CRISPR-Cas13 Inhibitors Block RNA Editing in Bacteria and Mammalian Cells.

Cas13 has demonstrated unique and broad utility in RNA editing, nucleic acid detection, and disease diagnosis; however, a constantly active Cas enzyme may induce unwanted effects. Bacteriophage- or prophage-region-encoded anti-CRISPR (acr) gene molecules provide the potential to control targeting specificity and potency to allow for optimal RNA editing and nucleic acid detection by spatiotemporally modulating endonuclease activities. Using integrated approaches to screen acrVI candidates and evaluate their effects on Cas13 function, we discovered a series of acrVIA1-7 genes that block the activities of Cas13a. These VI-A CRISPR inhibitors substantially attenuate RNA targeting and editing by Cas13a in human cells. Strikingly, type VI-A anti-CRISPRs (AcrVIAs) also significantly muffle the single-nucleic-acid editing ability of the dCas13a RNA-editing system. Mechanistically, AcrVIA1, -4, -5, and -6 bind LwaCas13a, while AcrVIA2 and -3 can only bind the LwaCas13-crRNA (CRISPR RNA) complex. These identified acr molecules may enable precise RNA editing in Cas13-based application and study of phage-bacterium interaction.

Light recovery after maize harvesting promotes soybean flowering in a maize-soybean relay strip intercropping system.

Moving from sole cropping to intercropping is a transformative change in agriculture, contributing to yield. Soybeans adapt to light conditions in intercropping by adjusting the onset of reproduction and the inflorescence architecture to optimize reproductive success. Maize-soybean strip intercropping (MS), maize-soybean relay strip intercropping (IS), and sole soybean (SS) systems are typical soybean planting systems with significant differences in light environments during growth periods. To elucidate the effect of changes in the light environment on soybean flowering processes and provide a theoretical basis for selecting suitable varieties in various planting systems to improve yields, field experiments combining planting systems (IS, MS, and SS) and soybean varieties (GQ8, GX7, ND25, and NN996) were conducted in 2021 and 2022. Results showed that growth recovery in the IS resulted in a balance in the expression of TERMINAL FLOWER 1 (TFL1) and FLOWERING LOCUS T (FT) in the meristematic tissues of soybeans, which promoted the formation of new branches or flowers. IS prolonged the flowering time (2-7 days) and increased the number of forming flowers compared with SS (93.0 and 169%) and MS (67.3 and 103.3%) at the later soybean flowering stage. The higher carbon and nitrogen content in the middle and bottom canopies of soybean contributed to decreased flower abscission by 26.7 and 30.2%, respectively, compared with SS. Canopy light environment recovery promoted branch and flower formation and transformation of flowers into pods with lower flower-pod abscission, which contributed to elevating soybean yields in late-maturing and multibranching varieties (ND25) in IS.

Mapping of cytosol-facing organelle outer membrane proximity proteome by proximity-dependent biotinylation in living Arabidopsis cells.

The cytosol-facing outer membrane (OM) of organelles communicates with other cellular compartments to exchange proteins, metabolites, and signaling molecules. Cellular surveillance systems also target OM-resident proteins to control organellar homeostasis and ensure cell survival under stress. However, the OM proximity proteomes have never been mapped in plant cells since using traditional approaches to discover OM proteins and identify their dynamically interacting partners remains challenging. In this study, we developed an OM proximity labeling (OMPL) system using biotin ligase-mediated proximity biotinylation to identify the proximity proteins of the OMs of mitochondria, chloroplasts, and peroxisomes in living Arabidopsis (Arabidopsis thaliana) cells. Using this approach, we mapped the OM proximity proteome of these three organelles under normal conditions and examined the effects of the ultraviolet-B (UV-B) or high light (HL) stress on the abundances of OM proximity proteins. We demonstrate the power of this system with the discovery of cytosolic factors and OM receptor candidates potentially involved in local protein translation and translocation. The candidate proteins that are involved in mitochondrion-peroxisome, mitochondrion-chloroplast, or peroxisome-chloroplast contacts, and in the organellar quality control system are also proposed based on OMPL analysis. OMPL-generated OM proximity proteomes are valuable sources of candidates for functional validation and suggest directions for further investigation of important questions in cell biology.

A systematic assessment of deep learning methods for drug response prediction: from in vitro to clinical applications.

Drug response prediction is an important problem in personalized cancer therapy. Among various newly developed models, significant improvement in prediction performance has been reported using deep learning methods. However, systematic comparisons of deep learning methods, especially of the transferability from preclinical models to clinical cohorts, are currently lacking. To provide a more rigorous assessment, the performance of six representative deep learning methods for drug response prediction using nine evaluation metrics, including the overall prediction accuracy, predictability of each drug, potential associated factors and transferability to clinical cohorts, in multiple application scenarios was benchmarked. Most methods show promising prediction within cell line datasets, and TGSA, with its lower time cost and better performance, is recommended. Although the performance metrics decrease when applying models trained on cell lines to patients, a certain amount of power to distinguish clinical response on some drugs can be maintained using CRDNN and TGSA. With these assessments, we provide a guidance for researchers to choose appropriate methods, as well as insights into future directions for the development of more effective methods in clinical scenarios.

A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Tungsten Nitride (W5N6): An Ultraresilient 2D Semimetal.

Two-dimensional transition metal nitrides offer intriguing possibilities for achieving novel electronic and mechanical functionality owing to their distinctive and tunable bonding characteristics compared to other 2D materials. We demonstrate here the enabling effects of strong bonding on the morphology and functionality of 2D tungsten nitrides. The employed bottom-up synthesis experienced a unique substrate stabilization effect beyond van-der-Waals epitaxy that favored W5N6 over lower metal nitrides. Comprehensive structural and electronic characterization reveals that monolayer W5N6 can be synthesized at large scale and shows semimetallic behavior with an intriguing indirect band structure. Moreover, the material exhibits exceptional resilience against mechanical damage and chemical reactions. Leveraging these electronic properties and robustness, we demonstrate the application of W5N6 as atomic-scale dry etch stops that allow the integration of high-performance 2D materials contacts. These findings highlight the potential of 2D transition metal nitrides for realizing advanced electronic devices and functional interfaces.

Exceptional Hole-Selective Properties of Ta2O5 Films via Sn4+ Doping for High Performance Silicon Heterojunction Solar Cells.

Carrier-selective passivating contacts using transition metal oxides (TMOs) have attracted great attention for crystalline silicon (c-Si) heterojunction solar cells recently. Among them, tantalum oxide (Ta2O5) exhibits outstanding advantages, such as a wide bandgap, good surface passivation, and a small conduction band offset with c-Si, which is typically used as an electron-selective contact layer. Interestingly, it is first demonstrated that solution-processed Ta2O5 films exhibit a high hole selectivity, which blocks electrons and promotes hole transport simultaneously. Through the ozone pre-treatment of Ta2O5/p-Si interface and optimization of the film thickness (≈9 nm), the interfacial recombination is suppressed and the contact resistivity is reduced from 178.0 to 29.3 mΩ cm2. Moreover, the Sn4+ doping increases both the work function and oxygen vacancies of the film, contributing to the improved hole-selective contact performance. As a result, the photoelectric conversion efficiencies of Ta2O5/p-Si heterojunction solar cells are significantly improved from 14.84% to 18.47%, with a high thermal stability up to 300 °C. The work has provided a feasible strategy to explore new features of TMOs for carrier-selective contact applications, that is, bipolar carrier transport properties.

Single-cell and spatially resolved transcriptomics for liver biology.

Single-cell transcriptomics enables the identification of rare cell types and the inference of state transitions, whereas spatially resolved transcriptomics allows the quantification of cells and genes in the context of tissues. The recent progress in these new technologies is improving our understanding of the cell landscape and its roles in diseases. Here, we review key biological insights into liver homeostasis, development, regeneration, chronic liver disease, and cancer obtained from single-cell and spatially resolved transcriptomics. We highlight recent progress in the liver cell atlas that characterizes the comprehensive cellular composition; diversity and function; the spatial architecture such as liver zonation, cell communication, and proximity; the cell identity conversion and cell-specific alterations that are associated with liver pathology; and new therapeutic targets. We further discuss outstanding challenges, advanced experimental technologies, and computational methods that help to address these challenges.

Exploring the causal association between uric acid and lung cancer in east Asian and European populations: a mendelian randomization study.

BACKGROUND: Lung cancer still ranks first in the mortality rate of cancer. Uric acid is a product of purine metabolism in humans. Its presence in the serum is controversial; some say that its high levels have a protective effect against tumors, others say the opposite, that is, high levels increase the risk of cancer. Therefore, the aim of this study was to investigate the potential causal association between serum uric acid levels and lung cancer. METHODS: Mendelian randomization was used to achieve our aim. Sensitivity analyses was performed to validate the reliability of the results, followed by reverse Mendelian analyses to determine a potential reverse causal association. RESULTS: A significant causal association was found between serum uric acid levels and lung cancer in East Asian and European populations. Further sublayer analysis revealed a significant causal association between uric acid and small cell lung cancer, while no potential association was observed between uric acid and non-small cell lung cancer, squamous lung cancer, and lung adenocarcinoma. The sensitivity analyses confirmed the reliability of the results. Reverse Mendelian analysis showed no reverse causal association between uric acid and lung cancer. CONCLUSIONS: The results of this study suggested that serum uric acid levels were negatively associated with lung cancer, with uric acid being a potential protective factor for lung cancer. In addition, uric acid level monitoring was simple and inexpensive. Therefore, it might be used as a biomarker for lung cancer, promoting its wide use clinical practice.

ZNF32 prevents the activation of cancer-associated fibroblasts through negative regulation of TGFB1 transcription in breast cancer.

Breast cancer is the most frequently diagnosed malignancy and the leading cause of cancer-related deaths in women worldwide. Cancer-associated fibroblasts (CAFs) are one of the fundamental cellular components of the tumor microenvironment and play a critical role in the initiation, progression, and therapy resistance of breast cancer. However, the detailed molecular mechanisms of CAFs activation from normal fibroblasts (NFs) are still not well understood. In the present study, we reported that ZNF32 expression in breast cancer cells was negatively correlated with CAF-related markers (FSP1, α-SMA, and FAP) in stromal fibroblasts, and loss of ZNF32 promoted the activation of CAFs, as evidenced by the enhanced proliferation and contractility of CAFs. ZNF32 deficiency-mediated fibroblast activation promoted the growth and metastasis of breast cancer cells in vitro and in vivo. Mechanistically, we demonstrated that ZNF32 inhibited TGFB1 transcription by directly binding to the -1968/-1962 region of the TGFB1 promoter, leading to the prevention of fibroblast activation. Altogether, our findings reveal an important mechanism by which ZNF32 suppression increases the transcription of the TGFB1 gene in breast cancer cells, and subsequently, elevated levels of secretory TGF-ß stimulate NFs transformation into CAFs, which in turn facilitates the malignant progression of breast cancer. Our data implicated ZNF32 as a potential therapeutic strategy against breast cancer.