Microbial metabolite enhances immunotherapy efficacy by modulating T cell stemness in pan-cancer.

The immune checkpoint blockade (ICB) response in human cancers is closely linked to the gut microbiota. Here, we report that the abundance of commensal Lactobacillus johnsonii is positively correlated with the responsiveness of ICB. Supplementation with Lactobacillus johnsonii or tryptophan-derived metabolite indole-3-propionic acid (IPA) enhances the efficacy of CD8+ T cell-mediated αPD-1 immunotherapy. Mechanistically, Lactobacillus johnsonii collaborates with Clostridium sporogenes to produce IPA. IPA modulates the stemness program of CD8+ T cells and facilitates the generation of progenitor exhausted CD8+ T cells (Tpex) by increasing H3K27 acetylation at the super-enhancer region of Tcf7. IPA improves ICB responsiveness at the pan-cancer level, including melanoma, breast cancer, and colorectal cancer. Collectively, our findings identify a microbial metabolite-immune regulatory pathway and suggest a potential microbial-based adjuvant approach to improve the responsiveness of immunotherapy.

A Natural Product Chemist's Guide to Unlocking Silent Biosynthetic Gene Clusters.

Microbial natural products have provided an important source of therapeutic leads and motivated research and innovation in diverse scientific disciplines. In recent years, it has become evident that bacteria harbor a large, hidden reservoir of potential natural products in the form of silent or cryptic biosynthetic gene clusters (BGCs). These can be readily identified in microbial genome sequences but do not give rise to detectable levels of a natural product. Herein, we provide a useful organizational framework for the various methods that have been implemented for interrogating silent BGCs. We divide all available approaches into four categories. The first three are endogenous strategies that utilize the native host in conjunction with classical genetics, chemical genetics, or different culture modalities. The last category comprises expression of the entire BGC in a heterologous host. For each category, we describe the rationale, recent applications, and associated advantages and limitations.

Multi-organ proteomic landscape of COVID-19 autopsies.

The molecular pathology of multi-organ injuries in COVID-19 patients remains unclear, preventing effective therapeutics development. Here, we report a proteomic analysis of 144 autopsy samples from seven organs in 19 COVID-19 patients. We quantified 11,394 proteins in these samples, in which 5,336 were perturbed in the COVID-19 patients compared to controls. Our data showed that cathepsin L1, rather than ACE2, was significantly upregulated in the lung from the COVID-19 patients. Systemic hyperinflammation and dysregulation of glucose and fatty acid metabolism were detected in multiple organs. We also observed dysregulation of key factors involved in hypoxia, angiogenesis, blood coagulation, and fibrosis in multiple organs from the COVID-19 patients. Evidence for testicular injuries includes reduced Leydig cells, suppressed cholesterol biosynthesis, and sperm mobility. In summary, this study depicts a multi-organ proteomic landscape of COVID-19 autopsies that furthers our understanding of the biological basis of COVID-19 pathology.

Treg cell-derived osteopontin promotes microglia-mediated white matter repair after ischemic stroke.

The precise mechanisms underlying the beneficial effects of regulatory T (Treg) cells on long-term tissue repair remain elusive. Here, using single-cell RNA sequencing and flow cytometry, we found that Treg cells infiltrated the brain 1 to 5 weeks after experimental stroke in mice. Selective depletion of Treg cells diminished oligodendrogenesis, white matter repair, and functional recovery after stroke. Transcriptomic analyses revealed potent immunomodulatory effects of brain-infiltrating Treg cells on other immune cells, including monocyte-lineage cells. Microglia depletion, but not T cell lymphopenia, mitigated the beneficial effects of transferred Treg cells on white matter regeneration. Mechanistically, Treg cell-derived osteopontin acted through integrin receptors on microglia to enhance microglial reparative activity, consequently promoting oligodendrogenesis and white matter repair. Increasing Treg cell numbers by delivering IL-2:IL-2 antibody complexes after stroke improved white matter integrity and rescued neurological functions over the long term. These findings reveal Treg cells as a neurorestorative target for stroke recovery.

Recognition of methamphetamine and other amines by trace amine receptor TAAR1.

Trace amine-associated receptor 1 (TAAR1), the founding member of a nine-member family of trace amine receptors, is responsible for recognizing a range of biogenic amines in the brain, including the endogenous ß-phenylethylamine (ß-PEA)1 as well as methamphetamine2, an abused substance that has posed a severe threat to human health and society3. Given its unique physiological role in the brain, TAAR1 is also an emerging target for a range of neurological disorders including schizophrenia, depression and drug addiction2,4,5. Here we report structures of human TAAR1-G-protein complexes bound to methamphetamine and ß-PEA as well as complexes bound to RO5256390, a TAAR1-selective agonist, and SEP-363856, a clinical-stage dual agonist for TAAR1 and serotonin receptor 5-HT1AR (refs. 6,7). Together with systematic mutagenesis and functional studies, the structures reveal the molecular basis of methamphetamine recognition and underlying mechanisms of ligand selectivity and polypharmacology between TAAR1 and other monoamine receptors. We identify a lid-like extracellular loop 2 helix/loop structure and a hydrogen-bonding network in the ligand-binding pockets, which may contribute to the ligand recognition in TAAR1. These findings shed light on the ligand recognition mode and activation mechanism for TAAR1 and should guide the development of next-generation therapeutics for drug addiction and various neurological disorders.

scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless, challenges arise from prevalent sequencing dropout events and noise effects, impacting subsequent analyses. Here, we introduce a novel algorithm, Single-cell Gene Importance Ranking (scGIR), which utilizes a single-cell gene correlation network to evaluate gene importance. The algorithm transforms single-cell sequencing data into a robust gene correlation network through statistical independence, with correlation edges weighted by gene expression levels. We then constructed a random walk model on the resulting weighted gene correlation network to rank the importance of genes. Our analysis of gene importance using PageRank algorithm across nine authentic scRNA-seq datasets indicates that scGIR can effectively surmount technical noise, enabling the identification of cell types and inference of developmental trajectories. We demonstrated that the edges of gene correlation, weighted by expression, play a critical role in enhancing the algorithm's performance. Our findings emphasize that scGIR outperforms in enhancing the clustering of cell subtypes, reverse identifying differentially expressed marker genes, and uncovering genes with potential differential importance. Overall, we proposed a promising method capable of extracting more information from single-cell RNA sequencing datasets, potentially shedding new lights on cellular processes and disease mechanisms.

Potent and specific antibiotic combination therapy against Clostridioides difficile.

Keratinicyclins and keratinimicins are recently discovered glycopeptide antibiotics. Keratinimicins show broad-spectrum activity against Gram-positive bacteria, while keratinicyclins form a new chemotype by virtue of an unusual oxazolidinone moiety and exhibit specific antibiosis against Clostridioides difficile. Here we report the mechanism of action of keratinicyclin B (KCB). We find that steric constraints preclude KCB from binding peptidoglycan termini. Instead, KCB inhibits C. difficile growth by binding wall teichoic acids (WTAs) and interfering with cell wall remodeling. A computational model, guided by biochemical studies, provides an image of the interaction of KCB with C. difficile WTAs and shows that the same H-bonding framework used by glycopeptide antibiotics to bind peptidoglycan termini is used by KCB for interacting with WTAs. Analysis of KCB in combination with vancomycin (VAN) shows highly synergistic and specific antimicrobial activity, and that nanomolar combinations of the two drugs are sufficient for complete growth inhibition of C. difficile, while leaving common commensal strains unaffected.

Structure of GPR101-Gs enables identification of ligands with rejuvenating potential.

GPR101 is an orphan G protein-coupled receptor actively participating in energy homeostasis. Here we report the cryo-electron microscopy structure of GPR101 constitutively coupled to Gs heterotrimer, which reveals unique features of GPR101, including the interaction of extracellular loop 2 within the 7TM bundle, a hydrophobic chain packing-mediated activation mechanism and the structural basis of disease-related mutants. Importantly, a side pocket is identified in GPR101 that facilitates in silico screening to identify four small-molecule agonists, including AA-14. The structure of AA-14-GPR101-Gs provides direct evidence of the AA-14 binding at the side pocket. Functionally, AA-14 partially restores the functions of GH/IGF-1 axis and exhibits several rejuvenating effects in wild-type mice, which are abrogated in Gpr101-deficient mice. In summary, we provide a structural basis for the constitutive activity of GPR101. The structure-facilitated identification of GPR101 agonists and functional analysis suggest that targeting this orphan receptor has rejuvenating potential.

Vaccine Molecule Design Based on Phage Display and Computational Modeling against Rhabdovirus.

Rhabdoviruses with rich species lead a variety of high lethality and rapid transmission diseases to plants and animals around the globe. Vaccination is one of the most effective approaches to prevent and control virus disease. However, the key antigenic epitopes of glycoprotein being used for vaccine development are unclear. In this study, fish-derived Abs are employed for a Micropterus salmoides rhabdovirus (MSRV) vaccine design by phage display and bioinformatics analysis. We constructed an anti-MSRV phage Ab library to screen Abs for glycoprotein segment 2 (G2) (G129-266). Four M13-phage-displayed Abs (Ab-5, Ab-7, Ab-8 and Ab-30) exhibited strong specificity to target Ag, and Ab-7 had the highest affinity with MSRV. Ab-7 (300 µg/ml) significantly increased grass carp ovary cell viability to 83.40% and significantly decreased the titer of MSRV. Molecular docking results showed that the key region of Ag-Ab interaction was located in 10ESQEFTTLTSH20 of G2. G2Ser11 and G2Gln12 were replaced with alanine, respectively, and molecular docking results showed that the Ag-Ab was nonbinding (ΔG > 0). Then, the peptide vaccine KLH-G210-20 was immunized to M. salmoides via i.p. injection. ELISA result showed that the serum Ab potency level increased significantly (p < 0.01). More importantly, the challenge test demonstrated that the peptide vaccine elicited robust protection against MSRV invasion, and the relative percentage survival reached 62.07%. Overall, this study proposed an approach for screening key epitope by combining phage display technology and bioinformatics tools to provide a reliable theoretical reference for the prevention and control of viral diseases.

Structural basis of ligand recognition and self-activation of orphan GPR52.

GPR52 is a class-A orphan G-protein-coupled receptor that is highly expressed in the brain and represents a promising therapeutic target for the treatment of Huntington's disease and several psychiatric disorders1,2. Pathological malfunction of GPR52 signalling occurs primarily through the heterotrimeric Gs protein2, but it is unclear how GPR52 and Gs couple for signal transduction and whether a native ligand or other activating input is required. Here we present the high-resolution structures of human GPR52 in three states: a ligand-free state, a Gs-coupled self-activation state and a potential allosteric ligand-bound state. Together, our structures reveal that extracellular loop 2 occupies the orthosteric binding pocket and operates as a built-in agonist, conferring an intrinsically high level of basal activity to GPR523. A fully active state is achieved when Gs is coupled to GPR52 in the absence of an external agonist. The receptor also features a side pocket for ligand binding. These insights into the structure and function of GPR52 could improve our understanding of other self-activated GPCRs, enable the identification of endogenous and tool ligands, and guide drug discovery efforts that target GPR52.

SCTC: inference of developmental potential from single-cell transcriptional complexity.

Inferring the developmental potential of single cells from scRNA-Seq data and reconstructing the pseudo-temporal path of cell development are fundamental but challenging tasks in single-cell analysis. Although single-cell transcriptional diversity (SCTD) measured by the number of expressed genes per cell has been widely used as a hallmark of developmental potential, it may lead to incorrect estimation of differentiation states in some cases where gene expression does not decrease monotonously during the development process. In this study, we propose a novel metric called single-cell transcriptional complexity (SCTC), which draws on insights from the economic complexity theory and takes into account the sophisticated structure information of scRNA-Seq count matrix. We show that SCTC characterizes developmental potential more accurately than SCTD, especially in the early stages of development where cells typically have lower diversity but higher complexity than those in the later stages. Based on the SCTC, we provide an unsupervised method for accurate, robust, and transferable inference of single-cell pseudotime. Our findings suggest that the complexity emerging from the interplay between cells and genes determines the developmental potential, providing new insights into the understanding of biological development from the perspective of complexity theory.

Solving independent set problems with photonic quantum circuits.

An independent set (IS) is a set of vertices in a graph such that no edge connects any two vertices. In adiabatic quantum computation [E. Farhi, et al., Science 292, 472-475 (2001); A. Das, B. K. Chakrabarti, Rev. Mod. Phys. 80, 1061-1081 (2008)], a given graph G(V, E) can be naturally mapped onto a many-body Hamiltonian [Formula: see text], with edges [Formula: see text] being the two-body interactions between adjacent vertices [Formula: see text]. Thus, solving the IS problem is equivalent to finding all the computational basis ground states of [Formula: see text]. Very recently, non-Abelian adiabatic mixing (NAAM) has been proposed to address this task, exploiting an emergent non-Abelian gauge symmetry of [Formula: see text] [B. Wu, H. Yu, F. Wilczek, Phys. Rev. A 101, 012318 (2020)]. Here, we solve a representative IS problem [Formula: see text] by simulating the NAAM digitally using a linear optical quantum network, consisting of three C-Phase gates, four deterministic two-qubit gate arrays (DGA), and ten single rotation gates. The maximum IS has been successfully identified with sufficient Trotterization steps and a carefully chosen evolution path. Remarkably, we find IS with a total probability of 0.875(16), among which the nontrivial ones have a considerable weight of about 31.4%. Our experiment demonstrates the potential advantage of NAAM for solving IS-equivalent problems.

Modeling and analyzing single-cell multimodal data with deep parametric inference.

The proliferation of single-cell multimodal sequencing technologies has enabled us to understand cellular heterogeneity with multiple views, providing novel and actionable biological insights into the disease-driving mechanisms. Here, we propose a comprehensive end-to-end single-cell multimodal analysis framework named Deep Parametric Inference (DPI). DPI transforms single-cell multimodal data into a multimodal parameter space by inferring individual modal parameters. Analysis of cord blood mononuclear cells (CBMC) reveals that the multimodal parameter space can characterize the heterogeneity of cells more comprehensively than individual modalities. Furthermore, comparisons with the state-of-the-art methods on multiple datasets show that DPI has superior performance. Additionally, DPI can reference and query cell types without batch effects. As a result, DPI can successfully analyze the progression of COVID-19 disease in peripheral blood mononuclear cells (PBMC). Notably, we further propose a cell state vector field and analyze the transformation pattern of bone marrow cells (BMC) states. In conclusion, DPI is a powerful single-cell multimodal analysis framework that can provide new biological insights into biomedical researchers. The python packages, datasets and user-friendly manuals of DPI are freely available at https://github.com/studentiz/dpi.

Diverse Chiral Nanotubes Assembled from Identical DNA Strands.

DNA nanotubes with controllable geometries hold a wide range of interdisciplinary applications. When preparing DNA nanotubes of varying widths or distinct chirality, existing methods require repeatedly designing and synthesizing specific DNA sequences, which can be costly and laborious. Here, we proposed an intercalator-assisted DNA tile assembly method which enables the production of DNA nanotubes of diverse widths and chirality using identical DNA strands. Through adjusting the concentration of intercalators during assembly, the twisting direction and extent of DNA tiles could be modulated, leading to the formation of DNA nanotubes featuring controllable widths and chirality. Moreover, through introducing additional intercalators and secondary annealing, right-handed nanotubes could be reconfigured into distinct left-handed nanotubes. We expect that this method could be universally applied to modulating the self-assembly pathways of various DNA tiles and other chiral materials, advancing the landscape of DNA tile assembly.

Lithiophilic Chemistry Facilitated Ultrathin Lithium for Scalable Prelithiation.

Prelithiation plays a crucial role in advancing the development of high-energy-density batteries, and ultrathin lithium (UTL) has been proven to be a promising anode prelithiation reagent. However, there remains a need to explore an adjustable, efficient, and cost-effective method for manufacturing UTL. In this study, we introduce a method for producing UTL with adjustable thicknesses ranging from 1.5 to 10 µm through blade coating of molten lithium on poly(vinylidene fluoride)-modified copper current collectors. By employing the transfer-printing method, prelithiated graphite and Si-C composite electrodes are prepared, which exhibit significantly improved initial Coulombic efficiencies of 99.60% and 99.32% in half-cells, respectively. Moreover, the energy densities of Li(NiCoMn)1/3O2 and LiFePO4 full cells assembled with the prelithiated graphite electrodes increase by 13.1% and 23.6%, respectively.

Structural Mass Spectrometry Captures Residue-Resolved Comprehensive Conformational Rearrangements of a G Protein-Coupled Receptor.

G protein-coupled receptor (GPCR) structural studies with in-solution spectroscopic approaches have offered distinctive insights into GPCR activation and signaling that highly complement those yielded from structural snapshots by crystallography or cryo-EM. While most current spectroscopic approaches allow for probing structural changes at selected residues or loop regions, they are not suitable for capturing a holistic view of GPCR conformational rearrangements across multiple domains. Herein, we develop an approach based on limited proteolysis mass spectrometry (LiP-MS) to simultaneously monitor conformational alterations of a large number of residues spanning both flexible loops and structured transmembrane domains for a given GPCR. To benchmark LiP-MS for GPCR conformational profiling, we studied the adenosine 2A receptor (A2AR) in response to different ligand binding (agonist/antagonist/allosteric modulators) and G protein coupling. Systematic and residue-resolved profiling of A2AR conformational rearrangements by LiP-MS precisely captures structural mechanisms in multiple domains underlying ligand engagement, receptor activation, and allostery, and may also reflect local conformational flexibility. Furthermore, these residue-resolution structural fingerprints of the A2AR protein allow us to readily classify ligands of different pharmacology and distinguish the G protein-coupled state. Thus, our study provides a new structural MS approach that would be generalizable to characterizing conformational transition and plasticity for challenging integral membrane proteins.

PARP1-DOT1L transcription axis drives acquired resistance to PARP inhibitor in ovarian cancer.

BACKGROUND: Poly (ADP-ribose) polymerase inhibitor (PARPi) resistance poses a significant challenge in ovarian carcinoma (OC). While the role of DOT1L in cancer and chemoresistance is acknowledged, its specific role in PARPi resistance remains unclear. This study aims to elucidate the molecular mechanism of DOT1L in PARPi resistance in OC patients. METHODS: This study analyzed the expression of DOT1L in PARPi-resistant cell lines compared to sensitive ones and correlated it with clinical outcomes in OC patients. Comprehensive in vitro and in vivo functional experiments were conducted using cellular and mouse models. Molecular investigations, including RNA sequencing, chromatin immunoprecipitation (ChIP) and Cleavage Under Targets and Tagmentation (CUT&Tag) assays, were employed to unravel the molecular mechanisms of DOT1L-mediated PARPi resistance. RESULTS: Our investigation revealed a robust correlation between DOT1L expression and clinical PARPi resistance in non-BRCA mutated OC cells. Upregulated DOT1L expression in PARPi-resistant tissues was associated with diminished survival in OC patients. Mechanistically, we identified that PARP1 directly binds to the DOT1L gene promoter, promoting transcription independently of its enzyme activity. PARP1 trapping induced by PARPi treatment amplified this binding, enhancing DOT1L transcription and contributing to drug resistance. Sequencing analysis revealed that DOT1L plays a crucial role in the transcriptional regulation of PLCG2 and ABCB1 via H3K79me2. This established the PARP1-DOT1L-PLCG2/ABCB1 axis as a key contributor to PARPi resistance. Furthermore, we discovered that combining a DOT1L inhibitor with PARPi demonstrated a synergistic effect in both cell line-derived xenograft mouse models (CDXs) and patient-derived organoids (PDOs). CONCLUSIONS: Our results demonstrate that DOT1L is an independent prognostic marker for OC patients. The PARP1-DOT1L/H3K79me2-PLCG2/ABCB1 axis is identified as a pivotal contributor to PARPi resistance. Targeted inhibition of DOT1L emerges as a promising therapeutic strategy for enhancing PARPi treatment outcomes in OC patients.

NSUN6-mediated 5-methylcytosine modification of NDRG1 mRNA promotes radioresistance in cervical cancer.

BACKGROUND: Radioresistance is the leading cause of death in advanced cervical cancer (CC). Dysregulation of RNA modification has recently emerged as a regulatory mechanism in radiation and drug resistance. We aimed to explore the biological function and clinical significance of 5-methylcytosine (m5C) in cervical cancer radiosensitivity. METHODS: The abundance of RNA modification in radiotherapy-resistant and sensitive CC specimens was quantified by liquid chromatography-tandem mass spectrometry. The essential RNA modification-related genes involved in CC radiosensitivity were screened via RNA sequencing. The effect of NSUN6 on radiosensitivity was verified in CC cell lines, cell-derived xenograft (CDX), and 3D bioprinted patient-derived organoid (PDO). The mechanisms of NSUN6 in regulating CC radiosensitivity were investigated by integrative m5C sequencing, mRNA sequencing, and RNA immunoprecipitation. RESULTS: We found a higher abundance of m5C modification in resistant CC samples, and NSUN6 was the essential m5C-regulating gene concerning radiosensitivity. NSUN6 overexpression was clinically correlated with radioresistance and poor prognosis in cervical cancer. Functionally, higher NSUN6 expression was associated with radioresistance in the 3D PDO model of cervical cancer. Moreover, silencing NSUN6 increased CC radiosensitivity in vivo and in vitro. Mechanistically, NDRG1 was one of the downstream target genes of NSUN6 identified by integrated m5C-seq, mRNA-seq, and functional validation. NSUN6 promoted the m5C modification of NDRG1 mRNA, and the m5C reader ALYREF bound explicitly to the m5C-labeled NDRG1 mRNA and enhanced NDRG1 mRNA stability. NDRG1 overexpression promoted homologous recombination-mediated DNA repair, which in turn led to radioresistance in cervical cancer. CONCLUSIONS: Aberrant m5C hypermethylation and NSUN6 overexpression drive resistance to radiotherapy in cervical cancer. Elevated NSUN6 expression promotes radioresistance in cervical cancer by activating the NSUN6/ALYREF-m5C-NDRG1 pathway. The low expression of NSUN6 in cervical cancer indicates sensitivity to radiotherapy and a better prognosis.

Visceral and ectopic fat are more predictively associated with primary liver cancer than overall obesity from genetic sights: A Mendelian randomization study.

Several observational studies have reported an association between obesity and primary liver cancer (PLC), while the causality behind this association and the comparison of the risk effects of different obesity indicators on PLC remain unclear. In this study, we performed two-sample Mendelian randomization (MR) analyses to assess the associations of genetically determined liver fat, visceral adipose tissue (VAT), and body mass index (BMI) with the risk of PLC. The summary statistics of exposures were obtained from two genome-wide association studies (GWASs) based on the UK Biobank (UKB) imaging cohort and the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. GWAS summary statistics for PLC were obtained from FinnGen consortium R7 release data, including 304 PLC cases and 218 488 controls. Inverse-variance weighted (IVW) was used as the primary analysis, and a series of sensitivity analyses were performed to further verify the robustness of these findings. IVW analysis highlighted a significant association of genetically determined liver fat (OR per SD increase: 7.14; 95% CI: 5.10-9.99; P = 2.35E-30) and VAT (OR per SD increase: 5.70; 95% CI: 1.32-24.72; P = .020) with PLC but not of BMI with PLC. The findings were further confirmed by a series of MR methods. No evidence of horizontal pleiotropy between these associations existed. Our study suggested that genetically determined liver fat and VAT rather than BMI were associated with an increased risk of PLC, which suggested that visceral fat distribution is more predictive of the clinical risk of PLC than common in vitro measures.

Disulfidptosis: A new type of cell death.

Disulfidptosis is a novel form of cell death that is distinguishable from established programmed cell death pathways such as apoptosis, pyroptosis, autophagy, ferroptosis, and oxeiptosis. This process is characterized by the rapid depletion of nicotinamide adenine dinucleotide phosphate (NADPH) in cells and high expression of solute carrier family 7 member 11 (SLC7A11) during glucose starvation, resulting in abnormal cystine accumulation, which subsequently induces andabnormal disulfide bond formation in actin cytoskeleton proteins, culminating in actin network collapse and disulfidptosis. This review aimed to summarize the underlying mechanisms, influencing factors, comparisons with traditional cell death pathways, associations with related diseases, application prospects, and future research directions related to disulfidptosis.