Unique Molecules Research Articles

Fragment and torsion biasing algorithms for construction of small organic molecules in proteins using DOCK.

The computational construction of small organic molecules (de novo design), directly in a protein binding site, is an effective means for generating novel ligands tailored to fit the pocket environment. In this work, we present two new methods, which aim to improve de novo design outcomes using (1) biasing algorithms to prioritize selection and/or acceptance of fragments and torsions during growth, and (2) parallel-based clustering and pruning algorithms to remove duplicate molecules as candidate fragment are added. Large-scale testing encompassing thousands of simulations were employed to interrogate the methods in terms of multiple metrics which include numbers of duplicate molecules generated, pairwise-similarity, focused library reconstruction rates, fragment and torsion frequencies, fragment and torsion rank scores, interaction energy and drug-likeness scores, and 3D pose comparisons. The biasing algorithms, particularly those that include fragment and torsion components simultaneously, led to molecules that more closely mimicked the distributions of fragments and torsions found in drug-like libraries. The new parallel-based clustering and pruning algorithms, compared with the existing serial approach, also led to larger ensembles comprised of topologically unique molecules with much greater efficiency by removing redundant growth paths.

Single-cell sequencing analysis of multiple myeloma heterogeneity and identification of new theranostic targets

Multiple myeloma (MM) is a heterogeneous and incurable tumor characterized by the malignant proliferation of plasma cells. It is necessary to clarify the heterogeneity of MM and identify new theranostic targets. We constructed a single-cell transcriptome profile of 48,293 bone marrow cells from MM patients and health donors (HDs) annotated with 7 continuous B lymphocyte lineages. Through CellChat, we discovered that the communication among B lymphocyte lineages between MM and HDs was disrupted, and unique signaling molecules were observed. Through pseudotime analysis, it was found that the differences between MM and HDs were mainly reflected in plasma cells. These differences are primarily related to various biological processes involving mitochondria. Then, we identified the key subpopulation associated with the malignant proliferation of plasma cells. This group of cells exhibited strong proliferation ability, high CNV scores, high expression of frequently mutated genes, and strong glucose metabolic activity. Furthermore, we demonstrated the therapeutic potential of WNK1 as a target. Our study provides new insights into the development of B cells and the heterogeneity of plasma cells in MM and suggests that WNK1 is a potential therapeutic target for MM.

The beauty of symmetry: siRNA phosphorodithioate modifications reduce stereocomplexity, ease analysis, and can improve in vivo potency

Phosphorothioates (PS) can be essential in stabilizing therapeutic oligonucleotides against enzymatic degradation. However, unless synthesis is performed with stereo-defined amidites, each PS introduces a chemically undefined stereocenter, resulting in 2n unique molecules in the final product and affecting downstream analytics and purification. Replacing the second non-bridging oxygen with sulfur results in phosphorodithioate (PS2) linkages, thereby removing the stereocenter. We describe synthesis and analytical data for GalNAc-conjugated siRNAs with PS2 in the GalNAc cluster and at the siRNA termini. All siRNA conjugates with PS2 internucleotide linkages were produced with good yield and showed improved analytical properties. PS2 in the GalNAc cluster had no, or only minor, effect on in vitro and in vivo activity. Except for the 5′-antisense position, PS2 modifications were well tolerated at the siRNA termini, and a single PS2 internucleotide linkage gave similar or improved stabilization and in vitro activity as the two PS typically used for end stabilization. Surprisingly, several of the PS2-containing siRNA conjugates resulted in increased in vivo activity and duration of action compared to the same siRNA sequence stabilized with PS linkages, suggesting PS2 linkages as interesting options for siRNA strand design with reduced number of undefined stereocenters.

Thermally Activated Delayed Fluorescence and Beyond. Photophysics and Material Design Strategies.

This review focuses on thermally activated delayed fluorescence (TADF). Photophysical properties of Cu(I) complexes and unique organic molecules are addressed. Investigations, based on temperature‐dependent emission studies, micro‐ to femto‐second time‐resolved spectroscopy investigations, quantum mechanical considerations, state‐of‐art calculations, and organic light‐emitting diodes (OLED) device studies, address exciton harvesting mechanisms and photophysical impact of the energy gap ΔE(S1–T1) and spin‐orbit coupling (SOC). We disclose relationship between (i) ΔE(S1–T1) and transition rate k(S1–S0); (ii) SOC, phosphorescence, and intersystem crossing (ISC); (iii) internal/external rigidity, luminescence quantum yield, excitation self‐trapping, and concentration quenching; (iv) environment polarity and state energy tuning, as well as (v) SOC and combined ambient‐temperature TADF/phosphorescence, zero‐field splitting, and spin‐lattice relaxation (at T = 1.2 K). These studies guide us to milestone Cu(I) complexes. Moreover, we demonstrate that fast ISC in organic molecules requires state mixing with an additional, energetically close triplet state. Thus, a guide structure for unique organic TADF molecules with ultra‐fast ISC and reverse‐ISC rates (>109 s−1) combined with ΔE(S1–T1)<10 cm−1 (<1 meV) is presented allowing for ultra‐fast singlet‐triplet equilibrated fluorescence with sub‐microsecond decay. First OLEDs fabricated show high external quantum efficiency of ≈19%. Based on this breakthrough material class, a new exciton harvesting mechanism, the direct singlet harvesting (DSH), is presented.

Metathesis Mastery: Crafting Linear Triquinane Frameworks for Natural Product Synthesis

AbstractOrganic and medicinal chemists have shown great interest in developing simple approaches for creating linear triquinane structures, as they are crucial for synthesizing both natural and synthetic products. Linear triquinane shows various biological characteristics like being commonly used for antitumor, antibacterial, and antimalarial purposes, making it a popular subject of study among synthetic chemists. These come from different sources like marine organisms, plants, and fungi and make up a significant group of sesquiterpenoids. These can be classified as cis‐syn‐cis or cis‐anti‐cis based on the stereochemistry at the ring junction, with cis‐anti‐cis being the most common type and exhibiting significant biological activity. In this review, we summarized different methods using a combination of photothermal and olefin metathesis strategy to construct a triquinane framework by utilizing both traditional heat and microwave radiation. Some commonly used synthetic methods, including metathesis protocol and key reactions like Grignard reaction and Diels–Alder reaction, were utilized together with ring‐closing, ring‐opining, and ring‐rearrangement metathesis to create the linear triquinane core for use in total synthesis of natural products. Additionally, the use of photothermal olefin metathesis along with Hosomi‐Sakurai reaction conditions allows for the creation of intricate polyquinanes, which can be used to produce complex natural compounds and for designing unique molecules theoretically.

An Exopolysaccharide from the Cyanobacterium Arthrospira platensis May Utilize CH-π Bonding: A Review of the Isolation, Purification, and Chemical Structure of Calcium-Spirulan.

The CH-π bonding potential of a saccharide is determined primarily by the number of hydrogen atoms available for bonding and is reduced by side groups that interfere with the CH-π bond. Each hydrogen bond increases the total bond energy, while interfering hydroxyl groups and other side groups reduce the bond energy by repulsion. The disaccharide repeating units of Calcium-Spirulan (Ca-SP), a large exopolysaccharide sub fractionated from the supernatant of the cyanobacterium Arthrospira platensis, contain a unique monosaccharide that is completely devoid of hydroxyl groups and side groups on its entire beta surface, leaving five hydrogen atoms available for CH-π bonding in the planar conformation. While planar conformations of independent pyranose rings are rare-to-nonexistent, due to ring strain associated with that conformation, the binding site of a protein could provide the conformational energy needed to overcome that energy barrier. By enabling a planar conformation, a protein could also enable the sugar to form a novel 5-hydrogen CH-π bond configuration. One study of the anticoagulant property of Ca-SP shows that the molecule acts as an activator of Heparin Cofactor II (HC-II), boosting its anticoagulant kinetics by 104. In comparison, the longstanding anticoagulant drug Heparin boosts the HC-II kinetics by 103. The difference may be explained by this unique CH-π configuration. Here, we review current knowledge and experience on the isolation techniques, analytical methods, and chemical structures of Ca-SP. We emphasize a discussion of the CH-π bonding potential of this unique polysaccharide because it is a topic that has not yet been addressed. By introducing the topic of CH-π bonding to the cyanobacterial research community, this review may help to set the stage for further investigation of these unique molecules, their genetics, their biosynthetic pathways, their chemistry, and their biological functions.

Scutellaria barbata D.Don and Scleromitrion diffusum (Willd.) R.J.Wang inhibits the progression of triple negative breast cancer though the activation inhibition of NF-κB triggered by CAFs-derived IL6

Ethnopharmacological relevanceThe treatment options for triple-negative breast cancer (TNBC) are limited. Traditional Chinese Medicine (TCM) plays an important role in the treatment of TNBC. The herb pair Scutellaria barbata D.Don and Scleromitrion diffusum (Willd.) R.J.Wang (SH) is commonly used in clinical practice for its anti-tumor properties. It has been proven to have good therapeutic effects on tumor-related diseases, but the underlying molecular mechanisms are not yet fully explained. Aim of studyThrough bioinformatics, it was validated that IL6, primarily derived from cancer-associated fibroblasts (CAFs), is associated with poor prognosis. Additionally, cell and animal experiments confirmed that SH inhibits tumor proliferation, migration, and growth in an orthotopic tumor model by suppressing the IL6/NF-κB pathway. Materials and methodsGEO, TCGA and HPA databases were used to analyze the prognostic value of CAFs and IL6, then IL6 resource was detected. After the bioinformatics, the influence of CAFs and CAFs-derived IL6 on TNBC was verified by experiments both in vitro and in vivo. Cell clone formation assay, wound-Healing assay, and Transwell assay were used to detect the promotion of CAFs and CAFs-derived IL6 and the inhibition of SH in vitro. TNBC model in mice was used to prove the promotion of CAFs and CAFs-derived IL6 and the inhibition of SH in vivo. The biological pathway of NF-κB was explored by western blotting through detecting unique molecules. ResultsBioinformatics analysis revealed that higher proportion of CAFs and elevated level of IL6 were significantly associated with poor prognosis in TNBC. At the same time, IL6 was proved predominantly derived from CAFs. After the indication of bioinformatics, experiments in vitro demonstrated that both CAFs and IL6 could enhance the clone formation and migration ability of MDA-MD-231 cells (231), furthermore, the promotion of CAFs was related with the level of IL6. Based on these data, mechanism was detected that CAFs-derived IL6 enhancement was closely related to the activation of NF-κB signaling pathway, while the activation can be reduced by SH. In the end, the promotion of CAFs/CAFs-derived IL6/NF-κB and the efficacy of SH inhibition were both confirmed by experiments in vivo. ConclusionsBioinformatics data indicates that higher proportion of CAFs and higher level of CAFs-derived IL6 are significantly related to poorer survival of TNBC. CAFs and CAFs-derived IL6 were proved to promote the progression of TNBC both in vitro and in vivo, and the process of which was significantly related to the activation of NF-κB. SH inhibited the progress of TNBC, which was proved to be closely related to CAFs/CAFs-derived IL6/NF-κB.

MDK promotes M2 macrophage polarization to remodel the tumour microenvironment in clear cell renal cell carcinoma

The efficacy of immunotherapy for clear cell renal cell carcinoma (ccRCC), especially advanced ccRCC, is limited, presenting a clinical challenge. This limitation is closely tied to the immune regulation network. Understanding the heterogeneity of the tumour microenvironment (TME) is crucial for developing advanced ccRCC therapies. Using publicly available ccRCC data (scRNA-seq, bulk RNA-seq, and somatic mutation data), a multiomics study was performed to explore TME heterogeneity. Three distinct ccRCC immune subtypes were identified through combined scRNA-seq and bulk RNA-seq analysis. A prognostic model based on unique cell signalling molecules in immunosuppressive tumour subtype was validated in the TCGA and CheckMate cohorts. MDK emerged as a critical regulatory gene in the immunosuppressive subtype, predicting a poor ccRCC prognosis and a poor immunotherapy response. MDK promotes M2 macrophage polarization via the MDK-LRP1 interaction, and the inhibition of MDK suppressed M2 polarization. This study revealed the heterogeneity of the ccRCC TME and a reliable prognostic model, shedding light on the vital role of MDK in the immunosuppressive TME and paving the way for optimized ccRCC immunotherapy.

A Tool for the Assessment of HLA-DQ Heterodimer Variation in Hematopoietic Cell Transplantation

BackgroundWhen optimizing transplants, clinical decision-makers consider HLA-A, -B, -C, -DRB1 (8 matched alleles out of 8), and sometimes HLA-DQB1 (10 out of 10) matching between the patient and donor. HLA-DQ is a heterodimer formed by the β chain product of HLA-DQB1 and an α chain product of HLA-DQA1. In addition to molecules defined by the parentally-inherited cis haplotypes, α-β trans-dimerization is possible between certain alleles, leading to unique molecules and a potential source of mismatched molecules. Recently, researchers uncovered that clinical outcome after HLA-DQB1-mismatched unrelated donor HCT depends on the total number of HLA-DQ molecule mismatches and the specific α-β heterodimer mismatch. ObjectiveOur objective in this study is to develop an automated tool for analyzing HLA-DQ heterodimer data and validating it through numerous datasets and analyses. By doing so, we provide an HLA-DQ heterodimer tool for DQα-DQβ trans-heterodimer evaluation, HLA-DQ imputation, and HLA-DQ-featured source selection to the transplant field. Study designIn our study, we leverage 352,148 high-confidence, statistically-phased (via a modified expectation-maximization algorithm) HLA-DRB1∼DQA1∼DQB1 haplotypes, 1,052 pedigree-phased HLA-DQA1∼DQB1 haplotypes, and 13,663 historical transplants to characterize HLA-DQ heterodimers data. ResultsUsing our developed QLASSy (HLA-DQA1 and HLA-DQB1 Heterodimers Assessment) tool, we first assessed the data quality of HLA-DQ heterodimers in our data for trans-dimers, missing HLA-DQA1 typing, and unexpected HLA-DQA1 and HLA-DQB1 combinations. Since trans-dimers enable up to four unique HLA-DQ molecules in individuals, we provide in-silico validations for 99.7% of 275 unique trans-dimers generated by 176,074 U.S. donors with HLA-DQA1 and HLA-DQB1 data. Many individuals lack HLA-DQA1 typing, so we developed and validated high-confidence HLA-DQ annotation imputation via HLA-DRB1 with >99% correct predictions in 23,698 individuals. A select few individuals displayed unexpected HLA-DQ combinations. We revisited the typing of 61 donors with unexpected HLA-DQ combinations based on their HLA-DQA1 and HLA-DQB1 typing and corrected 22 out of 61 (36%) cases of donors through data review or retyping and used imputation to resolve unexpected combinations. After verifying the data quality of our datasets, we analyzed our datasets further: we explored the frequencies of observed HLA-DQ combinations to compare HLA-DQ across populations (for instance, we found more high-risk molecules in Asian/Pacific Islander and Black/African American populations), demonstrated the effect of HLA-DQA1 and HLA-DQB1 mismatching on HLA-DQ molecular mismatches, and highlighted where donor selections could be improved at the time of search for historical transplants with this new HLA-DQ information (where 51.9% of G2-mismatched transplants had lower-risk, G2-matched alternatives). ConclusionWe encapsulated our findings into a tool that imputes missing HLA-DQA1 as needed, annotates HLA-DQ (mis)matches, and highlights other important HLA-DQ data to consider for the present and future. Altogether, these valuable datasets, analyses, and a culminating tool serve as actionable resources to enhance donor selection and improve patient outcomes.

Harnessing Cytosine for Tunable Nanoparticle Self-Assembly Behavior Using Orthogonal Stimuli.

Nucleobases control the assembly of DNA, RNA, etc. due to hydrogen bond complementarity. By combining these unique molecules with state-of-the-art synthetic polymers, it is possible to form nanoparticles whose self-assembly behavior could be altered under orthogonal stimuli (pH and temperature). Herein, we report the synthesis of cytosine-containing nanoparticles via aqueous reversible addition-fragmentation chain transfer polymerization-induced self-assembly. A poly(N-acryloylmorpholine) macromolecular chain transfer agent (mCTA) was chain-extended with cytosine acrylamide, and a morphological phase diagram was constructed. By exploiting the ability of cytosine to form dimers via hydrogen bonding, the self-assembly behavior of cytosine-containing polymers was altered when performed under acidic conditions. Under these conditions, stable nanoparticles could be formed at longer polymer chain lengths. Furthermore, the resulting nanoparticles displayed different morphologies compared to those at pH 7. Additionally, particle stability post-assembly could be controlled by varying pH and temperature. Finally, small-angle X-ray scattering was performed to probe their dynamic behavior under thermal cycling.

3-[(Benzo-1,3-dioxol-5-yl)amino]-4-methoxycyclobut-3-ene-1,2-dione: polymorphism and twinning of a precursor to an antimycobacterial squaramide.

The title compound, 3-[(benzo-1,3-dioxol-5-yl)amino]-4-methoxycyclobut-3-ene-1,2-dione, C12H9NO5 (3), is a precursor to an antimycobacterial squaramide. Block-shaped crystals of a monoclinic form (3-I, space group P21/c, Z = 8, Z' = 2) and needle-shaped crystals of a triclinic form (3-II, space group P-1, Z = 4, Z' = 2) were found to crystallize concomitantly. In both crystal forms, R22(10) dimers assemble through N-H...O=C hydrogen bonds. These dimers are formed from crystallographically unique molecules in 3-I, but exhibit crystallographic Ci symmetry in 3-II. Twinning by pseudomerohedry was encountered in the crystals of 3-II. The conformations of 3 in the solid forms 3-I and 3-II are different from one another but are similar for the unique molecules in each polymorph. Density functional theory (DFT) calculations on the free molecule of 3 indicate that a nearly planar conformation is preferred.

AMCG: a graph dual atomic-molecular conditional molecular generator

Drug design is both a time consuming and expensive endeavour. Computational strategies offer viable options to address this task; deep learning approaches in particular are indeed gaining traction for their capability of dealing with chemical structures. A straightforward way to represent such structures is via their molecular graph, which in turn can be naturally processed by graph neural networks. This paper introduces AMCG, a dual atomic-molecular, conditional, latent-space, generative model built around graph processing layers able to support both unconditional and conditional molecular graph generation. Among other features, AMCG is a one-shot model allowing for fast sampling, explicit atomic type histogram assignation and property optimization via gradient ascent. The model was trained on the Quantum Machines 9 (QM9) and ZINC datasets, achieving state-of-the-art performances. Together with classic benchmarks, AMCG was also tested by generating large-scale sampled sets, showing robustness in terms of sustainable throughput of valid, novel and unique molecules.

Microbial Metabolites and Gut Immunology

The intestine is the largest peripheral lymphoid organ in animals, including humans, and interacts with a vast array of microorganisms called the gut microbiota. Comprehending the symbiotic relationship between the gut microbiota and our immune system is essential not only for the field of immunology but also for understanding the pathogenesis of various systemic diseases, including cancer, cardiometabolic disorders, and extraintestinal autoimmune conditions. Whereas microbe-derived antigens are crucial for activating the intestinal immune system, particularly T and B cells, as environmental cues, microbes and their metabolites play a critical role in directing the differentiation of these immune cells. Microbial metabolites are regarded as messengers from the gut microbiota, since bacteria have the ability to produce unique molecules that humans cannot, and many immune cells in the intestine express receptors for these molecules. This review highlights the distinct relationships between microbial metabolites and the differentiation and function of the immune system.

Long-range charge transfer mechanism of the III2IV2 mycobacterial supercomplex

Aerobic life is powered by membrane-bound redox enzymes that shuttle electrons to oxygen and transfer protons across a biological membrane. Structural studies suggest that these energy-transducing enzymes operate as higher-order supercomplexes, but their functional role remains poorly understood and highly debated. Here we resolve the functional dynamics of the 0.7 MDa III2IV2 obligate supercomplex from Mycobacterium smegmatis, a close relative of M. tuberculosis, the causative agent of tuberculosis. By combining computational, biochemical, and high-resolution (2.3 Å) cryo-electron microscopy experiments, we show how the mycobacterial supercomplex catalyses long-range charge transport from its menaquinol oxidation site to the binuclear active site for oxygen reduction. Our data reveal proton and electron pathways responsible for the charge transfer reactions, mechanistic principles of the quinone catalysis, and how unique molecular adaptations, water molecules, and lipid interactions enable the proton-coupled electron transfer (PCET) reactions. Our combined findings provide a mechanistic blueprint of mycobacterial supercomplexes and a basis for developing drugs against pathogenic bacteria.

Extrapolative Machine Learning for Accurate Efficiency Prediction in Non‐Fullerene Ternary Organic Solar Cells: Leveraging Computable Molecular Descriptors in High‐Throughput Virtual Screening

Adding a third component to binary organic solar cells (OSCs) enhances ternary OSCs, boosting power conversion efficiency (PCE). However, developing and optimizing appropriate donors, acceptors, and ternary materials remains a complex and demanding task. This study presents four machine‐learning (ML) predictive models using XGBoost and ANN approaches, utilizing both experimental and DFT‐derived HOMO and LUMO levels for efficient high‐throughput virtual screening (HTVS) of top candidates based on PCE. Two distinct latent databases were employed for HTVS: one consisting of 429 413 uniquely recombined ternary OSC systems from experimentally available data, and another comprising ≈2.3 million unique donor molecules from the Harvard Clean Energy Project database (CEPDB). The four ML models demonstrated notable predictive accuracy for PCE on a test dataset containing molecules closely aligned with the training set (interpolation). However, the XGBoost model showed constrained extrapolative ability for molecules significantly divergent from those in the training dataset. In contrast, the ANN models displayed a robust extrapolative capacity in HTVS, successfully predicting new potential ternary OSC systems and leading donors with PCE values exceeding 20%. Our ML models use HOMO and LUMO inputs for donors, acceptors, and ternaries, facilitating efficient optimization via rapid HTVS of high‐performance ternary materials.

Synthesis, molecular docking and molecular dynamics simulations, drug-likeness studies, ADMET prediction and biological evaluation of novel pyrazole-carboxamides bearing sulfonamide moiety as potent carbonic anhydrase inhibitors.

Pyrazoles are unique bioactive molecules with a versatile biological profile and they have gained an important place on pharmaceutical chemistry. Pyrazole compounds containing sulfonamide nuclei also attract attention as carbonic anhydrase (CA) inhibitors. In this study, a library of pyrazole-carboxamides were synthesized and the structures of the synthesized molecules were characterized using FT-IR, 1H-NMR, 13C-NMR and HRMS. Then the inhibition effects of newly synthesized molecules on human erythrocyte hCA I and hCA II isoenzymes were investigated. Ki values of the compounds were in the range of 0.063-3.368µM for hCA I and 0.007-4.235µM for hCA II. Molecular docking studies were performed between the most active compounds 6a, 6b and the reference inhibitor, acetazolamide (AAZ) and the hCA I and hCA II receptors to investigate the binding mechanisms between the compounds and the receptors. These compounds showed better interactions than the AAZ. ADMET analyzes were performed for the compounds and it was seen that the compounds did not show AMES toxicity. The stability of the molecular docking results over time was analysed by 50ns molecular dynamics simulations. Molecular dynamics simulations revealed that 6a and 6b exhibited good stability after docking to the binding sites of hCA I and hCA II receptors, with minor conformational changes and fluctuations.

Interaction between ABA and NO in plants under abiotic stresses and its regulatory mechanisms.

Abscisic acid (ABA) and nitric oxide (NO), as unique signaling molecules, are involved in plant growth, developmental processes, and abiotic stresses. However, the interaction between ABA and NO under abiotic stresses has little been worked out at present. Therefore, this paper reviews the mechanisms of crosstalk between ABA and NO in the regulation of plants in response to environmental stresses. Firstly, ABA-NO interaction can alleviate the changes of plant morphological indexes damaged by abiotic stresses, for instance, root length, leaf area, and fresh weight. Secondly, regulatory mechanisms of interaction between ABA and NO are also summarized, such as reactive oxygen species (ROS), antioxidant enzymes, proline, flavonoids, polyamines (PAs), ascorbate-glutathione cycle, water balance, photosynthetic, stomatal movement, and post-translational modifications. Meanwhile, the relationships between ABA and NO are established. ABA regulates NO through ROS at the physiological level during the regulatory processes. At the molecular level, NO counteracts ABA through mediating post-translational modifications. Moreover, we also discuss key genes related to the antioxidant enzymes, PAs biosynthesis, ABA receptor, NO biosynthesis, and flavonoid biosynthesis that are regulated by the interaction between ABA and NO under environmental stresses. This review will provide new guiding directions for the mechanism of the crosstalk between ABA and NO to alleviate abiotic stresses.

Data-driven, explainable machine learning model for predicting volatile organic compounds’ standard vaporization enthalpy

The accurate prediction of standard vaporization enthalpy (ΔvapHm°) for volatile organic compounds (VOCs) is of paramount importance in environmental chemistry, industrial applications and regulatory compliance. To overcome traditional experimental methods for predicting ΔvapHm° of VOCs, machine learning (ML) models enable a high-throughput, cost-effective property estimation. But despite a rising momentum, existing ML algorithms still present limitations in prediction accuracy and broad chemical applications.In this work, we present a data driven, explainable supervised ML model to predict ΔvapHm° of VOCs. The model was built on an established experimental database of 2410 unique molecules and 223 VOCs categorized by chemical groups. Using supervised ML regression algorithms, the Random Forest successfully predicted VOCs' ΔvapHm° with a mean absolute error of 3.02 kJ mol−1 and a 95% test score. The model was successfully validated through the prediction of ΔvapHm° for a known database of VOCs and through molecular group hold-out tests. Through chemical feature importance analysis, this explainable model revealed that VOC polarizability, connectivity indexes and electrotopological state are key for the model's prediction accuracy. We thus present a replicable and explainable model, which can be further expanded towards the prediction of other thermodynamic properties of VOCs.

Causal association of plasma circulating metabolites with nephritis: a Mendelian randomization study.

Nephritis is a pivotal catalyst in chronic kidney disease (CKD) progression. Although epidemiological studies have explored the impact of plasma circulating metabolites and drugs on nephritis, few have harnessed genetic methodologies to establish causal relationships. Through Mendelian randomization (MR) in two substantial cohorts, spanning large sample sizes, we evaluated over 100 plasma circulating metabolites and 263 drugs to discern their causal effects on nephritis risk. The primary analytical tool was the inverse variance weighted (IVW) analysis. Our bioinformatic scrutiny of GSE115857 (IgA nephropathy, 86 samples) and GSE72326 (lupus nephritis, 238 samples) unveiled anomalies in lipid metabolism and immunological characteristics in nephritis. Thorough sensitivity analyses (MR-Egger, MR-PRESSO, leave-one-out analysis) were undertaken to verify the instrumental variables' (IVs) assumptions. Unique lipoprotein-related molecules established causal links with diverse nephritis subtypes. Notably, docosahexaenoic acid (DHA) emerged as a protective factor for acute tubulointerstitial nephritis (ATIN) (OR1 = 0.84, [95% CI 0.78-0.90], p1 = 0.013; OR2 = 0.89, [95% CI 0.82-0.97], p2 = 0.007). Conversely, multivitamin supplementation minus minerals notably increased the risk of ATIN (OR = 31.25, [95% CI 9.23-105.85], p = 0.004). Reduced α-linolenic acid (ALA) levels due to lipid-lowering drugs were linked to both ATIN (OR = 4.88, [95% CI 3.52-6.77], p < 0.001) and tubulointerstitial nephritis (TIN) (OR = 7.52, [95% CI 2.78-20.30], p = 0.042). While the non-renal drug indivina showed promise for TIN treatment, the use of digoxin, hydroxocobalamin, and liothyronine elevated the risk of chronic tubulointerstitial nephritis (CTIN). Transcriptome analysis affirmed that anomalous lipid metabolism and immune infiltration are characteristic of IgA nephropathy and lupus nephritis. The robustness of these causal links was reinforced by sensitivity analyses and leave-one-out tests, indicating no signs of pleiotropy. Dyslipidemia significantly contributes to nephritis development. Strategies aimed at reducing plasma low-density lipoprotein levels or ALA supplementation may enhance the efficacy of existing lipid-lowering drug regimens for nephritis treatment. Renal functional status should also be judiciously considered with regard to the use of nonrenal medications.

Extrachromosomal circular DNA landscape of breast cancer with lymph node metastasis.

Breast cancer (BC) is a complex disease with diverse manifestations, often resulting in lymph node metastasis (LNM) and impacting patient prognosis. Extrachromosomal circular DNA (eccDNA) has emerged as a key player in tumorigenesis, yet its contribution to BC LNM remains elusive. Here, we examined primary tumors and matched LNM tissues from 19 BC patients using the Circle-Seq method. We identified a median count of 44,682 eccDNA in primary tumor tissues and 38,057 in their paired LNM tissues. Furthermore, a ladder-like size distribution is observed in both primary tumor and LNM tissues. Meanwhile, similar repeat sequence distribution and GC content are identified from both primary tissue and LNM tissues. Finally, we found that eccDNA from both groups are flanked with palindromic trinucleotide motifs. These observations indicate that eccDNA of primary tumor and LNM tissues are from similar chromosomal origins. However, a subset of miRNA-associated eccDNA displayed selective enrichment in metastatic lesions, such as miR-6730 and miR-548AA1 genes. This observation implicates the function of miRNA-related eccDNA in the metastatic cascade. Our study uncovers the potential significance of these unique eccDNA molecules, shedding light on their role in cancer metastasis.