Exploring the role of biotic factors in regulating the spatial variability in land surface phenology across four temperate forest sites.

Land surface phenology (LSP), the characterization of plant phenology with satellite data, is essential for understanding the effects of climate change on ecosystem functions. Considerable LSP variation is observed within local landscapes, and the role of biotic factors in regulating such variation remains underexplored. In this study, we selected four National Ecological Observatory Network terrestrial sites with minor topographic relief to investigate how biotic factors regulate intra-site LSP variability. We utilized plant functional type (PFT) maps, functional traits, and LSP data to assess the explanatory power of biotic factors for the start and end of season (SOS and EOS) variability. Our results indicate that PFTs alone explain only 0.8-23.4% of intra-site SOS and EOS variation, whereas including functional traits significantly improves explanatory power, with cross-validation correlations ranging from 0.50 to 0.85. While functional traits exhibited diverse effects on SOS and EOS across different sites, traits related to competitive ability and productivity were important for explaining both SOS and EOS variation at these sites. These findings reveal that plants exhibit diverse phenological responses to comparable environmental conditions, and functional traits significantly contribute to intra-site LSP variability, highlighting the importance of intrinsic biotic properties in regulating plant phenology.

Unraveling the drivers and impacts of leaf phenological diversity in a subtropical forest: A fine-scale analysis using PlanetScope CubeSats.

Leaf phenology variations within plant communities shape community assemblages and influence ecosystem properties and services. However, questions remain regarding quantification, drivers, and productivity impacts of intra-site leaf phenological diversity. With a 50-ha subtropical forest plot in China's Heishiding Provincial Nature Reserve (part of the global ForestGEO network) as a testbed, we gathered a unique dataset combining ground-derived abiotic (topography, soil) and biotic (taxonomic diversity, functional diversity, functional traits) factors. We investigated drivers underlying leaf phenological diversity extracted from high-resolution PlanetScope data, and its influence on aboveground biomass (AGB) using structural equation modeling (SEM). Our results reveal considerable fine-scale leaf phenological diversity across the subtropical forest landscape. This diversity is directly and indirectly influenced by abiotic and biotic factors (e.g. slope, soil, traits, taxonomic diversity; r2 = 0.43). While a notable bivariate relationship between AGB and leaf phenological diversity was identified (r = -0.24, P < 0.05), this relationship did not hold in SEM analysis after considering interactions with other biotic and abiotic factors (P > 0.05). These findings unveil the underlying mechanism regulating intra-site leaf phenological diversity. While leaf phenology is known to be associated with ecosystem properties, our findings confirm that AGB is primarily influenced by functional trait composition and taxonomic diversity rather than leaf phenological diversity.

6-Plex mdSUGAR Isobaric-Labeling Guide Fingerprint Embedding for Glycomics Analysis.

Glycans are vital biomolecules with diverse functions in biological processes. Mass spectrometry (MS) has become the most widely employed technology for glycomics studies. However, in the traditional data-dependent acquisition mode, only a subset of the abundant ions during MS1 scans are isolated and fragmented in subsequent MS2 events, which reduces reproducibility and prevents the measurement of low-abundance glycan species. Here, we reported a new method termed 6-plex mdSUGAR isobaric-labeling guide fingerprint embedding (MAGNI), to achieve multiplexed, quantitative, and targeted glycan analysis. The glycan peak signature was embedded by a triplicate-labeling strategy with a 6-plex mdSUGAR tag, and using ultrahigh-resolution mass spectrometers, the low-abundance glycans that carry the mass fingerprints can be recognized on the MS1 spectra through an in-house developed software tool, MAGNIFinder. These embedded unique fingerprints can guide the selection and fragmentation of targeted precursor ions and further provide rich information on glycan structures. Quantitative analysis of two standard glycoproteins demonstrated the accuracy and precision of MAGNI. Using this approach, we identified 304 N-glycans in two ovarian cancer cell lines. Among them, 65 unique N-glycans were found differentially expressed, which indicates a distinct glycosylation pattern for each cell line. Remarkably, 31 N-glycans can be quantified in only 1 × 103 cells, demonstrating the high sensitivity of our method. Taken together, our MAGNI method offers a useful tool for low-abundance N-glycan characterization and is capable of determining small quantitative differences in N-glycan profiling. Therefore, it will be beneficial to the field of glycobiology and will expand our understanding of glycosylation.

Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types.

Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (Vc,max ), an important trait for modelling photosynthesis, can be inferred from other easier-to-measure traits. However, whether trait-Vc,max relationships are robust across different forest types remains unclear. Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area-based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (Vc,max ), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. We found weak and forest type-specific relationships between Vc,max and the four morphological and biochemical traits (R2 ≤ 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait-Vc,max relationships across three forest biomes into a single robust model for Vc,max (R2 = 0.77), and also accurately estimated the four traits (R2 = 0.75-0.94). These findings challenge the traditional use of the empirical trait-Vc,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising Vc,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.

Strategy Based on Deglycosylation, Multiprotease, and Hydrophilic Interaction Chromatography for Large-Scale Profiling of Protein Methylation.

Reversible methylation of proteins regulates the majority of cellular processes, including signal transduction, mRNA splicing, transcriptional control, DNA repair, and protein translocation. A fundamental understanding of these biological processes at the molecular level requires comprehensive characterization of the methylated proteins. Methylation is often substoichiometric, and only a very limited number of methylated proteins and sites have been confidently identified to date. Although the intrinsically basic/hydrophilic methylated peptides can be enriched by the hydrophilic interaction liquid chromatography (HILIC), other hydrophilic peptides can coelute during the enrichment process and suppress the detection of methylated peptides. In addition, the modified Arg and Lys residues cannot be efficiently cleaved by trypsin, the most commonly used enzyme in shotgun proteomics. To overcome these caveats, we develop a novel de-glyco-assisted methylation site identification (DOMAIN) strategy which enables straightforward, fast, and reproducible analysis of protein methylation in a proteome-wide manner. Combining multidimensional fractionation and multiprotease digestion, our method enabled the identification of 573 methylated forms in 270 proteins, including 311 new methylation forms, in A549 cells. Combining this technique with stable isotope labeling quantitative proteomics and RNA interference, we determined the differential regulation of several putative methylated sites that are related to the protein arginine N-methyltransferase 3 (PRMT3). Collectively, our integrated proteomics workflow for comprehensive mapping of methylation sites enables a better understanding of protein methylation, while providing a rapid and effective approach for global protein methylation analysis in biomedical research.

M1 macrophage-membrane-cloaked paclitaxel/ß-elemene nanoparticles targeting cervical cancer for enhanced therapy.

Cervical cancer is a leading cause of cancer-related mortality in females worldwide, necessitating urgent solutions for effective treatment. Paclitaxel (PTX), a natural diterpene alkaloid compound, has the ability to inhibit mitosis and induce programmed apoptosis in tumor cells. However, its toxicity and drug resistance limit its efficacy in certain cervical cancer patients. ß-elemene (ß-ELE) can reverse multidrug resistance by inhibiting ATP-binding cassette transporters, thereby enhancing chemotherapy drug retention. Therefore, we propose a combination therapy using PTX/ß-ELE to improve chemotherapy sensitivity. To enhance targeted drug delivery, we developed M1-macrophage-membrane-coated nanoparticles (M1@PLGA/PTX/ß-ELE) for co-delivery of PTX&ß-ELE. Through both in vitro and in vivo cervical cancer models, we demonstrated that M1@PLGA/PTX/ß-ELE effectively suppressed tumor progression and polarization of tumor-associated macrophages. Furthermore, H&E staining confirmed the high therapeutic biosafety of M1@PLGA/PTX/ß-ELE as there was no significant damage observed in major organs throughout the entire therapeutic process. Overall, this study presents a targeted biomimetic nanoplatform and combinatorial strategy that synergistically enhances chemosensitivity in malignant tumors.

Multifunctional iron-apigenin nanocomplex conducting photothermal therapy and triggering augmented immune response for triple negative breast cancer.

Triple negative breast cancer (TNBC) presents a formidable challenge due to its low sensitivity to many chemotherapeutic drugs and a relatively low overall survival rate in clinical practice. Photothermal therapy has recently garnered substantial interest in cancer treatment, owing to its swift therapeutic effectiveness and minimal impact on normal cells. Metal-polyphenol nanostructures have recently garnered significant attention as photothermal transduction agents due to their facile preparation and favorable photothermal properties. In this study, we employed a coordinated approach involving Fe3+ and apigenin, a polyphenol compound, to construct the nanostructure (nFeAPG), with the assistance of ß-CD and DSPE-PEG facilitating the formation of the complex nanostructure. In vitro research demonstrated that the formed nFeAPG could induce cell death by elevating intracellular oxidative stress, inhibiting antioxidative system, and promoting apoptosis and ferroptosis, and near infrared spectrum irradiation further strengthen the therapeutic outcome. In 4T1 tumor bearing mice, nFeAPG could effectively accumulate into tumor site and exhibit commendable control over tumor growth. Futher analysis demonstrated that nFeAPG ameliorated the suppressed immune microenvironment by augmenting the response of DC cells and T cells. This study underscores that nFeAPG encompasses a multifaceted capacity to combat TNBC, holding promise as a compelling therapeutic strategy for TNBC treatment.

Optimised data-independent acquisition strategy recaptures the classification of early-stage hepatocellular carcinoma based on data-dependent acquisition.

Proteomics is increasingly used for exploring disease biomarkers and therapeutic targets. The data-independent acquisition (DIA) method collects all peptide signals in a sample, and provides a convenient way to archive disease-related molecular features for further exploration. In this study, we first established a high-coverage human hepatocellular carcinoma (HCC) spectral library containing 9393 protein groups, 119,903 peptides. Furthermore, we optimised the DIA method with respect to four key parameters: settings for mass spectrometry acquisition, gradient length, amount of sample loading, and length of analytical column. More than 6000 proteins from HepG2 cells could be stably quantified using the optimised one-shot DIA approach with a 2 h gradient time. One-shot DIA identified a similar number of proteins as did multi-fraction data-dependent acquisition (DDA) from the same group of HCC samples, but at a quarter of the total acquisition time. DIA data could recapture the classification results obtained from DDA data, thus paving the way for large-scale, multi-centre proteomics analysis of clinical samples. SIGNIFICANCE: The organ-specific spectral library for HCC and the optimised 2 h DIA approach met the urgent demands for large-scale quantitative proteomics analysis of HCC clinical samples. Compared with multi-fraction-DDA, the optimised one-shot DIA could reach a similar identification while consuming shorter acquisition time, thus making it possible to analyse thousands of clinical samples.

Metabolomics Analysis of Splenic CD19+ B Cells in Mice Chronically Infected With Echinococcus granulosus sensu lato Protoscoleces.

Background: The larval stages of Echinococcus granulosus sensu lato (E. granulosus s.l) infection can alter B cell function and affect host anti-infective immunity, but the underlying mechanism remains unclear. The newly emerging immunometabolism highlights that several metabolites are key factors in determining the fate of immune cells, which provides a new insight for exploring how larval E. granulosus s.l. infection remodels B cell function. This study investigated the metabolomic profiles of B cells in mice infected with E. granulosus s.l. protoscoleces (PSC). Results:Total CD19+ B cells, purified from the spleen of infected mice, showed significantly increased production of IL-6, TNF-α, and IL-10 after exposure to LPS in vitro. Moreover, the mRNA expression of metabolism related enzymes in B cells was remarkably disordered post infection. In addition, differential metabolites were identified in B cells after infection. There were 340 differential metabolites (83 upregulated and 257 downregulated metabolites) identified in the positive ion model, and 216 differential metabolites (97 upregulated and 119 downregulated metabolites) identified in the negative ion mode. Among these, 64 differential metabolites were annotated and involved in 68 metabolic pathways, including thyroid hormone synthesis, the metabolic processes of glutathione, fructose, mannose, and glycerophospholipid. Furthermore, several differential metabolites such as glutathione, taurine, and inosine were validated to regulate the cytokine production in LPS stimulated B cells. Conclusion:Infection with the larval E. granulosus s.l. causes metabolic reprogramming in the intrinsic B cells of mice, which provides the first evidence for understanding the role and mechanism of B cells in parasite anti-infective immunity from the viewpoint of immunometabolism.

Usefulness of heterologous promoters in the Pseudozyma flocculosa gene expression system.

The basidiomycetous fungus Pseudozyma flocculosa represents a promising new host for the expression of complex recombinant proteins. Two novel heterologous promoter sequences, the Ustilago maydis glyceraldehyde-3-phosphate dehydrogenase (GPD) and Pseudozyma tsukubaensis alpha-glucosidase promoters, were tested for their ability to provide expression in P. flocculosa. In liquid medium, these two promoters produced lower levels of intracellular green fluorescent protein (GFP) as compared to the U. maydis hsp70 promoter. However, GPD and alpha-glucosidase sequences behaved as constitutive promoters whereas the hsp70 promoter appeared to be morphology-dependent. When using the hsp70 promoter, the expression of GFP increased proportionally to the concentration of hygromycin in the culture medium, indicating possible induction of the promoter by the antibiotic. Optimal solid-state culture conditions were designed for high throughput screening of hygromycin-resistant transformants with the hsp70 promoter in P. flocculosa.

Recombinant protein secretion in Pseudozyma flocculosa and Pseudozyma antarctica with a novel signal peptide.

Secretion of recombinant proteins aims to reproduce the correct posttranslational modifications of the expressed protein while simplifying its recovery. In this study, secretion signal sequences from an abundantly secreted 34-kDa protein (P34) from Pseudozyma flocculosa were cloned. The efficiency of these sequences in the secretion of recombinant green fluorescent protein (GFP) was investigated in two Pseudozyma species and compared with other secretion signal sequences, from S. cerevisiae and Pseudozyma spp. The results indicate that various secretion signal sequences were functional and that the P34 signal peptide was the most effective secretion signal sequence in both P. flocculosa and P. antarctica. The cells correctly processed the secretion signal sequences, including P34 signal peptide, and mature GFP was recovered from the culture medium. This is the first report of functional secretion signal sequences in P. flocculosa. These sequences can be used to test the secretion of other recombinant proteins and for studying the secretion pathway in P. flocculosa and P. antarctica.

[Construction of a set of secreting expression vectors for Saccharomyces cerevisiea].

The DNA fragment ecoding the Signal peptide of inulinase of Kluyveromyces smarxianu was synthesized chemically. This fragment was cloned in-frame in the expression vector pYES2 of Saccharomyces cerevisiae, resulting in a set of new secreting expression vectors pYES2 I, pYES2 II, pYES2 III. The L-Asparaginase gene (ASN) of E. coli and alpha-acetylactate decarboxylase gene (ALDC) of B. brevis which were amplified by PCR and cloned into the new vectors respectively were transformed into Saccharomyces cerevisia, and most of enzyme activities were secreted into the medium. The new secreting expression vectors still have excellent segregational stability even after growth for 100 h in the absence of selective pressure.