SRSF2 plays an unexpected role as reader of m5C on mRNA, linking epitranscriptomics to cancer.

A common mRNA modification is 5-methylcytosine (m5C), whose role in gene-transcript processing and cancer remains unclear. Here, we identify serine/arginine-rich splicing factor 2 (SRSF2) as a reader of m5C and impaired SRSF2 m5C binding as a potential contributor to leukemogenesis. Structurally, we identify residues involved in m5C recognition and the impact of the prevalent leukemia-associated mutation SRSF2P95H. We show that SRSF2 binding and m5C colocalize within transcripts. Furthermore, knocking down the m5C writer NSUN2 decreases mRNA m5C, reduces SRSF2 binding, and alters RNA splicing. We also show that the SRSF2P95H mutation impairs the ability of the protein to read m5C-marked mRNA, notably reducing its binding to key leukemia-related transcripts in leukemic cells. In leukemia patients, low NSUN2 expression leads to mRNA m5C hypomethylation and, combined with SRSF2P95H, predicts poor outcomes. Altogether, we highlight an unrecognized mechanistic link between epitranscriptomics and a key oncogenesis driver.

Structure and topography of the synaptic V-ATPase-synaptophysin complex.

Synaptic vesicles are organelles with a precisely defined protein and lipid composition1,2, yet the molecular mechanisms for the biogenesis of synaptic vesicles are mainly unknown. Here we discovered a well-defined interface between the synaptic vesicle V-ATPase and synaptophysin by in situ cryo-electron tomography and single-particle cryo-electron microscopy of functional synaptic vesicles isolated from mouse brains3. The synaptic vesicle V-ATPase is an ATP-dependent proton pump that establishes the proton gradient across the synaptic vesicle, which in turn drives the uptake of neurotransmitters4,5. Synaptophysin6 and its paralogues synaptoporin7 and synaptogyrin8 belong to a family of abundant synaptic vesicle proteins whose function is still unclear. We performed structural and functional studies of synaptophysin-knockout mice, confirming the identity of synaptophysin as an interaction partner with the V-ATPase. Although there is little change in the conformation of the V-ATPase upon interaction with synaptophysin, the presence of synaptophysin in synaptic vesicles profoundly affects the copy number of V-ATPases. This effect on the topography of synaptic vesicles suggests that synaptophysin assists in their biogenesis. In support of this model, we observed that synaptophysin-knockout mice exhibit severe seizure susceptibility, suggesting an imbalance of neurotransmitter release as a physiological consequence of the absence of synaptophysin.

Piwi-interacting RNAs protect DNA against loss during Oxytricha genome rearrangement.

Genome duality in ciliated protozoa offers a unique system to showcase their epigenome as a model of inheritance. In Oxytricha, the somatic genome is responsible for vegetative growth, whereas the germline contributes DNA to the next sexual generation. Somatic nuclear development removes all transposons and other so-called "junk" DNA, which comprise ~95% of the germline. We demonstrate that Piwi-interacting small RNAs (piRNAs) from the maternal nucleus can specify genomic regions for retention in this process. Oxytricha piRNAs map primarily to the somatic genome, representing the ~5% of the germline that is retained. Furthermore, injection of synthetic piRNAs corresponding to normally deleted regions leads to their retention in later generations. Our findings highlight small RNAs as powerful transgenerational carriers of epigenetic information for genome programming.

Identification of SARS-CoV-2 inhibitors using lung and colonic organoids.

There is an urgent need to create novel models using human disease-relevant cells to study severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) biology and to facilitate drug screening. Here, as SARS-CoV-2 primarily infects the respiratory tract, we developed a lung organoid model using human pluripotent stem cells (hPSC-LOs). The hPSC-LOs (particularly alveolar type-II-like cells) are permissive to SARS-CoV-2 infection, and showed robust induction of chemokines following SARS-CoV-2 infection, similar to what is seen in patients with COVID-19. Nearly 25% of these patients also have gastrointestinal manifestations, which are associated with worse COVID-19 outcomes1. We therefore also generated complementary hPSC-derived colonic organoids (hPSC-COs) to explore the response of colonic cells to SARS-CoV-2 infection. We found that multiple colonic cell types, especially enterocytes, express ACE2 and are permissive to SARS-CoV-2 infection. Using hPSC-LOs, we performed a high-throughput screen of drugs approved by the FDA (US Food and Drug Administration) and identified entry inhibitors of SARS-CoV-2, including imatinib, mycophenolic acid and quinacrine dihydrochloride. Treatment at physiologically relevant levels of these drugs significantly inhibited SARS-CoV-2 infection of both hPSC-LOs and hPSC-COs. Together, these data demonstrate that hPSC-LOs and hPSC-COs infected by SARS-CoV-2 can serve as disease models to study SARS-CoV-2 infection and provide a valuable resource for drug screening to identify candidate COVID-19 therapeutics.

Polygonal tessellations as predictive models of molecular monolayers.

Molecular self-assembly plays a very important role in various aspects of technology as well as in biological systems. Governed by covalent, hydrogen or van der Waals interactions-self-assembly of alike molecules results in a large variety of complex patterns even in two dimensions (2D). Prediction of pattern formation for 2D molecular networks is extremely important, though very challenging, and so far, relied on computationally involved approaches such as density functional theory, classical molecular dynamics, Monte Carlo, or machine learning. Such methods, however, do not guarantee that all possible patterns will be considered and often rely on intuition. Here, we introduce a much simpler, though rigorous, hierarchical geometric model founded on the mean-field theory of 2D polygonal tessellations to predict extended network patterns based on molecular-level information. Based on graph theory, this approach yields pattern classification and pattern prediction within well-defined ranges. When applied to existing experimental data, our model provides a different view of self-assembled molecular patterns, leading to interesting predictions on admissible patterns and potential additional phases. While developed for hydrogen-bonded systems, an extension to covalently bonded graphene-derived materials or 3D structures such as fullerenes is possible, significantly opening the range of potential future applications.

Paxillin family proteins Hic-5 and LPXN promote lipid storage by regulating the ubiquitination degradation of CIDEC.

Many metabolic diseases are caused by disorders of lipid homeostasis. CIDEC, a lipid droplet (LD)-associated protein, plays a critical role in controlling LD fusion and lipid storage. However, regulators of CIDEC remain largely unknown. Here, we established a homogeneous time-resolved fluorescence (HTRF)-based high-throughput screening method and identified LPXN as a positive regulatory candidate for CIDEC. LPXN and Hic-5, the members of the Paxillin family, are focal adhesion adaptor proteins that contribute to the recruitment of specific kinases and phosphatases, cofactors, and structural proteins, participating in the transduction of extracellular signals into intracellular responses. Our data showed that Hic-5 and LPXN significantly increased the protein level of CIDEC and enhanced CIDEC stability not through triacylglycerol synthesis and FAK signaling pathways. Hic-5 and LPXN reduced the ubiquitination of CIDEC and inhibited its proteasome degradation pathway. Furthermore, Hic-5 and LPXN enlarged LDs and promoted lipid storage in adipocytes. Therefore, we identified Hic-5 and LPXN as novel regulators of CIDEC. Our current findings also suggest intervention with Hic-5 and LPXN might ameliorate ectopic fat storage by enhancing the lipid storage capacity of white adipose tissues.

Jasmonate mimic modulates cell elongation by regulating antagonistic bHLH transcription factors via brassinosteroid signaling.

Lodging restricts growth, development, and yield formation in maize (Zea mays L.). Shorter internode length is beneficial for lodging tolerance. However, although brassinosteroids (BRs) and jasmonic acid (JA) are known to antagonistically regulate internode growth, the underlying molecular mechanism is still unclear. In this study, application of the JA mimic coronatine (COR) inhibited basal internode elongation at the jointing stage and repressed expression of the cell wall-related gene XYLOGLUCAN ENDOTRANSGLUCOSYLASE/HYDROLASE 1 (ZmXTH1), whose overexpression in maize plants promotes internode elongation. We demonstrated that the basic helix-loop-helix (bHLH) transcription factor ZmbHLH154 binds directly to the ZmXTH1 promoter and induces its expression, whereas the bHLH transcription factor ILI1 BINDING BHLH 1 (ZmIBH1) inhibits this transcriptional activation by forming a heterodimer with ZmbHLH154. Overexpressing ZmbHLH154 led to longer internodes, whereas zmbhlh154 mutants had shorter internodes than the wild type. The core JA-dependent transcription factors ZmMYC2-4 and ZmMYC2-6 interacted with BRASSINAZOLE RESISTANT 1 (ZmBZR1), a key factor in BR signaling, and these interactions eliminated the inhibitory effect of ZmBZR1 on its downstream gene ZmIBH1. Collectively, these results reveal a signaling module in which JA regulates a bHLH network by attenuating BR signaling to inhibit ZmXTH1 expression, thereby regulating cell elongation in maize.

Comprehensive evaluation of molecule property prediction with ChatGPT.

The versatility of ChatGPT in performing a diverse range of tasks has elicited considerable interest on its potential applications within professional fields. Taking drug discovery as a testbed, this paper provides a comprehensive evaluation of ChatGPT's ability on molecule property prediction. The study focuses on three aspects: 1) Effects of different prompt settings, where we investigate the impact of varying prompts on the prediction outcomes of ChatGPT; 2) Comprehensive evaluation on molecule property prediction, where we conduct a comprehensive evaluation on 53 ADMET-related endpoints; 3) Analysis of ChatGPT's potential and limitations, where we make comparisons with models tailored for molecule property prediction, thus gaining a more accurate understanding of ChatGPT's capabilities and limitations in this area. Through comprehensive evaluation, we find that 1) With appropriate prompt settings, ChatGPT can attain satisfactory prediction outcomes that are competitive with specialized models designed for those tasks. 2) Prompt settings significantly affect ChatGPT's performance. Among all prompt settings, the strategy of selecting examples in few-shot has the greatest impact on results. Scaffold sampling greatly outperforms random sampling. 3) The capacity of ChatGPT to accomplish high-precision predictions is significantly influenced by the quality of examples provided, which may constrain its practical applicability in real-world scenarios. This work highlights ChatGPT's potential and limitations on molecule property prediction, which we hope can inspire future design and evaluation of Large Language Models within scientific domains.

Circumventing drug resistance in gastric cancer: A spatial multi-omics exploration of chemo and immuno-therapeutic response dynamics.

BACKGROUND: Gastric Cancer (GC) characteristically exhibits heterogeneous responses to treatment, particularly in relation to immuno plus chemo therapy, necessitating a precision medicine approach. This study is centered around delineating the cellular and molecular underpinnings of drug resistance in this context. METHODS: We undertook a comprehensive multi-omics exploration of postoperative tissues from GC patients undergoing the chemo and immuno-treatment regimen. Concurrently, an image deep learning model was developed to predict treatment responsiveness. RESULTS: Our initial findings associate apical membrane cells with resistance to fluorouracil and oxaliplatin, critical constituents of the therapy. Further investigation into this cell population shed light on substantial interactions with resident macrophages, underscoring the role of intercellular communication in shaping treatment resistance. Subsequent ligand-receptor analysis unveiled specific molecular dialogues, most notably TGFB1-HSPB1 and LTF-S100A14, offering insights into potential signaling pathways implicated in resistance. Our SVM model, incorporating these multi-omics and spatial data, demonstrated significant predictive power, with AUC values of 0.93 and 0.84 in the exploration and validation cohorts respectively. Hence, our results underscore the utility of multi-omics and spatial data in modeling treatment response. CONCLUSION: Our integrative approach, amalgamating mIHC assays, feature extraction, and machine learning, successfully unraveled the complex cellular interplay underlying drug resistance. This robust predictive model may serve as a valuable tool for personalizing therapeutic strategies and enhancing treatment outcomes in gastric cancer.

Temperature-sensitive hydrogel releasing pectolinarin facilitate scarless wound healing.

The dressing that promotes scarless healing is essential for both normal function and aesthetics after a wound. With a deeper understanding of the mechanisms involved in scar formation during the wound healing process, the ideal dressing becomes clearer and more promising. For instance, the yes-associated transcriptional regulator (YAP) has been extensively studied as a key gene involved in regulating scar formation. However, there has been limited attention given to pectolinarin, a natural flavonoid that may exhibit strong binding affinity to YAP, in the context of scarless healing. In this study, we successfully developed a temperature-sensitive Pluronic@F-127 hydrogel as a platform for delivering pectolinarin to promote scarless wound healing. The bioactive pectolinarin was released from the hydrogel, effectively enhancing endothelial cell migration, proliferation and the expression of angiogenesis-related genes. Additionally, a concentration of 20 µg/mL of pectolinarin demonstrated remarkable antioxidant ability, capable of counteracting the detrimental effects of reactive oxygen species (ROS). Our results from rat wound healing models demonstrated that the hydrogel accelerated wound healing, promoting re-epithelialization and facilitating skin appendage regeneration. Furthermore, we discovered that a concentration of 50 µg/mL of pectolinarin incorporated to the hydrogel exhibited the most favourable outcomes in terms of promoting wound healing and minimizing scar formation. Overall, our study highlights that the significant potential of locally released pectolinarin might substantially inhibit YAP and promoting scarless wound healing.

Merging Ring-Opening 1,2-Metallate Shift with Asymmetric C(sp3)-H Borylation of Aziridines.

Chiral secondary alkyl amines with a vicinal quaternary stereocenter are undoubtedly important and ubiquitous subunits in natural products and pharmaceuticals. However, their asymmetric synthesis remains a formidable challenge. Herein, we merge the ring-opening 1,2-metallate shift with iridium-catalyzed enantioselective C(sp3)-H borylation of aziridines to deliver these frameworks with high enantioselectivities. We also demonstrated the synthetic application by downstream transformations, including the total synthesis of two Amaryllidaceae alkaloids, (-)-crinane and (+)-mesmebrane.

Polyvalent Nanobody Structure Designed for Boosting SARS-CoV-2 Inhibition.

Coronavirus transmission and mutations have brought intensive challenges on pandemic control and disease treatment. Developing robust and versatile antiviral drugs for viral neutralization is highly desired. Here, we created a new polyvalent nanobody (Nb) structure that shows the effective inhibition of SARS-CoV-2 infections. Our polyvalent Nb structure, called "PNS", is achieved by first conjugating single-stranded DNA (ssDNA) and the receptor-binding domain (RBD)-targeting Nb with retained binding ability to SARS-CoV-2 spike protein and then coalescing the ssDNA-Nb conjugates around a gold nanoparticle (AuNP) via DNA hybridization with a desired Nb density that offers spatial pattern-matching with that of the Nb binding sites on the trimeric spike. The surface plasmon resonance (SPR) assays show that the PNS binds the SARS-CoV-2 trimeric spike proteins with a ∼1000-fold improvement in affinity than that of monomeric Nbs. Furthermore, our viral entry inhibition assays using the PNS against SARS-CoV-2 WA/2020 and two recent variants of interest (BQ1.1 and XBB) show an over 400-fold enhancement in viral inhibition compared to free Nbs. Our PNS strategy built on a new DNA-protein conjugation chemistry provides a facile approach to developing robust virus inhibitors by using a corresponding virus-targeting Nb with a desired Nb density.

Potential biomarkers for immunotherapy in non-small-cell lung cancer.

For individuals with advanced or metastatic non-small cell lung cancer (NSCLC), the primary treatment is platinum-based doublet chemotherapy. Immune checkpoint inhibitors (ICIs), primarily PD-1/PD-L1 and CTLA-4, have been found to be effective in patients with NSCLC who have no EGFR/ALK mutations. Furthermore, ICIs are considered a standard therapy. The quantity of fresh immunogenic antigens discovered by cytotoxic T cells was measured by PD-L1 expression and tumor mutational burden (TMB), which were the first biomarkers assessed in clinical trials. However, immunotherapy did not have response efficacy markers similar to targeted therapy, highlighting the significance of newly developed biomarkers. This investigation aims to review the research on immunotherapy for NSCLC, focusing primarily on the impact of biomarkers on efficacy prediction to determine whether biomarkers may be utilized to evaluate the effectiveness of immunotherapy.

Hypoxia-induced ALKBH5 aggravates synovial aggression and inflammation in rheumatoid arthritis by regulating the m6A modification of CH25H.

Previous studies have shown that epigenetic factors are involved in the occurrence and development of rheumatoid arthritis (RA). However, the role of N6-methyladenosine (m6A) methylation in RA has not been determined. The aim of this study was to investigate the role and regulatory mechanisms of hypoxia-induced expression of the m6A demethylase alkB homolog 5 (ALKBH5) in RA fibroblast-like synoviocytes (FLSs). Synovial tissues were collected from RA and osteoarthritis (OA) patients, and RA FLSs were obtained. ALKBH5 expression in RA FLSs and collagen-induced arthritis (CIA) model rats was determined using quantitative reverse transcription-PCR (qRT-PCR), western blotting and immunohistochemistry (IHC). Using ALKBH5 overexpression and knockdown, we determined the role of ALKBH5 in RA FLS aggression and inflammation. The role of ALKBH5 in RA FLS regulation was explored using m6A-methylated RNA sequencing and methylated RNA immunoprecipitation coupled with quantitative real-time PCR. The expression of ALKBH5 was increased in RA synovial tissues, CIA model rats and RA FLSs, and a hypoxic environment increased the expression of ALKBH5 in FLSs. Increased expression of ALKBH5 promoted the proliferation and migration of RA-FLSs and inflammation. Conversely, decreased ALKBH5 expression inhibited the migration of RA-FLSs and inflammation. Mechanistically, hypoxia-induced ALKBH5 expression promoted FLS aggression and inflammation by regulating CH25H mRNA stability. Our study elucidated the functional roles of ALKBH5 and mRNA m6A methylation in RA and revealed that the HIF1α/2α-ALKBH5-CH25H pathway may be key for FLS aggression and inflammation. This study provides a novel approach for the treatment of RA by targeting the HIF1α/2α-ALKBH5-CH25H pathway.

AnnoSM: An Automated Annotation Tool for Determining the Substituent Modes on the Parent Skeleton Based on a Characteristic MS/MS Fragment Ion Library.

Mass spectrometry (MS) is a powerful technology for the structural elucidation of known or unknown small molecules. However, the accuracy of MS-based structure annotation is still limited due to the presence of numerous isomers in complex matrices. There are still challenges in automatically interpreting the fine structure of molecules, such as the types and positions of substituents (substituent modes, SMs) in the structure. In this study, we employed flavones, flavonols, and isoflavones as examples to develop an automated annotation method for identifying the SMs on the parent molecular skeleton based on a characteristic MS/MS fragment ion library. Importantly, user-friendly software AnnoSM was built for the convenience of researchers with limited computational backgrounds. It achieved 76.87% top-1 accuracy on the 148 authentic standards. Among them, 22 sets of flavonoid isomers were successfully differentiated. Moreover, the developed method was successfully applied to complex matrices. One such example is the extract of Ginkgo biloba L. (EGB), in which 331 possible flavonoids with SM candidates were annotated. Among them, 23 flavonoids were verified by authentic standards. The correct SMs of 13 flavonoids were ranked first on the candidate list. In the future, this software can also be extrapolated to other classes of compounds.

Net-Shaped DNA Nanostructure-Based Lateral Flow Assays for Rapid and Sensitive SARS-CoV-2 Detection.

Lateral flow assay (LFA)-based rapid antigen tests are experiencing extensive global uptake as an expeditious and highly effective modality for the screening of viral infections during the COVID-19 pandemic. While these devices have played a significant role in alleviating the burden on the public healthcare system, their specificity and sensitivity fall short compared with molecular tests. In this study, we endeavor to address both limitations through the utilization of DNA nanotechnology in LFA format, wherein we substitute the target-specific antibody with designer DNA nanostructure-based molecular probes for recognizing the SARS-CoV-2 virus via multivalent, pattern-matching interactions. We meticulously designed a Net-shaped DNA nanostructure and strategically arranged trimeric clusters of aptamers that specifically recognize the spike proteins of SARS-CoV-2. This approach has proven instrumental in bolstering virus-binding affinity on the LFAs. Our findings indicate high LFA sensitivity, enabling the detection of viral loads ranging from 103 to 108 viral copies/mL. This notable sensitivity is maintained across various SARS-CoV-2 viral strains, obviating the need for intricate sample preparation protocols. The significance of this heightened sensitivity lies in the crucial role played by the designer DNA nanostructure, which facilitates the detection of extremely low levels of viral loads. This not only enhances the overall reliability of self-testing but also reduces the likelihood of false-negative results, especially in cases of low viral load within patient samples.

Optimized Two-Photon Imaging by Stimuli-Responsive Peptide Self-Assembly Facilitates Self-Assisted Counteraction of Cisplatin-Resistance in Cancer Cells.

Accurate diagnosis and effective treatment of tumors remain significant clinical challenges. While fluorescence imaging is essential for tumor detection, it has limitations in terms of specificity, penetration depth, and emission wavelength. Here, we report a novel glutathione (GSH)-responsive peptide self-assembly excimer probe (pSE) that optimizes two-photon tumor imaging and self-assisted counteraction of the cisplatin resistance in cancer cells. The GSH-responsive self-assembly of pSE induces a monomer-excimer transition of coumarin, promoting a near-infrared redshift of fluorescence emission under two-photon excitation. This process enhances penetration depth and minimizes interference from biological autofluorescence. Moreover, the intracellular self-assembly of pSE impacts GSH homeostasis, modulates relevant signaling pathways, and significantly reduces GSTP1 expression, resulting in decreased cisplatin efflux in cisplatin-resistant cancer cells. The proposed self-assembled excimer probe not only distinguishes cancer cells from normal cells but also enhances the efficacy of cisplatin chemotherapy, offering significant potential in tumor diagnosis and overcoming cisplatin-resistant tumors.

AntDAS-GCMS: A New Comprehensive Data Analysis Platform for GC-MS-Based Untargeted Metabolomics with the Advantage of Addressing the Time Shift Problem.

Over the years, a number of state-of-the-art data analysis tools have been developed to provide a comprehensive analysis of data collected from gas chromatography-mass spectrometry (GC-MS). Unfortunately, the time shift problem remains unsolved in these tools. Here, we developed a novel comprehensive data analysis strategy for GC-MS-based untargeted metabolomics (AntDAS-GCMS) to perform total ion chromatogram peak detection, peak resolution, time shift correction, component registration, statistical analysis, and compound identification. Time shift correction was specifically optimized in this work. The information on mass spectra and elution profiles of compounds was used to search for inherent landmarks within analyzed samples to resolve the time shift problem across samples efficiently and accurately. The performance of our AntDAS-GCMS was comprehensively investigated by using four complex GC-MS data sets with various types of time shift problems. Meanwhile, AntDAS-GCMS was compared with advanced GC-MS data analysis tools and classic time shift correction methods. Results indicated that AntDAS-GCMS could achieve the best performance compared to the other methods.

Overexpression of VEGFA mediated by HIF-1 is associated with higher rate of spread through air spaces in resected lung adenocarcinomas.

BACKGROUND: Spread through air spaces (STAS), a newly identified pattern of invasion in lung adenocarcinomas (LACs), is an unfavorable prognostic factor for patients with LAC, but the molecular characteristics and mechanisms underlying STAS have not been adequately explored. METHODS: In total, 650 pathologically confirmed invasive LAC patients who underwent curative resection between December 2019 and April 2020 were reviewed. Disease-free survival (DFS) and overall survival (OS) were analyzed using the log-rank test and the Cox proportional hazards model. A comparative deep sequencing analysis was conducted to explore the molecular characteristics underlying STAS. Vascular endothelial growth factor A (VEGFA) expression was evaluated by immunoblotting and immunohistochemical analysis using fresh tumor tissue and tissue microarray. RESULTS: STAS was more prevalent in patients with a smoking history (p < 0.001), high pathological TNM stage (p < 0.001), lymphovascular invasion (p < 0.001), visceral pleural invasion (p < 0.001) and micropapillary/solid histological subtypes (p < 0.001). STAS-negative patients had better DFS (p < 0.001) and OS (p = 0.003) compared to STAS-positive patients with invasive LACs, especially in the lymph node-negative population (p < 0.001). After RNA-sequencing analysis, hypoxia-inducible factor-1 (HIF-1) signaling was enriched and appeared to be strongly correlated with STAS, and more STAS-positive individuals were detected in the higher VEGFA-expressing group (p = 0.042). CONCLUSIONS: We demonstrated that STAS was an independent prognostic marker of poor clinical outcome, especially in lymph node-negative patients, and that higher VEGFA expression mediated by HIF-1 signaling was associated with an increased STAS rate.