Biology subjects

Zhang, C.

Publications and source records attributed to Zhang, C..

At least 433 records · Page 24Linked to original sources

bioRxiv2024

Decoding Continuous Character-based Language from Non-invasive Brain Recordings

Deciphering natural language from brain activity through non-invasive devices remains a formidable challenge. Previous non-invasive decoders either require multiple experiments with identical stimuli to pinpoint cortical regions and enhance signal-to-noise ratios in brain activity, or they are limited to discerning basic linguistic elements such as letters and words. We propose a novel approach to decoding continuous language from single-trial non-invasive fMRI recordings, in which a three-dimensional convolutional network augmented with information bottleneck is developed to automatically identify responsive voxels to stimuli, and a character-based decoder is designed for the semantic reconstruction of continuous language characterized by inherent character structures. The resulting decoder can produce intelligible textual sequences that faithfully capture the meaning of perceived speech both within and across subjects, while existing decoders exhibit significantly inferior performance in cross-subject contexts. The ability to decode continuous language from single trials across subjects demonstrates the promising applications of non-invasive language brain-computer interfaces in both healthcare and neuroscience.

neuroscience↗

bioRxiv2024

Evolutionary trajectory of TRPM2 channel activation by adenosine diphosphate ribose and calcium

Ion channel activation upon ligand gating triggers a myriad of biological events and, therefore, evolution of ligand gating mechanism is of fundamental importance. TRPM2, a typical ancient ion channel, is activated by adenosine diphosphate ribose (ADPR) and calcium and its activation has evolved from a simple mode in invertebrates to a more complex one in vertebrates, but the evolutionary process is still unknown. Molecular evolutionary analysis of TRPM2s from more than 280 different animal species has revealed that, the C-terminal NUDT9-H domain has evolved from an enzyme to a ligand binding site for activation, while the N-terminal MHR domain maintains a conserved ligand binding site. Calcium gating pattern has also evolved, from one Ca2+-binding site as in sea anemones to three sites as in human. Importantly, we identified a new group represented by olTRPM2, which has a novel gating mode and fills the missing link of the channel gating evolution. We conclude that the TRPM2 ligand binding or activation mode evolved through at least three identifiable stages in the past billion years from simple to complicated and coordinated. Such findings benefit the evolutionary investigations of other channels and proteins.

evolutionary biology↗

bioRxiv2024

All-atom protein sequence design based on geometric deep learning

The development of advanced deep learning methods has revolutionized computational protein design. Although the success rate of design has been significantly increased, the overall accuracy of de novo design remains low. Many computational sequence design approaches are devoted to recover the original sequences for given protein structures by encoding the environment of the central residue without considering atomic details of side chains. This may limit the exploration of new sequences that can fold into the same structure and restrain function design that depends on interaction details. In this study, we proposed a novel deep learning frame-work, GeoSeqBuilder, to learn the relationship between protein structure and sequence based on rotational and translational invariance by extracting the information from relative locations. We utilized geometric deep learning to fetch the spatial local geometric features from protein backbones and explicitly incorporated three-body interactions to learn the inter-residue coupling information, and then determined the central residue type. Our model recovers over 50% native residue types and simultaneously gives highly accurate prediction of side-chain conformations which gives the atomic interaction details and circumvents the dependence of protein structure prediction tools. We used the likelihood confidence logP as scoring function for sequence and structure consistence evaluation which exhibits strong correlation with TM-score, and can be applied to recognize near-native structures from protein decoys pool in protein structure prediction. We have used GeoSeqBuilder to design sequences for two proteins, including thiore-doxin and a de novo hallucinated protein. All of the 15 sequences experimentally tested can be expressed as soluble monomeric proteins with high thermal stability and correct secondary structures. We further solved one crystal structure for thioredoxin and two for the hallucinated structure and all the experimentally solved structures are in good agreement with the designed models. The two designed sequences for the hallucination structure are novel without any homologous sequences within the latest released database clust30. The ability of GeoSeqBuilder to design new sequences for given protein structures with atomic details makes it applicable, not only for de novo sequence design, but also for protein-protein interaction and functional protein design.

biophysics↗

bioRxiv2024

Deep learning based genomic breeding of pest-resistant grapevine

Crop pests have profoundly deleterious effects on crop yield and food security. However, conventional pest control depends heavily on the utilization of insecticides, which develops strong pesticide resistance and concerns of food safety. Crop and their wild relatives display diverse levels of pest resistance, indicating the feasibility for breeding of pest-resistant crop varieties. In this study, we integrate deep learning (DL)/machine learning (ML) algorithms, plant phenomics and whole genome sequencing (WGS) data to conduct genomic selection (GS) of pest-resistance in grapevine. We employ deep convolutional neural networks (DCNN) to accurately calculate the severity of damage by pests on grape leaves, which achieves a classification accuracy of 95.3% (Visual Geometry Group 16, VGG16, for binary trait) and a correlation coefficient of 0.94 in regression analysis (DCNN with Pest Damage Score, DCNN-PDS, for continuous trait). We apply DL models to predict and integrate phenotype (both binary and continuous) along with WGS data from 231 grape accessions, conducting Genome-Wide Association Studies (GWAS). This analysis detects a total of 69 QTLs, encompassing 139 candidate genes involved in pathways associated with pest resistance, including jasmonic acid (JA), salicylic acid (SA), ethylene, and other related pathways. Furthermore, through the combination with transcriptome data, we identify specific pest-resistant genes, such as ACA12 and CRK3, which play distinct roles in resisting herbivore attacks. Machine learning-based GS demonstrates a high accuracy (95.7%) and a strong correlation (0.90) in predicting the leaf area damaged by pests as binary and continuous traits in grapevine, respectively. In general, our study highlights the power of DL/ML in plant phenomics and GS, facilitating genomic breeding of pest-resistant grapevine.

plant biology↗

bioRxiv2024

Hour-long, kilohertz sampling rate 3D single-virus tracking in live cells enabled by StayGold fluorescent protein fusions

The viral infection process covers a large range of spatiotemporal scales. Tracking the viral infection process with fluorescent labels over long durations while maintaining a fast sampling rate requires bright and highly photostable labels. StayGold is a recently identified green fluorescent protein that has a greater photostability and higher signal intensity under identical illumination conditions as compared to existing fluorescence protein variants. Here, StayGold protein fusions were used to generate virus-like particles (StayGold-VLPs) to achieve hour-long 3D single-virus tracking (SVT) with one thousand localizations per second (kHz sampling rate) in live cells. The expanded photon budget from StayGold protein fusions prolonged the tracking duration, facilitating a comprehensive study of viral trafficking dynamics with high temporal resolution over long timescales. The development of StayGold-VLPs presents a simple and general VLP labeling strategy for better performance in SVT, enabling exponentially more information to be collected from single trajectories and allowing for the future possibility of observing the whole life cycle of a single virus. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/585070v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@96584corg.highwire.dtl.DTLVardef@7f1e4corg.highwire.dtl.DTLVardef@19e38ddorg.highwire.dtl.DTLVardef@a75aed_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2024

An unsupervised deep learning framework encodes super-resolved image features to decode bacterial cell cycle

Super-resolution microscopy can resolve cellular features at the nanoscale. However, increased spatial resolution comes with increased phototoxicity, and reduced temporal resolution. As a result, studies that require the highest spatial resolutions often rely on static or fixed images, lacking dynamic information. This is particularly true of bacteria, whose lateral dimensions approach the scale of the diffraction limit. In this work, we present Enso, a method based on unsupervised machine learning to recover bacterial cell cycle and cell type information from static single molecule localization microscopy (SMLM) images, whilst retaining nanoscale spatial resolution. Enso uses single-cell images as input, and orders cells according to their spatial pattern progression, ultimately linked to the cell cycle. Our method requires no a priori knowledge or categories, and is validated on both simulated and user-annotated experimental data.

bioinformatics↗

bioRxiv2024

FTO-mediated m6A demethylation of ULK1 mRNA promotes autophagy and activation of hepatic stellate cells in liver fibrosis

The activation of hepatic stellate cells (HSCs) is the central link in the occurrence and development of liver fibrosis. Our previous studies showed that autophagy promotes HSCs activation and ultimately accelerates liver fibrosis. Unc-51-like autophagy activating kinase 1 (ULK1) is an autophagic initiator in mammals and N6-methyladenosine (m6A) modification is closely related to autophagy. In this study, we find that m6A demethylase fat mass and obesity-associated protein (FTO) is upregulated during HSCs activation and bile duct ligation (BDL)-induced hepatic fibrosis, which is the m6A methylase with the most significant difference in expression. Importantly, we identify that FTO overexpression aggravates HSCs activation and hepatic fibrosis via autophagy. Mechanistically, compared with other autophagy-related genes, ULK1 is the target of FTO due to FTO mainly mediates the m6A demethylation of ULK1 and upregulates its expression, thereby enhancing autophagy and activation of HSCs. Noteworthy, m6A reader YTH domain-containing protein 2 (YTHDC2) decreases ULK1 mRNA level via recognizing the m6A binding site and ultimately inhibits autophagy and activation of HSCs. Taken together, our findings highlight m6A-dependent ULK1 as an essential regulator of HSCs autophagy and reveal ULK1 as a novel potential therapeutic target for hepatic fibrosis treatment. Graphical Abstractm6A demethylases FTO promoted autophagy via recognizing the ULK1 m6A binding site, thus triggering HSCs activation, and eventually leading to liver fibrosis. In this process, YTHDC2 participated in the translation of ULK1. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/584975v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@4fdd01org.highwire.dtl.DTLVardef@136791forg.highwire.dtl.DTLVardef@149f448org.highwire.dtl.DTLVardef@44ca9c_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2024

DNA-based ForceChrono Probes for Deciphering Single-Molecule Force Dynamics in Living Cells

Accurate measurement of mechanical forces in cells is key to understanding how cells sense and respond to mechanical stimuli, a central aspect of mechanobiology. However, accurately quantifying dynamic forces at the single-molecule level in living cells is a significant challenge. Here, weve developed the DNA-based ForceChrono probe to enable in-depth studies of integrin force dynamics at the single-molecule level in living cells. By illuminating two distinct mechanical points and circumventing the inherent fluctuations of single-molecule fluorescence, the ForceChrono probe enables analysis of the complex dynamics of mechanical forces at the single-molecule level, such as loading rates and durations. Our results refine previous broad estimates of cellular loading rates to a more precise range of 0.5 to 2 pN/s, shedding light on the specifics of cellular mechanics. In addition, this study reveals a critical link between the magnitude and duration of integrin forces, consistent with the catch-bond behavior demonstrated in vitro. The ForceChrono probe has distinct advantages, such as precise analysis of single-molecule force dynamics and robust resistance to fluorescence fluctuations, which will significantly advance our understanding of cell adhesion and mechanotransduction at the single-molecule level.

biophysics↗

bioRxiv2024

Dense, Continuous Membrane Labeling and Expansion Microscopy Visualization of Ultrastructure in Tissues

Lipid membranes are key to the nanoscale compartmentalization of biological systems, but fluorescent visualization of them in intact tissues, with nanoscale precision, is challenging to do with high labeling density. Here, we report ultrastructural membrane expansion microscopy (umExM), which combines a novel membrane label and optimized expansion microscopy protocol, to support dense labeling of membranes in tissues for nanoscale visualization. We validated the high signal-to-background ratio, and uniformity and continuity, of umExM membrane labeling in brain slices, which supported the imaging of membranes and proteins at a resolution of [~]60 nm on a confocal microscope. We demonstrated the utility of umExM for the segmentation and tracing of neuronal processes, such as axons, in mouse brain tissue. Combining umExM with optical fluctuation imaging, or iterating the expansion process, yielded [~]35 nm resolution imaging, pointing towards the potential for electron microscopy resolution visualization of brain membranes on ordinary light microscopes.

bioengineering↗

bioRxiv2024

Spatial Transcriptomics of Spinal Ependymoma in NF2-related Schwannomatosis

Spinal ependymoma (SP-EPN) is a central nervous system (CNS) tumor that is associated with high morbidity. The only effective treatment for end-stage SP-EPN is surgery, but this is associated with high risk of injury to the sensorimotor spinal tracts and paralysis. There is a critical need to understand the cellular origins of this tumor so that disease models of tumor progression can be generated for drug development. Recent genomic studies with bulkRNA sequencing suggest the molecular signature of SP-EPN matches that of ependymal cells (EPCs). However, large-scale genomic studies can often misrepresent rare cancer stem cell populations within the tumor. In this study, we performed spatial transcriptomics (ST) on a SP-EPN resected from a patient with NF2-related neurofibromatosis to examine the spatial heterogeneity within the tumor. The SP-EPN sample exhibited cellular heterogeneity with diffuse expression of astrocytic and EPC markers, and smaller pockets with RGC or stem cell markers, as well as overlap between progenitor cell and mature cell markers. These findings suggest that there may be a developmental hierarchy within the EPC lineage in SP-EPN tumors, which may stem from aberrant radial glia cells.

cancer biology↗

bioRxiv2024

Hairpin-locker mediated CRISPR/Cas tandem system for ultrasensitive detection of DNA without pre-amplification

Achieving ultra-sensitive detection of DNA is of paramount importance in the field of molecular analytics. Conventional amplification technologies such as polymerase chain reaction (PCR) currently play a leading role in ultrasensitive DNA detection. However, amplicon contamination common in these techniques may lead to false positives. To date, CRISPR-associated nucleases (type V & VI) with their programmable cleavage have been utilised for sensitive detection of unamplified nucleic acids in complex real samples. Nevertheless, without additional amplification strategies, the pM range sensitivity of such CRISPR/Cas sensors is not sufficient for clinical applications. Here, we established a hairpin-locker (H-locker) mediated Cas12-Cas13 tandem biosensing system (Cas12-13 tandem-sensor) for ultrasensitive detection of DNA targets. Without the need for any additional amplification reaction or device, this system is capable of detecting DNA at a notable 1 aM level (<1 copy/uL) sensitivity. In addition, the system was able to distinguish cancer mutations in colorectal cancer (CRC) mice. This is a significant advance for CRISPR/Cas biosensing technology offering simple, highly sensitive, and user-friendly diagnostics for next-generation nucleic acid detection. O_TBL View this table: org.highwire.dtl.DTLVardef@7fc32eorg.highwire.dtl.DTLVardef@1e716ceorg.highwire.dtl.DTLVardef@8a43e0org.highwire.dtl.DTLVardef@16e286dorg.highwire.dtl.DTLVardef@1202eb0_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 1.C_FLOATNO O_TABLECAPTIONDNA and RNA oligos used in this study. C_TABLECAPTION C_TBL

molecular biology↗

bioRxiv2024

Late-Life Alcohol Exposure Does Not Exacerbate Age-Dependent Reductions in Mouse Spatial Memory and Brain TFEB Activity

Alcohol consumption is believed to affect Alzheimers disease (AD) risk, but the contributing mechanisms are not well understood. A potential mediator of the proposed alcohol-AD connection is autophagy, a degradation pathway that maintains organelle and protein homeostasis. Autophagy is in turn regulated through the activity of Transcription factor EB (TFEB), which promotes lysosome and autophagy-related gene expression. To explore the effect of alcohol on brain TFEB and autophagy, we exposed young (3-month old) and aged (23-month old) mice to two alcohol-feeding paradigms and assessed biochemical, transcriptome, histology, and behavioral endpoints. In young mice, alcohol decreased hippocampal nuclear TFEB staining but increased SQSTM1/p62, LC3-II, ubiquitinated proteins, and phosphorylated Tau. Hippocampal TFEB activity was lower in aged mice than it was in young mice, and Gao-binge alcohol feeding did not worsen the age-related reduction in TFEB activity. To better assess the impact of chronic alcohol exposure, we fed young and aged mice alcohol for four weeks before completing Morris Water and Barnes Maze spatial memory testing. The aged mice showed worse spatial memory on both tests. While alcohol feeding slightly impaired spatial memory in the young mice, it had little effect or even slightly improved spatial memory in the aged mice. These findings suggest that aging is a far more important driver of spatial memory impairment and reduced autophagy flux than alcohol consumption.

animal behavior and cognition↗

bioRxiv2024

CAMKV Kinase Signaling Is a Novel Therapeutic Avenue with Prognostic Relevance in Neuroblastoma

Neuroblastoma (NB) can be a highly aggressive malignancy in children. However, the precise mechanisms driving NB tumorigenesis remain elusive. This study revealed the critical role of CREB phosphorylation in NB cell proliferation. By employing a CRISPR-Cas9 knockout screen targeting calcium/calmodulin-dependent protein kinase (CaMK) family members, we identified the CaM kinase-like vesicle-associated (CAMKV) protein as a kinase that mediates direct phosphorylation of CREB to promote NB cell proliferation. CAMKV was found to be a transcriptional target of MYCN/MYC in NB cells. CAMKV knockout and knockdown effectively suppressed NB cell proliferation and tumor growth both in vitro and in vivo. Bioinformatic analysis revealed that high CAMKV expression is significantly correlated with poor patient survival. High-risk NB frequently had high CAMKV protein levels by Immunohistochemical staining. Integrated transcriptomic and proteomic analyses of CAMKV knockdown cells unveiled downstream targets involved in CAMKV-regulated phosphorylation and signaling pathways, many of which are linked to neural development and cancer progression. We identified small molecule inhibitors targeting CAMKV and further demonstrated the efficacy of one inhibitor in suppressing NB tumor growth and prolonging the survival of mice bearing xenografted tumors. These findings reveal a critical role for CAMKV kinase signaling in NB growth and identified CAMKV kinase as a potential therapeutic target and prognostic marker for patients with NB.

cancer biology↗

bioRxiv2024

Pooled CRISPR interference screening identifies crucial transcription factors in gas-fermenting Clostridium ljungdahlii

Gas-fermenting Clostridium species hold tremendous promise for one-carbon biomanufacturing. To unlock their full potential, it is crucial to unravel and optimize the intricate regulatory networks that govern these organisms; however, this aspect is currently underexplored. In this study, we employed pooled CRISPR interference (CRISPRi) screening to uncover a wide range of functional transcription factors (TFs) in Clostridium ljungdahlii, a representative species of gas-fermenting Clostridium, with a special focus on the TFs associated with the utilization of carbon resources. Among the 425 TF candidates, we identified 75 and 68 TF genes affecting the heterotrophic and autotrophic growth of C. ljungdahlii, respectively. We directed our attention on two of the screened TFs, NrdR and DeoR, and revealed their pivotal roles in the regulation of deoxyribonucleotides (dNTPs) supply, carbon fixation, and product synthesis in C. ljungdahlii, thereby influencing the strain performance in gas fermentation. Based on this, we proceeded to optimize the expression of deoR in C. ljungdahlii by adjusting its promoter strength, leading to improved growth rate and ethanol synthesis of C. ljungdahlii when utilizing syngas. This study highlights the effectiveness of pooled CRISPRi screening in gas-fermenting Clostridium species, expanding the horizons for functional genomic research in these industrially important bacteria.

microbiology↗

bioRxiv2024

Unleashing precision and freedom of optical manipulation: Software-aided real-time precision opto-control of intracellular molecular activities and cell functions

The traditional method in biological science to regulate cell functions often employs chemical interventions, which commonly lack precision in space and time. While optical manipulation offers superior spatial precision, existing technologies are constrained by limitations in flexibility, accuracy, and response time. Here, we present an adaptable and interactive optical manipulation platform that integrates laser scanning, chemical sensing, synchronized multi-laser control, adaptable target selection, flexible decision-making, and real-time monitoring of sample responses. This software-assisted real-time precision opto-control (S-RPOC) platform facilitates automatic target selection driven by optical signals while permitting user-defined manual delineation. It allows the treatment of mobile or stationary targets with varying laser dosages and wavelengths simultaneously at diffraction-limited spatial precision and optimal accuracy. Significantly, S-RPOC showcases versatile capabilities including adaptive photobleaching, comprehensive quantification of protein dynamics, selective organelle perturbation, control of cell division, and manipulation of individual cell behaviors within a population. With its unprecedented spatiotemporal precision and adaptable decision-making, S-RPOC holds the potential for extensive applications in biological science.

cell biology↗

bioRxiv2024

Contrastive Fitness Learning: Reprogramming Protein Language Models for Low-N Learning of Protein Fitness Landscape

Machine learning (ML) is revolutionizing our ability to model the fitness landscape of protein sequences, which is critical to answering fundamental life science questions and addressing important protein engineering applications, such as quantifying the pathogenicity of disease variants, forecasting viral evolution in a pandemic, and engineering new antibodies. Recently, the protein language model (pLM) has emerged as an effective ML tool in deciphering the intrinsic semantics of protein sequences and become the foundation of state-of-the-art ML solutions for many problems in protein biology. However, significant challenges remain in leveraging pLMs for protein fitness prediction, in part due to the disparity between the scarce number of sequences functionally characterized by high-throughput assays and the massive data samples required for training large pLMs. To bridge this gap, we introduce Contrastive Fitness Learning (ConFit), a pLM-based ML method for learning the protein fitness landscape with limited experimental fitness measurements as training data. We propose a novel contrastive learning strategy to fine-tune the pre-trained pLM, tailoring it to achieve protein-specific fitness prediction while avoiding overfitting, even when using a small number (low-N) of functionally assayed mutant sequences for supervised fine-tuning. Evaluated across over 30 benchmark datasets of protein fitness, ConFit consistently provided accurate fitness predictions and outperformed several competitive baseline methods. Further analysis revealed that ConFits capability of low-N learning enabled sample-efficient active learning for identifying high-fitness protein variants. Collectively, our work represents a novel strategy to harness the potential of pLMs to elucidate the protein sequence-function relationship. The source code of ConFit is available at https://github.com/luo-group/ConFit.

bioinformatics↗

bioRxiv2024

vSPACE: Exploring Virtual Spatial Representation of Articular Chondrocytes at the Single-Cell Level

Single cell RNA sequencing technology has been dramatically changing how gene expression studies are performed. However, its use has been limited to identifying subtypes of cells by comparing cells gene expression levels in an unbiased manner to produce a 2D plot (e.g., UMAP/tSNE). We developed a new method of placing cells in 2D space. This system, called vSPACE, shows a virtual spatial representation of scRNAseq data obtained from human articular cartilage by emulating the concept of spatial transcriptomics technology, but virtually. This virtual 2D plot presentation of human articular cartage cells generates several zonal distribution patterns, in one or multiple genes at a time, reveling patterns that scientists can appreciate as imputed spatial distribution patterns along the zonal axis. The discovered patterns are explainable and remarkably consistent across all six healthy doners despite their respectively different clinical variables (age and sex), suggesting the confidence of the discovered patterns.

bioinformatics↗

bioRxiv2024

Lipoxin A4 yields an electrophilic 15-oxo metabolite that mediates FPR2 receptor-independent anti-inflammatory signaling

The enzymatic oxidation of arachidonic acid is proposed to yield trihydroxytetraene species (termed lipoxins) that resolve inflammation via ligand activation of the formyl peptide receptor, FPR2. While cell and murine models activate signaling responses to synthetic lipoxins, primarily 5S,6R,15S-trihydroxy-7E,9E,11Z,13E-eicosatetraenoic acid (lipoxin A4, LXA4), there are expanding concerns about the biological formation, detection and signaling mechanisms ascribed to LXA4 and related di- and tri-hydroxy {omega}-6 and {omega}-3 fatty acids. Herein, the generation and actions of LXA4 and its primary 15-oxo metabolite were assessed in control, LPS-activated and arachidonic acid supplemented RAW 264.7 macrophages. Despite protein expression of all enzymes required for LXA4 synthesis, both LXA4 and its 15-oxo-LXA4 metabolite were undetectable. Moreover, synthetic LXA4 and the membrane permeable 15-oxo-LXA4 methyl ester that is rapidly de-esterified to 15-oxo-LXA4, displayed no ligand activity for the putative LXA4 receptor FPR2, as opposed to the FPR2 ligand WKYMVm. Alternatively, 15-oxo-LXA4, an electrophilic ,{beta}-unsaturated ketone, alkylates nucleophilic amino acids such as cysteine to modulate redox-sensitive transcriptional regulatory protein and enzyme function. 15-oxo-LXA4 activated nuclear factor (erythroid related factor 2)-like 2 (Nrf2)-regulated gene expression of anti-inflammatory and repair genes and inhibited nuclear factor (NF)-{kappa}B-regulated pro-inflammatory mediator expression. LXA4 did not impact these macrophage anti-inflammatory and repair responses. In summary, these data show an absence of macrophage LXA4 formation and receptor-mediated signaling actions. Rather, if LXA4 were present in sufficient concentrations, this, and other more abundant mono- and poly-hydroxylated unsaturated fatty acids can be readily oxidized to electrophilic ,{beta}-unsaturated ketone products that modulate the redox-sensitive cysteine proteome via G-protein coupled receptor-independent mechanisms.

biochemistry↗