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.

Infection and inflammation stimulate expansion of a CD74+ Paneth cell subset to regulate disease progression.

Paneth cells (PCs), a specialized secretory cell type in the small intestine, are increasingly recognized as having an essential role in host responses to microbiome and environmental stresses. Whether and how commensal and pathogenic microbes modify PC composition to modulate inflammation remain unclear. Using newly developed PC-reporter mice under conventional and gnotobiotic conditions, we determined PC transcriptomic heterogeneity in response to commensal and invasive microbes at single cell level. Infection expands the pool of CD74+ PCs, whose number correlates with auto or allogeneic inflammatory disease progressions in mice. Similar correlation was found in human inflammatory disease tissues. Infection-stimulated cytokines increase production of reactive oxygen species (ROS) and expression of a PC-specific mucosal pentraxin (Mptx2) in activated PCs. A PC-specific ablation of MyD88 reduced CD74+ PC population, thus ameliorating pathogen-induced systemic disease. A similar phenotype was also observed in mice lacking Mptx2. Thus, infection stimulates expansion of a PC subset that influences disease progression.

CropGS-Hub: a comprehensive database of genotype and phenotype resources for genomic prediction in major crops.

The explosive amount of multi-omics data has brought a paradigm shift both in academic research and further application in life science. However, managing and reusing the growing resources of genomic and phenotype data points presents considerable challenges for the research community. There is an urgent need for an integrated database that combines genome-wide association studies (GWAS) with genomic selection (GS). Here, we present CropGS-Hub, a comprehensive database comprising genotype, phenotype, and GWAS signals, as well as a one-stop platform with built-in algorithms for genomic prediction and crossing design. This database encompasses a comprehensive collection of over 224 billion genotype data and 434 thousand phenotype data generated from >30 000 individuals in 14 representative populations belonging to 7 major crop species. Moreover, the platform implemented three complete functional genomic selection related modules including phenotype prediction, user model training and crossing design, as well as a fast SNP genotyper plugin-in called SNPGT specifically built for CropGS-Hub, aiming to assist crop scientists and breeders without necessitating coding skills. CropGS-Hub can be accessed at https://iagr.genomics.cn/CropGS/.

IL-17RA promotes pathologic epithelial inflammation in a mouse model of upper respiratory influenza infection.

The upper respiratory tract (nasopharynx or NP) is the first site of influenza replication, allowing the virus to disseminate to the lower respiratory tract or promoting community transmission. The host response in the NP regulates an intricate balance between viral control and tissue pathology. The hyper-inflammatory responses promote epithelial injury, allowing for increased viral dissemination and susceptibility to secondary bacterial infections. However, the pathologic contributors to influenza upper respiratory tissue pathology are incompletely understood. In this study, we investigated the role of interleukin IL-17 recetor A (IL-17RA) as a modulator of influenza host response and inflammation in the upper respiratory tract. We used a combined experimental approach involving IL-17RA-/- mice and an air-liquid interface (ALI) epithelial culture model to investigate the role of IL-17 response in epithelial inflammation, barrier function, and tissue pathology. Our data show that IL-17RA-/- mice exhibited significantly reduced neutrophilia, epithelial injury, and viral load. The reduced NP inflammation and epithelial injury in IL-17RA-/- mice correlated with increased resistance against co-infection by Streptococcus pneumoniae (Spn). IL-17A treatment, while potentiating the apoptosis of IAV-infected epithelial cells, caused bystander cell death and disrupted the barrier function in ALI epithelial model, supporting the in vivo findings.

Differences in Oligomerization of the SARS-CoV-2 Envelope Protein, Poliovirus VP4, and HIV Vpu.

Viroporins constitute a class of viral membrane proteins with diverse roles in the viral life cycle. They can self-assemble and form pores within the bilayer that transport substrates, such as ions and genetic material, that are critical to the viral infection cycle. However, there is little known about the oligomeric state of most viroporins. Here, we use native mass spectrometry in detergent micelles to uncover the patterns of oligomerization of the full-length SARS-CoV-2 envelope (E) protein, poliovirus VP4, and HIV Vpu. Our data suggest that the E protein is a specific dimer, VP4 is exclusively monomeric, and Vpu assembles into a polydisperse mixture of oligomers under these conditions. Overall, these results revealed the diversity in the oligomerization of viroporins, which has implications for the mechanisms of their biological functions as well as their potential as therapeutic targets.

Drug repurposing-based nanoplatform via modulating autophagy to enhance chemo-phototherapy against colorectal cancer.

Multi-modal combination therapy is regarded as a promising approach to cancer treatment. Combining chemotherapy and phototherapy is an essential multi-modal combination therapy endeavor. Ivermectin (IVM) is a potent antiparasitic agent identified as having potential antitumor properties. However, the fact that it induces protective autophagy while killing tumor cells poses a challenge to its further application. IR780 iodide (IR780) is a near-infrared (NIR) dye with outstanding photothermal therapy (PTT) and photodynamic therapy (PDT) effects. However, the hydrophobicity, instability, and low tumor uptake of IR780 limit its clinical applications. Here, we have structurally modified IR780 with hydroxychloroquine, an autophagy inhibitor, to synthesize a novel compound H780. H780 and IVM can form H780-IVM nanoparticles (H-I NPs) via self-assembly. Using hyaluronic acid (HA) to modify the H-I NPs, a novel nano-delivery system HA/H780-IVM nanoparticles (HA/H-I NPs) was synthesized for chemotherapy-phototherapy of colorectal cancer (CRC). Under NIR laser irradiation, HA/H-I NPs effectively overcame the limitations of IR780 and IVM and exhibited potent cytotoxicity. In vitro and in vivo experiment results showed that HA/H-I NPs exhibited excellent anti-CRC effects. Therefore, our study provides a novel strategy for CRC treatment that could enhance chemo-phototherapy by modulating autophagy.

Type III CRISPR-based RNA editing for programmable control of SARS-CoV-2 and human coronaviruses.

Gene-editing technologies, including the widespread usage of CRISPR endonucleases, have the potential for clinical treatments of various human diseases. Due to the rapid mutations of SARS-CoV-2, specific and effective prevention and treatment by CRISPR toolkits for coronavirus disease 2019 (COVID-19) are urgently needed to control the current pandemic spread. Here, we designed Type III CRISPR endonuclease antivirals for coronaviruses (TEAR-CoV) as a therapeutic to combat SARS-CoV-2 infection. We provided a proof of principle demonstration that TEAR-CoV-based RNA engineering approach leads to RNA-guided transcript degradation both in vitro and in eukaryotic cells, which could be used to broadly target RNA viruses. We report that TEAR-CoV not only cleaves SARS-CoV-2 genome and mRNA transcripts, but also degrades live influenza A virus (IAV), impeding viral replication in cells and in mice. Moreover, bioinformatics screening of gRNAs along RNA sequences reveals that a group of five gRNAs (hCoV-gRNAs) could potentially target 99.98% of human coronaviruses. TEAR-CoV also exerted specific targeting and cleavage of common human coronaviruses. The fast design and broad targeting of TEAR-CoV may represent a versatile antiviral approach for SARS-CoV-2 or potentially other emerging human coronaviruses.

FKF1b controls reproductive transition associated with adaptation to geographical distribution in maize.

Maize (Zea mays subspecies mays) is an important commercial crop across the world, and its flowering time is closely related to grain yield, plant cycle and latitude adaptation. FKF1 is an essential clock-regulated blue-light receptor with distinct functions on flowering time in plants, and its function in maize remains unclear. In this study, we identified two FKF1 homologs in the maize genome, named ZmFKF1a and ZmFKF1b, and indicated that ZmFKF1a and ZmFKF1b independently regulate reproductive transition through interacting with ZmCONZ1 and ZmGI1 to increase the transcription levels of ZmCONZ1 and ZCN8. We demonstrated that ZmFKF1b underwent artificial selection during modern breeding in China probably due to its role in geographical adaptation. Furthermore, our data suggested that ZmFKF1bHap_C7 may be an elite allele, which increases the abundance of ZmCONZ1 mRNA more efficiently and adapt to a wider range of temperature zone than that of ZmFKF1bHap_Z58 to promote maize floral transition. It extends our understanding of the genetic diversity of maize flowering. This allele is expected to be introduced into tropical maize germplasm to enrich breeding resources and may improve the adaptability of maize at different climate zones, especially at temperate region.

Effect of prehabilitation on postoperative outcomes in the frail older people: A systematic review and meta-analysis.

OBJECTIVE: The study investigates the impact of preoperative rehabilitation on the surgical prognosis of frail older patients. METHOD: The effect sizes of all studies retrieved and included by the nine databases were analyzed and expressed as RR and WMD. RESULTS: 8 studies with 902 participants met the criteria for inclusion. A significant reduction in total complications (RR = 0.84, 95 % CI = 0.73 to 0.97, P = 0.021) and the 6MWT after surgery (WMD = 74.76, 95 % CI = 44.75 to 104.77, P = 0.000) was observed in the prehabilitation group. But it had no differences in mortality(RR = 1.89, 95 % CI = 0.75 to 4.72, P = 0.176), readmission rates(RR = 1.04, 95 % CI = 0.56 to 1.91, P = 0.906) and LOS(WMD = -0.24, 95 % CI = -1.00 to 0.52, P = 0.540). CONCLUSIONS: Prehabilitation had positive effect on postoperative complications and functional recovery in frail older patients.

Dynamic impact of virome on colitis and colorectal cancer: Immunity, inflammation, prevention and treatment.

The gut microbiome includes a series of microorganism genomes, such as bacteriome, virome, mycobiome, etc. The gut microbiota is critically involved in intestine immunity and diseases, including inflammatory bowel disease (IBD) and colorectal cancer (CRC); however, the underlying mechanism remains incompletely understood. Clarifying the relationship between microbiota and inflammation may profoundly improve our understanding of etiology, disease progression, patient management, and the development of prevention and treatment. In this review, we discuss the latest studies of the influence of enteric viruses (i.e., commensal viruses, pathogenic viruses, and bacteriophages) in the initiation, progression, and complication of colitis and colorectal cancer, and their potential for novel preventative approaches and therapeutic application. We explore the interplay between gut viruses and host immune systems for its effects on the severity of inflammatory diseases and cancer, including both direct and indirect interactions between enteric viruses with other microbes and microbial products. Furthermore, the underlying mechanisms of the virome's roles in gut inflammatory response have been explained to infer potential therapeutic targets with examples in specific clinical trials. Given that very limited literature has thus far discussed these various topics with the gut virome, we believe these extensive analyses may provide insight into the understanding of the molecular pathogenesis of IBD and CRC, which could help add the design of improved therapies for these important human diseases.

DeepCAC: a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network.

Understanding gene expression processes necessitates the accurate classification and identification of transcription factors, which is supported by high-throughput sequencing technologies. However, these techniques suffer from inherent limitations such as time consumption and high costs. To address these challenges, the field of bioinformatics has increasingly turned to deep learning technologies for analyzing gene sequences. Nevertheless, the pursuit of improved experimental results has led to the inclusion of numerous complex analysis function modules, resulting in models with a growing number of parameters. To overcome these limitations, it is proposed a novel approach for analyzing DNA transcription factor sequences, which is named as DeepCAC. This method leverages deep convolutional neural networks with a multi-head self-attention mechanism. By employing convolutional neural networks, it can effectively capture local hidden features in the sequences. Simultaneously, the multi-head self-attention mechanism enhances the identification of hidden features with long-distant dependencies. This approach reduces the overall number of parameters in the model while harnessing the computational power of sequence data from multi-head self-attention. Through training with labeled data, experiments demonstrate that this approach significantly improves performance while requiring fewer parameters compared to existing methods. Additionally, the effectiveness of our approach  is validated in accurately predicting DNA transcription factor sequences.

DL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning.

PURPOSE: Sequenced Protein-Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets. RESULTS: In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction.

Proteomics Profiling Reveals Regulation of Immune Response to Salmonella enterica Serovar Typhimurium Infection in Mice.

Regulation of the immune response to Salmonella enterica serovar Typhimurium (S. Typhimurium) infection is a complex process, influenced by the interaction between genetic and environmental factors. Different inbred strains of mice exhibit distinct levels of resistance to S. Typhimurium infection, ranging from susceptible (e.g., C57BL/6J) to resistant (e.g., DBA/2J) strains. However, the underlying molecular mechanisms contributing to the host response remain elusive. In this study, we present a comprehensive proteomics profiling of spleen tissue from C57BL/6J and DBA/2J strains with different doses of S. Typhimurium infection by tandem mass tag labeling coupled with two-dimensional liquid chromatography-tandem mass spectrometry (TMT-LC/LC-MS/MS). We identified and quantified 3,986 proteins, resulting in 475 differentially expressed proteins (DEPs) between C57BL/6J and DBA/2J strains. Functional enrichment analysis unveiled that the mechanisms of innate immune responses to S. Typhimurium infection could be associated with several signaling pathways, including the interferon (IFN) signaling pathway. We experimentally validated the roles of the IFN signaling pathway in the innate immune response to S. Typhimurium infection using an IFN-γ neutralization assay. We further illustrated the importance of macrophage and proinflammatory cytokines in the mechanisms underlying the resistance to S. Typhimurium using quantitative reverse transcription-PCR (qRT-PCR). Taken together, our results provided new insights into the genetic regulation of the immune response to S. Typhimurium infection in mice and might lead to the discovery of potential protein targets for controlling salmonellosis.

Identifying Membrane Protein-Lipid Interactions with Lipidomic Lipid Exchange-Mass Spectrometry.

Lipids can play important roles in modulating membrane protein structure and function. However, it is challenging to identify natural lipids bound to membrane proteins in complex bilayers. Here, we developed lipidomic lipid exchange-mass spectrometry (LX-MS) to study the lipid affinity for membrane proteins on a lipidomic scale. We first mix membrane protein nanodiscs with empty nanodiscs that have no embedded membrane proteins. After allowing lipids to passively exchange between the two populations, we separate the two types of nanodiscs and perform lipidomic analysis on each with liquid chromatography and MS. Enrichment of lipids in the membrane protein nanodiscs reveals the affinity of individual lipids for binding the target membrane protein. We apply this approach to study three membrane proteins. With the Escherichia coli ammonium transporter AmtB and aquaporin AqpZ in nanodiscs with E. coli polar lipid extracts, we detected binding of cardiolipin and phosphatidyl-glycerol lipids to the proteins. With the acetylcholine receptor in nanodiscs with brain polar lipid extracts, we discovered a complex set of lipid interactions that depended on the head group and tail composition. Overall, lipidomic LX-MS provides a detailed understanding of the lipid-binding affinity and thermodynamics for membrane proteins in complex bilayers and provides a unique perspective on the chemical environment surrounding membrane proteins.

Investigating Daptomycin-Membrane Interactions Using Native MS and Fast Photochemical Oxidation of Peptides in Nanodiscs.

Daptomycin is a cyclic lipopeptide antibiotic that targets the lipid membrane of Gram-positive bacteria. Membrane fluidity and charge can affect daptomycin activity, but its mechanisms are poorly understood because it is challenging to study daptomycin interactions within lipid bilayers. Here, we combined native mass spectrometry (MS) and fast photochemical oxidation of peptides (FPOP) to study daptomycin-membrane interactions with different lipid bilayer nanodiscs. Native MS suggests that daptomycin incorporates randomly and does not prefer any specific oligomeric states when integrated into bilayers. FPOP reveals significant protection in most bilayer environments. Combining the native MS and FPOP results, we observed that stronger membrane interactions are formed with more rigid membranes, and pore formation may occur in more fluid membranes to expose daptomycin to FPOP oxidation. Electrophysiology measurements further supported the observation of polydisperse pore complexes from the MS data. Together, these results demonstrate the complementarity of native MS, FPOP, and membrane conductance experiments to shed light on how antibiotic peptides interact with and within lipid membranes.

Drug Repurposing-Based Brain-Targeting Self-Assembly Nanoplatform Using Enhanced Ferroptosis against Glioblastoma.

Glioblastoma (GBM), the most aggressive and lethal form of malignant brain tumor, is a therapeutic challenge due to the drug filtration capabilities of the blood-brain barrier (BBB). Interestingly, glioblastoma tends to resist apoptosis during chemotherapy, but is susceptible to ferroptosis. Developing therapies that can effectively target glioblastoma by crossing the BBB and evoke ferroptosis are, therefore, crucial for improving treatment outcomes. Herein, a versatile biomimetic nanoplatform, L-D-I/NPs, is designed that self-assembled by loading the antimalarial drug dihydroartemisinin (DHA) and the photosensitizer indocyanine green (ICG) onto lactoferrin (LF). This nanoplatform can selectively target glioblastoma by binding to low-density lipoprotein receptor-related protein-1 (LRP1) and crossing the BBB, thus inducing glioblastoma cell ferroptosis by boosting intracellular reactive oxygen species (ROS) accumulation and iron overload. In addition, L-D-I/NPs have demonstrated the ability to effectively suppress the progression of orthotopic glioblastoma and significantly prolong survival in a mouse glioblastoma model. This nanoplatform has facilitated the application of non-chemotherapeutic drugs in tumor treatment with minimal adverse effects, paving the way for highly efficient ferroptosis-based therapies for glioblastoma.

Cellular Heterogeneity and Molecular Reprogramming of the Host Response during Influenza Acute Lung Injury.

An exuberant host response contributes to influenza A virus (IAV) (or influenza)-mediated lung injury. However, despite significant information on the host response to IAV, the cellular framework and molecular interactions that dictate the development of acute injury in IAV-infected lungs remain incompletely understood. We performed an unbiased single-cell RNA sequencing (scRNAseq) analysis to examine the cellular heterogeneity and regulation of host responses in the IAV model of acute lung injury. At the cellular level, IAV infection promoted the overwhelming recruitment of monocytes that exhibited the cell differentiation trajectory to monocyte-derived macrophages. Together, monocytes and monocyte-derived myeloid cells constituted over 50% of the total immune cells in IAV-infected lungs. In contrast, IAV infection resulted in a significant loss of nonhematopoietic cells. Molecularly, our data show the multidimensional cell-cell communication dynamics of interferon and chemokine signaling between immune and nonimmune cells and the cell-specific molecular pathways regulating the host responses during IAV-induced lung injury. Our data provide a foundation for further exploring the mechanistic association of the IAV host response with acute lung injury. IMPORTANCE A dysregulated host response develops acute lung injury during IAV infection. However, the pathological immune mechanism(s) associated with acute lung injury during IAV infection is yet to be elucidated. In this study, we performed scRNAseq to examine the dynamics of host responses during the peak of IAV-mediated lung injury. At the cellular level, our data reveal significant myelopoiesis predominated by monocytes and macrophages and the simultaneous disruption of the nonhematopoietic cell framework, crucial for regulating inflammation and barrier integrity in IAV-infected lungs. Molecularly, we observed a complex cellular network involving cell-cell communications and a number of unique regulons dictating the outcome of interferon and chemokine responses during peak lung injury. Our data present a unique atlas of cellular changes and the regulation of global and cell-specific host responses during IAV infection. We expect that this information will open new avenues to identify targets for therapeutic intervention against IAV lung injury.

Metastasis organotropism in colorectal cancer: advancing toward innovative therapies.

Distant metastasis remains a leading cause of mortality among patients with colorectal cancer (CRC). Organotropism, referring to the propensity of metastasis to target specific organs, is a well-documented phenomenon in CRC, with the liver, lungs, and peritoneum being preferred sites. Prior to establishing premetastatic niches within host organs, CRC cells secrete substances that promote metastatic organotropism. Given the pivotal role of organotropism in CRC metastasis, a comprehensive understanding of its molecular underpinnings is crucial for biomarker-based diagnosis, innovative treatment development, and ultimately, improved patient outcomes. In this review, we focus on metabolic reprogramming, tumor-derived exosomes, the immune system, and cancer cell-organ interactions to outline the molecular mechanisms of CRC organotropic metastasis. Furthermore, we consider the prospect of targeting metastatic organotropism for CRC therapy.

Gut Microbiota Regulate Gut-Lung Axis Inflammatory Responses by Mediating ILC2 Compartmental Migration.

Gut microbiota is increasingly linked to the development of various pulmonary diseases through a gut-lung axis. However, the mechanisms by which gut commensal microbes impact trafficking and functional transition of immune cells remain largely unknown. Using integrated microbiota dysbiosis approaches, we uncover that the gut microbiota directs the migration of group 2 innate lymphoid cells (ILC2s) from the gut to the lung through a gut-lung axis. We identify Proteobacteria as a critical species in the gut microbiome to facilitate natural ILC2 migration, and increased Proteobacteria induces IL-33 production. Mechanistically, IL-33-CXCL16 signaling promotes the natural ILC2 accumulation in the lung, whereas IL-25-CCL25 signals augment inflammatory ILC2 accumulation in the intestines upon abdominal infection, parabiosis, and cecum ligation and puncture in mice. We reveal that these two types of ILC2s play critical but distinct roles in regulating inflammation, leading to balanced host defense against infection. Overall results delineate that Proteobacteria in gut microbiota modulates ILC2 directional migration to the lung for host defense via regulation of select cytokines (IL-33), suggesting novel therapeutic strategies to control infectious diseases.

Optical image conversion and encryption based on structured light illumination and a diffractive neural network.

In this paper, an optical image encryption method is proposed based on structured light illumination and a diffractive neural network (DNN), which can realize conversion between different images. With the use of the structured phase mask (SPM) in the iterative phase retrieval algorithm, a plaintext image is encoded into a DNN composed of multiple phase-only masks (POMs) and ciphertext. It is worth noting that ciphertext is a visible image such that the conversion of one image to another is achieved, leading to high concealment of the proposed optical image encryption method. In addition, the wavelength of the illuminating light, all Fresnel diffraction distances, the optical parameters of the adopted SPM such as focal length and topological charge number, as well as all POMs in the DNN are all considered as security keys in the decryption process, contributing to a large key space and high level of security. Numerical simulations are performed to demonstrate the feasibility of the proposed method, and simulation results show that it exhibits high feasibility and safety as well as strong robustness.