Sources and human health risks associated with potentially toxic elements (PTEs) in urban dust: A global perspective.

Long-term exposure to urban dust containing potentially toxic elements (PTEs) poses detrimental impacts on human health. However, studies estimating human health risks in urban dusts from a global perspective are scarce. We evaluated data for twelve PTEs in urban dusts across 59 countries from 463 published articles, including their concentrations, input sources, and probabilistic risks to human health. We found that 34.1 and 60.3% of those investigated urban dusts have been heavily contaminated with As and Cd, respectively. The input of PTEs was significantly correlated with economic structure due to emissions of industrial activities and traffic emissions being the major sources. Based on the Monte Carlo simulation, we found that the mean hazard index below the safe threshold (1.0) could still cause non-negligible risks to human health. Arsenic and Cr were the major PTEs threatening human health, and relatively high risk levels were observed in cities in China, Korea, Chile, Malaysia, and Australia. Importantly, our analysis suggested that PTEs threaten the health of approximately 92 million adults and 280 million children worldwide. Overall, our study provides important foundational understanding and guidance for policy decision-making to reduce the potential risks associated with PTE exposure and to promote sustainable development of urban economies.

A meta-analysis on crop growth and heavy metals accumulation with PGPB inoculation in contaminated soils.

Plant growth-promoting bacteria (PGPB) offer a promising solution for mitigating heavy metals (HMs) stress in crops, yet the mechanisms underlying the way they operate in the soil-plant system are not fully understood. We therefore conducted a meta-analysis with 2037 observations to quantitatively evaluate the effects and determinants of PGPB inoculation on crop growth and HMs accumulation in contaminated soils. We found that inoculation increased shoot and root biomass of all five crops (rice, maize, wheat, soybean, and sorghum) and decreased metal accumulation in rice and wheat shoots together with wheat roots. Key factors driving inoculation efficiency included soil organic matter (SOM) and the addition of exogenous fertilizers (N, P, and K). The phylum Proteobacteria was identified as the keystone taxa in effectively alleviating HMs stress in crops. More antioxidant enzyme activity, photosynthetic pigment, and nutrient absorption were induced by it. Overall, using PGPB inoculation improved the growth performance of all five crops, significantly increasing crop biomass in shoots, roots, and grains by 33 %, 35 %, and 20 %, respectively, while concurrently significantly decreasing heavy metal accumulation by 16 %, 9 %, and 37 %, respectively. These results are vital to grasping the benefits of PGPB and its future application in enhancing crop resistance to HMs.

Advances in phage-host interaction prediction: in silico method enhances the development of phage therapies.

Phages can specifically recognize and kill bacteria, which lead to important application value of bacteriophage in bacterial identification and typing, livestock aquaculture and treatment of human bacterial infection. Considering the variety of human-infected bacteria and the continuous discovery of numerous pathogenic bacteria, screening suitable therapeutic phages that are capable of infecting pathogens from massive phage databases has been a principal step in phage therapy design. Experimental methods to identify phage-host interaction (PHI) are time-consuming and expensive; high-throughput computational method to predict PHI is therefore a potential substitute. Here, we systemically review bioinformatic methods for predicting PHI, introduce reference databases and in silico models applied in these methods and highlight the strengths and challenges of current tools. Finally, we discuss the application scope and future research direction of computational prediction methods, which contribute to the performance improvement of prediction models and the development of personalized phage therapy.

Meta-analysis of impacts of microplastics on plant heavy metal(loid) accumulation.

The co-occurrence of microplastics (MPs) and heavy metal(loid)s (HMs) has attracted growing scientific interest because of their wide distribution and environmental toxicity. Nevertheless, the interactions between MPs and HMs in soil-plant systems remain unclear. We conducted a meta-analysis with 3226 observations from 87 independent studies to quantify the impact of MPs addition on the plant biomass and HMS accumulation. Co-occurrence of MPs and HMs (except for As) induced synergistic toxicity to plant growth. MPs promoted their uptake in the shoot by 11.0% for Cd, 30.0% for Pb, and 47.1% for Cu, respectively. In contrast, MPs caused a significant decrease (22.6%, 17.9-26.9%) in the shoot As accumulation. The type and dose of MPs were correlated with the accumulation of HMs. MPs increased available concentrations of Cd, Pb, and Cu, but decreased available As concentration in soils. Meanwhile, MPs addition significantly lowered soil pH. These findings may provide explanations for MPs-mediated effects on influencing the accumulation of HMs in plants. Using a machine learning approach, we revealed that soil pH and total HMs concentration are the major contributors affecting their accumulation in shoot. Overall, our study indicated that MPs may increase the environmental risks of HMs in agroecosystems, especially metal cations.

PB2 residue 473 contributes to the mammalian virulence of H7N9 avian influenza virus by modulating viral polymerase activity via ANP32A.

Since the first human infection reported in 2013, H7N9 avian influenza virus (AIV) has been regarded as a serious threat to human health. In this study, we sought to identify the virulence determinant of the H7N9 virus in mammalian hosts. By comparing the virulence of the SH/4664 H7N9 virus, a non-virulent H9N2 virus, and various H7N9-H9N2 hybrid viruses in infected mice, we first pinpointed PB2 as the primary viral factor accounting for the difference between H7N9 and H9N2 in mammalian virulence. We further analyzed the in vivo effects of individually mutating H7N9 PB2 residues different from the closely related H9N2 virus and consequently found residue 473, alongside the well-known residue 627, to be critical for the virulence of the H7N9 virus in mice and the activity of its reconstituted viral polymerase in mammalian cells. The importance of PB2-473 was further strengthened by studying reverse H7N9 substitutions in the H9N2 background. Finally, we surprisingly found that species-specific usage of ANP32A, a family member of host factors connecting with the PB2-627 polymorphism, mediates the contribution of PB2 473 residue to the mammalian adaption of AIV polymerase, as the attenuating effect of PB2 M473T on the viral polymerase activity and viral growth of the H7N9 virus could be efficiently complemented by co-expression of chicken ANP32A but not mouse ANP32A and ANP32B. Together, our studies uncovered the PB2 473 residue as a novel viral host range determinant of AIVs via species-specific co-opting of the ANP32 host factor to support viral polymerase activity.IMPORTANCEThe H7N9 avian influenza virus has been considered to have the potential to cause the next pandemic since the first case of human infection reported in 2013. In this study, we identified PB2 residue 473 as a new determinant of mouse virulence and mammalian adaptation of the viral polymerase of the H7N9 virus and its non-pathogenic H9N2 counterparts. We further demonstrated that the variation in PB2-473 is functionally linked to differential co-opting of the host ANP32A protein in supporting viral polymerase activity, which is analogous to the well-known PB2-627 polymorphism, albeit the two PB2 positions are spatially distant. By providing new mechanistic insight into the PB2-mediated host range determination of influenza A viruses, our study implicated the potential existence of multiple PB2-ANP32 interfaces that could be targets for developing new antivirals against the H7N9 virus as well as other mammalian-adapted influenza viruses.

Potent and broadly neutralizing antibodies against sarbecoviruses induced by sequential COVID-19 vaccination.

The current SARS-CoV-2 variants strikingly evade all authorized monoclonal antibodies and threaten the efficacy of serum-neutralizing activity elicited by vaccination or prior infection, urging the need to develop antivirals against SARS-CoV-2 and related sarbecoviruses. Here, we identified both potent and broadly neutralizing antibodies from a five-dose vaccinated donor who exhibited cross-reactive serum-neutralizing activity against diverse coronaviruses. Through single B-cell sorting and sequencing followed by a tailor-made computational pipeline, we successfully selected 86 antibodies with potential cross-neutralizing ability from 684 antibody sequences. Among them, PW5-570 potently neutralized all SARS-CoV-2 variants that arose prior to Omicron BA.5, and the other three could broadly neutralize all current SARS-CoV-2 variants of concern, SARS-CoV and their related sarbecoviruses (Pangolin-GD, RaTG13, WIV-1, and SHC014). Cryo-EM analysis demonstrates that these antibodies have diverse neutralization mechanisms, such as disassembling spike trimers, or binding to RBM or SD1 to affect ACE2 binding. In addition, prophylactic administration of these antibodies significantly protects nasal turbinate and lung infections against BA.1, XBB.1, and SARS-CoV viral challenge in golden Syrian hamsters, respectively. Importantly, post-exposure treatment with PW5-5 and PW5-535 also markedly protects against XBB.1 challenge in these models. This study reveals the potential utility of computational process to assist screening cross-reactive antibodies, as well as the potency of vaccine-induced broadly neutralizing antibodies against current SARS-CoV-2 variants and related sarbecoviruses, offering promising avenues for the development of broad therapeutic antibody drugs.

PB-LKS: a python package for predicting phage-bacteria interaction through local K-mer strategy.

Bacteriophages can help the treatment of bacterial infections yet require in-silico models to deal with the great genetic diversity between phages and bacteria. Despite the tolerable prediction performance, the application scope of current approaches is limited to the prediction at the species level, which cannot accurately predict the relationship of phages across strain mutants. This has hindered the development of phage therapeutics based on the prediction of phage-bacteria relationships. In this paper, we present, PB-LKS, to predict the phage-bacteria interaction based on local K-mer strategy with higher performance and wider applicability. The utility of PB-LKS is rigorously validated through (i) large-scale historical screening, (ii) case study at the class level and (iii) in vitro simulation of bacterial antiphage resistance at the strain mutant level. The PB-LKS approach could outperform the current state-of-the-art methods and illustrate potential clinical utility in pre-optimized phage therapy design.

Silicon reduces toxicity and accumulation of arsenic and cadmium in cereal crops: A meta-analysis, mechanism, and perspective study.

Arsenic (As) and cadmium (Cd) are two toxic metal(loid)s that pose significant risks to food security and human health. Silicon (Si) has attracted substantial attention because of its positive effects on alleviating the toxicity and accumulation of As and Cd in crops. However, our current knowledge of the comprehensive effects and detailed mechanisms of Si amendment is limited. In this study, a global meta-analysis of 248 original articles with over 7000 paired observations was conducted to evaluate Si-mediated effects on growth and As and Cd accumulation in rice (Oryza sativa L.), wheat (Triticum aestivum L.), and maize (Zea mays L.). Si application increases the biomass of these crops under As and/or Cd contamination. Si amendment also decreased shoot As and Cd accumulation by 24.1 % (20.6 to 27.5 %) and 31.9 % (29.0 to 31.9 %), respectively. Furthermore, the Si amendment reduced the human health risks posed by As (2.6 %) and Cd (12.9 %) in crop grains. Si-induced inhibition of Cd accumulation is associated with decreased Cd bioavailability and the downregulation of gene expression. The regulation of gene expression by Si addition was the driving factor limiting shoot As accumulation. Overall, our analysis demonstrated that Si amendment has great potential to reduce the toxicity and accumulation of As and/or Cd in crops, providing a scientific basis for promoting food safety globally.

Characterization of the metabolites of Eucommiae Cortex in rats provides a further insight into its estrogen-like effective substances.

Eucommiae Cortex is one of important traditional Chinese medicines (TCMs) used in Asia for preventing and treating osteoporosis induced by estrogen deficiency. However, the low exposure of prototype components in Eucommiae Cortex in vivo is difficult to interpret its efficacy. Under the guidance of UPLC-Q/TOF-MS, 42 metabolites including 32 lignans and 10 phenolics, 21 of which were new compounds, were isolated from rat urine and feces after oral administration of aqueous extract of E. ulmoides Oliv. by various chromatographic techniques. Their structures were determined based on extensive physicochemical analyses and spectral data. Their absolute configurations were determined by experimental and calculated ECD spectra, along with the calculated NMR with DP4 evaluation. Additionally, all isolated metabolites were evaluated for their estrogen-like activities, and there are 15 metabolites having estrogen-like effects after assessing influences in MCF-7 cells. Further, Dual Luciferase Reporter Gene Assay was used to determine their activation with estrogen receptor, M10 and M11 mixtures, M14, M19, M33, M27, M31, M38-M41 could activate ERα, and M19 and M41 could activate ERß. These results not only clarify the pharmacological substances of Eucommiae Cortex, but also provide a basis for guiding its clinical application.

Interaction of microplastics with heavy metals in soil: Mechanisms, influencing factors and biological effects.

Microplastics (MPs) and heavy metals (HMs) in soil contamination are considered an emerging global problem that poses environmental and health risks. However, their interaction and potential biological effects remain unclear. Here, we reviewed the interaction of MPs with HMs in soil, including its mechanisms, influencing factors and biological effects. Specifically, the interactions between HMs and MPs mainly involve sorption and desorption. The type, aging, concentration, size of MPs, and the physicochemical properties of HMs and soil have significant impacts on the interaction. In particular, MP aging affects specific surface areas and functional groups. Due to the small size and resistance to decomposition characteristics of MPs, they are easily transported through the food chain and exhibit combined biological effects with HMs on soil organisms, thus accumulating in the human body. To comprehensively understand the effect of MPs and HMs in soil, we propose combining traditional experiments with emerging technologies and encouraging more coordinated efforts.

One HA stalk topping multiple heads as a novel influenza vaccine.

Classic chimeric hemagglutinin (cHA) was designed to induce immune responses against the conserved stalk domain of HA. However, it is unclear whether combining more than one HA head domain onto one stalk domain is immunogenic and further induce immune responses against influenza viruses. Here, we constructed numerous novel cHAs comprising two or three fuzed head domains from different subtypes grafted onto one stalk domain, designated as cH1-H3, cH1-H7, cH1-H3-H7, and cH1-H7-H3. The three-dimensional structures of these novel cHAs were modelled using bioinformatics simulations. Structural analysis showed that the intact neutralizing epitopes were exposed in cH1-H7 and were predicted to be immunogenic. The immunogenicity of the cHAs constructs was evaluated in mice using a chimpanzee adenoviral vector (AdC68) vaccine platform. The results demonstrated that cH1-H7 expressed by AdC68 (AdC68-cH1-H7) induced the production of high levels of binding antibodies, neutralizing antibodies, and hemagglutinin inhibition antibodies against homologous pandemic H1N1, drifted seasonal H1N1, and H7N9 virus. Moreover, vaccinated mice were fully protected from a lethal challenge with the aforementioned influenza viruses. Hence, cH1-H7 cHAs with potent immunogenicity might be a potential novel vaccine to provide protection against different subtypes of influenza virus.

Dynamically adjustable SVEIR(MH) model of multiwave epidemics: Estimating the effects of public health measures against COVID-19.

The COVID-19 pandemic was characterized by multiple subsequent, overlapping outbreaks, as well as extremely rapid changes in viral genomes. The information about local epidemics spread and the epidemic control measures was shared on a daily basis (number of cases and deaths) via centralized repositories. The vaccines were developed within the first year of the pandemic. New modes of monitoring and sharing of epidemic data were implemented using Internet resources. We modified the basic SEIR compartmental model to include public health measures, multiwave scenarios, and the variation of viral infectivity and transmissibility reflected by the basic reproduction number R0 of emerging viral variants. SVEIR(MH) model considers the capacity of the medical system, lockdowns, vaccination, and changes in viral reproduction rate on the epidemic spread. The developed model uses daily infection reports for assessing the epidemic dynamics, and daily changes of mobility data from mobile phone networks to assess the lockdown effectiveness. This model was deployed to six European regions Baden-Württemberg (Germany), Belgium, Czechia, Lombardy (Italy), Sweden, and Switzerland for the first 2 years of the pandemic. The correlation coefficients between observed and reported infection data showed good concordance for both years of the pandemic (ρ = 0.84-0.94 for the raw data and ρ = 0.91-0.98 for smoothed 7-day averages). The results show stability across the regions and the different epidemic waves. Optimal control of epidemic waves can be achieved by dynamically adjusting epidemic control measures in real-time. SVEIR(MH) model can simulate different scenarios and inform adjustments to the public health policies to achieve the target outcomes. Because this model is highly representative of actual epidemic situations, it can be used to assess both the public health and socioeconomic effects of the public health measures within the first 7 days of the outbreak.

Neo-intline: integrated pipeline enables neoantigen design through the in-silico presentation of T-cell epitope.

Neoantigen vaccines are one of the most effective immunotherapies for personalized tumour treatment. The current immunogen design of neoantigen vaccines is usually based on whole-genome sequencing (WGS) and bioinformatics prediction that focuses on the prediction of binding affinity between peptide and MHC molecules, ignoring other peptide-presenting related steps. This may result in a gap between high prediction accuracy and relatively low clinical effectiveness. In this study, we designed an integrated in-silico pipeline, Neo-intline, which started from the SNPs and indels of the tumour samples to simulate the presentation process of peptides in-vivo through an integrated calculation model. Validation on the benchmark dataset of TESLA and clinically validated neoantigens illustrated that neo-intline could outperform current state-of-the-art tools on both sample level and melanoma level. Furthermore, by taking the mouse melanoma model as an example, we verified the effectiveness of 20 neoantigens, including 10 MHC-I and 10 MHC-II peptides. The in-vitro and in-vivo experiments showed that both peptides predicted by Neo-intline could recruit corresponding CD4+ T cells and CD8+ T cells to induce a T-cell-mediated cellular immune response. Moreover, although the therapeutic effect of neoantigen vaccines alone is not sufficient, combinations with other specific therapies, such as broad-spectrum immune-enhanced adjuvants of granulocyte-macrophage colony-stimulating factor (GM-CSF) and polyinosinic-polycytidylic acid (poly(I:C)), or immune checkpoint inhibitors, such as PD-1/PD-L1 antibodies, can illustrate significant anticancer effects on melanoma. Neo-intline can be used as a benchmark process for the design and screening of immunogenic targets for neoantigen vaccines.

Spike substitution T813S increases Sarbecovirus fusogenicity by enhancing the usage of TMPRSS2.

SARS-CoV Spike (S) protein shares considerable homology with SARS-CoV-2 S, especially in the conserved S2 subunit (S2). S protein mediates coronavirus receptor binding and membrane fusion, and the latter activity can greatly influence coronavirus infection. We observed that SARS-CoV S is less effective in inducing membrane fusion compared with SARS-CoV-2 S. We identify that S813T mutation is sufficient in S2 interfering with the cleavage of SARS-CoV-2 S by TMPRSS2, reducing spike fusogenicity and pseudoparticle entry. Conversely, the mutation of T813S in SARS-CoV S increased fusion ability and viral replication. Our data suggested that residue 813 in the S was critical for the proteolytic activation, and the change from threonine to serine at 813 position might be an evolutionary feature adopted by SARS-2-related viruses. This finding deepened the understanding of Spike fusogenicity and could provide a new perspective for exploring Sarbecovirus' evolution.

SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs.

Identifying the exact epitope positions for a monoclonal antibody (mAb) is of critical importance yet highly challenging to the Ab design of biomedical research. Based on previous versions of SEPPA 3.0, we present SEPPA-mAb for the above purpose with high accuracy and low false positive rate (FPR), suitable for both experimental and modelled structures. In practice, SEPPA-mAb appended a fingerprints-based patch model to SEPPA 3.0, considering the structural and physic-chemical complementarity between a possible epitope patch and the complementarity-determining region of mAb and trained on 860 representative antigen-antibody complexes. On independent testing of 193 antigen-antibody pairs, SEPPA-mAb achieved an accuracy of 0.873 with an FPR of 0.097 in classifying epitope and non-epitope residues under the default threshold, while docking-based methods gave the best AUC of 0.691, and the top epitope prediction tool gave AUC of 0.730 with balanced accuracy of 0.635. A study on 36 independent HIV glycoproteins displayed a high accuracy of 0.918 and a low FPR of 0.058. Further testing illustrated outstanding robustness on new antigens and modelled antibodies. Being the first online tool predicting mAb-specific epitopes, SEPPA-mAb may help to discover new epitopes and design better mAbs for therapeutic and diagnostic purposes. SEPPA-mAb can be accessed at http://www.badd-cao.net/seppa-mab/.

Molecular epidemiology analysis of symptomatic and asymptomatic norovirus infections in Chinese infants.

BACKGROUND: Norovirus is a leading cause of acute gastroenteritis among children. Previous studies based on symptomatic infections indicated that mutations, rather than recombination drove the evolution of the norovirus ORF2. These characteristics were found in hospital-based symptomatic infections, whereas, asymptomatic infections are frequent and contribute significantly to transmission. METHODS: We conducted the first norovirus molecular epidemiology analysis covering both symptomatic and asymptomatic infections derived from a birth cohort study in the northern China. RESULTS: During the study, 14 symptomatic and 20 asymptomatic norovirus infections were detected in 32 infants. Out of the 14 strains that caused symptomatic infections, 12 strains were identified as GII.3[P12], and others were GII.4[P31]. Conversely, 17 asymptomatic infections were caused by GII.4[P31], two by GII.2[P16], and one by GII.4[P16]. Regardless of symptomatic and asymptomatic infections, the mutations were detected frequently in the ORF2 region, and almost all recombination were identified in the RdRp-ORF2 region. The majority of the mutations were located around the predefined epitope regions of P2 subdomain indicating a potential for immune evasion. CONCLUSION: The role of symptomatic as well as asymptomatic infections in the evolution of norovirus needs to be evaluated continuously.

Establishment and validation of a nomogram for predicting the surgical difficulty of lateral retroperitoneal laparoscopic adrenalectomy.

Background: Lateral retroperitoneal laparoscopic adrenalectomy (LRLA) is widely performed for the resection of adrenal disorders, but when larger and more malignant lesions are involved, the difficulty of LRLA increases. We aimed to develop and evaluate a predictive model for the surgical difficulty of LRLA. Methods: A retrospective, observational, single-center study was performed involving all consecutive cases of unilateral RLA for adrenal disease from 2012.01 to 2021.12. Cases were randomly divided into training and validation cohorts (split ratio =7:3), then the least absolute shrinkage and selection operator (LASSO) regression was applied to reduce data dimension and select predictors. Multivariate logistic regression followed to develop the prediction nomogram for the surgical difficulty of LRLA. Finally, receiver operating characteristic (ROC) curve, calibration curve plot and decision curve analysis (DCA) were used to evaluate the nomogram's discrimination, calibration, and clinical usefulness, respectively. Results: A total of 621 cases were enrolled with a median age of 53 years and a median tumor diameter of 1.7 cm. After LASSO regression analysis, surgeon's experience, tumor diameter, resection procedure, histological type, patient's sex and body mass index (BMI) were identified to establish the nomogram. The model displayed good discrimination with area under the curve (AUC) in both the training cohort (0.754, 95% CI: 0.701-0.806) and validation cohort (0.742, 95% CI: 0.646-0.838). Additionally, excellent calibration curves were revealed for surgical difficulty evaluation in both the training cohort (P=0.999) and validation cohort (P=0.444). DCA results indicated the prediction model was clinically useful. Conclusions: Our novel and effective predictive model can be used to assess the individual surgical difficulty of LRLA. By stratifying patients at risk of having a difficult LRLA for adrenal disease, the model could contribute to improvements in perioperative strategy and therapy.

Crop residue return sustains global soil ecological stoichiometry balance.

Although soil ecological stoichiometry is constrained in natural ecosystems, its responses to anthropogenic perturbations are largely unknown. Inputs of inorganic fertilizer and crop residue are key cropland anthropogenic managements, with potential to alter their soil ecological stoichiometry. We conducted a global synthesis of 682 data pairs to quantify the responses of soil carbon (C), nitrogen (N), and phosphorus (P) and grain yields to combined inputs of crop residue plus inorganic fertilizer compared with only inorganic fertilizer application. Crop residue inputs enhance soil C (10.5%-12%), N (7.63%-9.2%), and P (2.62%-5.13%) contents, with an increase in C:N (2.51%-3.42%) and C:P (7.27%-8.00%) ratios, and grain yields (6.12%-8.64%), indicating that crop residue alleviated soil C limitation caused by inorganic fertilizer inputs alone and was able to sustain balanced stoichiometry. Moreover, the increase in soil C and C:N(P) ratio reached saturation in ~13-16 years after crop residue return, while grain yield increase trend discontinued. Furthermore, we identified that the increased C, N, and P contents and C:N(P) ratios were regulated by the initial pH and C content, and the increase in grain yield was not only related to soil properties, but also negatively related to the amount of inorganic N fertilizer input to a greater extent. Given that crop residual improvement varies with soil properties and N input levels, we propose a predictive model to preliminary evaluate the potential for crop residual improvement. Particularly, we suggest that part of the global budget should be used to subsidize crop residue input management strategies, achieving to a win-win situation for agricultural production, ecological protection, and climate change mitigation.

Synergistic interplay between Azospirillum brasilense and exogenous signaling molecule H2S promotes Cd stress resistance and growth in pak choi (Brassica chinensis L.).

Inoculation with growth-promoting rhizobacteria inoculation and the addition of exogenous signaling molecules are two distinct strategies for improving heavy metal resistance and promoting growth in crops through several mechanisms. However, whether rhizobacteria and phyllosphere signaling molecules can act synergistically alleviate heavy metal stress and promote growth and the mechanisms underlying these effects remain unclear. Here, a novel strategy involving the co-application of growth-promoting rhizobacteria and an exogenous signaling molecule was developed to reduce cadmium (Cd) phytotoxicity and promote pak choi growth in Cd-contaminated soil. We found that the co-application of Azospirillum brasilense and hydrogen sulfide (H2S) resulted in significant improvements in shoot biomass and antioxidant enzyme content and a decline in the levels of Cd translocation factors. In addition, this co-application significantly improved pak choi Cd resistance. Furthermore, we observed a significant negative correlation between abscisic acid concentration and Cd content of pak choi and a positive correlation between H2S concentration and biomass. These findings revealed that the co-application of rhizobacteria and exogenous signaling molecules synergistically promoted the growth of vegetable crops subjected to heavy metal stress. Our results may serve as a guide for improving the food safety of crops grown in soil contaminated with heavy metals.

Identification of prognostic immune-related lncRNAs in pancreatic cancer.

Long noncoding RNAs (lncRNAs) play a critical role in the immune regulation and tumor microenvironment of pancreatic cancer (PaCa). To construct a novel immune-related prognostic risk model for PaCa and evaluate the prognostic prediction of lncRNAs, essential immune-related lncRNAs (IRlncRNAs) were identified by Pearson correlation analysis of differentially expressed immune-related genes (IRGs) and IRlncRNAs in PaCa from The Cancer Genome Atlas (TCGA) and GTEx databases. Least absolute shrinkage and selection operator (LASSO) regression was also applied to construct a prognostic risk model of IRlncRNAs, and gene set enrichment analysis (GSEA) was further applied for functional annotation for these IRlncRNAs. A total of 148 IRlncRNAs were identified in PaCa to construct a prognostic risk model. Among them, lncRNA LINC02325, FNDC1-AS1, and ZEB2-AS1 were significantly upregulated in 69 pairs of PaCa tissues by qRT-PCR. ROC analyses showed that LINC02325 (AUC = 0.80), FNDC1-AS1 (AUC = 0.76), and ZEB2-AS1 (AUC = 0.75) had a good predictive effect on 5-year survival prognosis. We demonstrated that high expression levels of ZEB2-AS1 and LINC02325 were not only positively associated with tumor size and CA199, but elevated levels of ZEB2-AS1 and FNDC1-AS1 were also positively correlated with tumor stage. GSEA further revealed that immune-related pathways were mainly enriched in the high-risk groups. Several immune-related algorithms demonstrated that four IRlncRNAs were related to immune infiltration, immune checkpoints, and immune-related functions. Thus, the prognostic risk model based on IRlncRNAs in Paca indicates that the four IRlncRNA signatures may serve as predictors of survival and potential predictive biomarkers of the pancreatic tumor immune response.