Merging Excited-State Copper Catalysis and Triplet Nitro(hetero)arenes for Direct Synthesis of 2-Aminophenol Derivatives.

Nitro(hetero)arene derivatives are essential commodity chemicals used in various products, such as drugs, polymers, and agrochemicals. In this study, we leverage the excited-state reactivities of copper catalysts and nitro(hetero)arenes, and the Umpolung reactivity of acyl radicals to convert readily available nitro(hetero)arenes directly to valuable 2-aminophenol derivatives, which are important scaffolds in many top-selling pharmaceuticals. This reaction is applicable to a variety of nitro(hetero)arenes, acyl chlorides, and late-stage modifications of complex molecules, making it a useful tool for the discovery of new functional molecules. Mechanistic studies, including radical trapping experiments, Stern Volmer quenching studies, light ON/OFF experiments, and 18O-labeling studies, suggest a reaction mechanism involving photoexcitation of a copper complex, diradical couplings, and an in-cage contact ion pair (CIP) migration. Our findings offer a streamlined protocol for synthesizing essential pharmacophores from nitro(hetero)arenes while simultaneously advancing knowledge in excited-state and radical chemistry and stimulating new reaction design and development.

Synthesis of Ketonylated Carbocycles via Excited-State Copper-Catalyzed Radical Carbo-Aroylation of Unactivated Alkenes.

Carbocycles are core skeletons in natural and synthetic organic compounds possessing a wide diversity of important biological activities. Herein, we report the development of an excited-state copper-catalyzed radical carbo-aroylation of unactivated alkenes to synthesize ketonylated tetralins, di- and tetrahydrophenanthrenes, and cyclopentane derivatives. The reaction is operationally simple and features mild reaction conditions that tolerate a broad range of functional groups. Preliminary mechanistic studies suggest a reaction pathway beginning with photoexcitation of [CuI-BINAP]2 and followed by a single electron transfer (SET), radical aroylation of unactivated alkenes, radical cyclization, and re-aromatization, affording the desired ketonylated carbocycles.

Linear Scaling Relationships for Furan Hydrodeoxygenation over Transition Metal and Bimetallic Surfaces.

Brønsted-Evans-Polanyi (BEP) and transition-state-scaling (TSS) relationships have become valuable tools for the rational design of catalysts for complex reactions like hydrodeoxygenation (HDO) of bio-oil (containing heterocyclic and homocyclic molecules). In this work, BEP and TSS relationships are developed for all the elementary steps of furan activation (C and O hydrogenation and CHx -OHy scission, for both ring and open-ring intermediates) to oxygenates, ring-saturated compounds and deoxygenated products on the most stable facets of Ni, Co, Rh, Ru, Pt, Pd, Fe and Ir surfaces using Density Functional Theory (DFT) calculations. Furan ring opening barriers were found to be facile and strongly dependent on carbon and oxygen binding strength on the investigated surfaces. Our calculations suggest linear chain oxygenates form on Ir, Pt, Pd and Rh surfaces due to their low hydrogenation and high CHx -OHy scission barriers, while deoxygenated linear products are favoured on Fe and Ni surfaces due to their low CHx -OHy scission and moderate hydrogenation barriers. Bimetallic alloy catalysts were also screened for their potential HDO activity and PtFe catalysts were found to significantly lower the ring opening and deoxygenation barriers relative to the corresponding pure metals. The developed BEPs for monometallic surfaces can be extended to estimate the barriers on bimetallic surfaces for ring opening and ring hydrogenation reactions but fails to predict the barriers for open-ring activation reactions due to the change in transition state binding sites on the bimetallic surface. The obtained BEP and TSS relationships can be used to develop microkinetic models for facilitating accelerated catalyst discovery for HDO.

SARS-CoV-2 severity classification from plasma sample using confocal Raman spectroscopy.

The world is on the brink of facing coronavirus's (COVID-19) fourth wave as the mutant forms of viruses are escaping neutralizing antibodies in spite of being vaccinated. As we have already witnessed that it has encumbered our health system, with hospitals swamped with infected patients observed during the viral outbreak. Rapid triage of patients infected with SARS-CoV-2 is required during hospitalization to prioritize and provide the best point of care. Traditional diagnostics techniques such as RT-PCR and clinical parameters such as symptoms, comorbidities, sex and age are not enough to identify the severity of patients. Here, we investigated the potential of confocal Raman microspectroscopy as a powerful tool to generate an expeditious blood-based test for the classification of COVID-19 disease severity using 65 patients plasma samples from cohorts infected with SARS-CoV-2. We designed an easy manageable blood test where we used a small volume (8 µl) of inactivated whole plasma samples from infected patients without any extra solvent usage in plasma processing. Raman spectra of plasma samples were acquired and multivariate exploratory analysis PC-LDA (principal component based linear discriminant analysis) was used to build a model, which segregated the severe from the non-severe COVID-19 group with a sensitivity of 83.87%, specificity of 70.60% and classification efficiency of 76.92%. Among the bands expressed in both the cohorts, the study led to the identification of Raman fingerprint regions corresponding to lipids (1661, 1742), proteins amide I and amide III (1555, 1247), proteins (Phe) (1006, 1034), and nucleic acids (760) to be differentially expressed in severe COVID-19 patient's samples. In summary, the current study exhibits the potential of confocal Raman to generate simple, rapid, and less expensive blood tests to triage the severity of patients infected with SARS-CoV-2.

The First Pituitary Proteome Landscape From Matched Anterior and Posterior Lobes for a Better Understanding of the Pituitary Gland.

To date, very few mass spectrometry (MS)-based proteomics studies are available on the anterior and posterior lobes of the pituitary. In the past, MS-based investigations have focused exclusively on the whole pituitary gland or anterior pituitary lobe. In this study, for the first time, we performed a deep MS-based analysis of five anterior and five posterior matched lobes to build the first lobe-specific pituitary proteome map, which documented 4090 proteins with isoforms, mostly mapped into chromosomes 1, 2, and 11. About 1446 differentially expressed significant proteins were identified, which were studied for lobe specificity, biological pathway enrichment, protein-protein interaction, regions specific to comparison of human brain and other neuroendocrine glands from Human Protein Atlas to identify pituitary-enriched proteins. Hormones specific to each lobe were also identified and validated with parallel reaction monitoring-based target verification. The study identified and validated hormones, growth hormone and thyroid-stimulating hormone subunit beta, exclusively to the anterior lobe whereas oxytocin-neurophysin 1 and arginine vasopressin to the posterior lobe. The study also identified proteins POU1F1 (pituitary-specific positive transcription factor 1), POMC (pro-opiomelanocortin), PCOLCE2 (procollagen C-endopeptidase enhancer 2), and NPTX2 (neuronal pentraxin-2) as pituitary-enriched proteins and was validated for their lobe specificity using parallel reaction monitoring. In addition, three uPE1 proteins, namely THEM6 (mesenchymal stem cell protein DSCD75), FSD1L (coiled-coil domain-containing protein 10), and METTL26 (methyltransferase-like 26), were identified using the NeXtProt database, and depicted tumor markers S100 proteins having high expression in the posterior lobe. In summary, the study documents the first matched anterior and posterior pituitary proteome map acting as a reference control for a better understanding of functional and nonfunctional pituitary adenomas and extrapolating the aim of the Human Proteome Project towards the investigation of the proteome of life.

A protein microarray-based serum proteomic investigation reveals distinct autoantibody signature in colorectal cancer.

PURPOSE: Colorectal cancer (CRC) has been reported as the second leading cause of cancer death worldwide. The 5-year annual survival is around 50%, mainly due to late diagnosis, striking necessity for early detection. This study aims to identify autoantibody in patients' sera for early screening of cancer. EXPERIMENTAL DESIGN: The study used a high-density human proteome array with approximately 17,000 recombinant proteins. Screening of sera from healthy individuals, CRC from Indian origin, and CRC from middle-east Asia origin were performed. Bio-statistical analysis was performed to identify significant autoantibodies altered. Pathway analysis was performed to explore the underlying mechanism of the disease. RESULTS: The comprehensive proteomic analysis revealed dysregulation of 15 panels of proteins including CORO7, KCNAB1, WRAP53, NDUFS6, KRT30, and COLGALT2. Further biological pathway analysis for the top dysregulated autoantigenic proteins revealed perturbation in important biological pathways such as ECM degradation and cytoskeletal remodeling etc. CONCLUSIONS AND CLINICAL RELEVANCE: The generation of an autoimmune response against cancer-linked pathways could be linked to the screening of the disease. The process of immune surveillance can be detected at an early stage of cancer. Moreover, AAbs can be easily extracted from blood serum through the least invasive test for disease screening.

Excited-State Copper-Catalyzed [4 + 1] Annulation Reaction Enables Modular Synthesis of α,ß-Unsaturated-γ-Lactams.

Synthesis of α,ß-unsaturated-γ-lactams continue to attract attention due to the importance of this structural motif in organic chemistry. Herein, we report the development of a visible-light-induced excited-state copper-catalyzed [4 + 1] annulation reaction for the preparation of a wide range of γ-H, -OH, and -OR-substituted α,ß-unsaturated-γ-lactams using acrylamides as the 4-atom unit and aroyl chlorides as the 1-atom unit. This modular synthetic protocol features mild reaction conditions, broad substrate scope, and high functional group tolerance. The reaction is amenable to late-stage diversification of complex molecular architectures, including derivatives of marketed drugs. The products of the reaction can serve as versatile building blocks for further derivatization. Preliminary mechanistic studies suggest an inner-sphere catalytic cycle involving photoexcitation of the Cu(BINAP) catalyst, single-electron transfer, and capture of radical intermediates by copper species, followed by reductive elimination or protonation to give the desired γ-functionalized α,ß-unsaturated-γ-lactams.

Metal-Free N-Doped Carbon Catalyst Derived from Chitosan for Aqueous Formic Acid-Mediated Selective Reductive Formylation of Quinoline and Nitroarenes.

A chitosan-derived metal-free N-doped carbon catalyst was synthesized and investigated for selective reductive formylation of quinoline to N-formyl-tetrahydroquinoline and nitroarenes to N-formyl anilides via aqueous formic acid (FA)-mediated catalytic transformation. FA dissociated on the catalyst surface and acted as a hydrogenating and formylating source for selective N-formylation of N-heteroarenes. The carbonized catalyst prepared at 700 °C offered the best activity. A 92 % yield of N-formyl-tetrahydroquinoline after 14 h and >99 % yield for N-formyl anilide after 12 h at 160 °C were obtained. The excellent catalytic activity was correlated with the type of "N" species and the basicity of the catalyst. Density functional theory calculations revealed that a water-assisted FA decomposition pathway (deprotonation and dehydroxylation) generated the surface adsorbed -H and -HCOO species, required for the formation of N-formylated products. In addition, the selective formation of N-formyl-tetrahydroquinoline and N-formyl anilides was explained by a comprehensive reaction energetics analysis.

Metabolomics Profiling of Pituitary Adenomas by Raman Spectroscopy, Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy, and Mass Spectrometry of Serum Samples.

To date, no studies are available in which pituitary adenomas (PAs) have been studied using techniques like confocal Raman spectroscopy, attenuated total reflection-Fourier transform infrared (FT-IR), and liquid chromatography-tandem mass spectrometry (LC-MS/MS) in the same serum samples. To understand the metabolomics fingerprint, Raman spectra of 16 acromegaly, 19 Cushing's, and 33 nonfunctional PA (NFPA) and ATR-FTIR spectral acquisition of 16 acromegaly, 18 Cushing's, and 22 NFPA patient's serum samples were acquired. Next, Principal component-based linear discriminant analysis (PC-LDA) models were developed, Raman spectral analysis classified acromegaly with an accuracy of 79.17%, sensitivity of 75%, and specificity of 81.25%, Cushing's with an accuracy of 66.67%, sensitivity of 100%, and specificity of 52.63%, and NFPA with an accuracy of 73.17%, sensitivity of 75%, and specificity of 72.73%. ATR-FTIR spectral analysis classified acromegaly with an accuracy of 95.83%, sensitivity of 100%, and specificity of 93.75%, Cushing's with an accuracy of 65.38%, sensitivity of 87.5%, and specificity of 55.56%, and NFPA with an accuracy of 70%, sensitivity of 87.5%, and specificity of 43.75%. In either of the cases, healthy individual cohorts were clearly segregated from the disease cohort, which identified differential regulated regions of nucleic acids, lipids, amides, phosphates, and polysaccharide/C-C residue α helix regions. Furthermore, LC-MS/MS-based analysis of sera samples resulted in the identification of various sphingosine, lipids, acylcarnitines, amino acids, ethanolamine, choline, and their derivatives that differentially regulated in each tumor cohort. We believe cues obtained from the study may be used to generate the metabolite-based test to diagnose PAs from serum in addition to conventional techniques and also to understand disease biology for better disease management, point of care, and improving quality of life in PA patients.

Evaluation of autoantibody signatures in pituitary adenoma patients using human proteome arrays.

PURPOSE: To identify the specific diagnostic biomarkers related to pituitary adenomas (PAs), we performed serological antibody profiles for three types of PAs, namely Acromegaly, Cushing's and Nonfunctional Pituitary Adenomas (NFPAs), using the human proteome (HuProt) microarray. This is the first study describing the serum autoantibody profile of PAs. EXPERIMENTAL DESIGN: We performed serological autoantibody profiling of four healthy controls, four Acromegaly, three Cushing's and three NFPAs patient samples to obtain their autoantibody profiles, which were used for studying expression, interaction and altered biological pathways. Further, significant autoantibodies of PAs were compared with data available for glioma, meningioma and AAgAtlas for their specificity. RESULTS: Autoantibody profile of PAs led to the identification of differentially expressed significant proteins such as AKNAD1 (AT-Hook Transcription Factor [AKNA] Domain Containing 1), NINJ1 (Nerve injury-induced protein 1), L3HYPDH (Trans-3-hydroxy-L-proline dehydratase), RHOG (Rho-related GTP-binding protein) and PTP4A1 (Protein Tyrosine Phosphatase Type IVA 1) in Acromegaly. Protein ABR (Active breakpoint cluster region-related protein), ST6GALNAC6 (ST6 N-acetylgalactosaminide alpha-2, 6-sialyltransferase 6), NOL3 (Nucleolar protein 3), ANXA8 (Annexin A8) and POLR2H (RNA polymerase II, I and III subunit H) showed an antigenic response in Cushing's patient's serum samples. Protein dipeptidyl peptidase 3 (DPP3) and reticulon-4 (RTN4) exhibited a very high antigenic response in NFPA patients. These proteins hold promise as potential autoantibody biomarkers in PAs.

Green syntheses of graphene and its applications in internet of things (IoT)-a status review.

Internet of Things (IoT) is a trending technological field that converts any physical object into a communicable smarter one by converging the physical world with the digital world. This innovative technology connects the device to the internet and provides a platform to collect real-time data, cloud storage, and analyze the collected data to trigger smart actions from a remote location via remote notifications, etc. Because of its wide-ranging applications, this technology can be integrated into almost all the industries. Another trending field with tremendous opportunities is Nanotechnology, which provides many benefits in several areas of life, and helps to improve many technological and industrial sectors. So, integration of IoT and Nanotechnology can bring about the very important field of Internet of Nanothings (IoNT), which can re-shape the communication industry. For that, data (collected from trillions of nanosensors, connected to billions of devices) would be the 'ultimate truth', which could be generated from highly efficient nanosensors, fabricated from various novel nanomaterials, one of which is graphene, the so-called 'wonder material' of the 21st century. Therefore, graphene-assisted IoT/IoNT platforms may revolutionize the communication technologies around the globe. In this article, a status review of the smart applications of graphene in the IoT sector is presented. Firstly, various green synthesis of graphene for sustainable development is elucidated, followed by its applications in various nanosensors, detectors, actuators, memory, and nano-communication devices. Also, the future market prospects are discussed to converge various emerging concepts like machine learning, fog/edge computing, artificial intelligence, big data, and blockchain, with the graphene-assisted IoT field to bring about the concept of 'all-round connectivity in every sphere possible'.

Mass spectrometry and proteome analysis to identify SARS-CoV-2 protein from COVID-19 patient swab samples.

With new emerging SARS-CoV-2 strains and their increased pathogenicity, diagnosis has become more challenging. Molecular diagnosis often involves the use of nasopharyngeal swabs and subsequent real-time PCR-based tests. Although this test is the gold standard, it has several limitations; therefore, more complementary assays are required. This protocol describes how to identify SARS-CoV-2 protein from patients' nasopharyngeal swab samples. We first introduce the approach of label-free quantitative proteomics. We then detail target verification by triple quadrupole mass spectrometry (MS)-based targeted proteomics. For complete details on the use and execution of this profile, please refer to Bankar et al. (2021).

Excited-State Copper Catalysis for the Synthesis of Heterocycles.

Heterocycles are one of the largest groups of organic moieties with significant medicinal, chemical, and industrial applications. Herein, we report the discovery and development of visible-light-induced, synergistic excited-state copper catalysis using a combination of Cu(IPr)I as a catalyst and rac-BINAP as a ligand, which produces more than 10 distinct classes of heterocycles. The reaction tolerates a broad array of functional groups and complex molecular scaffolds, including derivatives of peptides, natural products, and marketed drugs. Preliminary mechanistic investigation suggests in situ generations of [Cu(BINAP)2 ]+ and [Cu(IPr)2 ]+ catalysts that work cooperatively under visible-light irradiation to facilitate catalytic carbo-aroylation of unactivated alkenes, affording a wide range of useful heterocycles.

Rapid Classification of COVID-19 Severity by ATR-FTIR Spectroscopy of Plasma Samples.

The coronavirus disease 2019 (COVID-19) pandemic continues to ravage the world, with many hospitals overwhelmed by the large number of patients presenting during major outbreaks. A rapid triage for COVID-19 patient requiring hospitalization and intensive care is urgently needed. Age and comorbidities have been associated with a higher risk of severe COVID-19 but are not sufficient to triage patients. Here, we investigated the potential of attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy as a rapid blood test for classification of COVID-19 disease severity using a cohort of 160 COVID-19 patients. A simple plasma processing and ATR-FTIR data acquisition procedure was established using 75% ethanol for viral inactivation. Next, partial least-squares-discriminant analysis (PLS-DA) models were developed and tested using data from 130 and 30 patients, respectively. Addition of the ATR-FTIR spectra to the clinical parameters (age, sex, diabetes mellitus, and hypertension) increased the area under the ROC curve (C-statistics) for both the training and test data sets, from 69.3% (95% CI 59.8-78.9%) to 85.7% (78.6-92.8%) and 77.8% (61.3-94.4%) to 85.1% (71.3-98.8%), respectively. The independent test set achieved 69.2% specificity (42.4-87.3%) and 94.1% sensitivity (73.0-99.0%). Diabetes mellitus was the strongest predictor in the model, followed by FTIR regions 1020-1090 and 1588-1592 cm-1. In summary, this study demonstrates the potential of ATR-FTIR spectroscopy as a rapid, low-cost COVID-19 severity triage tool to facilitate COVID-19 patient management during an outbreak.

Recent advances in proteomics and its implications in pituitary endocrine disorders.

Pituitary adenoma is considered as one of the most frequent intracranial tumors having salient impact on human health such as mass effects, hypopituitarism and visual defects etc. During the past few decades, there has been enormous advancement in mass spectrometry (MS)-based proteomics. However, very little is known about the molecular pathogenesis of pituitary adenomas in the context of proteomics. In this review article, we have focused on the provenance of pituitary tumors and their pathogenesis with the help of MS-based proteomics approaches. Recent advancements in quantitative proteomic approaches are outlined here that would be useful in the near pituitary adenoma proteomics research. This review discusses the enormous potential of pituitary adenomas research through proteomics with a common aim of deciphering disease pathobiology and identifying the work done in studying pituitary tumors during past decade.

Influence of Indium as a Promoter on the Stability and Selectivity of the Nanocrystalline Cu/CeO2 Catalyst for CO2 Hydrogenation to Methanol.

Stable catalyst development for CO2 hydrogenation to methanol is a challenge in catalysis. In this study, indium (In)-promoted Cu nanoparticles supported on nanocrystalline CeO2 catalysts were prepared and explored for methanol production from CO2. In-promoted Cu catalysts with ∼1 wt % In loading showed a methanol production rate of 0.016 mol gCu-1 h-1 with 95% methanol selectivity and no loss of activity for 100 h. It is found that the addition of indium remarkably increases Cu dispersion and decreases Cu particle size. In addition led to an increased metal-support interaction, which stabilizes Cu particles against sintering during the reaction, leading to high stability and activity. In addition, density functional theory calculations suggested that the reaction is proceeding via reverse water gas shift (RWGS) mechanism where the presence of In stabilized intermediate species and lowered CO2 activation energy barriers.

Profiling Autoantibody Responses to Devise Novel Diagnostic and Prognostic Markers Using High-Density Protein Microarrays.

Protein microarrays are a diverse and high-throughput platform for screening biomolecular interactions, autoantigens, and protein expression profiles across tissues, etc. Autoantibodies produced against aberrant protein expression are often observed in malignancies which makes protein microarrays a powerful platform to elucidate biomarkers of translational interest. Early diagnosis of malignancies is an enduring clinical problem that has a direct impact on disease prognosis. Here, we provide an overview of a method employed to screen autoantibodies using patient sera in brain tumors. In case of brain malignancies, early diagnosis is particularly challenging and often requires highly invasive brain biopsies as a confirmatory test. This chapter summarizes the various considerations for applying a serum-based autoantibody biomarker discovery pipeline that could provide a minimally invasive initial diagnostic screen, potentiating classical diagnostic approaches.

Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential.

The pestilential pathogen SARS-CoV-2 has led to a seemingly ceaseless pandemic of COVID-19. The healthcare sector is under a tremendous burden, thus necessitating the prognosis of COVID-19 severity. This in-depth study of plasma proteome alteration provides insights into the host physiological response towards the infection and also reveals the potential prognostic markers of the disease. Using label-free quantitative proteomics, we performed deep plasma proteome analysis in a cohort of 71 patients (20 COVID-19 negative, 18 COVID-19 non-severe, and 33 severe) to understand the disease dynamics. Of the 1200 proteins detected in the patient plasma, 38 proteins were identified to be differentially expressed between non-severe and severe groups. The altered plasma proteome revealed significant dysregulation in the pathways related to peptidase activity, regulated exocytosis, blood coagulation, complement activation, leukocyte activation involved in immune response, and response to glucocorticoid biological processes in severe cases of SARS-CoV-2 infection. Furthermore, we employed supervised machine learning (ML) approaches using a linear support vector machine model to identify the classifiers of patients with non-severe and severe COVID-19. The model used a selected panel of 20 proteins and classified the samples based on the severity with a classification accuracy of 0.84. Putative biomarkers such as angiotensinogen and SERPING1 and ML-derived classifiers including the apolipoprotein B, SERPINA3, and fibrinogen gamma chain were validated by targeted mass spectrometry-based multiple reaction monitoring (MRM) assays. We also employed an in silico screening approach against the identified target proteins for the therapeutic management of COVID-19. We shortlisted two FDA-approved drugs, namely, selinexor and ponatinib, which showed the potential of being repurposed for COVID-19 therapeutics. Overall, this is the first most comprehensive plasma proteome investigation of COVID-19 patients from the Indian population, and provides a set of potential biomarkers for the disease severity progression and targets for therapeutic interventions.

Proteomic investigation reveals dominant alterations of neutrophil degranulation and mRNA translation pathways in patients with COVID-19.

The altered molecular proteins and pathways in response to COVID-19 infection are still unclear. Here, we performed a comprehensive proteomics-based investigation of nasopharyngeal swab samples from patients with COVID-19 to study the host response by employing simple extraction strategies. Few of the host proteins such as interleukin-6, L-lactate dehydrogenase, C-reactive protein, Ferritin, and aspartate aminotransferase were found to be upregulated only in COVID-19-positive patients using targeted multiple reaction monitoring studies. The most important pathways identified by enrichment analysis were neutrophil degranulation, interleukin-12 signaling pathways, and mRNA translation of proteins thus providing the detailed investigation of host response in COVID-19 infection. Thus, we conclude that mass spectrometry-detected host proteins have a potential for disease severity progression; however, suitable validation strategies should be deployed for the clinical translation. Furthermore, the in silico docking of potential drugs with host proteins involved in the interleukin-12 signaling pathway might aid in COVID-19 therapeutic interventions.

A thermally insulating vermiculite nanosheet-epoxy nanocomposite paint as a fire-resistant wood coating.

Conventional fire-retardant composite coatings are typically made of organic-based materials that reduce flame spread rates. However, the associated chemical reactions and starting precursors produce toxic and hazardous gases, affecting the environment and contributing to climate change. Wood is one of the most common materials used in construction and households, and thin-film fire-retardant coatings are needed to protect it from fire. Here, we derive high-performance nanocomposite paint-based coatings from naturally occurring and highly insulating layered vermiculite. The coatings are made using different weight percentages of shear-exfoliated vermiculite nanosheets in an epoxy matrix and are brush-coated onto teak wood. A series of tests using coated wooden rods and standard fire retardancy tests confirm a reduction in flame spread and combustion velocity with minimal toxic smoke release. Samples coated with the vermiculite/epoxy nanocomposite paint resist fire propagation, and post-combustion analysis indicates their resistance to thermal degradation. Our results offer a novel and eco-efficient solution to minimize the flame propagation rate, enhancing char development, and expand the scope of applications of ultra-thin vermiculite in nanocomposite coatings as a fire retardant, exploiting its low thermal conductivity in thermal insulation systems.