Hierarchical clustering based on metaproteomic responses of individual microbiomes resulted in two clusters. The noncaloric artificial sweetener (NAS) cluster was composed of NASs and two sugar alcohols with shorter carbon backbones (4 or 5 carbon atoms), and the carbohydrate (CHO) cluster was composed of the remaining sugar alcohols. The metaproteomic functional responses of the CHO cluster were clustered with those of the prebiotics fructooligosaccharides and kestose. The sugar substitute sweeteners in the CHO cluster showed the ability to modulate the metabolism of Clostridia. This study provides a comprehensive evaluation of the direct effects of commonly used sugar substitute sweeteners on the functions of the human gut microbiome using a functional metaproteomic approach, improving our understanding of the roles of sugar substitute sweeteners on microbiome-associated human health and disease issues.The composition and function of the human gut microbiome are often associated with health and disease status. Sugar substitute sweeteners are widely used food additives, although many studies using animal models have linked sweetener consumption to gut microbial changes and health issues. Whether sugar substitute sweeteners directly change the human gut microbiome functionality remains largely unknown. In this study, we systematically investigated the responses of five human gut microbiomes to 21 common sugar substitute sweeteners, using an approach combining high-throughput IMPORTANCE The human gut microbiome is closely related to human health. Sugar substitute sweeteners as commonly used food additives are increasingly consumed and have potential impacts on microbiome functionality. Although many studies have evaluated the effects of a few sweeteners on gut microbiomes using animal models, the direct effect of sugar substitute sweeteners on the human gut microbiome remains largely unknown. Our results revealed that the sweetener-induced metaproteomic responses of individual microbiomes had two major patterns, which were associated with the chemical properties of the sweeteners. This study provided a comprehensive evaluation of the effects of commonly used sugar substitute sweeteners on the human gut microbiome. Dietary components, which include carbohydrates (CHO), proteins, fats, minerals, food additives, and other compounds, have been shown to play major roles in shaping the composition and function of the gut microbiome and the associated health consequences , 2. Sugaex vivo human gut microbiome. Most of the studies were conducted using animal models and focused on only a few sweeteners . To the best of our knowledge, this is the first study using metaproteomic approaches to simultaneously examine the effects of 21 sugar substitute sweeteners on human gut microbiomes. Our results revealed that the sweetener-induced metaproteomic responses of individual microbiomes had two major patterns, which were associated with the chemical properties of the sweeteners.Here, we report a systematic study of the effects of sugar substitute sweeteners on individual human gut microbiomes. Briefly, 21 common sweeteners, covering all sweeteners approved by Health Canada (HC) , the U.SIn this study, fecal samples from five healthy volunteers were cultured individually in the presence of each sweetener in an anaerobic workstation for 24 h . A totalThe comparison of Bray-Curtis distances between the sweetener-treated microbiome and nontreated microbiome metaproteomic profiles revealed seven sweeteners  that significantly altered the metaproteome across all five individual microbiomes . Of the We evaluated the effect of sugar substitute sweeteners on total microbial biomass by measuring the total proteins obtained in each sample using a detergent-compatible (DC) protein assay, as described in Materials and Methods. For most sweeteners, their effects on the individual microbiomes varied, while ISO and thaumatin at 2 mg/mL (THA2) increased the total biomass in all five microbiomes .Dialister, Parabacteroides, Ruminococcus, Phascolarctobacterium, Butyrivibrio, Blautia, and Marvinbryantia. Sugar alcohols SOR, MAN, and HSH clustered with controls fructooligosaccharide (FOS), kestose (KES), and glucose. This cluster featured significant increases in Actinobacteria genera Bifidobacterium and Collinsella and decreases in Dorea, Clostridium, Lachnoclostridium, Alistipes, Roseburia, and Flavonifractor. Genera Coprococcus, Oscillibater, Anaerostipes, and Butyrivibrio were increased by XYL but not by any other sugar alcohols.Genus-level protein abundance was then calculated from the distinctive peptide intensities of each genus measured by LC-MS/MS and the total microbial protein biomass of each sample. Most sweeteners showed significant effects on the abundance of at least one genus. Clustering based on the effects of genus-level biomass shows that the genus-level protein biomass composition was affected in patterns by different classes of sugar substitute sweeteners . GlycosiOf the identified protein groups, 93.5% had COG functional annotation. Sweeteners were categorized into two major clusters using COG abundances . We nameIn the NAS cluster, all NASs were included, plus sugar alcohols ERY and XYL, which have shorter carbon backbones . Interestingly, although four sugar alcohols, MAL, lactitol monohydrate, XYL, and ISO, were clustered with three of the NASs, i.e., REB, NDC, and MFE, in the taxonomic analysis in Partial least-squares discriminant analysis (PLS-DA) was performed to identify the most important differences in functional effects between the CHO cluster and the NAS cluster  and B. AClostridia (Roseburia (21 proteins), Clostridium (6 proteins), Blautia (4 proteins), Eubacterium (4 proteins), Lachnoclostridium (3 proteins), Ruminococcus (3 proteins), Anaerostipes (1 protein), Coprococcus (1 protein), Faecalibacterium (1 protein), Fusicatenibacter (1 protein), and Oscillibacter (1 protein). Functional enrichment analysis of each group of discriminative proteins from Clostridia was performed. The CHO elevated group was enriched in COG categories C (energy production and conversion), J (translation), and O , while the CHO depleted group was enriched in COG categories C, E (amino acid metabolism and transport), and G (CHO metabolism and transport) . Pathway analysis also showed that proteins from the two groups were from different metabolic pathways  were from class ostridia . Forty-sansport)  and F. Apathways . In partin vitro to 21 common sugar substitute sweeteners. Among the 21 sweeteners, 13 are NASs, with diverse chemical properties and high sweetness intensities, and 8 are sugar alcohols, which are CHOs with low digestibility  significantly altered the metaproteome across the five gut microbiomes . The remro assay , we obsero assay , 15. Froro assay , which iA total of 1,075 protein groups were significantly altered by one or more of the sugar substitute sweeteners (see Table S3). Although it is not possible to assess the possible functional impact of the changes in each protein group, some examples are noteworthy. For example, an average of 54 CAZymes, an important group of enzymes for microbiome function, were identified in each sample, a level consistent with a previous study . Most ofAlistipes was decreased by four sugar substitute sweeteners. OmpA is one of the most abundant OMPs  classified as outer membrane protein (OMP) OmpA from the genus ant OMPs  and is iant OMPs . Its decFirmicutes. A single-species-based study revealed that XYL was largely utilized by Anaerostipes caccae from Firmicutes to produce butyrate and promoted the growth of the species  and sugar alcohols  significantly increased protein abundance from several genera in  species . The effClostridia. Clostridia accounts for at least 10 to 40% of the total bacteria in gut microbiota  and proteins with greater abundances in the NAS cluster (CHO depleted group) were both enriched in COG category C. Enriched proteins from the two groups corresponded to different functions (see Table S4), with differences in specific pathways, as shown by the metabolic pathway analysis  and O . The greater abundance of proteins from these two categories indicated that the CHO cluster sweeteners might promote the cellular growth of Clostridia. A previous study showed that the traditional prebiotic FOS and probiotic Lactobacillus can increase the proportion of Clostridia in gut microbiomes , and the proportion of consumed sweeteners that could reach the colon . Twelve of the tested sweeteners had ADI data defined by FDA or EFSA. The culturing concentration that met the ADI (cADI) for each sweetener was calculated based on consumption from an average participant body weight of 70.3 kg, normalized to 200 g of colon content. Calculation of the cADI was conducted based on the following formula:Sweeteners with cADI values much lower than 2 mg/mL were tested both at the cADI and at 2 mg/mL to facilitate the comparison of their effects on the microbiome with those of other sweeteners. For sweeteners without defined ADI, in the case of all sugar alcohols the concentration used was standardized to 2 mg/mL, which would represent consumption levels lower than those in the general public –36. Sinc–2, 5% CO2, and 90% N2, at 37°C). A 20% (wt/vol) stool slurry was made in sterile prereduced PBS (pH 7.6) containing 10% (vol/vol) glycerol and 1 g/L l-cysteine, vortex homogenized, and filtered through gauze. The filtered slurry aliquots were stored at −80°C until culturing.The study was approved by the Ottawa Health Science Network Research Ethics Board at the Ottawa Hospital (protocol number 20160585-01 H). Informed consent was obtained from all subjects involved in the study. Stool samples from five healthy volunteers  were included in this study. Exclusion criteria included the diagnosis of irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), or diabetes mellitus; antibiotic use or gastroenteritis episode in the past 3 months; use of probiotic/prebiotic, laxative, or antidiarrheal drugs in the past month; or pregnancy. Volunteers included in this study were self-assessed as non-sweetener consumers, based on their food and nutritional supplement consumption. The stool samples were collected on site and immediately transferred into an anaerobic workstation  in the anaerobic workstation at 37°C for 24 h. Following anaerobic culture, samples were processed to isolate bacterial pellets as described previously  into 96-deep-well plates containing prereduced, optimized microbiome culture medium , 25 and eviously . Bacterig at 8°C for 10 min to remove cell debris. Supernatant protein concentrations were measured with the Bio-Rad DC protein assay reagent in triplicate and were used to calculate the total biomass of the microbiome after culturing.Proteins were extracted from bacterial pellets according to the method described by Li et al. . Brieflyg at 4°C for 25 min. Proteins were washed three times with 1 mL of −20°C acetone and pelleted by centrifugation at 16,000 × g at 4°C for 25 min. Following acetone washes, proteins were dissolved in 6 M urea-50 mM ammonium bicarbonate (ABC) (pH 8.0). Protein concentrations were determined as described above. Protein aliquots (50 μg) were reduced with 10 mM dithiothreitol at 56°C for 30 min, with shaking. Protein alkylation was then performed with 20 mM iodoacetamide in the dark for 40 min at room temperature. Samples were diluted 10-fold with 50 mM ABC (pH 8.0). Trypsin  was added to a trypsin/protein mass ratio of 1:50, and digestion was performed at 37°C for 19 h at 850 rpm. Trypsin digestion was stopped by adding 50 μL 10% (vol/vol) formic acid to a final pH of 2 to 3. Desalting was performed on in-house-made 96-channel filter tip plates packed with 5 mg 10-μm C18 resin . Desalted samples were freeze-dried and stored at −20°C.Proteins from each sample were precipitated overnight at −20°C by mixing lysis supernatant with a protein precipitation buffer containing 50% (vol/vol) acetone, 50% (vol/vol) ethanol, and 0.1% (vol/vol) acetic acid, at 1:5 ratio (vol/vol). Precipitated proteins were collected by centrifugation at 16,000 × 18 resin . A 90-min gradient with acetonitrile changing from 5% to 30% (vol/vol) was used, at a flow rate of 300 nL/min. Solvent A was composed of 0.1% (vol/vol) formic acid, and solvent B was composed of 0.1% (vol/vol) formic acid and 80% (vol/vol) acetonitrile. MS analysis was performed with a Q Exactive mass spectrometer (Thermo Fisher Scientific Inc.). Full MS scans were performed from m/z 300 to m/z 1,800, and data-dependent MS/MS scans were performed for the 12 most intense ions. MS and MS/MS scans were performed with resolutions of 70,000 and 17,500, respectively. Samples were loaded in a randomized order. In this study, 197 samples were analyzed over a period of 23 days. All raw data from LC-MS/MS have been deposited with the ProteomeXchange Consortium (http://www.proteomexchange.org) via the PRIDE (An Eksigent NanoLC system (nano2D ultra) coupled with a Q Exactive mass spectrometer (Thermo Fisher Scientific Inc.) was used for analysis. Tryptic peptides were reconstituted in 0.1% (vol/vol) formic acid to approximately 0.25 μg/μL, and 1 μg of peptides was loaded. The column used for peptide separation was of 75-μm inner diameter and was 15 cm long, packed with reverse-phase Che PRIDE  partner The MetaLab software (version 1.2.0) was used for peptide/protein identification and quantification, peptide taxonomic assignment, and protein functional annotation . The sea10 transformed, and PCA was performed using the R package stats. To reduce the effect of interindividual microbiome variation on data analysis, the distribution of each protein group among individual microbiomes was fitted to the same empirical distribution using an empirical Bayesian algorithm with ComBat  and DIAMOND   .Taxonomic assignment of each peptide was performed based on the lowest common ancestor (LCA) algorithm. If one peptide is identified in more than one taxon, then the peptide is assigned to the LCA of those taxa. Using a previously constructed pep2tax database integrated with the MetaLab software (version 1.2.0), each identified peptide was assigned to a taxon . The abuFunctional annotation was carried out in the MetaLab software, and each identified protein group was assigned to a COG category. The relative abundance of each COG was calculated based on the summed LFQ intensities of all assigned protein groups. Clustering of sweeteners was based on the fold change of relative COG intensities, averaged across all tested sweeteners. The Euclidean distance between sweeteners was calculated, and the clustering was performed with the ward.D method, using the R package stats. Bootstrapping evaluation  of the thttps://shiny.imetalab.ca), and the P value threshold was set at <0.05. Visualization of pathways was performed using COG accession numbers in iPath3  via the PRIDE partner repository (PXD030458).All raw data from the LC-MS/MS analyses have been deposited with the ProteomeXchange Consortium (


Medicago sativa) can dwell in water-deficient habitats, where it is difficult to predict dry mass (DM) production and forage quality due to understory transmittance. Mowing is a recommended practice for alfalfa populations under drought, but its effect on forested land receives less attention. In a controlled indoor experiment, we found that drought better reduces shoot DM weight and crude fiber content (CFi) in blue light  than red light . Mowing decreases carbon (C) isotope signature (δ13C), CFi, and total C content in shoots but increases their accumulations in DM, nonstructural carbohydrates, and crude fat content (CFa). The results also demonstrated that mown alfalfa has higher starch content when exposed to green light  compared to the other two spectra. Multiple factorial regression indicated that higher soluble sugar content accounted for the increase of CFa and DM weight for CFi. Overall, mowing in blue-light–enriched understory stands is recommended and produces high-forage–quality alfalfa, which can be used as a lowered crude fiber component.Alfalfa ( Medicago sativa) is a reliable species of legume that provides an abundance of forage for feeding ruminants  and nutrient detergent fiber (NDF); both reduce the forage quality of alfalfa hay . The alf3 plant perceives the stress of drought, it will mostly downregulate the discrimination of the heavier carbon (C) isotope (13C) against the lighter one (12C) . The less a plant discriminates between the two isotopes (δ13C more positive), the more stomatal conductance is controlled to limit gas exchange, and the greater water-use efficiency (WUE) is increased (13C) was proven to be a sensitive parameter to indicate WUE in alfalfa subjected to drought (When a Cncreased . The C i drought . In thisJuglans regia L.)-alfalfa agroforestry system  in southern Taihang Mountain. Seeds were evenly distributed to planting cavities  in trays filled with growing substrates . The tray surface was covered by a moist towel, and the moisture (>95%) was maintained every day by spraying with distilled water. When plantlets were germinated, the number of specimens was thinned to leave about 8–10 individuals per cavity to eliminate unnecessary competition. Thinned seedlings were cultured with a nutritional solution adapted from 3), 0.4 mM monopotassium phosphate (KH2PO4), 1 mM potassium sulfate (K2SO4), 3 mM calcium chloride (CaCl2), 0.5 mM magnesium sulfate (MgSO4), 0.15 mM dipotassium phosphate (K2HPO4), 0.2 mM iron–sodium ethylene diamine tetraacetic acid (Fe–Na EDTA), 14 μM boric acid (H3BO3), 5 μM manganese sulfate (MnSO4), 3 μM zinc sulfate (ZnSO4), 0.7 mM ammonium molybdate ((NH4)6Mo7O2), and 0.1 Mm cobalt chloride (CoCl2).Alfalfa seeds were collected from Nanshan Forest Farm, Jiyuan, Henan, China. Planted alfalfa populations were used to collect seeds from the understory layer of a walnut , and the other half were well-watered (control). The water deficit was induced by a 7-day withdrawal of water input. That is, drought-treated seedlings were watered every 14 days, while the controlled seedlings were watered every day. Thus, for alfalfa, drought stress can be induced after 7, 14, or even 21 days of water deficit, but only the 14-day period of drought-induced mostly frequent negative responses . BecauseAlfalfa seedlings were raised under artificial LED lighting. Throughout the experiment, seedlings were exposed to a 12-h photoperiod from 08:00 am to 20:00 pm. This amount of time was shorter than that (~18 h/day) used for woody plants  because −2 s−1 . It was determined that daytime PPFD under forest canopy ranged from 3.60 to 175.67 μmol m−2 s−1 . For eac  and water deficit (df = 1) were replicated three times, each of which was assigned as a tray of alfalfa seedlings. When the maximum height of most alfalfa per tray nearly reached ~50 cm (tips touched the panel), seedlings were mowed to remove all above-ground parts. Seedlings were clipped to mimic mowing about 50 days after sowing, and 58 days later the seedlings were clipped again to harvest for sampling. The mowing treatment was incorporated into the experimental arrangement as a repeated manipulation and did not increase the number of fixed-factor replicates. Most aerial organs were mowed, leaving shoots at a height of about 5 cm, as suggested by The combined treatments of light spectra  following the equation:Oven-dried samples were ground to pass a 1.0-mm screen. Soluble sugar and starch contents were determined by a colorimetric method . A 0.5-gRSample and RStandard are the ratios of 13C/12C in plant samples and the standard (Pee Dee Belemnite). Total C content was determined by an element analyzer .where α = 0.05). To reveal the joint driving forces of ecophysiological parameters, multivariate linear regression was used to regress the contributions of crude fat and fiber contents. Pearson correlation was used to detect relationships between pairs of ecophysiological parameters.Results were analyzed in a mixed-model analysis of variance (ANOVA), where light spectra and drought treatment were two fixed factors that were replicated three times, and seedlings were sampled twice pre/postmowing. The random placement of trays was designated as a random factor. SAS software  was used to analyze the data. Factors of water deficit, mowing treatment, and light spectra were combined as a multiple-factorial interaction design. When significant effects were indicated, results were compared across treatments following the Tukey test . The green-light spectrum resulted in higher soluble sugar content (6.77 ± 2.22 mg g−1 DW) compared to the blue- (3.64 ± 1.70 mg g−1 DW) and red-light (4.94 ± 1.52 mg g−1 DW) spectrums. Starch content was the highest in mowed seedlings exposed to green light  that may contribute to the accumulation of crude fat Table 3.13C.Apart from forage-quality variables, physiological parameters had close relationships Table 4.Aralia elata, a woody species, under plant factory conditions  and lettuce cultivars, red light caused greater DM production than blue light   . Alfalfa] Pers.) . The lowWe found that across treatments, crude fat contents were positively associated with soluble sugar content. Soluble sugars can be dissolved in glycerin because of the formation of hydrogen bonds between glucose and glycerin molecules . These cThrough a simulated experiment, we found that droughts can interact with the understory light spectrum, which affects DM production in alfalfa. The blue-light spectrum depressed DM production by controlling stomata conductance. Thus, it should be avoided for alfalfa production unless a lower fiber ratio is proposed as the study objective. Mowing can be a reliable approach to activate photosynthetic assimilation/production and improve forage quality by increasing the crude fat ratio and controlling fiber content in alfalfa shoots. Drought depressed DM production and reduced forage quality. Overall, the management of understory alfalfa populations should be considered with forests, where mowing is recommended for alfalfa that are exposed to sunlight transmittance in a higher blue-light spectrum. There, one can expect high-forage quality with lowered crude fibers. In a dry season or when interspecific water competition occurs, irrigation measures will be needed in an agroforestry system to improve alfalfa dry biomass and forage quality.While we found significant responses of growth, physiology, and forage quality in alfalfa seedlings in this study, all were obtained in a 1-year study, and random factors cannot be fully eliminated from the results. Future studies are encouraged to use more alfalfa genotypes and test the results by cross-year bioassays. Field trials are also suggested for future works to identify indoor findings and to guide agroforestry management with alfalfa as an understory forage species.The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.Conceptualization, CH and JG; methodology, JG; software, YZ; validation, YZ, YW, and JC; formal analysis, CH; investigation, YW; resources, JC; data curation, CH; writing—original draft preparation, CH; writing—review and editing, JZ; visualization, JC; supervision, JZ; project administration, JG; funding acquisition, JG and JZ. All authors have read and agreed to the published version of the manuscript.This research was funded by Fundamental Research Funds for the Central Non-profit Research Institution of CAF (grant number CAFYBB2020SY001 and CAFZC2017M005).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Enterobacteriaceae pathogens to rapid environmental changes. It has been shown that synergistic and antagonistic actions exist between the signaling molecules (p)ppGpp and DksA in several foodborne pathogens; however, the biological function of these molecules and their interactions in Yersinia are still unclear. This study systematically investigated the role of stringent response in Yersinia enterocolitica, a typical foodborne Enterobacteriaceae pathogen, by deleting the (p)ppGpp and DksA biosynthesis genes. (p)ppGpp and DksA copositively regulated most phenotypes, such as motility, antibiotic resistance, and tolerance to oxidative stress, whereas they exhibited independent and/or divergent roles in the growth and biofilm synthesis of Y. enterocolitica. Gene expression analysis revealed that (p)ppGpp- and DksA-deficiency reduced the transcription of flagellar synthesis genes (fliC and flgD) and biofilm synthesis genes (bssS and hmsHFRS), which could potentially contribute to changes in motility and biofilm formation. These results indicate that stringent response regulators (p)ppGpp and DksA have a synergistic role and independent or even completely opposite biological functions in regulating genes and phenotypes of Y. enterocolitica. Our findings revealed the biofunctional relationships between (p)ppGpp and DksA and the underlying molecular mechanisms in the regulation of the pathogenic phenotype of Y. enterocolitica.Stringent response plays an important role in the response of IMPORTANCE The synergetic actions between the stringent response signaling molecules, (p)ppGpp and DksA, have been widely reported. However, recent transcriptomic and phenotypic studies have suggested that independent or even opposite actions exist between them. In this study, we demonstrated that the knockout of (p)ppGpp and DksA affects the polymorphic phenotype of Yersinia enterocolitica. Although most of the tested phenotypes, such as motility, antibiotic resistance, and tolerance to oxidative stress, were copositively regulated by (p)ppGpp and DksA, it also showed inconsistencies in biofilm formation ability as well as some independent phenotypes. This study deepens our understanding of the strategies of foodborne pathogens to survive in complex environments, so as to provide theoretical basis for the control and treatment of these microorganisms. When the external environment changes, bacteria rapidly reintegrate intracellular resources and energy by stopping the synthesis of DNA, stable RNA and membrane components and rapidly synthesizing factors that are important for stress resistance, thereby ensuring their survival . This soVerrucomicrobia, Chlamydiae, and Planctomycetes ppGpp . The con5–omycetes . In Gramomycetes , 2. Furtsubtilis  and Mycomegmatis .70-dependent promoters and release more RNAP to bind to the alternative σ-factors, then direct RNAP to regulate target gene transcription ((p)ppGpp exerts transcriptional and physiological effects through indirect or direct mechanisms. Direct mechanisms of action are achieved by binding to target proteins. This includes the regulation of enzyme activity through interaction with RNA polymerase (RNAP) in cooperation with DksA. The positive or negative effects of (p)ppGpp and DksA are determined by the properties of involved promoter . In addi12–cription .Firmicutes or Thermophilic. However, it should be emphasized that this statement is not definitive due to the incomplete understanding of the function and structure of DksA homologs  virulence gene transcription , and type 3 secretion system (T3SS), indicating the importance of DksA in nutrition uptake, host adaptation and virulence (In addition to (p)ppGpp, the 17-kDa transcription factor DksA play a crucial role in the stringent response. Based on bioinformatic alignment, it is generally believed that DksA is present in most proteobacteria species but not in homologs . It was ectively , 6. Bothcription . In XantDksA acts as a cofactor to enhance ppGpp transcriptional regulation. Several studies have reported synergistic actions between them, including an anti-oxidation effect . HoweverS. enterica serovar Typhi ppGpp-deficient strains and focused on the differences in the regulatory mechanisms of the two signaling molecules. Using phenotypic characterization and target gene expression determination, this study provides new insights into the function of stringent response regulators and interactions between (p)ppGpp and DksA in Y. enterocolitica.Stringent response not only helps bacteria to quickly adapt to the stressful environment, but also affects the virulence. For foodborne pathogens, several reports have shown regulation of stringent response signaling molecules (p)ppGpp and DksA on bacterial virulence-related phenotypes, such as ar Typhi , E. colippGpp in a low nutrient environment, we constructed the ΔrelA strain (designated YENR) and ΔrelAΔspoT strain (designated YENRS) based on wild-type strain Y. enterocolitica ATCC 23715 ppGpp  experiments showed that the expression levels of relA and spoT in YENRS and relA in YENR were undetectable but returned to a relatively stable level through plasmid complementation .In general, nutrient-adequate conditions required for bacterial growth are only available under laboratory conditions, and in the natural environment bacteria need to quickly adapt to nutrient-poor conditions to ensure their survival and spread. To explore in more detail the effect of CC 23715 . Althoug(p)ppGpp , 28. MorY. enterocolitica was dependent on (p)ppGpp synthesis in an oligotrophic environment. Interestingly, relA single deletion mutant showed roughly the same growth curve as the WT, rather than an absolute loss of growth ability as in YENRS. Similar results were observed in YENRS strains complemented by the spoT gene. Considering spoT possesses (p)ppGpp synthesis and hydrolysis activities, we speculated that SpoT could synthesize sufficient (p)ppGpp in Y. enterocolitica to cope with auxotrophic conditions.Growth rates of the wild type (WT), YENR, and YENRS strains in rich lysogeny broth (LB), LBNS , and M63 minimal media were determined  to C. CoY. enterocolitica, tolerance to acids, alkalis, high osmosis, and oxidants is very important for the strain to adapt to complex food environments quickly ppGpp synthesis activity of SpoT. A similar phenomenon was observed in the WT and mutant strains after treatment with high osmotic pressure (LBNS supplemented with 0.5 M NaCl). The survival rate of the YENRS mutant was approximately one-third that of the WT strain, which can be complemented by the survival rate of YENR by expressing SpoT induced by l-arabinose. Treatment with oxidative stress conditions resulted in a half survival rate decrease in YENRS mutant (7.87%) compared to the WT strain (15.62%). Neither the YENR nor YENRS mutants showed an evident decrease in resistance to base stress (pH 10.0). These results confirmed that (p)ppGpp contributes to bacterial resistance to acid and high osmotic and oxidative stress in Y. enterocolitis.Previous studies have suggested that stringent responses play a role in bacterial resistance to multiple environmental stresses . For the quickly , 30. The quickly  to D. Afin vivo. Previous studies suggested that deletion of relA and spoT in Proteobacteria would lead to motility deficiency and changes in biofilm formation ppGpp resulted in a deficiency in motility and reduction in flagellum synthesis in Y. enterocolitis.Motility and biofilm formation are two phenotypes closely related to the virulence of pathogens ormation , 32; thecroscopy . The resrelA), and YENRS (pBAD2- poT) strains by crystal violet staining at 24, 48, and 72 h in 24-well polystyrene microtiter plates , cells, and fibrin attached to a contact surface ppGpp in biofilm formation, we quantified the biofilm amounts of the WT, YENR, YENRS, YENR ppGpp is a potential target for developing broad-spectrum bacteriostatic agents owing to its distinct absence in humans and intricate regulation of catalytic activity . To inveY. enterocolitica, we constructed a DksA-deficient strain (YEND) and knocked out the relA and spoT genes to obtain a dksA-(p)ppGpp double-deficient (YENDRS) strain.DksA binds to and widens the RNAP secondary channel as a stringent response factor, thereby influencing core orientation and shelf modules . MoreovedksA gene in the YEND strain ppGpp instead of DksA was involved in Y. enterocolitica acid and osmotic stress responses ppGpp affected polymyxin B resistance in Y. enterocolitis. These results suggest that both DksA and (p)ppGpp confers chloramphenicol and ampicillin resistance in Y. enterocolitis.Similar to (p)ppGpp, DksA increased the resistance of flgD, flgH, and fliM exhibited an increase in YEND compared to in the WT, the expression of fliC and flhA was significantly decreased 5- and 10-fold, respectively. Moreover, the transcription of these motility-associated genes was downregulated by 2- to 10-fold in YENRS and exhibited a combined effect in YENDRS. We also analyzed the transcription levels of biofilm-associated genes, including bssS encoding biofilm formation regulatory protein and hmsHFRS required for the biosynthesis of poly-β-1,6-N-acetylglucosamine exopolysaccharide. The results revealed that all these biofilm-associated genes were repressed in the absence of DksA or (p)ppGpp but could be restored when complemented with PBAD24- dksA or spoT plasmids. It is not surprising that the change ratio of motility- and biofilm-associated genes in YENR was more stable than in YENRS since the phenotypes of Y. enterocolitis in the absence of RelA also exhibited no significant changes. A previous study reported that RelA positively regulates T6SS through the RovA pathway in Yersinia pseudotuberculosis, which raised our interest in the effect of Y. enterocolitis (p)ppGpp and DksA on rovA transcription (relA mutant. These results indicated that (p)ppGpp and DksA are required for motility and biofilm gene expression.To better understand how (p)ppGpp and DksA affect motility and biofilm formation, we determined the expression of effector genes during the midlogarithmic growth phase using qRT-PCR ppGpp-deficient and DksA-deficient strain.Stringent response is a global regulatory mechanism that is widely present in bacteria and is induced by multiple stresses, such as nutritional starvation and environmental downshift. The signaling molecules (p)ppGpp and DksA act on diverse target genes, resulting in various bacterial phenotypes . Their sY. enterocolitica requires (p)ppGpp to grow in M63 minimal medium rather than in LB rich medium (Erwinia amylovora that (p)ppGpp is necessary for growth in MBMA minimal medium, which is due to the dependence of (p)ppGpp on bacterial de novo amino acid synthesis ppGpp under nutrient poor medium conditions, thus maintaining the normal growth of Y. enterocolitica on nutrient starved minimal media. In contrast, the deletion of RelA in S. enterica serovar Typhi result in defective growth under similar under nutrient poor medium ppGpp regulates tolerance to acid and hyperosmolar environments, whereas DksA is involved in regulating tolerance to oxidants. A similar study has been previously described in Y. pseudotuberculosis ΔRelA strains. Yang et al. considered that RelA regulates the type VI secretion system (T6SS4) expression through the RovM/RovA pathway, thus generating a stress response to adverse stresses (rovA in the (p)ppGpp deficient strain ppGpp to activate RNA polymerase when exposed to hydrogen peroxide, and its regulatory activity differentially modulated by varying hydrogen peroxide concentration ppGpp and DksA are potential targets for developing antibiotic adjuvants. In this study, we found that (p)ppGpp and DksA could confer bacterial resistance to chloramphenicol and ampicillin. A recent study reported that (p)ppGpp is involved in maintaining cysteine homeostasis while inhibiting protein synthesis with chloramphenicol. In general, the intracellular cysteine concentration increased when bacteria were exposed to chloramphenicol, which caused damage to DNA and cellular components owing to the high ability of cysteine to reduce free iron . HoweverY. enterocolitica. However, the regulatory roles of (p)ppGpp and DksA on bacterial motility are controversial, and interpretation of this mechanism requires consideration of both direct and indirect effects of the two signal molecules, as well as the specificity of regulatory mechanisms across species. The effects of (p)ppGpp on flagellar synthesis are almost uniform across in different species. In S. enterica and X. citri, deletion of relA and spoT leads to a decrease in bacterial flagellar synthesis and pathogenicity, and E. coli requires (p)ppGpp for flagellar gene activation and chemotaxis ppGpp-deficient strains, and it was shown that there was an opposite regulatory role in Y. enterocolitica, in which (p)ppGpp inhibited biofilm formation, whereas DksA positively regulated biofilm formation. However, the expression of biofilm genes, such as bssS and hmsHFRS in (p)ppGpp-deficient strains was suppressed, indicating that that other regulators or mechanisms may be involved in biofilm formation. Liu et al.ppGpp reduced the expression level of σS by affecting transcriptional elongation, resulting in the enhancement of the binding capacity of RNAP and alternative σ factor RpoD. Therefore, the expression of fleQ, which is positively regulated by RpoD, increased, and FleQ further enhanced the expression of lapA and bcs, resulting in enhanced biofilm formation.Motility and biofilm formation are important for bacterial spread and colonization, thereby affecting pathogenicity and infectivity. In this study, we found that both (p)ppGpp and DksA were able to inhibit flagella formation and attenuate the motility of emotaxis , 6, 24. cholerae , 42, 43. E. coli . Justin  E. coli . In our  E. coli . Therefomotility . We attriu et al. may explY. enterocolitica regulation of different traits was developed ppGpp and DksA, which demonstrated that DksA does not always act as a cofactor to enhance the function of (p)ppGpp in the stringent response, but also exerts transcriptional regulation independently of (p)ppGpp. Together, our investigations revealed the biological function of (p)ppGpp and DksA and their relationship with the response to environmental stress, which is important for understanding the complex mechanism underlying Y. enterocolitica transmissibility and pathogenicity.Based on the results of this study and previous observations, a working model of how (p)ppGpp and DksA contribute to eveloped . (p)ppGpE. coli DH5α, used for plasmid construction and amplification, was cultured at 37°C in Luria–Bertani (LB) broth consisting of 5 g/L yeast extract, 10 g/L tryptone, and 5 g/L NaCl. Y. enterocolitica ATCC 23715 (biotype 1B and serotype O:8) was used as the parent strain for the construction of Y. enterocolitica mutants and maintained in LB, defined M63 minimal medium  supplemented with 0.2% (wt/vol) glucose or LBNS (LB without NaCl) at 26°C. In addition, 100 μg/mL ampicillin, 16 μg/mL chloramphenicol, 15 μg/mL cefsulodin, 4 μg/mL irgasan, and 2.5 μg/mL novobiocin were added to the growth medium when appropriate.The bacterial strains used in the present study are listed in Y. enterocolitica genome for each target gene . A second fusion PCR was performed to connect the two fragments using the forward primer for the upstream fragment and the reverse primer for the downstream fragment. The resultant fragment was digested with XbaI and SacI and cloned into pDS132 to yield suicide plasmids for gene deletion.All plasmids used in this study are listed in Y. enterocolitica genome, digested with two appropriate enzymes, and inserted into pBAD24. The coding gene was expressed by the inducible araBAD promoter with the addition of l-arabinose in the growth medium.To construct plasmids for the complemented strains, a DNA fragment covering the entire coding region was amplified from the E. coli S17-1λpir via electroporation and mobilized into Y. enterocolitica via conjugation. A two-step homologous recombination process was executed, deletion mutant was obtained after PCR verification and gene sequencing. Note that the double mutant YENRS was generated from YENR and the triple mutant YENDRS was generated in the background of YENRS.The strategy used to generate the deletion mutants was based on a two-step sucrose counter selection procedure, as described previously . Briefly600 of approximately 0.05 and incubated at 26°C and 180 rpm for 30 h. Samples (200 μL) of each strain were taken every 3 h and measured at 600 nm using a microplate spectrophotometer . The experiment was performed in triplicate at least twice, and the error bars represent the standard deviation.Fresh overnight bacterial culture was diluted into 250-mL flasks containing 100 mL LB, LBNS, or M63 medium to an OD2O2 for 30 min). Simultaneously, each strain incubated in an LBNS medium without stress was used as the control. The cultures were then diluted 5,000-fold and plated onto LBNS agar plates. The plate colonies were counted after incubating at 26°C for 48 h, and the survival rate was calculated by dividing the number of CFU of stressed cells by the CFU number of the control , or NaOH ((pH 10.0) and incubated at 26°C for 1 h, or 1.0 mM H control , 46. The600 = 1.0. In addition, 1 μL of diluted culture was inoculated into the center of swim agar plates supplemented with 0.02 g/L l-arabinose and incubated at 26°C for 48 h without being inverted. The experiment was performed in triplicate at least twice, and the error bars represent the standard deviation.Bacterial motility experiments were performed on semisolid LBNS plates containing 0.35% agar. A single colony was inoculated into the LB medium, incubated overnight at 26°C and 180 rpm, and then diluted to ODl-arabinose. LBNS without inoculation was used as blank. After incubation at 26°C for 24 h, 48 h, or 72 h, planktonic cells were discarded by washing with phosphate-buffered saline (PBS), and the formed biofilm was fixed for 2 h at 60°C. Fixed bacteria were stained with 0.1% crystal violet for 10 min. Each tube was washed with water and treated with 1,800 μL 33% acetic acid to release the dye bound to the biofilm, which was then measured at 595 nm using a microplate reader . The experiment was repeated thrice in six replicates, and the error bars represent the standard deviation.Crystal violet was used to quantify the amount of biofilm as previously described . BrieflyY. enterocolitica strains, were determined using a 2-fold broth dilution method . LB broth without inoculum was used as the negative control. After incubation at 26°C for 24 h, the lowest concentration at which no visible Y. enterocolitica was detected was defined as the MIC.The MIC of three antibiotics, polymyxin B, chloramphenicol, and ampicillin, for the eight n method . Briefly600 of 0.01 in a 24-well plate containing 1200 μL LBNS medium. After static incubation at 26°C for 48 h, the bacterial mass and polysaccharides produced by bacterial cells were collected by centrifuging at 10,000 × g for 5 min, and the supernatant was discarded. The pellet was resuspended in 600 μL of 100 μg/mL Congo red in 0.9% saline and incubated for 1.5 h at 25°C while it was shaken. The polysaccharide-bound Congo red was then sedimented by centrifuging at 10000 × g for 5 min, and the optical density of the supernatant was measured at 490 nm. Finally, the percentage of Congo red was calculated by dividing the optical density of supernatant by the optical density of 100 μg/mL Congo red in 0.9% saline.The Congo red binding assay was performed to evaluate the production of polysaccharides as described previously, with a few modifications , 27. BriY. enterocolitica strains grown on LBNS medium was observed using a transmission electron microscope . The extracted RNA was then tested for its concentration and quality using Nanodrop 2000c  and agarose gel electrophoresis. RNA (200 ng) was subjected to cDNA synthesis using PrimeScript RT Master Mix  under the following cycling conditions: 15 min at 37°C and 5 s at 85°C. RT-qPCRs were performed using TB Green Premix Ex Taq II (TaKaRa), and 16sRNA was used as a reference for normalization. Data analysis was performed as previously described (RNA was extracted from escribed . The rea


Macrophage-like cells (MLCs) located at the ILM were observed in live human retinas using adaptive optics optical coherence tomography (OCT) as well as clinically-used OCT. The study aimed to quantitatively analyzing MLCs at the vitreoretinal interface (VRI) in diabetic retinopathy (DR) using en face OCT and swept-source optical coherence tomography angiography (SS-OCTA).190 DR eyes were included in the study, with 70 proliferative diabetic retinopathy (PDR) eyes and 120 non- proliferative diabetic retinopathy (NPDR) eyes. Sixty-three eyes from normal subjects were included as controls. MLCs were visualized in a 5 μm en face OCT slab above the VRI centered on the fovea. Mann-Whitney U test and Kruskal-Wallis H test were used to compare the OCTA parameters and the MLC parameters among groups. We evaluated the MLC density among groups on binarized images after image processing. We also investigated the relationship between MLC density and other OCT parameters including retina thickness and vessel density (VD).2 vs.6.14 (8.78) cells/mm2, P = 0.013; PDR vs. normal, 8.97 (8.40) cells/mm2vs. 6.48 (6.71) cells/mm2, P = 0.027) and diabetic macular edema (DME) eyes  vs.6.09 (9.00), P = 0.005). After adjusting for age and gender, MLC density in NPDR eyes negatively correlated to VD of deep capillary plexus (DCP) (P = 0.01).The MLC density significantly increased in PDR eyes  cells/mmSS-OCTA is a non-invasive and simple method for the characterization of MLCs at the VRI. PDR and DME are two factors that increase MLC density. MLC density also correlated with VD.The online version contains supplementary material available at 10.1186/s12886-023-02794-8. Diabetic retinopathy (DR) is a leading cause of vision loss and lead to heavy economic burden in many countries . 40% of Macrophages in the vitreous humor were known as hyalocytes, parts of which located above internal limiting membranes (ILM) –13. In pBased on studies using animal models and a little available research on human retina, we presumed that macrophages involved in the pathological process of DR. The current study collected patients of non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR), aiming to observe changes in the quantity, distribution characteristics, and morphology of MLCs through clinical swept source OCT (SS- OCT) and en face OCT.This retrospective cross-sectional study included PDR and NPDR patients and normal subjects between April 2021 to May 2022 at the Eye Center of the Renmin Hospital of Wuhan University in Wuhan, China. The study was approved by the Institutional Review Board of the Renmin Hospital of Wuhan University (WDRY2021-k162) and conducted in accordance with the tenets of the Declaration of Helsinki. Informed consent was obtained from all participants. Both eyes of all participants underwent comprehensive ophthalmologic examinations, including visual acuity, fundus fluorescein angiography (FFA), color fundus photography (CFP) and slit lamp examination. Diabetic maculae edema (DME) was diagnosed by OCT volumes centered on macular. Some apparent retinal thickening or hard exudates in posterior pole was considered as DME. Diagnosis and staging of DR was based on FFA, CFP or ultra-wide field images. Eyes with neovascularization and/or vitreous/preretinal hemorrhage were considered as PDR eyes. Eyes with microaneurysms, intraretinal hemorrhages, venous beading, intraretinal microvascular abnormalities and no signs of proliferative retinopathy were NPDR eyes. All diagnoses were performed by an experienced ophthalmologist. Exclusion criteria included: (1) prior surgical procedures such as pars plana vitrectomy; (2) concomitant with other retinal diseases ; (3) vitreous hemorrhage, cataract, or poor fixation affecting imaging quality; (4) eyes with large area of epiretinal membrane (ERM) affecting image analysis; (5) subjects with refractive error > 3.0 diopters(D) or < − 6.0 D; (6) eyes received intravitreal injection of steroids or received anti-VEGF therapy within the last 3 months.OCTA images were acquired using a commercial SS-OCTA instrument . OCTA was performed using a raster scan protocol of 512  × 512  which covered an area of 6 mm × 6 mm centered on the fovea. OCT B-scans corresponding to OCTA images were acquired at the same time. OCTA images of the peripheral retina were taken in the same scan mode as the macular area by guiding eye position. Only the OCTA images centering on the fovea were applied to quantitative analysis of MLC density. Repeated images  at the same location were acquired to increase the credibility of the results. The MLC layer was segmented by a 5-μm OCT slab located from 5 to 10 μm above the ILM to display the MLCs clearly. The segmentation lines were adjusted manually if needed.VD of the deep capillary plexus (DCP), superficial capillary plexus (SCP), mean retina thickness (MRT), and central fovea thickness (CFT) were also automatically measured in 6 mm × 6 mm OCTA images centered on the fovea by the built-in software of the SS-OCTA instrument (version 1.36.4). OCTA parameters including VD of DCP, VD of SCP, MRT and CFT were further separately measured in the parafoveal region (1–3 mm), and perifoveal region (3–6 mm).The en face OCT images were processed by ImageJ software. First, we used the White Top Hat algorithm, the Subtract Background and Gaussian Blur to remove some artifacts. Second, the Trainable WeKa Segmentation was used to mark MLCs. Finally, we applied the Analyze Particles tool to analyze the number of MLCs A semi-automated macro program for image processing were used. Specific processes were shown in the supplement S.We used SPSS Statistics . Continuous variables coincided with normal distribution are presented as the mean ± standard deviation (SD). Continuous variables not coincided with normal distribution are presented as median (interquartile range [IQR]). After using the Shapiro-Wilk test to test the normality of the data and Levene’s test for the homogeneity test of variance, we performed independent samples Mann-Whitney U test and Kruskal-Wallis H test to compare the OCTA parameters and the MLC parameters among PDR eyes, NPDR eyes and the control group. Chi-square test or Fisher’s exact test were used for categorical variables. Based on the data distribution type, the Spearman correlation coefficient and multiple linear regression were used to verify the correlation between the MLC density and the OCTA parameters in NPDR and PDR groups. Generalized estimating equations (GEE) were used to eliminate the effect of repeatability and relevance in data.190 DR eyes from 128 patients were included in the study ,including bilateral eyes from 62 patients and unilateral eyes from 66 patients. Among the included eyes, PDR and NPDR eyes accounted for 36.8% (70/190) and 63.2% (120/190) respectively. There were 77 eyes with DME (40.5%) and 53 eyes received panretinal photocoagulation (PRP) (27.9%). We also included 63 normal eyes from 63 normal subjects as the control group .2 vs. 6.48 (6.71) cells/mm2, P = 0.566). Compared with NPDR eyes and normal eyes, MLC density in PDR group increased significantly (8.97 (8.40) cells/mm2 vs.6.14 (8.78) cells/mm2, P = 0.013; 8.97 (8.40) cells/mm2vs. 6.48 (6.71) cells/mm2, P = 0.027). Examples are shown in Fig. P = 0.005, Fig. P = 0.002). In PDR eyes, no significant difference in MLC density was found between eyes with DME and without DME (P = 0.713). And MLC density was comparable between NPDR with DME (n = 50) and PDR (n = 70) (8.60 (11.07) vs 8.97 (8.40), P = 0.91). Considering that we included bilateral eyes giving rise to non-independent, correlated data points, GEE was applied to eliminate the effect of correlation. PDR  and DME  are two factors for the increase in MLC density after adjusting for correlation effect. Also, there was no statistical difference in MLC density comparing with eyes received PRP with those not received PRP in either NPDR group (7.49 (7.01) vs. 5.85 (9.06), P = 0.052) or PDR group (11.52 (13.40) vs. 8.79 (7.15), P = 0.95). Statistical analysis of the DR eyes and normal eyes in Mann-Whitney U test found that the MRT and CFT were significantly thicker in DR eyes (P < 0.001) and the VD of DCP decreased in DR eyes (P < 0.001). Also, VD of DCP also decreased with disease progression  vs. 43.68 (11.62), P < 0.001). And there is no significant difference between PDR and NPDR groups in other OCTA parameters.The MLC density and OCTA parameters are summarized in Table  6.48 6.7 cells/mmr = 0.292, P = 0.016). In NPDR eyes, MLC density positively correlated to MRT  and negatively correlated to VD of DCP . After adjusting for age and gender, only VD of DCP still negatively correlated with MLC density in NPDR (P = 0.01). The results of the correlation analysis are shown in the Fig. We also investigated that whether MLC density associated with VD and retina thickness. The results seemed to be different in PDR and NPDR groups, which was summarized in Tables In addition, we investigated morphological and distribution characteristics of MLCs. MLCs appeared as small protrusions at the VRI on B scans Fig. A-D&G-J, In the current study, we used en face OCT image to investigate the density changes of MLCs located at the VRI in NPDR and PDR eyes. We found that PDR and DME were two factors that increased MLC density in the macular area. The study also demonstrated an association between VD and MLC density. To our best knowledge, this is the first large cohort study suggesting that MLC density increased in PDR and DME eyes.The results that MLC density increased in DR especially PDR eyes is consistent with the previous study with a small sample size . The uneAlthough more MLCs were found in the en face OCT centered on fovea in most DR eyes, we found that PDR and DME are two factors that significantly increase MLC density. PDR pathogenesis involved in not only severe ischemia burden but the injury of neurons and glial cells, dysfunction of endothelial progenitor cells and activation of inflammatory cells , 28. TheDME, a critical pathological hallmark, often accompanies all stages of DR and results in the vision loss and economic burden in DR patients. Macular edema results from an imbalance between fluid entry and drainage. The increase of vascular permeability, vessels abnormalization and neovascularization lead to more fluid entry the retina . NumerouAnother result in the current study is that MLC density correlates negatively with VD of DCP in NPDR eyes. It has been known that the DCP is more susceptible to ischemic damage and more vulnerable to injury because the deep layer of the retinal circulation sits next to high oxygen requirements of the outer plexiform layer , 42. LarThere are some limitations in our study. First, there are some potential factors that may influence MLCs distribution and MLC density including hypertension, dyslipidemia, diabetes type and hemoglobin A1c. We didn’t include these factors in this study due to insufficient data. Second, we didn’t find the influence of PRP on MLC density. And we didn’t include subjects receiving intravitreal injection of steroids or anti-VEGF therapy within 3 months. Considering the inconsistent time interval between observation and PRP treatment in the retrospective study, the current study cannot draw any definite conclusion about the relationship between MLCs and PRP treatment. Third, although we acquired some images of the peripheral retina, the images were insufficient for statistical analysis. And the morphology and distribution were described rather than analyzed statistically. We thought that MLCs were not only located in the macular area but widely distributed at the VRI. Observations on a larger scale using wide-field en face OCT may help have a much better global picture of the dynamics and distribution of MLCs. Besides, we included patients with DME and analyzed the VD in the current study. The existence of DME and exudation may influence the segmentation and measurement of VD and contribute to the inaccurate results. To eliminate the effect, we examined the OCTA images and adjusted the segmentation manually if it was needed. Lastly, we found the increased level of MLCs density in DR. However, the discovery is insufficient to establish a causative pathogenic link. Further experimental studies are needed to explore the mechanism, the origin and the function of the cells.PDR and DME are two factors associated with the significantly increasing number of MLCs at the VRI. The current study provides an clinical insight that MLCs participate in DR progression and DME. The investigation of MLCs may be helpful for understanding their pathophysiology and finding new therapeutic targets for DR. The observation of MLCs provides a non-invasive way to evaluate the inflammatory response in vivo. Considered as a biomarker, MLCs might be useful in estimating the prognosis of DR and guide treatment in the clinic.Additional file 1.


Lung adenocarcinoma (LUAD) is the most common histological subtype of non-small cell lung cancer (NSCLC) with a low 5-year survival rate, which may be associated with the presence of metastatic tumors at the time of diagnosis, especially lymph node metastasis (LNM). This study aimed to construct a LNM-related gene signature for predicting the prognosis of patients with LUAD.  RNA sequencing data and clinical information of LUAD patients were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Samples were divided into metastasis (M) and nonmetastasis (NM) groups based on LNM status. Differentially expressed genes (DEGs) between M and NM groups were screened, and then WGCNA was applied to identify key genes. Furthermore, univariate Cox and LASSO regression analyses were conducted to construct a risk score model, and the predictive performance of model was validated by GSE68465, GSE42127, and GSE50081. The protein and mRNA expression level of LNM-associated genes were detected by human protein atlas (HPA) and GSE68465.  A prognostic model based on eight LNM-related genes  was developed. Patients in the high-risk group had poorer overall survival than those in the low-risk group, and validation analysis showed that this model had potential predictive value for patients with LUAD. HPA analysis supported the upregulation of ANGPTL4, KRT6A, BARX2, RGS20 and the downregulation of GPR98 in LUAD compared with normal tissues.  Our results indicated that the eight LNM-related genes signature had potential value in the prognosis of patients with LUAD, which may have important practical implications. Almost a quarter of cancer-related deaths are caused by lung cancer, which ranks among the top ten causes of cancer deaths in both men and women . Non-smaThe lymphatic system is the main route of LUAD metastasis, and lymphatic metastasis is an important indicator influencing the prognosis and staging of it . It has In this study, we explored the potential prognostic value of LNM genes in LUAD via integration of the LNM-associated genes and clinical data obtained from The Cancer Genome Altas (TCGA) portal. Samples from the TCGA database were divided into metastasis (M) and nonmetastasis (NM) groups, and then genes differentially expressed in these two groups were identified. Next, weighed gene coexpression network analysis (WGCNA) was performed to screen the key modules and genes related to LNM, followed by LASSO regression analysis to construct an optimal prognostic model. Further, the predictive performance of model was assessed by using three gene expression omnibus (GEO) datasets. Meanwhile, the expression level of genes in the model was assessed by using the GEO dataset and human protein atlas (HPA) database. The constructed model could be used as a prognostic signature to improve the management of metastatic patients, which might ultimately be applied to assist clinicians in treatment selection and prognostic evaluation for LUAD patients with LNM.https://gdc-portal.nci.nih.gov/). Among these, the N0 stage was considered as the NM group and N1–N3 stages were regarded as the M group. Next, after eliminating the samples with missing information on survival time, 493 samples remained to construct the prognostic model.The mRNA expression data and clinical follow-up information of 505 LUAD samples were downloaded from the TCGA database  were employed as validation cohorts, including GSE68465 (based on the GPL96 platform) [Moreover, three external datasets downloaded from the GEO database (latform) , GSE4212latform) , 16, andlatform) . After ehttps://bioconductor.org/packages/release/bioc/html/limma.html) [p value < 0.05 and |log2 fold change (FC)| > 0.5 were considered as DEGs. The heatmap for DEGs was plotted via pheatmap package  and the volcano plot was visualized by the ggplot2 package in R. Furthermore, DAVID  was employed to perform Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The p value less than 0.05 as the threshold for significant enriched terms.Differentially expressed analysis between M and NM groups was conducted by using the limma package of R software  , and genhttps://cran.r-project.org/web/packages/WGCNA/) [The WGCNA approach is used to excavate the gene modules that are highly related to the sample phenotype in high-throughput data; among these, the most central genes are identified as key genes that serve a crucial role in the module. In this analysis, with the lymph node metastasis and nonmetastasis as properties, the WGCNA package   was usedhttps://bioconductor.org/packages/survivalr/) to screen prognosis-related genes, with p value < 0.05 as cutoff value. Then, optimal gene signature was obtained via LASSO analysis using Lars package . The following formula was used to calculate the risk score (RS): RS = ∑Coefgene × Expgene. Here, Coef represents the LASSO coefficient, and Exp represents the expression level of the gene.Further, 493 samples with complete prognostic information in the TCGA database were used as a training cohort to develop the prognostic model. Based on the mRNA expression levels of the overlapping genes, univariate Cox regression analysis was performed by using the survival package  and low-risk (LR) groups based on the median value of RS. The Kaplan–Meier (KM) approach was used to evaluate the association between the different risk groups and LUAD prognosis. In addition, the receiver operating characteristic (ROC) curves were plotted to evaluate the prognostic performance of the RS model. The area under the ROC curve (AUC) at different endpoints  was calculated by using the time ROC package (Version 0.4) in R3.6.1.p value < 0.05 and ∣normalized enrichment score (NES)∣ > 1 were set as cutoff threshold criteria.GSEA was used to analyze the functional pathways enriched by HS and LS groups, and nominal (NOM)In the training cohort, the KM method was used to compare the survival time of each gene between the HR and LR groups by using the survival package (Version 2.41-1).p value < 0.05 was considered statistically significant.By combining the clinical information from LUAD, the correlation between RS and clinical characteristics  was analyzed. https://www.proteinatlas.org/) to verify the difference in protein expression levels of genes between normal and tumor groups.The immunohistochemical staining map of genes in the RS model was downloaded from the HPA database (https://mexpress.be/).The correlation between each biomarker and its corresponding methylation site as well as copy number was analyzed using MEXPREWSS  to verify the mRNA expression levels of genes in the RS model. The paired According to the state of cancer metastasis, 172 and 333 samples in the TCGA database were classified into NM and M groups, respectively. A total of 294 DEGs were screened between M and NM groups. The specific distribution of DEGs was visualized by the heatmap  and volcFunctional enrichment analyses showed that these DEGs were significantly enriched in 21 GO-biological process (BP) terms, 12 GO-cellular component (CC) terms, 7 GO-molecular function (MF) terms, and 3 KEGG pathways . In brieR2 reached to 0.85 for the first time (red line), that is, power = 4. Based on the hierarchical clustering and dynamic tree-cutting algorithms, highly correlated genes were clustered into modules, and finally 13 modules were obtained , turquoise , black , and magenta  were positively correlated with the LNM  + (0.0158 ∗ ExpBARX2) + (−0.0131 ∗ ExpGPR98) + (0.0232 ∗ ExpKRT6A) + (0.024 ∗ ExpPTPRH) + (0.0852 ∗ ExpRGS20) + (0.0124 ∗ ExpTCN1) + (0.0211 ∗ ExpTNS4).First, univariate Cox regression analysis showed 36 genes had prognostic values. Next, LASSO analysis indicated that eight was considered as the optimal number based on the lambda values Figures . Eight gp < 0.05, p < 0.05, In the training and validation sets, samples were assigned into HR and LR groups based on the median of RS. In the TCGA training set, the distribution and survival status of patients are presented in Fourteen different signaling pathways were identified between the HR and LR groups. Among these, eight pathways were associated with the LR group, such as valine leucine and isoleucine degradation, taste transduction, and nitrogen metabolism; six pathways were closely corrected with the HR group, including the P53 signaling pathway, pathogenic Escherichia coli infection, ubiquitin-mediated proteolysis, the proteasome, pyrimidine metabolism, and pancreatic cancer .p value < 0.05, Figures p value < 0.05, Based on the median of gene expression level, patients were divided into low-expression and high-expression groups. KM curves showed that patients with low expression of ANGPTL4, KRT6A, TCN1, TNS4, PTPRH, and RGS20 had significantly longer survival times , higher N stage (N1–N3), and advanced stages (stage III + IV) . A heatmHR group . MeanwhiHR group .The HPA database was applied to display the protein level of genes in the RS model. The immunohistochemical images of PTPRH, TCN1, and TNS4 were not recorded in this database. The representational plots of ANGPTL4, KRT6A, BARX2, RGS20, and GPR98 are shown in The methylation sites and copy number of genes in the RS model were analyzed by using the MEXPREWSS website, while the information for GPR98 was not retrieved in this database. We found that ANGPTL4, BARX2, KRT6A, PTPRH, RGS20, TCN1, and TNS4 were significantly associated with 8, 12, 6, 15, 19, 3, and 8 methylation sites, respectively.p value < 0.05).To further observe the expression level of genes, we used GSE68465 to verify the difference in the mRNA expression level between the M and NM groups. As shown in LNM is one of the main factors affecting the prognosis of LUAD, and it significantly reduces the survival rate of patients with LUAD , which iThe focus of this study was to compare M with NM samples, and the screened DEGs were associated with LNM states. Eight key genes were further obtained via univariate and LASSO regression analyses. Angiopoietin-like 4 (ANGPTL4) encodes a glycosylated secreted protein that acts as a serum hormone to regulate blood glucose homeostasis and lipid metabolism; meanwhile, the encoded protein can serve as an apoptotic survival factor for vascular endothelia cells that may prevent metastasis by inhibiting vascular growth and tumor cell invasion , 22. PreAfter constructing the RS model, the patients were divided into LR and HR groups. Survival analysis revealed that patients in the HR group had shorter overall survival than those in the LR group. Next, performance evaluation showed that the established model had better performance in predicting the prognosis of LUAD patients. To explore the pathways involved in the gene sets from the HR group, a GSEA was performed. Results revealed that several key pathways were closely corrected with the HR group, such as P53 signaling pathway, pathogenic escherichia coli infection, and ubiquitin-mediated proteolysis. Evidence indicated that genes such as PAQR3 could regulate the progression of NSCLC via the p53 signaling pathway , and genBased on the gene expression profiling from public databases (TCGA and GEO) and a two-step design including development and validation, our study provided reliable evidence for the value of the LNM-related gene signature in the prognostic evaluation of LUAD. Although there have been studies to construct a prognostic model of LUAC based on LNM-related genes , the advIn summary, we constructed and validated a LNM-related gene signature to predict the prognosis for patients with LUAD. This prognostic model contained eight genes and had better specificity and predictive performance, which may assist clinicians in making a correct diagnosis and discovering the prognostic risk of LUAD patients in advance.


Differential diagnosis between tuberculous peritonitis and peritoneal carcinomatosis remains challenging in clinical practice; thus, in-patients diagnosed with tuberculous peritonitis or peritoneal carcinomatosis were retrospectively enrolled, and diagnostic values of ascitic tumor markers and adenosine deaminase were determined.Consecutive patients diagnosed with tuberculous peritonitis or peritoneal carcinomatosis were retrospectively enrolled. The pertinent data of 169 patients enrolled were collected.A panel of ascitic tumor makers  had high specificity (96.83%) and accuracy (94.67%) in the differentiation of peritoneal carcinomatosis from tuberculous peritonitis; and ascitic ADA was a good discriminator between these patients, with an accuracy of 91.72%. Combined use of ascitic tumor makers and ADA (ascitic ADA < 22.5 IU/L or ascitic CEA > 3.65 ng/mL or CA15-3 > 42.70 U/mL or CA19-9 > 25.10 U/mL) performed high sensitivity (99.06%) and accuracy (94.08%) for the diagnosis of peritoneal carcinomatosis. In addition, combined ascitic ADA and tumor marker (positive ascitic tumor makers and ADA < 22.50 IU/L) had 100% of the specificity in diagnosing peritoneal carcinomatosis.Combined use of ascitic tumor markers and adenosine deaminase showed excellent efficiency in the differential diagnosis between tuberculous peritonitis and peritoneal carcinomatosis, thus these two simple and cost‐effective parameters should be determined when tuberculous peritonitis or peritoneal carcinomatosis was suspected in clinic practice.The online version contains supplementary material available at 10.1186/s12876-022-02480-x. Tuberculous peritonitis (TBP) and peritoneal carcinomatosis (PC) are two of the most common causes of non-portal hypertensive ascites in developing countries , 2, and Laparoscopic peritoneal biopsy is considered to be an excellent choice in distinguishing TBP from PC , but itsIn this retrospective cohort study, patients over 18 years old with new‐onset ascites who were admitted to Union Hospital of Huazhong University of Science and Technology  were assessed for eligibility from May 2015 to January 2022. All patients had laboratory tests such as peripheral blood count, serum biochemical tests, ascitic cell count and biochemical tests, and ascitic tumor marker assay. The diagnosis criteria of TBP was based on the biopsy of the peritoneal nodules, or complete clinical and laboratory response after anti-tuberculous therapy when other causes of ascites were excluded , which was consistent with previous study . Secondly, we collected the data on the biomarkers of liver or kidney function, such as alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, γ-glutamyl transpeptidase, creatinine and uric acid, and there was no significant difference between the two groups.A total of 169 patients were enrolled for this study, 63 patients were diagnosed with tuberculous peritonitis and 106 patients with peritoneal carcinomatosis. In patients with peritoneal carcinomatosis, the etiological distribution was presented in Additional file p < 0.01] and ascitic ADA  in TBP were significantly higher than those in PC, with 0.65 and 0.89 of the AUC, respectively  g/L vs 45.30  g/L, 5, 56.08 Tumor markers are produced directly by the tumor or by non-tumor cells as a response to the presence of a tumor, which offers a putative clinical use in the screening, diagnosis and treatment of various cancers , 17.Our Above data demonstrated the concentration of ascitic ADA in patients with TBP was significantly higher than those with PC Table , then weSince ascitic tumor makers had high specificity and accuracy in diagnosing PC, and ascitic fluid ADA performed differentiating value in TBP and PC. Next, we determined the diagnostic performance of combined ascitic tumor makers and ADA. As shown in Table Abdominal paracentesis is likely the most rapid and cost‐effective method of diagnosing the cause of ascites. In this current study we illustrated that ascitic tumor makers had high specificity and accuracy in differential diagnosis of PC from TBP; and ascitic ADA was also a good discriminator in these patients. Combined ascitic tumor makers and ADA were superior to individual index in clinic practice.Ascitic tumor markers were widely used to differentiate malignant ascites from benign ascites –19. Our Our previous study has used cut-off values of 50 ng/mL for ascitic CEA, 75 U/mL for ascitic CA15-3 and 200U/mL for ascitic CA19-9 in diagnosing malignant or benign ascites [3, or extravasation of contrast material or peritoneal free air on radiography or computerized tomography, and/or perforation of the intestinal wall demonstrated at surgery [In decades, ascites ADA has been proposed as a useful diagnostic test in discriminating TBP from non-TB ascites, but there were also some debates about its clinical application in defining the etiologies of ascites. Donald et al. has indicated that ascitic fluid ADA was insensitive in detecting TBP in the United States . 59%  and tumor markers.


Through synthesizing studies regarding neurobehavioral impairment of pediatric brain tumor survivors (PBTS) in the past decade, this meta-analysis found that PBTS are at higher risk of attention problems, emotional difficulties and psychosocial problems compared to the healthy population. Future studies should focus on exploring potential interventions for PBTS at risk of neurobehavioral impairment to improve the long-term psychological outcomes.Purpose: The neurocognitive outcomes of pediatric brain tumor survivors have been extensively studied but the risk and predictors for neurobehavioral impairment are less clearly defined. We systematically analyzed the rates of emotional, psychosocial, and attention problems in pediatric brain tumor survivors. Methods: PubMed, Web of Science, Embase, Scopus, and Cochrane were searched for articles published between January 2012 to April 2022. Eligible studies reported neurobehavioral outcomes for PBTS aged 2 to <23 years with a brain tumor diagnosis before 18 years of age. A random-effect meta-analysis was performed in R. Results: The search yielded 1187 unique publications, of which 50 were included in the quantitative analysis. The estimated risk of having emotional, psychosocial, and attention problems were 15% (95%CI 10–20%), 12% (95%CI 9–16%), and 12% (95%CI 9–16%), respectively. PBTS were more likely to have emotional difficulties (Hedge’s g = 0.43 [95%CI 0.34–0.52]), psychosocial problems (Hedge’s g = 0.46 [95%CI 0.33–0.58]), and attention problems (Hedge’s g = 0.48 [95%CI 0.34–0.63]) compared to normal/healthy control subjects. There was no significant difference in the rates of neurobehavioral impairment between children with and without history of cranial radiotherapy. Conclusions: PBTS are at elevated risk of neurobehavioral impairment. Neurobehavioral monitoring should be considered as the standard of care for PBTS. Brain tumors are the most common solid tumors affecting children and adolescents, accounting for approximately 27% of pediatric cancers and affecting approximately 3000 children per year in the United States . The proNeurobehavioral disorders are very common in children and adolescents, affecting 4.4–9.8% of the general pediatric population . HoweverGiven the increased survival in PBTS, there needs to be more efforts to understand and improve the long-term outcomes , particuThis meta-analysis was conducted to examine the risk of emotional, psychosocial, and attention problems in PBTS by focusing on studies in the past decade. The study also aimed to identify the risk factors pre-disposing PBTS to poorer neurobehavioral outcomes.We searched PubMed, Scopus Embase, Web of Science, and Cochrane in April 2022 for articles published from 1 January 2012 to 20 April 2022. The following string was used to search the databases [“CNS tumor” OR “brain tumor” OR “brain Oncology” or neuro-oncology OR medulloblastomas OR “pilocytic astrocytoma” OR craniopharyngiomas OR “germ cell tumors” OR glioma OR ependymal OR glioneuronal OR embryonal in Title Abstract Keyword] AND [children OR pediatric OR adolescent OR toddler OR preschool OR teen OR teenager OR childhood in Title Abstract Keyword] AND [“social difficulties” OR “social outcome” OR “social problems” OR “social deficits” OR “emotional difficulties” OR “emotional problems” OR “attention deficits” OR “attention problems” OR ADHD OR “attention deficit hyperactivity disorder” OR “autism spectrum disorder” OR autism OR “developmental outcomes” OR “behavioral difficulties” OR neurobehavior OR neuropsychological OR psychiatric OR psychosocial OR depression OR anxiety OR internalizing OR externalizing in Title Abstract Keyword]. Word variations were also searched. References from the identified studies and relevant reviews were also retrieved and searched. See Assessed articles are screened by two independent reviewers according to the following inclusion and exclusion criteria:The inclusion criteria were as follows: (1) participants diagnosed with brain tumor before the age of 18 years; (2) assessed participants between the age of two and 23; (3) assessed at least one of the three aspects of neurobehavioral impairment by validated standard scales: (a) autistic features/psychosocial problems/psychosocial outcomes; (b) emotional problems/internalizing problem/externalizing problem; (c) attention deficits/attention problems; (4) reported original research data; and (5) studies published in English.The exclusion criteria were as follows: (1) case study, conference abstract and papers; (2) no validated standard scales measuring neurobehavioral impairment; (3) norm/clinical cut-off or healthy control scores were not provided for the scale; (4) data not retrievable for calculating either the absolute risk or the standard mean difference  of the psychosocial/emotion/attention problems in PBT participants; (5) researched on paediatric cancer survivor cohort while CNS paediatric cancer survivor’s data are not provided separately; (6) assessed overall psychological/neurobehavioral impairment while psychosocial/emotion/attention scores are not provided separately.Titles and abstracts of assessed papers were first screened by the two reviewers (YW and WWYT) for potentially eligible studies. Those identified studies were then reviewed in full text. In each step, disagreement was solved through consensus by the two reviewers. The inter-rater reliability is calculated in the inclusion process.Data  required to calculate the standard mean difference and absolute risk for neurobehavioral impairments in PBTS were retrieved from each study. The assessment results at baseline and at all follow-up time points were also retrieved from the studies. For studies containing more than one independent cohort, the data of these cohorts were recorded separately. For studies reporting more than one measurement in one aspect of the neurobehavioral impairment , the pooled standard mean difference was calculated . The ris2 statistics, with I2 ≥ 50% indicating substantial heterogeneity; and the significance of heterogeneity was examined by an χ2 test. For pooling the absolute risk and standard mean differences, self-reported data was used for children aged 12 and above, whereas parent-reported data was used for children below the age of 12. Subgroup analysis was conducted to examine categorical moderating factors, including reporting methods , comparison groups , and treatments (with or without a history of radio therapy). Peters’ Regression Test [p-value < 0.05 was considered to be statistically significant. All analyses were conducted in R 4.1.1 using the ‘meta’ and ‘esc’ packages [A meta-analysis was conducted to synthesize the findings on the risk of neurobehavioral problems in PBTS in the following two aspects: (1) the absolute risk: the proportion of PBTS who were below the clinical cut-offs for psychosocial, emotional, and attention problems from each identified study; and (2) the standard mean difference: the psychosocial, emotional, and attention problems in PBTS compared to the population norm and healthy controls. A random-effect model was used to pool the results from the different studies. The standard mean difference was measured by Hedge’s g. The heterogeneity across studies was evaluated by Iion Test  and Eggeion Test  were usepackages .This meta-analysis follows the PRISMA guidelines  and is rThe database searches yielded 3360 results, of which 1387 unique publications were further reviewed, and 50 studies were included in the final meta-analysis see . The Cohn = 1251)  and no significant publication bias was identified t (15) = 0.36, p = 0.72, The proportion of PBTS whose attention problems were below the clinical cut-off was reported in 14 studies ( = 1251) a. The pon = 1257) . No significant publication bias was observed t (22) = −0.47, p = 0.646  b. The po = 0.646 .n = 1699) (p < 0.01). Publication bias was not significant t (20) = 0.12, p = 0.908  c. The po = 0.908 . 2 = 67%, p < 0.01). The publication bias was not significant, as revealed by Egger’s test t (18) = 0.92, p = 0.369 . Notably, there was insignificant heterogeneity in the comparison with healthy controls (I2 = 31%), whereas the heterogeneity remained high in the subgroup that was compared with the population norm (I2 = 69%). No significant publication bias was observed, t (29) = −0.16, p = 0.877 . No significant publication bias was identified t (31) = 0.35, p = 0.730 , The included studies were separated into subgroups according to the reporting method . No significant differences were observed for attention problems and emotional difficulties regarding both their absolute risk and standard mean difference . For psyTen studies exclusively reported PBTS with a history of RT (RT-only) and three studies exclusively reported PBTS without a history of RT (no-RT). Thirty-seven studies reported a heterogenous sample including participants that both underwent RT and those that did not (mix-RT). p > 0.3 in all regression models). A trend was identified whereby the follow-up time was associated with the standard mean difference , although it did not reach the significant level.A meta-regression was conducted with the standard mean difference and absolute risk as the criteria, and age at assessment, age at diagnosis, and follow-up time as the predictors, respectively. Three different aspects of neurobehavioral impairment  were pooled together to increase the standard mean difference. Age at assessment and age at diagnosis were not significant predictors of either absolute risk or standard mean difference of neurobehavioral impairment in PBTS (n < 30) and/or were rated as having a ‘high’ risk of bias. The result revealed that there was no significant difference compared to the main analysis, see A sensitivity analysis was conducted through excluding studies with small sample size (As the survival of children with brain tumors has improved with advancements in cancer treatment, it becomes essential for healthcare professionals and childcare workers to have a better understanding of the long-term neurobehavioral sequelae of PBTS. This meta-analysis is one of the first to synthesize the recent evidence on the prevalence of neurobehavioral impairment in PBTS. The analysis showed that PBTS have a higher risk of neurobehavioral impairments compared to healthy subjects or the population norm. 18.9% and 15% of PBTS were found to have emotional difficulties and attention problems, respectively, when compared to a rate of 5.1% and 4.4% of the pediatric population with emotional problems and symptoms of inattention/hyperactivity according to a recent U.S. National Health Interview Survey .14.4% ofDespite the well-reported detrimental effects of cranial radiotherapy on cognition and memory in PBTS, our study did not find significant differences in the rates of neurobehavioral impairments between children with or without cranial radiotherapy treatment, although these inconsistent findings might be related to the small sample sizes and high heterogeneity among studies. The impact of radiotherapy could vary due to irradiation dosage , tumor lFor the long-term monitoring of neurobehavioral function in PBTS, the screening of attention problems and emotional difficulties could be achieved using self-reported and/or parent/proxy-reported questionnaires , as our In order to monitor the trajectory of neurobehavioral problems among PBTS, we propose that all children newly diagnosed with brain tumors should have comprehensive neurocognitive and behavioral evaluation by healthcare professionals. The initial assessment should include diagnostic interviews conducted by healthcare professionals as well as using parent and self-report questionnaires. All PBTS should have regular monitoring for neurobehavioral impairment using parent and self-report questionnaires . For parThis study had several limitations that need to be considered. There was significant heterogeneity among the included studies due to variations in patient characteristics and types of treatments across studies. High heterogeneity was also reported by Schulte et al., 2019  in a sysIn conclusion, neurobehavioral impairments, including emotional, psychosocial, and attention problems, are more common in PBTS. Survivor-reported or proxy-reported questionnaires might serve as good screening tools for PBTS at risk of neurobehavioral problems. Survivorship programs should offer long-term monitoring of neurobehavioral function in PBTS. Future studies should focus on exploring potential interventions for PBTS at risk of neurobehavioral impairment.