targets

Multiparameter sensing such as vector magnetometry often involves complex setups due to various external fields needed in explicitly connecting one measured signal to one parameter. Here, we propose a paradigm of indirect encoding for vector atomic magnetometry based on machine learning. We encode the three-dimensional magnetic-field information in the set of four simultaneously acquired signals associated with the optical rotation of a laser beam traversing the atomic sample. The map between the recorded signals and the vectorial field information is established through a pre-trained deep neural network. We demonstrate experimentally a single-shot all optical vector atomic magnetometer, with a simple scalar-magnetometer design employing only one elliptically-polarized laser beam and no additional coils. Magnetic field amplitude sensitivities of about 100 fT / Hz and angular sensitivities of about 100 ~ 200 μ r a d / Hz (for a magnetic field of around 140 nT) are derived from the neural network. Our approach can reduce the complexity of the architecture of vector magnetometers, and may shed light on the general design of multiparameter sensing. Multiparameter sensors in quantum optics are often complex due to use of external fields. Here the authors demonstrate a simple single-shot all-optical vector atomic magnetometer based on machine learning for the correspondence of the measured signals and the magnetic field.

Auxin response factors (ARFs) encode transcriptional factors that function in the regulation of plant development processes. A tomato ARF gene, SlARF5 , was observed to be expressed at high levels in emasculated ovaries but maintained low expression levels in pollinated ovaries. The ami RNA SlARF5 lines exhibited ovary growth and formed seedless fruits following emasculation. These parthenocarpic fruits developed fewer locular tissues, and the fruit size and weight were decreased in transgenic lines compared to those of wild-type fruits. Gene expression analysis demonstrated that several genes involved in the auxin-signaling pathway were downregulated, whereas some genes involved in the gibberellin-signaling pathway were enhanced by the decreased SlARF5 mRNA levels in transgenic plants, indicating that SlARF5 may play an important role in regulating both the auxin- and gibberellin-signaling pathways during fruit set and development.

Human 53BP1 is primarily known as a key player in regulating DNA double strand break (DSB) repair choice; however, its involvement in other biological process is less well understood. Here, we report a previously uncharacterized function of 53BP1 at heterochromatin, where it undergoes liquid-liquid phase separation (LLPS) with the heterochromatin protein HP1α in a mutually dependent manner. Deletion of 53BP1 results in a reduction in heterochromatin centers and the de-repression of heterochromatic tandem repetitive DNA. We identify domains and residues of 53BP1 required for its LLPS, which overlap with, but are distinct from, those involved in DSB repair. Further, 53BP1 mutants deficient in DSB repair, but proficient in LLPS, rescue heterochromatin de-repression and protect cells from stress-induced DNA damage and senescence. Our study suggests that in addition to DSB repair modulation, 53BP1 contributes to the maintenance of heterochromatin integrity and genome stability through LLPS. The 53BP1 protein is well-known for its involvement in double-strand break (DSB) repair. Here the authors show 53BP1 undergoes significant liquid-liquid phase separation (LLPS) to play a role in heterochromatin maintenance and identify distinct domains/residues that function in DSB repair versus LLPS.

GaN-based micro-size light-emitting diodes (µLEDs) have a variety of attractive and distinctive advantages for display, visible-light communication (VLC), and other novel applications. The smaller size of LEDs affords them the benefits of enhanced current expansion, fewer self-heating effects, and higher current density bearing capacity. Low external quantum efficiency (EQE) resulting from non-radiative recombination and quantum confined stark effect (QCSE) is a serious barrier for application of µLEDs. In this work, the reasons for the poor EQE of µLEDs are reviewed, as are the optimization techniques for improving the EQE of µLEDs.

The chemokine ligand C-X-C motif chemokine ligand 11 (CXCL11) is involved in the progression of various cancers, but its biological roles in colorectal cancer (CRC) remain confused. Therefore, the prognostic value and underlying mechanism of CXCL11 in CRC were preliminarily evaluated. Three independent datasets were used for mRNA-related analysis: one dataset from the Cancer Genome Atlas (TCGA, n = 451) and two single-cell RNA sequencing (scRNA-seq) datasets from Gene Expression Omnibus (GEO): GSE146771 and GSE132465. In addition, a colon adenocarcinoma (COAD) patient cohort (the Yijishan Hospital cohort, YJSHC, n = 108) was utilized for analysis of cell infiltration by immunohistochemistry. We determined the distribution of CXCL11 in tumor tissue across all TCGA cancers and found that CXCL11 expression was significantly upregulated in both COAD and rectal adenocarcinoma (READ). However, the upregulation of CXCL11 mRNA was associated with a better prognosis in COAD, but not in READ. Within the YJSHC, the patients with a high abundance of intratumoral CXCL11 + cells had prolonged survival ( p = 0.001). Furthermore, we found that the high CXCL11 expression group had a higher proportion of antitumor immune cells, and a lower proportion of protumor immune cells. Additionally, we discovered the changes of gene expression and enriched immune pathway network mediated by CXCL11. Interestingly, both cytotoxic genes (IFNG, GZMA, GZMB, GZMK, GZMM, and PRF1) and immunosuppressive molecules, including PD-L1, were positively correlated with CXCL11 expression. CXCL11, which promoted antitumor immunity to benefit survival, was identified as an independent prognostic biomarker in patients with COAD.

Introduction: Triply periodic minimal surface (TPMS) is widely used in the design of bone scaffolds due to its structural advantages. However, the current approach to designing bone scaffolds using TPMS structures is limited to a forward process from microstructure to mechanical properties. Developing an inverse bone scaffold design method based on the mechanical properties of bone structures is crucial. Methods: Using the machine learning and genetic algorithm, a new inverse design model was proposed in this research. The anisotropy of bone was matched by changing the number of cells in different directions. The finite element (FE) method was used to calculate the TPMS configuration and generate a back propagation neural network (BPNN) data set. Neural networks were used to establish the relationship between microstructural parameters and the elastic matrix of bone. This relationship was then used with regenerative genetic algorithm (RGA) in inverse design. Results: The accuracy of the BPNN-RGA model was confirmed by comparing the elasticity matrix of the inverse-designed structure with that of the actual bone. The results indicated that the average error was below 3.00% for three mechanical performance parameters as design targets, and approximately 5.00% for six design targets. Discussion: The present study demonstrated the potential of combining machine learning with traditional optimization method to inversely design anisotropic TPMS bone scaffolds with target mechanical properties. The BPNN-RGA model achieves higher design efficiency, compared to traditional optimization methods. The entire design process is easily controlled.

AlGaN-based deep ultraviolet light-emitting diodes (DUV LEDs) have great application prospects in sterilization, UV phototherapy, biological monitoring and other aspects. Due to their advantages of energy conservation, environmental protection and easy miniaturization realization, they have garnered much interest and been widely researched. However, compared with InGaN-based blue LEDs, the efficiency of AlGaN-based DUV LEDs is still very low. This paper first introduces the research background of DUV LEDs. Then, various methods to improve the efficiency of DUV LED devices are summarized from three aspects: internal quantum efficiency (IQE), light extraction efficiency (LEE) and wall-plug efficiency (WPE). Finally, the future development of efficient AlGaN-based DUV LEDs is proposed.