Biology subjects

Zhang, H.

Publications and source records attributed to Zhang, H..

At least 505 records · Page 28Linked to original sources

bioRxiv2024

A novel pan class-I glucose transporter inhibitor DRB18 exhibits synergistic effects in vitro and in vivo with paclitaxel against human non-small cell lung cancer

PurposeCancer cells depend on glucose for biomass synthesis, cell proliferation, and drug resistance. Glucose transporter (GLUT) transcripts as well as proteins are upregulated in human lungs and other cancers and are negatively correlated with patient survival, particularly GLUT1 and GLUT3. Thus, inhibiting GLUT function has been an attractive anticancer strategy. We previously characterized WZB117 and DRB18, first- and second-generation pan-class I GLUT inhibitors, respectively. DRB18 strongly inhibits glucose transport mediated by GLUT1-4 in non-small lung cancer (NSCLC) A549 cells in vitro and in vivo. Here, we report DRB18 as a more stable and potent anticancer compound, compared to WZB117. MethodsImmunohistochemistry analysis was performed in Lung adenocarcinoma (LUAD) tissue array to investigate GLUT1 and GLUT3 protein expression between normal, lower and higher stage LUAD patients. Bioinformatics analysis was performed to examine additive effect of GLUT3 to GLUT1 mediated prognosis in LUAD. Glucose uptake and resazurin dye-based proliferation assays were used to determine glucose uptake inhibitory and cell proliferation inhibitory against panel of human cancer cell lines A549, Panc1 and Hela. DRB18 potency was tested against the presence of extracellular nutrients glucose, glutamine and ATP. Synergism between DRB18 and clinically approved anticancer drugs was tested against cancer cells. DRB18 and advanced NSCLC drug Paclitaxel were tested for synergy in vitro and in vivo. ResultsGLUT1/3 combination exhibited higher hazard ratio than either GLUT1 and GLUT3 alone in many cancer types including LUAD. DRB18 reduced glucose uptake in NSCLC A549, pancreatic Panc1, and cervical Hela cancer cells with varied but strong anticancer potencies in the presence or absence of extracellular nutrients such as ATP and glucose. Combined with different clinical and pre-clinical anticancer compounds such as V9302, CB839, Sutent, Brigatinib, DRB18 significantly increased death of A549 and Panc1 cells. Noteworthy, DRB18 exhibited strong anticancer synergy with paclitaxel, an approved chemo drug for NSCLC, drastically reducing cancer cell proliferation in vitro and growth of A549 tumors grafted on the flank of nude mice without significant side effects, compared to single drug treatments. ConclusionsCollectively, our results demonstrate anticancer efficacy of pan class-I GLUT inhibitor DRB18 in combination with paclitaxel, providing a potentially more efficacious therapeutic strategy for treating advanced NSCLC and other cancers.

cancer biology↗

bioRxiv2024

Intra-tumor microbiome-based tumor survival indices predict immune interaction and drug sensitivity on pan-cancer scale

Growing research evidence indicates a substantial influence of the intra-tumor microbiome on tumor outcome. However, there is currently no consistent criterion for identifying the association of microbes with tumor progression and response to treatment across various types of cancer. In this study, we concentrate on the intra-tumor microbiome and develop the Tumor Microbiome Survival Index (TMSI), a measure indicative of cancer patient survival risk. Our indices revealed notable distinctions between two stratified risk groups for each of the 10 cancer types and could precisely predict patients overall survival. For each type of cancer, our findings unveiled two distinct gene expression profiles and shed light on the varying patterns of immune and stromal cell enrichment between the two risk groups. Additionally, we noted that the high-TMSI group exhibited substantially elevated IC50 values for a number of drugs, indicating that individuals in the low-TMSI group might experience superior therapeutic effects from chemotherapy. These findings illuminate the complex dynamics between the tumor microbiome, the patients immune reaction, and medical outcomes, thus shedding light on microbiome-based personalized therapeutic interventions. HighlightsO_LIThe TMSI model generates a survival risk score based on the intra-tumor microbiome. C_LIO_LIThe TMSI model predicts survival rates accurately across multiple cancer types. C_LIO_LIMolecular-level analysis highlights distinct immune interactions with tumor microbes. C_LIO_LIHigh-TMSI groups showed altered drug sensitivity and implying potential differences in treatment response. C_LI

bioinformatics↗

bioRxiv2024

Multimodal single-cell analyses reveal molecular markers of neuronal senescence in human drug-resistant epilepsy

The histopathological neurons in the brain tissue of drug-resistant epilepsy exhibit aberrant cytoarchitecture and imbalanced synaptic circuit function. However, the gene expression changes of these neurons remain unknown, making it difficult for the diagnosis or to dissect the mechanism of drug-resistant epilepsy. By integrating whole-cell patch clamp recording and single-cell RNA sequencing approaches, we identified a transcriptionally distinct subset of cortical pyramidal neurons. These neurons highly expressed genes CDKN1A (P21), CCL2 and NFKBIA that associated with mTOR pathway, inflammatory response and cellular senescence. We confirmed the expression of senescent marker genes in a subpopulation of cortical pyramidal neurons with enlarged soma size in the brain tissue of drug-resistant epilepsy. We further revealed the expression of senescent cell markers P21, P53, COX2, {gamma}-H2AX, {beta}-Gal and reduction of nuclear integrity marker Lamin B1 in histopathological neurons in the brain tissue of drug-resistant epilepsy patients with different pathologies, but not in control brain tissue with no history of epilepsy. Additionally, chronic, but not acute, epileptic seizures induced senescent markers expression in cortical neurons in mouse models of drug-resistant epilepsy. These results provide important molecular markers for histopathological neurons and new insights into the pathophysiological mechanisms of drug-resistant epilepsy.

neuroscience↗

bioRxiv2024

Rational molecular design for improved ZHD101 thermal stability based on the introduction of disulfide bonds at the dimer interface and B-factor analysis

AbstractZearalenone hydrolase 101 (ZHD101), derived from Clonostachys rosea, is known to effectively degrade the main contaminant (zearalenone) in animal feed, but the thermal instability of ZHD101 limits its industrial application. In this study, we successfully enhanced the thermal stability of ZHD101 through two iterative rounds of rational molecular design. First, after the prediction of disulfide bond sites, ZHD101T229C was obtained, and a new disulfide bond was formed between two single ZHD101 subunits to construct a dimer ZHD101. Then, based on ZHD101T229C, two high-vibration hotspot amino acids N137 and D170 were selected by analyzing atomic position fluctuations and dynamic information A small and precise mutant library containing three mutants (ZHD101T229C/N137L, ZHD101T229C/D170L, and ZHD101T229C/D170C) was obtained by saturation mutagenesis and calculation of binding energy in silico. Compared with the wild type, the thermal half-inactivation temperature (T50) of ZHD101T229C/D170C increased by 7{degrees}C, its half-life (t1/2) increased by 200% at 50{degrees}C, and its melting temperature (Tm) increased by 18.1{degrees}C. Molecular docking suggested that new covalent bond formation and shorter bond distance may contribute to the improved thermal stability of ZHD101. The rational design strategy proposed in this work can provide a reference for the thermal stability modification and optimization of other proteins. ImportanceZearalenone (ZEN) is a nonsteroidal estrogenic mycotoxin that poses a significant threat to animal feed safety. ZEN hydrolase 101 (ZHD101) catalyzes the conversion of ZEN into a nontoxic product, offering a promising detoxification strategy. However, the thermal instability of ZHD101 severely limits its industrial application. In this study, the thermal stability of ZHD101 was significantly improved through two rounds of rational design, resulting in the ZHD101T229C/D170C mutant. ZHD101T229C/D170C exhibited the improved half-inactivation temperature (T50), half-life (t1/2) and the highest melting temperature (Tm) reported thus far. Overall, the iterative combinatiorial mutation strategy involved the introduction of a disulfide bond between two single subunits and the application of B-factor analysis to identify hotspot amino acids residues. This approach effectively enhanced the enzymes structural stability, paving the way for its industrial application. ZHD101T229C/D170C and the rational design strategy presented in this work provide a robust framework for the thermal stability optimization of similar biocatalysts, advancing the practical use of ZHD101 in the feed industry.

molecular biology↗

bioRxiv2024

Current transcriptome database and biomarker discovery for immunotherapy by immune checkpoint blockade

Immune checkpoint blockade (ICB) has revolutionized the current immuno-oncology and significantly improved clinical outcome for cancer treatment. Despite the advancement in clinics, only a small subset of patients derives immune response to the ICB therapy. Therefore, a robust predictive biomarker that identifies potential candidate becomes increasingly crucial in delivering this technology to the public. In this review, we first discuss the biomarkers that focus on tumor genome, tumor microenvironment and tumor-host interaction. Then, we compare existing databases for biomarker discovery for ICB response. We also present IOhub - an interactive web portal that incorporates 36 bulk and 10 single-cell transcriptome datasets for benchmark analysis of the current biomarkers. Finally, we highlight the trending interest in antibody drug conjugate and combination treatment and their use in precision immuno-oncology.

bioinformatics↗

bioRxiv2024

Integrative analysis of transcriptome and metabolism reveals functional roles of redox homeostasis in low light and salt combined stress in Leymus chinensis

Soil salinity (Salt) is one of the major limiting factors of Leymus chinensis (named sheepgrass) performance and alters their primary metabolic process and gene expression. Salt stress could accelerate inhibitive effects concomitant with low light (LL-Salt). However, little is known about physiological and molecular mechanisms under such LL-Salt in sheepgrass. This study aims to uncover the key reprogrammed metabolic pathways induced by LL-Salt through an integrated analysis of transcriptome and metabolism. Plant seedlings were exposed to six combinations of light intensity (moderate light, ML, and LL) and NaCl concentrations (0, 50, and 200mM) for 20 days. Results suggest that the growth of sheepgrass seedlings was dramatically inhibited with severe lodging phenotype, especially when exposed to LL-Salt combined conditions. Activities of an antioxidant enzyme, catalase, were significantly increased in LL but significantly decreased in salt stress, leading to a reverse pattern for H2O2 in LL and salt stress. Transcriptome analysis reveals 4921 downregulated differentially expressed genes (DEGs) with carbon metabolism pathways were significantly enriched compared to light response without salt (ML_0 vs LL_0). There are 194 overlapped downregulated DEGs induced by two salt treatments (ML_50 vs ML_0 and ML_200 vs ML_0), where the carbon metabolism pathway was also significantly enriched. In terms of interactive effects of LL-Salt treatments, we found that there are 16 overlapped DEGs, including a phytochrome-interacting transcription factor 4 (PIF4) with upregulation in LL treatment while downregulation in salt treatment, which was validated by qPCR. Metabolism analysis confirmed that in carbon metabolism, the contents of seven differentially abundant metabolites (DAMs), including sucrose, were all downregulated in both LL and salt treatment. In contrast, serine and glycolate in the photorespiration pathway were downregulated in LL while upregulated in salt treatment. A photorespiration regulatory gene, glycolate oxidase expression, was confirmed by qPCR. Collectively, we found that serval antioxidant redox pathways, including photorespiration, GSG/GSSH redox, and ABA signaling, participated in response to LL and salt combined events and highlighted the roles of cellular redox homeostasis in LL-Salt response in sheepgrass.

plant biology↗

bioRxiv2024

BioMedGraphica: An All-in-One Platform for Biomedical Prior Knowledge and Omic Signaling Graph Generation

Multi-omic data analysis is essential for scientific discovery in precision medicine. However, translating statistical results of omic data analysis into novel scientific hypothesis remains a significant challenge. Human experts must manually review analysis results and generate new hypothesis based on extensive and inter-connected biomedical prior knowledge, which is subjective and not scalable. While large language models (LLMs) can accelerate the discovery, their reasoning improves when grounded in structured, auditable and comprehensive biomedical prior knowledge. Biomedical knowledge, however, is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of AI systems to fully leverage biomedical data for scientific discovery. To address these challenges, we developed BioMedGraphica, an all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified knowledge graph containing 2,306,921 entities and 27,232,091 relations. In addition, to the best of our knowledge, this is the first work to propose a novel Textual-Numeric Graph (TNG) data-structure for multi-omics data analysis. In TNG, textual information captures prior biological knowledge (e.g., transcription start sites, functions, mechanisms), while numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data-structure for the development of graph foundation models, with the potential to improve prediction performance and interpretability, while also augmenting LLMs by supplying graph-structured mechanistic context to strengthen reasoning. The details for BioMedGraphica code can be accessed by github link: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph data can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica

bioinformatics↗

bioRxiv2024

Age-related remodeling of the glycocalyx drives T cell exhaustion

Cell surface glycans, termed the glycocalyx, are essential regulators of cellular signaling and thus cellular development and functions, but how aging impacts the glycocalyx remains poorly understood. Here, using immune cells as a model system for studying the relationship between aging and glycocalyx remodeling, we show that 2,6-linked sialic acid - a terminal glycan epitope typically associated with inhibitory signaling - becomes downregulated in T cells from older animals. This downregulation is tightly correlated with age-associated accumulation of effector T cells, which are decorated with little to no 2,6-linked sialic acids. T cell aging renders older individuals more vulnerable to infections and cancers. To understand the role of 2,6-linked sialic acids in T cell physiology, we generated a mouse model with T cell-specific deletion of the sialyltransferase gene St6gal1. The chronic depletion of 2,6-linked sialic acids led to naive T (TN) cells expansion in the periphery and premature T cell exhaustion. As a result, these mice were less able to control acute Listeria infection and chronic tumor growth. Blockade of the PD-1 pathway can partially restore the ability of St6gal1-deficient T cells to control tumor growth. Together, these data suggest that 2,6-linked sialic acids are critical for maintaining long-term T cell responsiveness, and the loss of 2,6-linked sialic acids may directly contribute to age-related T cell exhaustion.

immunology↗

bioRxiv2024

Lymphatic activation of ACKR3 signaling regulates lymphatic response after ischemic heart injury.

2.BackgroundIschemic heart disease is a prevalent cause of death and disability worldwide. Recent studies reported a rapid expansion of the cardiac lymphatic network upon ischemic heart injury and proposed that cardiac lymphatics may attenuate tissue edema and inflammatory mechanisms after ischemic heart injury. Nevertheless, the mechanisms through which hypoxic conditions affect cardiac lymphangiogenesis and function remain unclear. Here, we aimed to characterize the role of the adrenomedullin decoy receptor atypical chemokine receptor 3 (ACKR3) in the lymphatic response following ischemic heart injury. MethodsSpatial assessment of ACKR3 signaling in the heart after ischemic heart injury was conducted using ACKR3-TangoGFP reporter mice. Roles of ACKR3 after ischemic heart injury were characterized in Ackr3{Delta}Lyve1 mice and in cultured human lymphatic endothelial cells (LECs) exposed to hypoxia. ResultsUsing the novel ACKR3-Tango-GFP reporter mice, we detected activation of ACKR3 signaling in cardiac lymphatics adjacent to the site of ischemic injury of left anterior descending artery (LAD) ligation. Ackr3{Delta}Lyve1 mice exhibited better survival and were protected from the formation of acute tissue edema after ischemic cardiac injury. Ackr3{Delta}Lyve1 mice exhibited a denser cardiac lymphatic network after LAD ligation, especially in the injured tissues. Transcriptomic analysis revealed changes in cardiac lymphatic gene expression patterns that have been associated with extracellular matrix remodeling and immune activation. We also found that ACKR3 plays a critical role in the regulating continuous cell-cell junction dynamics in LECs under hypoxic conditions. ConclusionsLymphatic expression of ACKR3 governs numerous processes following ischemic heart injury, including the lymphangiogenic response, edema protection and overall survival. These results expand our understanding of how the heart failure biomarker adrenomedullin, regulated by lymphatic ACKR3, may exert its cardioprotective roles after ischemic cardiac injury.

physiology↗

bioRxiv2024

The evolution of pectate lyase-like genes across land plants, and their roles in haustorium formation in parasitic plants

Parasitic plants in Orobanchaceae are noxious agricultural pests that severely impact crops worldwide. These plants acquire water and nutrients from their hosts through a specialized organ called the haustorium. A key step in haustorium development involves cell wall modification. In this study, we identified and analyzed the evolutionary relationships of pectate lyase-like (PLL) genes across parasitic plants and other non-parasitic land plant lineages. To support detailed examination of gene models and paralogous gene family members, we used published parasitic plant genomes, as well as a recently generated draft genome assembly and annotation of Triphysaria versicolor. One particular PLL gene, denoted as PLL1 in parasitic Orobanchaceae, emerged as an important candidate gene for parasitism. Our previous comparative transcriptomic analyses showed that PLL1 underwent neofunctionalization via an expression shift from floral tissues in non-parasitic relatives to haustoria in parasitic species. It belongs to the largest sub-clade of the PLL gene family, is highly upregulated in haustoria, and shows signatures of relaxed purifying selection and 15 individual sites with signatures of adaptive evolution. To explore its function in haustorium development, we manipulated PLL1 expression in T. versicolor, a model parasitic species from Orobanchaceae, using direct transformation with the parasite and host-induced-gene-silencing (HIGS). For HIGS, we generated transgenic Medicago hosts expressing hairpin RNAs targeting the PLL1 gene in T. versicolor. An average 60% reduction of PLL1 transcript level was observed in both direct transformation and HIGS treatments, leading to an increased frequency of poorly adhered parasites with fewer xylem connections and a smaller proportion of mature haustoria. These findings demonstrate that PLL1 plays a crucial role in haustorium development and suggest it as a promising target for managing parasitic weeds. Notably, the success of HIGS even before the establishment of a functional haustorium highlights the possibility of early intervention against parasitism.

plant biology↗

bioRxiv2024

Single amino acid mutations in histone H3.3 illuminate the functional significance of H3K4 methylation in plants

Although numerous data have revealed correlations between histone modifications and chromatin activities such as transcription, proofs of their causal importance in gene expression regulation remain limited. Sequence variants within each histone family expand chromatin diversity and may carry specific modifications, further raising the question of how histone modifications coordinate with different variants. Here, we investigate the regulatory role of lysine 4 (K4) of Arabidopsis histone H3 by point mutating K4 in two major H3 variants, H3.1 and H3.3. K4 is essential for the function of H3.3 but not H3.1 in plant development. H3K4 methylation levels decrease drastically upon K4 mutation in H3.3, and the associated transcriptome changes are similar to those observed in a mutant lacking SDG2, a major enzyme responsible for depositing H3K4 trimethylation (H3K4me3). Moreover, H3.3K4 and SDG2 are required for de novo gene activation and RNA Pol II elongation. H3K4 methylation is preferentially accumulated on H3.3 compared to H3.1, likely due to the close association of the H3.3 deposition and H3K4 methylation machineries. Furthermore, we reveal the diverse impacts of K4 nearby residue mutations on H3K4 methylation and H3.3 function. Collectively, these findings suggest that H3.3 serves as a critical substrate for H3K4 methylation, which is important for gene expression regulation. In addition, our work highlights the potential of using plants as a platform to directly investigate the importance of histone amino acids and their modifications.

plant biology↗

bioRxiv2024

Simple One-step Vial-based Pretreatment for Deep Single-cell Proteomics and Its Application to Oocyte Aging

Single-cell proteomics is a pivotal technology for studying cellular phenotypes, offering unparalleled insights into cellular heterogeneity and dynamic functions. Technical improvement in mass spectrometry instrument and sample preparation has made single-cell proteomics feasible in recent years. Yet, developing a simple and robust sample preparation method to enable deep proteomics profiling of single cells remains a significant challenge. Herein, we developed a simple one-step vial-based pretreatment (SOViP) for deep label-free single-cell proteomics. SOViP integrates all sample preparation procedures into a single step in autosampler vials, yet it is highly efficient and high-throughput. SOViP can be finished within [~]2 h, with hands-on time limited to merely a few minutes. We show that on average over 6,500 can be quantified from a single mouse oocyte using SOViP. In total 6,983 protein groups were identified from single mouse oocytes across an entire reproductive lifespan, offering a valuable proteomics resource for oocyte aging. Unique molecular characteristics of oocytes at different ages were revealed, and a classifier consisting of ten proteins demonstrated accurate age-group classification and fertility-level prediction. Although demonstrated using mouse oocytes in this study, SOViP is adaptable to rare cell samples and other large cells, including follicles and preimplantation embryo cells, among others.

biochemistry↗

bioRxiv2024

MAAPE: A Modular Approach to Evolutionary Analysis of Protein Embeddings

We present MAPPE, a novel algorithm integrating a k-nearest neighbor (KNN) similarity network with co-occurrence matrix analysis to extract evolutionary insights from protein language model (PLM) embeddings. The KNN network captures diverse evolutionary relationships and events, while the co-occurrence matrix identifies directional evolutionary paths and potential signals of gene transfer. MAPPE overcomes the limitations of traditional sequence alignment methods in detecting structural homology and functional associations in low-similarity protein sequences. By employing sliding windows of varying sizes, it analyzes embeddings to uncover both local and global evolutionary signals encoded by PLMs. We have benchmarked MAAPE approach on two well-characterized protein family datasets: the Als regulatory system (AlsS/AlsR) and the Rad DNA repair protein families. In both cases, MAAPE successfully reconstructed evolutionary networks that align with established phylogenetic relationships. This approach offers a deeper understanding of evolutionary relationships and holds significant potential for applications in protein evolution research, functional prediction, and the rational design of novel proteins.

bioinformatics↗

bioRxiv2024

SS-31 protects against bleomycin-induced lung injury and fibrosis

ObjectiveThe aim of this research was to investigate if the mitochondria-targeting peptide SS-31 could serve as a protective measure against bleomycin-induced pulmonary fibrosis in mice. MethodMice were split into four groups named CON group, SS-31 group, BLM group, and the BLM+ SS-31 group. SS-31 was administered daily from the day prior to the experiment for the control and model groups. Mice were euthanized after 28 days of the experiment, following which blood, bronchoalveolar lavage fluid, and lung tissue were collected for analysis. ResultsThe study demonstrated that SS-31 could potentially mitigate the reduction in mice. It was observed through HE and Masson staining, immunohistochemistry, hydroxyproline detection, and fibrosis index measurement via Western blot that SS-31 could alleviate pulmonary fibrosis caused by BLM. Electron microscopy and ATP detection further suggested that SS-31 might help protect mitochondrial structure and function. It was also found that SS-31 could reduce reactive oxygen species and myeloperoxidase, thereby alleviating the reduction of antioxidant factor MPO and SOD, as well as diminishing the inflammatory factors TNF-, IL-1 {beta}, and IL-6. ConclusionThe mitochondria-targeting drug SS-31 exhibited potential in mitigating bleomycin-induced pulmonary fibrosis, improving mitochondrial structural and functional damage, stabilizing the balance between oxidative and antioxidant systems, reducing inflammatory factor expression, and improving apoptosis in lung tissue.

pharmacology and toxicology↗

bioRxiv2024

General assessment of genetically-encoded reporters in Yarrowia lipolytica

The development of genetically-encoded reporters is important to characterize gene expression dynamics and investigate cellular-level events. Unlike Bakers yeast, the thick cell wall and the high oil content in oleaginous yeast have restricted our ability to establish efficient fluorescence or enzyme-based reporters. In this book chapter, we detailed the protocol for how to clone and construct fluorescence reporters including hrGFP, TurboGFP, and mScarlet-I3, and the Nanoluc luciferase reporters. We quantified the fluorescence/luciferase reporter efficiency with 96-well microplate readers, flow cytometry, and confocal microscopy. Our results indicate that TurboGFP and hrGFP have a relatively low signal/noise ratio, and mScarlet-I3 yields a very high signal/noise ratio. Compared to fluorescence, luciferase Nanoluc exhibits the highest signal/noise ratio. The downside of using luciferase is the relatively laborious procedure and the related high cost. This chapter may guide us to establish an efficient and reliable reporter system to study gene expression or protein-labeling in nonconventional yeast.

bioengineering↗

bioRxiv2024

Dual-timescale motor circuit dynamics underlies fast head exploratory behavior and efficient locomotion

Motor systems must balance flexibility and structure to enable efficient and adaptive movements. Traditional hierarchical descending models struggle to explain the intrinsic dynamism in natural behaviors. Here, we investigate the head exploratory behavior of Caenorhabditis elegans, a minimal system capable of intricate motor patterns. Using variational mode decomposition, we identified two distinct motor dynamics: slow rhythmic bends propagating along the body and fast, phase-specific head casts influencing directional bias. Combinatorial ablations of three types of cholinergic motor neurons, in conjunction with dynamical systems analysis, revealed their distinct and overlapping roles: RMD contributes to head casts, SMD sustains bending states, SMB and SMD enable slow rhythmic bending and head-body coupling. Collectively, these neurons form a central pattern generator (CPG) driving forward locomotion. In addition, RMD subgroups displayed unique calcium dynamics correlated with rapid head movements and slow transitions between behavior states. We propose that dual-proprioceptive feedback underlies these multi-timescale dynamics, with slow feedback coordinating rhythmic bending and fast feedback shaping head casts, thereby optimizing roaming efficiency. These findings highlight how intrinsic dynamics and structured composition emerge from highly interactive low-level circuits.

neuroscience↗

bioRxiv2024

Mapping Pesticide-Induced Metabolic Alterations in Human Gut Bacteria

Pesticides can modulate gut microbiota (GM) composition, but their specific effects on GM remain largely elusive. Our study demonstrated that pesticides inhibit or promote growth in various GM species, even at low concentrations, and can accumulate in GM to prolong their presence in the host. Meanwhile, the pesticide induced changes in GM composition are associated with significant alterations in gut bacterial metabolism that reflected by the changes of hundreds of metabolites. We generated a pesticide-GM-metabolites (PMM) network that not only reveals pesticide-sensitive gut bacteria species but also report specific metabolic changes in 306 pesticide-GM pairs (PGPs). Using an in vivo mice model, we further demonstrated a PGPs interactions and verified the inflammation-inducing effects of pesticides on the host through dysregulated lipid metabolism of microbes. Taken together, our findings generate a PMM interactions atlas, and shed light on the molecular level of how pesticides impact host health by modulating GM metabolism. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/623895v1_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@17ed5f5org.highwire.dtl.DTLVardef@10d3e0org.highwire.dtl.DTLVardef@c61b43org.highwire.dtl.DTLVardef@27d035_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

Accurate structure prediction of immune proteins using parameter-efficient transfer learning

Accurate prediction of immune protein structures is crucial for understanding the immune system and advancing immunotherapy development. While deep learning methods have significantly advanced protein structure prediction by extracting evolutionary constraints from homologous sequences of a target protein, they struggle with immune proteins due to the limited number of known structures and the lack of homologous sequences in hypervariable regions. To address this challenge, we propose ImmuneFold, a transfer learning approach that fine-tunes ESMFold specifically for immune proteins. We leverage low-rank adaption (LoRA), a parameter-efficient fine-tuning technique that requires considerably less memory and substantially fewer parameters. Evaluations on various immune proteins, including T-cell receptors, antibodies, and nanobodies, demonstrate that ImmuneFold outperforms existing methods in prediction accuracy. Furthermore, we apply ImmuneFold to develop a zero-shot protocol for TCR-epitope binding prediction. Unlike previous supervised methods suffering from severe overfitting due to limited experimental binding data, our approach first predicts TCR-epitope structure using ImmuneFold and then directly estimates the binding affinity by calculating Rosseta energy. Evaluations on experimental binding datasets suggest that our method is robust and accurate in predicting TCR-epitope binding. In summary, ImmuneFold demonstrates accurate predictions of immune protein structures and TCR-epitope binding, highlighting its potential to advance the development of immunotherapies.

bioinformatics↗