High exposure of global tree diversity to human pressure.

Safeguarding Earth's tree diversity is a conservation priority due to the importance of trees for biodiversity and ecosystem functions and services such as carbon sequestration. Here, we improve the foundation for effective conservation of global tree diversity by analyzing a recently developed database of tree species covering 46,752 species. We quantify range protection and anthropogenic pressures for each species and develop conservation priorities across taxonomic, phylogenetic, and functional diversity dimensions. We also assess the effectiveness of several influential proposed conservation prioritization frameworks to protect the top 17% and top 50% of tree priority areas. We find that an average of 50.2% of a tree species' range occurs in 110-km grid cells without any protected areas (PAs), with 6,377 small-range tree species fully unprotected, and that 83% of tree species experience nonnegligible human pressure across their range on average. Protecting high-priority areas for the top 17% and 50% priority thresholds would increase the average protected proportion of each tree species' range to 65.5% and 82.6%, respectively, leaving many fewer species (2,151 and 2,010) completely unprotected. The priority areas identified for trees match well to the Global 200 Ecoregions framework, revealing that priority areas for trees would in large part also optimize protection for terrestrial biodiversity overall. Based on range estimates for >46,000 tree species, our findings show that a large proportion of tree species receive limited protection by current PAs and are under substantial human pressure. Improved protection of biodiversity overall would also strongly benefit global tree diversity.

Temporal dynamics of Grime's CSR strategies in plant communities during 60 years of succession.

Grime's competitive, stress-tolerant, ruderal (CSR) theory predicts a shift in plant communities from ruderal to stress-tolerant strategies during secondary succession. However, this fundamental tenet lacks empirical validation using long-term continuous successional data. Utilizing a 60-year longitudinal data of old-field succession, we investigated the community-level dynamics of plant strategies over time. Our findings reveal that while plant communities generally transitioned from ruderal to stress-tolerant strategies during succession, initial abandonment conditions crucially shaped early successional strategies, leading to varied strategy trajectories across different fields. Furthermore, we found a notable divergence in the CSR strategies of alien and native species over succession. Initially, alien and native species exhibited similar ruderal strategies, but in later stages, alien species exhibited higher ruderal and lower stress tolerance compared to native species. Overall, our findings underscore the applicability of Grime's predictions regarding temporal shifts in CSR strategies depending on both initial community conditions and species origin.

Gastrodin regulates the expression of renin-angiotensin system-SIRT3 and proinflammatory mediators in reactive astrocytes via activated microglia.

Gastrodin, an anti-inflammatory herbal agent, is known to suppress microglia activation. Here, we investigated whether it would exert a similar effect in reactive astrocytes and whether it might act through the renin-angiotensin system (RAS) and sirtuin 3 (SIRT3). Angiotensinogen (ATO), angiotensin-converting enzyme (ACE), angiotensin II type 1 (AT1) and type 2 (AT2) receptor and SIRT3 expression was detected in TNC-1 astrocytes treated with BV-2 microglia conditioned medium (CM) with or without gastrodin and lipopolysaccharide (LPS) pre-treatment by RT-PCR, immunofluorescence and western blotting analysis. Expression of C3 (A1 astrocyte marker), S100A10 (A2 astrocyte marker), proinflammatory cytokines and neurotrophic factors was then evaluated. The results showed a significant increase of ATO, ACE, AT1, SIRT3, C3, proinflammatory cytokines and neurotrophic factors expression in TNC-1 astrocytes incubated in CM + LPS when compared with cells incubated in the CM, but AT2 and S100A10 expression was reduced. TNC-1 astrocytes responded vigorously to BV-2 CM treated with gastrodin + LPS as compared with the control. This was evident by the decreased expression of the abovementioned protein markers, except for AT2 and S100A10. Interestingly, SIRT3, IGF-1 and BDNF expression was enhanced, suggesting that gastrodin inhibited the expression of RAS and proinflammatory mediators but promoted the expression of neurotrophic factors. And gastrodin regulated the phenotypic changes of astrocytes through AT1. Additionally, azilsartan (a specific inhibitor of AT1) inhibited the expression of C3 and S100A10, which remained unaffected in gastrodin and azilsartan combination treatment. These findings provide evidence that gastrodin may have a therapeutic effect via regulating RAS-SIRT3.

Rapid Sample Pretreatment Facilitating SERS Detection of Trace Weak Organic Acids/Bases in Complex Matrices.

Fast and efficient sample pretreatment is the prerequisite for realizing surface-enhanced Raman spectroscopy (SERS) detection of trace targets in complex matrices, which is still a big issue for the practical application of SERS. Recently, we have proposed a highly performed liquid-liquid extraction (LLE)-back extraction (BE) for weak acids/bases extraction in drinking water and beverage samples. However, the performance efficiency decreased drastically on facing matrices like food and biological blood. Based on the total interaction energies among target, interferent, and extractant molecules, solid-phase extraction (SPE) with a higher selectivity was introduced in advance of LLE-BE, which enabled the sensitive (µg L-1 level) and rapid (within 10 min) SERS detection of both koumine (a weak base) and celastrol (a weak acid) in different food and biological samples. Further, the high SERS sensitivity was determined unmanned by Vis-CAD (a machine learning algorithm), instead of the highly demanded expert recognition. The generality of SPE-LLE-BE for various weak acids/bases (2 < pKa < 12), accompanied by the high efficiency, easy operation, and low cost, offers SERS as a powerful on-site and efficient inspection tool in food safety and forensics.

Patch-Based Convolutional Encoder: A Deep Learning Algorithm for Spectral Classification Balancing the Local and Global Information.

Molecular vibrational spectroscopies, including infrared absorption and Raman scattering, provide molecular fingerprint information and are powerful tools for qualitative and quantitative analysis. They benefit from the recent development of deep-learning-based algorithms to improve the spectral, spatial, and temporal resolutions. Although a variety of deep-learning-based algorithms, including those to simultaneously extract the global and local spectral features, have been developed for spectral classification, the classification accuracy is still far from satisfactory when the difference becomes very subtle. Here, we developed a lightweight algorithm named patch-based convolutional encoder (PACE), which effectively improved the accuracy of spectral classification by extracting spectral features while balancing local and global information. The local information was captured well by segmenting the spectrum into patches with an appropriate patch size. The global information was extracted by constructing the correlation between different patches with depthwise separable convolutions. In the five open-source spectral data sets, PACE achieved a state-of-the-art performance. The more difficult the classification, the better the performance of PACE, compared with that of residual neural network (ResNet), vision transformer (ViT), and other commonly used deep learning algorithms. PACE helped improve the accuracy to 92.1% in Raman identification of pathogen-derived extracellular vesicles at different physiological states, which is much better than those of ResNet (85.1%) and ViT (86.0%). In general, the precise recognition and extraction of subtle differences offered by PACE are expected to facilitate vibrational spectroscopy to be a powerful tool toward revealing the relevant chemical reaction mechanisms in surface science or realizing the early diagnosis in life science.

Revealing the Denoising Principle of Zero-Shot N2N-Based Algorithm from 1D Spectrum to 2D Image.

Denoising is a necessary step in image analysis to extract weak signals, especially those hardly identified by the naked eye. Unlike the data-driven deep-learning denoising algorithms relying on a clean image as the reference, Noise2Noise (N2N) was able to denoise the noise image, providing sufficiently noise images with the same subject but randomly distributed noise. Further, by introducing data augmentation to create a big data set and regularization to prevent model overfitting, zero-shot N2N-based denoising was proposed in which only a single noisy image was needed. Although various N2N-based denoising algorithms have been developed with high performance, their complicated black box operation prevented the lightweight. Therefore, to reveal the working function of the zero-shot N2N-based algorithm, we proposed a lightweight Peak2Peak algorithm (P2P) and qualitatively and quantitatively analyzed its denoising behavior on the 1D spectrum and 2D image. We found that the high-performance denoising originates from the trade-off balance between the loss function and regularization in the denoising module, where regularization is the switch of denoising. Meanwhile, the signal extraction is mainly from the self-supervised characteristic learning in the data augmentation module. Further, the lightweight P2P improved the denoising speed by at least ten times but with little performance loss, compared with that of the current N2N-based algorithms. In general, the visualization of P2P provides a reference for revealing the working function of zero-shot N2N-based algorithms, which would pave the way for the application of these algorithms toward real-time (in situ, in vivo, and operando) research improving both temporal and spatial resolutions. The P2P is open-source at https://github.com/3331822w/Peak2Peakand will be accessible online access at https://ramancloud.xmu.edu.cn/tutorial.

Signal2signal: Pushing the Spatiotemporal Resolution to the Limit by Single Chemical Hyperspectral Imaging.

There is growing interest in developing a high-performance self-supervised denoising algorithm for real-time chemical hyperspectral imaging. With a good understanding of the working function of the zero-shot Noise2Noise-based denoising algorithm, we developed a self-supervised Signal2Signal (S2S) algorithm for real-time denoising with a single chemical hyperspectral image. Owing to the accurate distinction and capture of the weak signal from the random fluctuating noise, S2S displays excellent denoising performance, even for the hyperspectral image with a spectral signal-to-noise ratio (SNR) as low as 1.12. Under this condition, both the image clarity and the spatial resolution could be significantly improved and present an almost identical pattern with a spectral SNR of 7.87. The feasibility of real-time denoising during imaging was well demonstrated, and S2S was applied to monitor the photoinduced exfoliation of transition metal dichalcogenide, which is hard to accomplish by confocal Raman spectroscopy. In general, the real-time denoising capability of S2S offers an easy way toward in situ/in vivo/operando research with much improved spatial and temporal resolution. S2S is open-source at https://github.com/3331822w/Signal2signal and will be accessible online at https://ramancloud.xmu.edu.cn/tutorial.

Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives.

Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.

Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients.

BACKGROUND: The non-invasive biomarkers for predicting immunotherapy response are urgently needed to prevent both premature cessation of treatment and ineffective extension. This study aimed to construct a non-invasive model for predicting immunotherapy response, based on the integration of deep learning and habitat radiomics in patients with advanced non-small cell lung cancer (NSCLC). METHODS: Independent patient cohorts from three medical centers were enrolled for training (n = 164) and test (n = 82). Habitat imaging radiomics features were derived from sub-regions clustered from individual's tumor by K-means method. The deep learning features were extracted based on 3D ResNet algorithm. Pearson correlation coefficient, T test and least absolute shrinkage and selection operator regression were used to select features. Support vector machine was applied to implement deep learning and habitat radiomics, respectively. Then, a combination model was developed integrating both sources of data. RESULTS: The combination model obtained a strong well-performance, achieving area under receiver operating characteristics curve of 0.865 (95% CI 0.772-0.931). The model significantly discerned high and low-risk patients, and exhibited a significant benefit in the clinical use. CONCLUSION: The integration of deep-leaning and habitat radiomics contributed to predicting response to immunotherapy in patients with NSCLC. The developed integration model may be used as potential tool for individual immunotherapy management.

Breaking Scaling Relations for Highly Efficient Electroreduction of CO2 to CO on Atomically Dispersed Heteronuclear Dual-Atom Catalyst.

Conversion of CO2 into value-added products by electrocatalysis provides a promising way to mitigate energy and environmental problems. However, it is greatly limited by the scaling relationship between the adsorption strength of intermediates. Herein, Mn and Ni single-atom catalysts, homonuclear dual-atom catalysts (DACs), and heteronuclear DACs are synthesized. Aberration-corrected annular dark-field scanning transmission electron microscopy (ADF-STEM) and X-ray absorption spectroscopy characterization uncovered the existence of the Mn─Ni pair in Mn─Ni DAC. X-ray photoelectron spectroscopy and X-ray absorption near-edge spectroscopy reveal that Mn donated electrons to Ni atoms in Mn─Ni DAC. Consequently, Mn─Ni DAC displays the highest CO Faradaic efficiency of 98.7% at -0.7 V versus reversible hydrogen electrode (vs RHE) with CO partial current density of 16.8 mA cm-2. Density functional theory calculations disclose that the scaling relationship between the binding strength of intermediates is broken, resulting in superior performance for ECR to CO over Mn─Ni─NC catalyst.

Nuclear Magnetic Resonance-Based Metabolomics and Risk of CKD.

RATIONALE & OBJECTIVE: Chronic kidney disease (CKD) leads to lipid and metabolic abnormalities, but a comprehensive investigation of lipids, lipoprotein particles, and circulating metabolites associated with the risk of CKD has been lacking. We examined the associations of nuclear magnetic resonance (NMR)-based metabolomics data with CKD risk in the UK Biobank study. STUDY DESIGN: Observational cohort study. SETTING & PARTICIPANTS: A total of 91,532 participants in the UK Biobank Study without CKD and not receiving lipid-lowering therapy. EXPOSURE: Levels of metabolites including lipid concentration and composition within 14 lipoprotein subclasses, as well as other metabolic biomarkers were quantified via NMR spectroscopy. OUTCOME: Incident CKD identified using ICD codes in any primary care data, hospital admission records, or death register records. ANALYTICAL APPROACH: Cox proportional hazards regression models were used to estimate hazard ratios and 95% confidence intervals. RESULTS: We identified 2,269 CKD cases over a median follow-up period of 13.1 years via linkage with the electronic health records. After adjusting for covariates and correcting for multiple testing, 90 of 142 biomarkers were significantly associated with incident CKD. In general, higher concentrations of very-low-density lipoprotein (VLDL) particles were associated with a higher risk of CKD whereas higher concentrations of high-density lipoprotein (HDL) particles were associated with a lower risk of CKD. Higher concentrations of cholesterol, phospholipids, and total lipids within VLDL were associated with a higher risk of CKD, whereas within HDL they were associated with a lower risk of CKD. Further, higher triglyceride levels within all lipoprotein subclasses, including all HDL particles, were associated with greater risk of CKD. We also identified that several amino acids, fatty acids, and inflammatory biomarkers were associated with risk of CKD. LIMITATIONS: Potential underreporting of CKD cases because of case identification via electronic health records. CONCLUSIONS: Our findings highlight multiple known and novel pathways linking circulating metabolites to the risk of CKD. PLAIN-LANGUAGE SUMMARY: The relationship between individual lipoprotein particle subclasses and lipid-related traits and risk of chronic kidney disease (CKD) in general population is unclear. Using data from 91,532 participants in the UK Biobank, we evaluated the associations of metabolites measured using nuclear magnetic resonance testing with the risk of CKD. We identified that 90 out of 142 lipid biomarkers were significantly associated with incident CKD. We found that very-low-density lipoproteins, high-density lipoproteins, the lipid concentration and composition within these lipoproteins, triglycerides within all the lipoprotein subclasses, fatty acids, amino acids, and inflammation biomarkers were associated with CKD risk. These findings advance our knowledge about mechanistic pathways that may contribute to the development of CKD.

The impact of MIF deficiency on alterations of fecal microbiota in C57BL/6 mice induced by Trichinella spiralis infection.

Trichinellosis caused by Trichinella spiralis (T. spiralis) is a major food-borne parasitic zoonosis worldwide. Prevention of trichinellosis is an effective strategy to improve patient quality of life. Macrophage migration inhibitory factor (MIF) is closely related to the occurrence and development of several parasitic diseases. Studying the impact of MIF deficiency (Mif-/- ) on the alterations in host fecal microbiota due to T. spiralis infection may contribute to proposing a novel dual therapeutic approach for trichinellosis. To reveal the diversity and differences in fecal microbial composition, feces were collected from T. spiralis-uninfected and T. spiralis-infected wild-type (WT) and MIF knockout (KO) C57BL/6 mice at 0, 7, 14, and 35 days post-infection (dpi), and the samples were sent for 16S rRNA amplicon sequencing on the Illumina NovaSeq platform. Flow cytometry was used to determine the expression levels of IFN-γ and IL-4 in the CD4+ /CD8+ T-cell sets of mouse spleens. The results showed that operational taxonomic unit (OTU) clustering, relative abundance of microbial composition, alpha diversity, and beta diversity exhibited significant changes among the eight groups. The LEfSe analysis selected several potential biomarkers at the genus or species level, including Akkermansia muciniphila, Lactobacillus murinus, Coprococcus catus, Firmicutes bacterium M10_2, Parabacteroides sp. CT06, and Bacteroides between the KTs and WTs groups. The predicted bacterial functions of the fecal microbiota were mainly involved in metabolism, such as the metabolism of carbohydrates, amino acids, energy, cofactors, vitamins, nucleotides, glycans, and lipids. Flow cytometry revealed an increased CD3+ CD8- /CD3+ CD8+ T-cell ratio and increased IFN-γ and IL-4 levels in CD3+ CD8- T-cell sets from WT and MIF KO mice at 7 dpi. The results indicated that both MIF KO and infection time have a significant influence on the CD3+ CD8- IFN-γ+ and CD3+ CD8- IL-4+ response in mice after T. spiralis. In conclusion, this research showed alterations of the fecal microbiota and immune response in both WT and MIF KO mice before and after T. spiralis infection. These results revealed a potential role of MIF in regulating the pathogenesis of trichinellosis related to the intestinal microbiota. Importantly, the selected potential biomarkers combined with MIF will also offer a novel therapeutic approach to treat trichinellosis in the future.

Peptide RL-QN15 promotes regeneration of epidermal nerve fibers and recovery of sensory function in diabetic skin wounds.

Epidermal nerve fiber regeneration and sensory function are severely impaired in skin wounds of diabetic patients. To date, however, research on post-traumatic nerve regeneration and sensory reconstruction remains scarce, and effective clinical therapeutics are lacking. In the current study, localized treatment with RL-QN15, considered as a drug candidate for intervention in skin wounds in our previous research, accelerated the healing of full-thickness dorsal skin wounds in diabetic mice and footpad skin wounds in diabetic rats. Interestingly, nerve density and axonal plasticity in the skin wounds of diabetic rats and mice, as well as plantar sensitivity in diabetic rats, were markedly enhanced by RL-QN15 treatment. Furthermore, RL-QN15 promoted the proliferation, migration, and axonal length of neuron-like PC12 cells, which was likely associated with activation of the phosphatidylinositol-3 kinase/protein kinase B (PI3K/Akt) signaling pathway. The therapeutic effects of RL-QN15 were partially reduced by blocking the PI3K/Akt signaling pathway with the inhibitor LY294002. Thus, RL-QN15 showed positive therapeutic effects on the distribution of epidermal nerve fibers and stimulated the recovery of sensory function after cutaneous injury. This study lays a solid foundation for the development of RL-QN15 peptide-based therapeutics against diabetic skin wounds.

Altered pupil responses to social and non-social stimuli in Shank3 mutant dogs.

Pupillary response, an important process in visual perception and social and emotional cognition, has been widely studied for understanding the neural mechanisms of neuropsychiatric disorders. However, there have been few studies on pupil response to social and non-social stimuli in animal models of neurodevelopmental disorders including autism spectrum disorder (ASD) and attention deficit hyperactivity disorder. Here, we developed a pupilometer using a robust eye feature-detection algorithm for real-time pupillometry in dogs. In a pilot study, we found that a brief light flash induced a less-pronounced and slower pupil dilation response in gene-edited dogs carrying mutations in Shank3; mutations of its ortholog in humans were repeatedly identified in ASD patients. We further found that obnoxious, loud firecracker sound of 120 dB induced a stronger and longer pupil dilation response in Shank3 mutant dogs, whereas a high reward food induced a weaker pupillary response in Shank3 mutants than in wild-type control dogs. In addition, we found that Shank3 mutants showed compromised pupillary synchrony during dog-human interaction. These findings of altered pupil response in Shank3 mutant dogs recapitulate the altered sensory responses in ASD patients. Thus, this study demonstrates the validity and value of the pupilometer for dogs, and provides an effective paradigm for studying the underlying neural mechanisms of ASD and potentially other psychiatric disorders.

Identification of Fasudil as a collaborator to promote the anti-tumor effect of lenvatinib in hepatocellular carcinoma by inhibiting GLI2-mediated hedgehog signaling pathway.

Lenvatinib is a frontline tyrosine kinase inhibitor for patients with advanced hepatocellular carcinoma (HCC). However, just 25% of patients benefit from the treatment, and acquired resistance always develops. To date, there are neither effective medications to combat lenvatinib resistance nor accurate markers that might predict how well a patient would respond to the lenvatinib treatment. Thus, novel strategies to recognize and deal with lenvatinib resistance are desperately needed. In the current study, a robust Lenvatinib Resistance index (LRi) model to predict lenvatinib response status in HCC was first established. Subsequently, five candidate drugs (Mercaptopurine, AACOCF3, NU1025, Fasudil, and Exisulind) that were capable of reversing lenvatinib resistance signature were initially selected by performing the connectivity map (CMap) analysis, and fasudil finally stood out by conducting a series of cellular functional assays in vitro and xenograft mouse model. Transcriptomics revealed that the co-administration of lenvatinib and fasudil overcame lenvatinib resistance by remodeling the hedgehog signaling pathway. Mechanistically, the feedback activation of EGFR by lenvatinib led to the activation of the GLI2-ABCC1 pathway, which supported the HCC cell's survival and proliferation. Notably, co-administration of lenvatinib and fasudil significantly inhibited IHH, the upstream switch of the hedgehog pathway, to counteract GLI2 activation and finally enhance the effectiveness of lenvatinib. These findings elucidated a novel EGFR-mediated mechanism of lenvatinib resistance and provided a practical approach to overcoming drug resistance in HCC through meaningful drug repurposing strategies.

Decomposed FDG PET-based phenotypic heterogeneity predicting clinical prognosis and decision-making in temporal lobe epilepsy patients.

OBJECTIVE: This study utilized a data-driven Bayesian model to automatically identify distinct latent disease factors represented by overlapping glucose metabolism patterns from 18F-Fluorodeoxyglucose PET (18F-FDG PET) to analyze heterogeneity among patients with TLE. METHODS: We employed unsupervised machine learning to estimate latent disease factors from 18F-FDG PET scans, representing whole-brain glucose metabolism patterns in seventy patients with TLE. We estimated the extent to which multiple distinct factors were expressed within each participant and analyzed their relevance to epilepsy burden, including seizure onset, duration, and frequency. Additionally, we established a predictive model for clinical prognosis and decision-making. RESULTS: We identified three latent disease factors: hypometabolism in the unilateral temporal lobe and hippocampus (factor 1), hypometabolism in bilateral prefrontal lobes (factor 2), and hypometabolism in bilateral temporal lobes (factor 3), variably co-expressed within each patient. Factor 3 demonstrated the strongest negative correlation with the age of onset and duration (r = - 0.33, - 0.38 respectively, P < 0.05). The supervised classifier, trained on latent disease factors for predicting patient-specific antiepileptic drug (AED) responses, achieved an area under the curve (AUC) of 0.655. For post-surgical seizure outcomes, the AUC was 0.857, and for clinical decision-making, it was 0.965. CONCLUSIONS: Decomposing 18F-FDG PET-based phenotypic heterogeneity facilitates individual-level predictions relevant to disease monitoring and personalized therapeutic strategies.

High Expression Level of TRIP6 is Correlated with Poor Prognosis in Colorectal Cancer and Promotes Tumor Cell Proliferation and Migration.

Globally, colorectal cancer (CRC) is one of the leading causes of health problems. More reliable molecular biomarkers for early diagnosis in CRC patients are needed. A crucial role for thyroid hormone receptor interacting protein 6 (TRIP6) is played in tumorigenesis and tumor growth. Our study aims to determine the diagnostic and prognostic roles of TRIP6 at CRC. TRIP6 gene expression levels were analyzed in this study from public databases. The relationship between TRIP6 expression and clinicopathological characteristics was explored by logistic regression analysis. Based on Kaplan-Meier (K-M) survival curves and receiver operating characteristic curves (ROC) analysis, the prognostic and diagnostic values of TRIP6 were determined. Protein-protein interaction (PPI) networks analysis were performed using the STRING database. A Spearman's correlation analysis applied for examining the correlation between TRIP6 expression, immune cell infiltration, and immune checkpoint genes. Moreover, colony formation assay and transwell assay were used to investigate the functions of TRIP6. TRIP6 was highly expressed in CRC cancer tissues and cells. K-M survival analysis indicated that a high expression of TRIP6 was associated with poor prognosis. TRIP6 expression was obviously associated with immune cell infiltration and immune checkpoint gene expression. For validation, the results of collected clinical CRC samples show that TRIP6 levels in CRC tumor tissue were higher than those of paired adjacent colorectal tissues. Additionally, in vitro experiments suggested that TRIP6 knockdown suppressed proliferation and migration in CRC cell line RKO. TRIP6 overexpression promoted the proliferation and migration of normal colon cell line NCM460. High TRIP6 expression is associated with poor prognosis in colorectal cancer and promotes tumor cell proliferation and migration which may be a potential diagnostic and prognostic biomarker and a promising therapeutic target for CRC, providing new insights into its role in CRC.

Ginger polysaccharide alleviates the effects of acute exposure to carbonate in crucian carp (Carassius auratus) by regulating immunity, intestinal microbiota, and intestinal metabolism.

Alkaline stress poses a significant challenge to the healthy growth of fish. Ginger polysaccharide (GP) is one of the main active substances in ginger and has pharmacological effects, such as anti-oxidation and immune regulation. However, the physiological regulatory mechanism of GP addition to diet on alkalinity stress in crucian carp remains unclear. This study aimed to investigate the potential protective effects of dietary GP on antioxidant capacity, gene expression levels, intestinal microbiome, and metabolomics of crucian carp exposed to carbonate (NaHCO3). The CK group (no GP supplementation) and COG group (NaHCO3 stress and no GP supplementation) were set up. The GPCS group (NaHCO3 stress and 0.4% GP supplementation) was stressed for seven days. Based on these data, GP significantly increased the activities of total antioxidant capacity (T-AOC), superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-PX), acid phosphatase (ACP), and alkaline phosphatase (AKP) in carp under alkalinity stress (p < 0.05) and decreased the activity of malon dialdehyde (MDA) (p < 0.05). GP restored the activity of GSH-PX, ACP, and AKP to CK levels. The expression levels of tumor necrosis factor ß (TGF-ß), tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), and interleukin 8 (IL-8) genes were decreased, and the expression levels of determination factor kappa-B (NF-κB) and interleukin 10 (IL-10) genes were increased (p < 0.05). Based on 16 S rRNA high-throughput sequencing, GP improved the changes in the intestinal microbial diversity and structural composition of crucian carp caused by NaHCO3 exposure. In particular, GP increased the relative abundance of Proteobacteria and Bacteroidetes and decreased the relative abundance of Actinobacteria. The metabolic response of GP to NaHCO3 exposed crucian carp guts was studied using LC/MS. Compared to the COG group, the GPCS group had 64 different metabolites and enriched 10 metabolic pathways, including lipid metabolism, nucleotide metabolism, and carbohydrate metabolism. The addition of GP to feed can promote galactose metabolism and provide an energy supply to crucian carp, thus alleviating the damage induced by alkalinity stress. In conclusion, GP can mitigate the effects of NaHCO3 alkalinity stress by regulating immune function, intestinal flora, and intestinal metabolism in crucian carp. These findings provide a novel idea for studying the mechanism of salt-alkali tolerance in crucian carp by adding GP to feed.

Selection and Validation of Reference Genes for Gene Expression in Bactericera gobica Loginova under Different Insecticide Stresses.

Insecticide resistance has long been a problem in crop pest control. Bactericera gobica is a major pest on the well-known medicinal plants Lycium barbarum L. Investigating insecticide resistance mechanisms of B. gobica will help to identify pesticide reduction strategies to control the pest. Gene expression normalization by RT-qPCR requires the selection and validation of appropriate reference genes (RGs). Here, 15 candidate RGs were selected from transcriptome data of B. gobica. Their expression stability was evaluated with five algorithms (Delta Ct, GeNorm, Normfinder, BestKeeper and RefFinder) for sample types differing in response to five insecticide stresses and in four other experimental conditions. Our results indicated that the RGs RPL10 + RPS15 for Imidacloprid and Abamectin; RPL10 + AK for Thiamethoxam; RPL32 + RPL10 for λ-cyhalothrin; RPL10 + RPL8 for Matrine; and EF2 + RPL32 under different insecticide stresses were the most suitable RGs for RT-qPCR normalization. EF1α + RPL8, EF1α + ß-actin, ß-actin + EF2 and ß-actin + RPS15 were the optimal combination of RGs under odor stimulation, temperature, developmental stages and both sexes, respectively. Overall, EF2 and RPL8 were the two most stable RGs in all conditions, while α-TUB and RPL32 were the least stable RGs. The corresponding suitable RGs and one unstable RG were used to normalize a target cytochrome P450 CYP6a1 gene between adult and nymph stages and under imidacloprid stress. The results of CYP6a1 expression were consistent with transcriptome data. This study is the first research on the most stable RG selection in B. gobica nymphs exposed to different insecticides, which will contribute to further research on insecticide resistance mechanisms in B. gobica.