Lagenaria siceraria) is a commercially important cucurbitaceous vegetable with health-promoting properties whose collections and cultivars differ considerably in their flavor aspects. However, the metabolomic profile related to flavor has not yet been elucidated. In the present study, a comprehensive metabolite analysis revealed the metabolite profile of the strong-flavor collection “J120” and weak-flavor collection “G32”. The major differentially expressed metabolites included carboxylic acids, their derivatives, and organooxygen compounds, which are involved in amino acid biosynthesis and metabolism. QTL-seq was used to identify candidate genomic regions controlling flavor in a MAGIC population comprising 377 elite lines. Three significant genomic regions were identified, and candidate genes likely associated with flavor were screened. Our study provides useful information for understanding the metabolic causes of flavor variation among bottle gourd collections and cultivars. Furthermore, the identified candidate genomic regions may facilitate rational breeding programs to improve bottle gourd quality.Bottle gourd ( Lagenaria siceraria (Mol.) Standl.) (2n = 2x = 22) is among the oldest plant species of the Cucurbitaceae family used by humans [With domestication records dating back 11,000 years, bottle gourd  were identified as being associated with free glutamate, which, as mentioned earlier, is a key factor conferring umami taste on bottle gourd . HoweverQTL-seq technology, based on the combination of bulked segregant analysis and whole-genome sequencing, provides a facile method for locating QTLs, an approach that has been applied to the study of agronomic traits in rice, cucumber, and tomato ,16,17. TIn this study, we used a previously constructed MAGIC population with a total of 377 MAGIC population elite lines  and two g for 12 min at 4 °C, filtered through a 0.22 μM microporous membrane , and transferred to an LC–MS/MS injection bottle for analysis.At least six marketable fruits were pooled to create a single biological sample after peeling. Three biological replicates were used for each accession. Samples were freeze-dried using a rotary vane vacuum pump  and then ground to powder for 2 min at 30 Hz in a grinder  that uses zirconia beads. Subsequently, 100 mg of each sample powder was immersed in 1.2 mL of 70% aqueous methanol overnight at a constant temperature of 4 °C. The sample solutions were centrifuged at 11,000× Bottle gourd samples were analyzed using an LC-ESI-MS/MS system . The chromatographic columns were Waters ACQUITY UPLC HSS T3 C18 columns . A 0.04% acetic acid aqueous solution was used as mobile phase A and 0.04% acetic acid acetonitrile as mobile phase B. The gradient was 0 min, 5% B; 5% to 95% B from 0 to 10 min, and maintained at 95% for 1 min; 95% to 5% B from 11 min to 11.1 min, and maintained at 5% to 14 min. The flow rate was 0.35 mL/min. The temperature of the column was 40 °C, and the injection volume was 2 μL. All the reagents were purchased from Merck .2 gas was set to 0.35 MPa. The dispersion potential and collision energy were optimized for individual MRM transitions. A specific set of MRM transitions in each period was monitored based on metabolite elution.The eluent was connected to an ESI triple-quadrupole–linear ion trap (Q TRAP) mass spectrometer (MS)  API 4500 Q TRAP LC/MS/MS System equipped with an ESI Turbo Ion-Spray interface, which was operated in positive-ion mode and controlled by Analyst 1.6.3 software . This system was used to acquire linear ion trap (LIT) and triple-quadrupole (QQQ) scans. The ESI source operation parameters were as follows: the ion source was a turbo spray with a 550 °C source temperature; ion-spray voltage was 5500 V; ion source gas I, gas II, and curtain gas were all supplied at 0.2 MPa; and collision-activated dissociation was set at “high”. Polypropylene glycol solutions of 10 and 100 μmol/L were used for instrument tuning and mass calibration in the QQQ and LIT modes, respectively. QQQ scans were acquired in multiple reaction monitoring (MRM) mode, and the collision Nt-test (two-tailed) with a p value < 0.05 was used to assess significant differences for metabolite abundance between the two collections. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database was used to determine pathway associations. Pathway enrichment analysis was performed using the web-based server Metabolite Set Enrichment Analysis. Metabolite identification and quantification were performed as previously described . MetabolMarketable fruit (8–10 days after pollination) from 377 MAGIC population elite lines were harvested and compared for flavor using a previously published, sensory evaluation method . BrieflyA total of four Illumina libraries  were prepared using the Plant Genomic DNA Kit . Total DNA was quantified using a Qubit 3.0 Fluorometer . One microgram of DNA from each sample was used to construct paired-end DNA sequencing libraries using the Illumina TruSeq Nano DNA Sample Prep Kit , and the Illumina NovaSeq 6000 platform was used for sequencing.After trimming and filtering, the short reads obtained from the strong- and weak-flavor pools were mapped against the bottle gourd reference genome  using BWt-test was used for statistical analyses .The data were statistically analyzed using Microsoft Office Excel 2010  and SPSS software . Data are presented as the mean ± standard error (SE). Student’s Although planted simultaneously under the same conditions, the two parent lines (“G32” and “J120”) of the previously constructed MAGIC population  showed dFlavor is a complex trait, and free glutamate content is one of the biochemical determinants of flavor. Therefore, the identification and evaluation of bottle gourd flavor at a broader metabolomic level and further identification of the relationship between identified metabolites other than free glutamate are of great significance for the analysis of the main components of gourd fruit flavor and will certainly contribute to breeding efforts aimed at improving fruit quality. Moreover, metabolomics has been previously used to reveal the nutritional aspects of a wide range of food and vegetables ,26,27.To better understand the flavor differences between “G32” and “J120”, metabolomic analysis was performed to identify the metabolite profiles of these parents that differed significantly in flavor. A total of 1920 metabolites were identified, including 875 negative ionization (NEG) metabolites and 1045 positive ionization (POS) metabolites. PCA was conducted separately under the NEG and POS modes. PCA clearly separated the two materials and quality control samples under both NEG and POS modes A,B. ThusSubsequently, a log2_FC (case_mean/control_mean) transformation of each metabolite peak area was applied, and hierarchical cluster analysis was subsequently performed in both NEG and POS modes. Two distinct groups were found to be associated with “G32” and “J120” in both NEG and POS modes C,D. Const-test at a p value < 0.05. Only 17 and 36 differential metabolites were identified between “G32” and “J120” in the NEG and POS modes, respectively  or <0 (downregulated) were selected for comparison between the two collections. A VIP value from the OPLS-DA model was used to screen metabolites with a VIP value ≥ 1.0, followed by a ectively . Among tectively A. In conectively B. The diectively C,D, but ectively .Seventeen and thirty-six differential metabolites from the “J120” strong- and “G32” weak-flavor parents identified in the NEG and POS modes, respectively, were mapped to the KEGG database. The results show that most of these metabolites were mapped to “Amino acid metabolism”, “Carbohydrate metabolism”, “Nucleotide metabolism”, and “Metabolism of cofactors and vitamins”, and a few of them belonged to other system information categories, such as “Biosynthesis of other secondary metabolites”, “Transport”, and “Energy metabolism” A,B. SubsA total of 346 MAGIC population elite lines of marketable fruits were harvested and compared for flavor using a previously published sensory evaluation method . After tFour Illumina libraries (two for flavor bulks and two for parents) were constructed and subjected to Illumina high-throughput resequencing. The results show 99,923,628, 100,072,612, 107,140,402, and 83,218,116 high-quality clean reads (150 + 150 bp in length) obtained from “G32” (50.3× depth coverage and 93.83% coverage), “J120” (50.5× depth coverage and 93.88% coverage), the strong-flavor line pool (41.9× depth coverage and 98.74% coverage), and the weak-flavor line pool (54.0× depth coverage or 99.03% coverage), respectively . A totalTo identify candidate genomic regions presumably responsible for the differences between the strong- and weak-flavor bulks, the SNP index of each identified SNP was calculated and compared. Sliding window analysis with a 1 Mb window size and a 10 kb step increment was applied to calculate the average of the SNP index. In turn, average SNP-index graphs for the strong- and weak-flavor pools were generated A,B by plHG_GLEAN_10013305 and HG_GLEAN_10013403; an amino acid transporter ANT1-encoding gene, HG_GLEAN_10017438; and two cationic amino acid transporter-coding genes, i.e., HG_GLEAN_10017483 and HG_GLEAN_10017484. Amino acid permeases are important transmembrane proteins involved in amino acid uptake and transport in plants [SlCAT2) increased moderately during early fruit development [In these three genomic regions, 408 putative protein-coding genes were predicted, according to genome annotation . Among tn plants ,33. Aminelopment . In addielopment . FurtherThe comprehensive metabolite profile of strong-flavor collection “J120” and weak-flavor collection “G32” has been elucidated. The major differential metabolites were carboxylic acids, their derivatives, and organooxygen compounds involved in amino acid biosynthesis- and metabolism-related pathways. Three flavor-associated genomic regions were identified, and five interesting candidate genes likely associated with bottle gourd flavor were predicted by genome-wide SNP profiling of the extreme flavor bulks from a MAGIC population including 377 bottle gourd elite lines. The results may facilitate rational breeding programs to improve bottle gourd quality.


There is increasing interest in the validation of pediatric preference-based health-related quality of life measurement instruments. It is critical that children with various degrees of health-related quality of life (HRQoL) impact are included in validation studies. To inform patient sample selection for validation studies from a pragmatic perspective, this study explored HRQoL impairments between known-groups and HRQoL changes over time across 27 common chronic child health conditions and identified conditions with the largest impact on HRQoL.The health dimensions of two common preference-based HRQoL measures, the EQ-5D-Y and CHU9D, were constructed using Pediatric Quality of Life Inventory items that overlap conceptually. Data was from the Longitudinal Study of Australian Children, a nationally representative sample with over 10,000 children at baseline. Seven waves of data were included for the analysis, with child age ranging from 2 to18 years. Impacts to specific health dimensions and overall HRQoL between those having a specific condition versus not were compared using linear mixed effects models. HRQoL changes over time were obtained by calculating the HRQoL differences between two consecutive time points, grouped by “Improved” and “Worsened” health status. Comparison among various health conditions and different age groups  were made.Conditions with the largest statistically significant total HRQoL impairments of having a specific condition compared with not having the condition were recurrent chest pain, autism, epilepsy, anxiety/depression, irritable bowel, recurrent back pain, recurrent abdominal pain, and attention deficit hyperactivity disorder (ADHD) for the total sample (2–18 years). Conditions with largest HRQoL improvement over time were anxiety/depression, ADHD, autism, bone/joint/muscle problem, recurrent abdominal pain, recurrent pain in other part, frequent headache, diarrhea and day-wetting. The dimensions included in EQ-5D-Y and CHU9D can generally reflect HRQoL differences and changes. The HRQoL impacts to specific health dimensions differed by condition in the expected direction. The conditions with largest HRQoL impacts differed by age group.The conditions with largest HRQoL impact were identified. This information is likely to be valuable for recruiting patient samples when validating pediatric preference-based HRQoL instruments pragmatically.The online version contains supplementary material available at 10.1186/s12955-023-02091-4. Economic evaluation is increasingly used by policy makers to inform healthcare resource allocation decisions due to limited health budgets . The preTwo popular preference-based HRQoL instruments for children and adolescents are EQ-5D-Y-3L and the Child Health Utility 9 Dimensions (CHU9D) . EQ-5D-YPediatric Quality of Life Inventory (PedsQL) is an established, non-preference based profile generic instrument for HRQoL assessment in children and adolescents available across the age ranges of 2–18 years . It is pThere are limitations with existing validation studies for EQ-5D-Y-3L and CHU9D. A frequently mentioned methodological limitation is that some dimensions of the measures are less validated , 21, 22.It is critical that children with various degree of impairment in HRQoL or expected changes in HRQoL are included in those validation studies. Otherwise, results may be misleading and lack generalizability. Validated instruments are in turn critical for evaluation and priority setting of programs, services, treatments and supports for children and their families. Therefore, it is important for us to know more about expected HRQoL decrements and changes across a range of common childhood conditions.The aim of this study was to investigate the HRQoL impairment and HRQoL changes over time based on dimensions captured in CHU9D and EQ-5D-Y descriptive system inferred from PedsQL across a wide range of common pediatric conditions and child age. The results can help inform the recruitment of samples in future validation studies.Data were from the Longitudinal Study of Australian Children (LSAC), a geographically representative sample of Australian children and their families. The LSAC commenced in 2004, followed by repeated biennial assessment (‘waves’) of over 10,000 children across two age cohorts (a birth cohort of children aged 0–1 year and a kindergarten cohort of children aged 4–5 years in 2003–2004). The LSAC sampling design and field methods are detailed elsewhere . LSAC waHRQoL data were available in the form of the PedsQL (Version 4.0), which measures four health dimensions: (1) physical, (2) emotional, (3) social, and (4) school functioning and contains 23 items (21 items for 2–4 years) . The PedWe used PedsQL items to construct scores that closely mirror the dimensions in EQ-5D-Y and CHU9D. EQ-5D-Y is the youth version of the commonly used EQ-5D, containing five dimensions: 1) mobility, (2) looking after myself, (3) usual activities, (4) pain or discomfort, and (5) worried, sad or unhappy. EQ-5D-Y has two versions, 3L (3 response levels for each dimension) and 5L (5 response levels for each dimension). The inferred EQ-5D-Y scores are based on PedsQL items and PedsQL scoring algorithm and thus do not differentiate between EQ-5D-Y-3L and EQ-5D-Y-5L. As EQ-5D-Y 3L and 5L share the same 5 dimensions, our results apply to both versions of EQ-5D-Y and thus we only use the term of “EQ-5D-Y” in the following text. CHU9D was developed for children from its inception , contain mobilityWithin the LSAC survey parents reported whether their child had any ongoing health conditions, defined as a health problem that ‘exists for some period of time  or re-occurs regularly’. If the answer was ‘yes’, parents were directed to select from a group of different health conditions which varied by age. In total, 33 ongoing conditions were identified for children from 2 to 18 years old. We excluded diseases with a sample size smaller than 30 in order to keep this task manageable and focused on common health conditions as opposed to those rarely reported . We also excluded diseases that were not specific, such as “other illness” because they can provide little instructive information . There were finally 27 health conditions included , and whether the child had special health care needs . ChildreTo estimate the HRQoL impairment of having a specific condition compared with not having that condition, we used linear mixed effects models with random intercepts accounting for the hierarchical data structure due to repeated measurement of each child in the longitudinal dataset . The chiTo assess the responsiveness to health changes over time, the HRQoL change between two consecutive time points was calculated using the HRQoL in the current wave minus the HRQoL in the previous wave . Three groups were defined: “Worse”, “Improved”, and “Unchanged”  for dimensions with multiple PedsQL items to see if there is any difference in the result. The detailed items included in each version of sensitivity analysis and its corresponding results are at Additional file Table The top 10 conditions with significant coefficients in HRQoL total scores are presented. Across all age groups, having a health condition was negatively associated with HRQoL. The shared eight conditions among the top 10 for inferred EQ-5D-Y and CHU9D are recurrent chest pain, autism, epilepsy, anxiety/depression, irritable bowel, recurrent back pain, recurrent abdominal pain, and attention deficit hyperactivity disorder (ADHD) for 2–18 year olds . For children aged 2–4 years the top conditions with highest HRQoL impairment include ADHD, frequent headaches, soiling and diarrhoea. For children aged 5–18 years, anxiety/depression, autism, recurrent pain and epilepsy were amongst conditions with relatively high HRQoL impairment.In general, ‘mobility’ in EQ-5D-Y and ‘daily routine’ in CHU9D had relatively small HRQoL impairment associated with having health conditions. The influence of a health condition on various health dimensions were as expected for typical conditions. For example, for anxiety/depression, the dimensions ‘worried, sad or unhappy’, ‘worried’ and ‘sad’ were associated with the largest HRQoL impairment, while for frequent headache, recurrent pain and bone/joint/muscle problem, the dimensions ‘pain or discomfort’ and ‘pain’ showed the largest HRQoL decrement. To be noted, anxiety/depression also showed significant HRQoL impairment in the dimensions ‘pain or discomfort’ and ‘pain’.As hypothesized HRQoL changes were generally positive in the “Improved” group and negative in the “Worse” group, except among the 2–4 year olds Figs.  and 4. TAround half of the top 10 conditions in all age groups except 2–4 years had small to moderate changes in overall HRQoL improvement (SRM: 0.2–0.5) for both instruments, with another half having small changes (SRM < 0.2). The 2–4 years group all had small changes (SRM < 0.2) except ear infection. Inferred CHU9D had larger HRQoL changes than inferred EQ-5D-Y, with anxiety/depression demonstrating moderate to large effects sizes (SRM: 0.5–0.8) when conditions worsened in 5–18 years old.In 2–4 year olds, both “Improved” and “Worse” groups showed HRQoL improvement in the ‘looking after myself’, ‘pain/discomfort’ (EQ-5D-Y), ‘sleep’, ‘daily routine’ and ‘pain’ (CHU9D) dimensions. Additionally, the HRQoL change over time could be different by age group for the same condition. For example, 5–12 years old had larger HRQoL loss in ‘sleep’ and ‘annoyed’ when developing autism than 13–18 years old.Generally, ‘pain/discomfort’ and ‘worried/sad/unhappy’ in EQ-5D-Y, and ‘worried’, ‘sad’ and ‘pain’ in CHU9D had relatively large HRQoL change over time. Again, the HRQoL changes across dimensions for typical health conditions were as expected. It is worth noting that depression had a larger HRQoL improvement than anxiety when conditions improved in 13–18 years old, with ‘mobility’ (EQ-5D-Y) and ‘join in activities’ (CHU9D) contributing most to this HRQoL increase.The analyses using narrower sets of PedsQL items to represent the EQ-5D-Y and CHU9D dimensions were consistent with the main result  seem larger than equivalent changes for the better , 39. TheThe consistent results from sensitivity analysis using narrower sets of PedsQL items for EQ-5D-Y and CHU9D dimensions confirmed the robustness of our results considering slightly different wording and number of items for one dimension. The common results between inferred CHU9D and real CHU9D confirmed that they had basically the same concept, while the differences may be due to difference in the degree of detailed description, recall period and proxy report or self-report . The sensitivity analysis using real CHU9D data provided some support for our conceptual mapping method using PedsQL data, but also indicated the importance to consider the influence of the other factors on HRQoL measurement. The sensitivity analysis comparing inferred CHU9D and real CHU9D provided valuable information on the impact of parent proxy versus self-completion and recall period. The inferred CHU9D generally had larger or similar overall HRQoL impact than the real CHU9D  was used. We included 27 common chronic pediatric conditions and allowed for comparison of their impact on HRQoL based on PBM constructs within a single study, which has not been reported previously. A wide childhood ages (2–18 years) enables comparison between age groups, which is valuable. We included dimensions that mirror both EQ-5D-Y and CHU9D, providing useful comparisons across instruments. Furthermore, taking advantage of the longitudinal dataset, the exploration of the HRQoL changes over time can inform responsiveness testing across a wide range of conditions which has been relatively less studied .There are also some limitations. First, we used PedsQL items to mirror EQ-5D-Y and CHU9D dimensions due to not having direct PBM measurement across ages. Although the conceptualization of EQ-5D-Y and CHU9D dimensions overlapped with PedsQL items, there exist some differences, such as the exact wording of questions, the recall period and the different number of PedsQL items informing different dimensions, that may lead to different HRQoL scores. However, PedsQL has the same number of levels for each response as the CHU9D and the increasingly used EQ-5D-Y 5L, which added to its suitability to reflect the descriptive system of CHU9D and EQ-5D-Y 5L. Sensitivity analysis including narrower set of items for EQ-5D-Y dimension showed consistent results. Comparison between inferred CHU9D and real CHU9D measurement in a small subset of children showed the potential difference for appropriate interpretation and use of our results. Second, only three conditions were included in the analysis of HRQoL changes over time in 2–4 year olds due to the lack of data available for 0–1 years and small sample sizes for some health conditions. Thus, the conditions with largest HRQoL changes over time in 2–4 years old need to be interpreted with caution. Third, for 2–4 year olds, the two year interval may be too long to capture meaningful HRQoL changes over time because this young age is associated with rapid natural development whereby dimensions such as ‘daily routine’ and ‘sleep’ have a strong natural history of improvement linked to development/growing. Further HRQoL responsiveness research is needed for this age group. Finally, our analyses have focused on the descriptive systems of two common generic preference-based measures EQ-5D-Y and CHU9D, and the results may not apply to instruments with some very different health dimensions.In conclusion, the relationship between childhood health conditions and HRQoL varies by health dimensions and age groups. Validation studies for children should include various conditions with a range of expected HRQoL impacts where relevant and possible. When there is difficulty to include patients from some disease areas, top candidates from this study may be considered based on resources and aim of the validation study.Additional file 1. Supplementary methods, results, tables and figures.


However, such transient PSI over-reduction was not observed by transitioning from 59 to 501 or 923 μmol photons m−2 s−1. Consequently, FL (59-1809) induced a significantly stronger PSI photoinhibition than FL (59-501) and FL (59-923). Compared with the proton gradient (∆pH) level after transition to high light for 60 s, tomato leaves almost formed a sufficient ∆pH after light transition for 10 s in FL (59-501) but did not in FL (59-923) or FL (59-1809). The difference in ∆pH between 10 s and 60 s was tightly correlated to the extent of PSI over-reduction and PSI photoinhibition induced by FL. Furthermore, the difference in PSI photoinhibition between (59-923) and FL (59-1809) was accompanied by the same level of linear electron flow. Therefore, PSI photoinhibition induced by different intensities of FL is more related to the kinetics of ∆pH formation rather than linear electron flow.Fluctuating light (FL) can cause the selective photoinhibition of photosystem I (PSI) in angiosperms. In nature, leaves usually experience FL conditions with the same low light and different high light intensities, but the effects of different FL conditions on PSI redox state and PSI photoinhibition are not well known. In this study, we found that PSI was highly reduced within the first 10 s after transition from 59 to 1809 μmol photons m Light intensity usually fluctuates in nature, and this condition of light is called fluctuating light (FL)  × 0.84 × 0.5, where PPFD is the photosynthetic photons flux density, 0.84 is the assumed light absorption of incident irradiance, and 0.5 is the fraction of absorbed light reaching PSI or PSII. We calculated the relative CEF by subtracting ETRII from ETRI.The chlorophyll fluorescence parameters were calculated as follows: the quantum yield of PSII photochemistry, Y(II) = (ST) was measured. Leaves were then illuminated at 1455 μmol photons m−2 s−1 for 5 min for light adaptation, followed by illumination at 59 μmol photons m−2 s−1 for 2 min. Afterwards, light intensity was changed to 501, 923 or 1809 μmol photons m−2 s−1, respectively, and ECS dark interval relaxation kinetics (DIRKECS) were recorded after this light transition for 10 s or 60 s according to the method previously reported. The total proton motive force (pmf), the proton gradient (ΔpH) component of proton motive force, and the chloroplast ATP synthase activity (gH+) were estimated from the DIRKECS  (We first measured the dynamic changes in PSI and PSII redox state under FL conditions with the same low light (59 μmol photons my [Y(I)] A. Moreovy [Y(I)] B,C, indi−2 s−1 (Similar to Y(I), the value of quantum yield of PSII photochemistry [Y(II)] in high light phases in FL (59-1809) was lower than FL (59-501) and FL (59-923) A. After −2 s−1 C. A highDuring the fluctuating light treatments, electron transport rates through PSI and PSII (ETRI and ETRII) during low light phases did not differ significantly among these three FL treatments A,B. ConsmP). Therefore, we measured the values of mP before and after FL treatments to evaluate the extent of FL-induced PSI photoinhibition. After FL treatments, mP decreased by 15%, 7%, and 6% in FL (59-1809), FL (59-923), and FL (59-501), respectively , meaning severer over-reduction in PSI, led to stronger PSI photoinhibition. PSI photoinhibition can be manifested by the decrease in the maximum photo-oxidizable P700 (ectively A. As a rectively B. In FL gH+) in regulating the redox state of PSI under high light, we examined the kinetics of ∆pH and gH+ after the transition from low to high light in these three FL conditions. In FL (59-501), no significant difference in ∆pH was observed between 10 s and 60 s after transitioning from low to high light. However, a significant difference in ∆pH was observed between 10 s and 60 s in the other two FL conditions, especially in FL (59-1809)  A. Therefand 60 s B, showin60 s − ∆pH10 s) and PSI photoinhibition induced by FL, and found that the value of ∆pH60 s − ∆pH10 s was tightly correlated to the average Y(NA)10 s and the decrease in mP (60 s − ∆pH10 s were accompanied by low levels of PSI over-reduction and PSI photoinhibition. A high level of ∆pH60 s − ∆pH10 s in FL (59-1809) was accompanied by a high value of Y(NA) and a drastic decrease in mP. The transient PSI over-reduction in FL (59-1809) was tightly correlated to an insufficient ∆pH , while such transient PSI over-reduction was not observed in FL (59–501) and FL (59-923) C. After 09, whileed by FL B. TherefArabidopsis pgr5 mutants with the impairment of CEF, the restriction of ∆pH formation under high light resulted in prolonged PSI over-reduction under high light, leading to severe PSI photoinhibition when exposed to FL [In angiosperms, ∆pH is the key signal for regulating PSI redox state when faced with FL ,39,40. Ied to FL ,11. Afteed to FL ,31,35. Aed to FL . TherefoArabidopsis pgr5 mutant, a decrease in LEF upon moderate PSII photoinhibition could alleviate PSI over-reduction and protect PSI activity under high light [Arabidopsis pgr5 mutant, PSI over-reduction was not observed under high light, and the lethal phenotype under FL was rescued [Paris polyphylla [In addition to ∆pH formation, LEF is thought to be important for PSI redox state . In the gh light . When th rescued . Low caplyphylla  and tomalyphylla . TherefoA. thaliana, Bletilla striata, and Cerasus cerasoides [b6f complex [−2 s−1, CEF rapidly increased to the peak in 10 s, which accelerated the formation of ∆pH during high light phases. However, such rapid activation of CEF did not appear by transitioning from 59 to 1809 μmol photons m−2 s−1 (As we know, the Calvin–Benson cycle and photorespiratory pathway in plants needs the ATP/NADPH ratios of 1.5 and 1.75, respectively . Howeverrasoides ,9,45. In complex . Moreove m−2 s−1 C, result m−2 s−1 A, the ov m−2 s−1 C, and FL m−2 s−1 A. This iIn this study, we examined the response of PSI to FL conditions with the same low light and different intensities of high light. We found that extreme high light could aggravate the PSI over-reduction and thus accelerate FL-induced PSI photoinhibition, which was mainly caused by insufficient ∆pH formation rather than the operation of LEF. When plants are inevitably faced with FL conditions, decreasing the intensity of high light is crucial for diminishing FL-induced PSI photoinhibition. For these reasons, we here provide insight into FL-induced PSI photoinhibition and strategies for crop cultivation under natural field FL conditions.


Tissue fibrosis is a key factor leading to disability and death worldwide; however, thus far, there are no approved treatments for fibrosis. Transforming growth factor (TGF)-β is a major pro-fibrotic cytokine, which is expected to become a target in the treatment of fibrosis; however, since TGF-β has a wide range of biological functions involving a variety of biological processes in the body, a slight change in TGF-β may have a systematic effect. Indiscriminate inhibition of TGF-β can lead to adverse reactions, which can affect the efficacy of treatment. Therefore, it has become very important to explore how both the TGF-β signaling pathway is inhibited and the safe and efficient TGF-β small molecule inhibitors or neutralizing antibodies are designed in the treatment of fibrotic diseases. In this review, we mainly discuss the key role of the TGF-β signaling pathway in fibrotic diseases, as well as the development of fibrotic drugs in recent years, and explore potential targets in the treatment of fibrotic diseases in order to guide subsequent drug development. According to relevant statistics, in the USA, nearly 45% of deaths for patients who suffered from various diseases can be attributed to tissue fibroproliferative diseases . Thus faThere are many factors leading to fibrosis, including occupation, hereditary disease, lifestyle, aging , 8, as wIn the process of fibrosis formation, a variety of molecular mechanisms and changes in the microenvironment are involved. For example, TGF-β, platelet-derived growth factor (PDGF), connective tissue growth factor (CTGF), interleukin-4 (IL-4), interleukin-13 (IL-13), and other cytokineshave chemotactic effects on fibroblasts and regulate the synthesis and degradation of collagen, which in turn is closely related to the occurrence and development of fibrosis –19. In aJudging from the current clinical incidence rate of fibrotic diseases, pulmonary fibrosis, renal fibrosis, and liver fibrosis are the most common fibrotic diseases, and there is an enormous need for the treatment of these diseases clinically which is not being met –38. An uTGF-β superfamily members can regulate the proliferation, differentiation, apoptosis, adhesion, and migration of a variety of cells, such as macrophages, T cells, B cells, immature hematopoietic cells, neutrophils, and dendritic cells, etc. –61. The TGF-β has three subtypes in mammals, namely TGF-β1, TGF-β2, and TGF-β3. Although their structures are highly similar, they perform different functions. TGF-β1 plays an important role in maintaining the stability of the body. It can activate fibroblasts and promote the synthesis of the extracellular matrix . StudiesTGF-β receptors are divided into three categories: transforming growth factor-β type I receptor (TβRI), transforming growth factor-β type II receptor (TβRII), and transforming growth factor-β type III receptor (TβRIII), 69. TβRThe TGF-β family members are composed of latency-related peptide (LAP), precursor domain, and C-terminal TGF-β fragment, where the precursor domain and growth factor are connected by a cleavage site of proprotein convertase (PC) , 78. TheThe activation of latent TGF-β complex involves several important molecular mechanisms as shown in Fig. Once activated, mature TGF-β initiates transmembrane signaling by binding itself to two distinct transmembranes Ser/Thr protein kinases, termed as TβRI and TβRII receptors. Among them, transforming growth factor-β can also bind to the helper receptor TβRIII, and then the signal molecules are transmitted to TβRI and TβRII by T-β RIII. TβRII can phosphorylate specific cytoplasmic GS domains of TβRI, leading to conformational regulation of TβRI so as to phosphorylate Smad2 and Smad3 proteins. Subsequently, phosphorylated Smad2 and Smad3 proteins bind to Smad4 protein to form heterologous complexes, with the formed heterotrimers translocating into the nucleus and bound to DNA to regulate the transcription of multiple target genes , 88–90. More and more pieces of evidence clearly demonstrate that in addition to activating classic Smad-dependent signaling, TGF-β also participates in nonclassical signaling pathways, which mainly include MAP kinases, PI-3 kinase-Akt, and Rho-like GTPase –96. FiguIt has been well known that fibrosis is not a disease but a result of tissue damage repair dysfunction . TGF-β pFibrosis is a common manifestation of chronic tissue damage. A further understanding of the mechanisms of fibrotic diseases will help accelerate the development of its clinical treatment. At present, the main mechanism of action used by drugs under development against fibrosis is related to the inhibition of various factors in the formation of fibrosis, including inhibition of the signaling of cytokines such as TGF-β, PDGF, and CTGF, inhibition of fibroblast division and proliferation, regulation of collagen synthesis and degradation, and regulation of oxidative stress, inflammation, and other reaction processes that contribute to the formation of fibrosis , 54, 115It is well known that the tissues surrounding the tumor microenvironment are rich in dense fibrotic cells, which are usually referred to as cancer-associated fibroblasts (CAF) . TherefoWith increasing in-depth research on the pathogenesis of fibrotic diseases, it has been shown that the TGF-β signaling pathway is closely related to organ fibrosis , 83, 91.Since a group of abnormal proliferation cells with high fibrotic characteristics also exists in most tumor microenvironments in addition to being a popular target for fibrotic diseases, TGF-β signaling is also one of the most popular targets in the field of tumor immunotherapy in recent years . The bif


A significant decline in pediatric vaccination uptake due to the COVID-19 pandemic has been documented. Little is known about the parental willingness and associated factors of pediatric vaccination during the COVID-19 pandemic. An extensive literature search in the databases of PubMed, Scopus, Web of Science, and EBSCOhost were conducted. A total of 20 eligible studies published from 2020–2022 were included for systematic summary by a thematic analysis, among which 12 studies were included in a meta-analysis conducted with R-4.2.1. The prevalence of parental willingness to childhood/routine vaccination and seasonal influenza vaccination was 58.6% (95%CI 2.8–98.6%) and 47.3% (95%CI 25.3–70.5%). Moreover, there is no sufficient evidence of significant change in parental willingness to childhood/routine vaccination, human papillomavirus vaccination, or pneumococcal conjugate vaccination during the pandemic. However, a significant increase in parental willingness to vaccinate their children against seasonal influenza was found. In addition to the factors of parental vaccination willingness/hesitancy that are well-studied in literature, children/parents’ history of COVID-19 and children’s perceived vulnerability to COVID-19 were associated with parental willingness. Developing synergetic strategies to promote COVID-19 vaccination together with other pediatric vaccination is warranted during the pandemic. This may help to improve and/or catch up the vaccine uptake of children during and/or after the COVID-19 pandemic. Vaccines have a positive impact on children’s health and development . Based oThe outbreak of the coronavirus disease 2019 (COVID-19) had a great negative influence on pediatric vaccination on a global scale. A recently published study documented a decline in the number of administered doses of diphtheria tetanus toxoid and pertussis (DTP3) and measles-containing vaccine first dose (MCV1) worldwide in June 2020 . The larTo address the knowledge gaps, this study aimed to systematically search and summarize the evidence from literature reporting parental willingness/hesitancy about the vaccination uptake in their children in the time of COVID-19  vaccination, and pneumococcal conjugate vaccination (PCV)). In addition, we also summarized the factors associated with parental willingness/hesitancy of pediatric vaccination during the COVID-19 pandemic.(a)“impact” or “effect” or “influence” or “outcome*” or “result*” or “consequence” in topic/title/abstract;(b)“willingness” or “intention” or “acceptance” or “attitude” or “perception” in topic/title/abstract;(c)“COVID-19” or “coronavirus” or “2019-ncov” or “SARS-CoV-2” or “cov-19” in topic/title/abstract;(d)“pandemic” or “epidemic” or “outbreak” in topic/title/abstract;(e)“pediatric” or “child*” or “infant” or “adolescent” or “early childhood” or “routine” in topic/title/abstract;(f)“vaccin*” or “immunizations” in topic/title/abstract;(g)a OR b;(h)c AND d;(i)e AND f;(j)g AND h AND i.This systematic review was registered in PROSPERO (ref#: PROSPERO 2022 CRD42022350081). The academic articles were identified by searching the electronic databases PubMed, Web of Science (including SSCI and A&HI), EBSCOhost , and Scopus (including both published papers and preprinted services), which cover the published periods of 1966–2022, 1975–2022, 1981–2022, and 1970–2022 respectively. The Boolean operator was used in the search strategy conducted, with “OR” and/or “AND” used to link search terms, while the asterisk “*” was used as a wildcard symbol appended at the end of the terms to search for variations of those terms. The complete search strategy is described below:We reviewed articles for relevance through screening titles and abstracts. After removing the duplicates and excluding those who did not meet the inclusion criteria, the remaining 31 articles were read in full to identify if they met the inclusion criteria. In addition, another research paper was included by checking through the reference lists of previous literature ,12,13,14The articles included in this review are original studies that were published in peer-reviewed journals in English using quantitative and/or qualitative methodology, and therefore providing a higher quality of evidence than a typical case study or case series, which tends to be more subjective. The eligible studies should report the willingness, intention, attitudes, acceptance, or perception of parents/caregivers toward pediatric/childhood/routine vaccination of their children during the COVID-19 pandemic. The participants in the reviewed studies were parents and/or caregivers of children. An article was excluded if it was (1) not clarified to be designed and conducted during the COVID-19 pandemic; (2) a study for the development or validation of scale/tool; (3) a study for the development of a study protocol; (4) a study that was unclear about whether it included parents/caregivers of children; (5) a study focused on the uptake/administration/coverage of pediatric/childhood/routine vaccination; (6) a study focused on modeling or projection for prediction of vaccination coverage; and/or (7) a study focused on perception/attitude of health professionals or stakeholders.We used the tool developed by Hawker et al. to assess the quality of the studies. The tool covers nine domains: (1) abstract and title (did they provide a clear description of the study?), (2) introduction and aims (was there a good background and clear statement of the aims of the research?), (3) method and data (is the method appropriate and clearly explained?), (4) sampling (was the sampling strategy appropriate to address the aims?), (5) data analysis , (6) ethics and bias , (7) results (are these a clear statement of the findings?), (8) transferability and generalizability , and (9) implications and usefulness (how important are these findings to policy and practice?). These domains are rated 1 = very poor, 2 = poor, 3 = fair, and 4 = good. The detailed assessment criteria for each domain were listed in the footnote of I2 statistic test was used to measure heterogeneity. I2 values of <25%, 25 to 75%, and > 75% indicate low, moderate, and high heterogeneity, respectively. Furthermore, visual inspection was employed to assess the asymmetry of funnel plots and the Egger’s test was applied to detect potential publication bias. Sensitivity analysis was conducted by removing one study each time.The meta-analysis was conducted with the metaphor package of R version 4.2.1  to combine data, calculated pooled proportions, and 95% confidence intervals (CI). During analysis, a random-effects model was used based on the generalized linear mixed-effects method. The The flowchart describing the search process is presented in A total of 20 papers published between 2020–2022 were included in the final analysis and summary, while the full list and the key information of the included studies are shown in p value ranged from 0.34–0.79 in all analyses , i.e., 7.8% during September–October 2020, 15.1% during February–March 2021, and 5.5% during May–Jun 2021 [p < 0.01) [According to the meta-analysis, the pooled prevalence of hesitancy towards childhood/routine vaccination was 18.5%  .I2 = 99%; Furthermore, the pooled prevalence of childhood/routine delayed vaccination was 38.2%  [p = 0.032) [Parents’ demographics, socioeconomic status, parental knowledge/attitude toward childhood vaccination, trust in information, and information source were factors associated with parental willingness of childhood vaccination in the included studies. Regarding parents’ demographics, older age , higher= 0.039, ; p = 0.0= 0.016, ; p = 0.0= 0.026, ; p = 0.0 0.0288, ), higher 0.0288, , and bei 0.0001)  were ass= 0.032) . Parents= 0.032) . Regiona= 0.032) . Moreove= 0.032) ,24,27. A= 0.032)  increase= 0.032) , while a= 0.032) . Obtaini= 0.032) ,25. The = 0.032) . In SaudFurthermore, the main reasons for parents to accept childhood vaccination included (1) trust in the vaccination information given by health professionals (85.1% ); (2) beI2 = 99%; Studies consistently found a significant increase in the parental willingness of children’s seasonal influenza vaccination during the COVID-19 pandemic . Based oSome sociodemographic characteristics of the children and/or parents were associated with parental willingness of children’s seasonal influenza vaccination during the COVID-19 pandemic. Having a daughter , youngerPerceived low risk of infection (24.7%) was the most common reason for parents’ hesitancy for children’s seasonal influenza vaccination, followed by concerns about side effects (19.6%), and children’s fear of needles/syringes (19.4%) . The maiMeta-analysis was not performed towards PCV due to limited information. Two studies looked at PCV among children during the COVID-19 pandemic. In Turkey, 30.8% of parents had their children with asthma complete PCV between October 2020 and March 2021 . HoweverThere was limited information in this session. Only one study investigated the psychological association and potential factors between willingness to accept HPV and COVID-19 vaccination in children and themselves . The stuSeveral limitations were reported by the included studies. Methodological issues included (1) being unable to establish a causal relationship due to the cross-sectional study design ,23,30,33Although a recently published systematic review and meta-analysis estimated parents’ hesitancy to vaccinate their children against COVID-19 , there wAssociated factors of the parental willingness of pediatric vaccination during the COVID-19 pandemic were categorized into three levels following the socioecological model . Factorsp < 0.001) when it was compared with regular clinical practice (17 ± 9.57 min). In addition to the positive feedback from both children and their parents, an apparently increased trend in monthly uptake of children vaccination was also revealed . Apart from this, providing free vaccination was potentially useful to increase parental willingness . In lincination .The review has some limitations. Limitations that existed in the pooled studies were inevitable. First, no causal relationship was established, as all included studies were cross-sectional. Second, self-reported data might reduce the validity and accuracy of the study conclusions. Third, articles were retrieved from eight electronic databases, and the selected studies were limited for reviewing because only those with specific terms mentioned in the title/abstract were screened for further analysis. Those in a non-English language, with the publications formed as a conference abstract, government report, textbook, or unpublished dissertation, were not included.The current review summarized and analyzed the parental willingness of children’s vaccination  during the COVID-19 pandemic. Associated factors were also revealed and compared with the situation before the time of COVID-19. The existing interventions to maintain or enhance pediatric vaccination in the era of COVID-19 were also interpreted and discussed in this study. Based on the evidence provided by this review, it is necessary to design and implement the parental intervention programs of children’s vaccination promotion targeting particular local context/circumstance in different countries/regions. This may contribute to the protection of children from vaccine-preventable diseases during or after the COVID-19 pandemic.


Staphylococcusaureus, Pseudomonas aeruginosa, and Escherichia coli K12) and fungi . Specifically, sinusoidal (1D), checkerboard (C), and herringbone(H) patterns were fabricated by mechanical wrinkling of plasma-oxidizedpolydimethylsiloxane (PDMS) bilayers and contrasted with flat (F)surfaces. Microbial deformation and orientation were found to correlatewith the aspect ratio and commensurably with surface pattern dimensionsand local pattern order. Significantly, the proliferation of P. aeruginosa could be described by a linear scaling betweenbacterial area coverage and available surface area, defined as a fractionof the line integral along each profile with negative curvature. However,in the early stages of proliferation (up to 6 h examined), that Cand H patterns disrupt the spatial arrangement of bacteria, impedingproliferation for several hours and reducing it (by ∼50%) thereafter.Our findings suggest a simple framework to rationalize the impactof micrometer-scale topography on microbial action and demonstratethat multiaxial patterning order provides an effective strategy todelay and frustrate the early stages of bacterial proliferation.We investigate theeffect of micrometer-scale surface wrinklingon the attachment and proliferation of model bacteria ( The spatial periodicity, amplitude,profile shape, and distribution of topographic patterns (includingtortuosity) are all expected to play an important role in bacterialattachment. For example, topography-mediated antifouling effects wereinvestigated on line patterns,36 micropits, or wells of (circular or honeycomb),38 square,41 and hexagonal lattices,43 suggesting that bacteria are constrained to prescribed arrangementsthat reduce the area fraction available for fouling,44 the number of contact points for adhesion,46 and surface curvature,47 thus reducingbacterial motility and interconnections.Nano- and microscale topography has beenshown to impact bacterialattachment and subsequent biofilm formation through a range of mechanisms.50 Expectedly,this has led to inconsistency of results and contradictions giventhe multiple definitions of roughness51 and measurement technicalities, but primarily due to the fact thatdistinct surface topographies can yield identical “roughness”descriptors.Studies of topographicaleffects in microbial fouling generallyuse some  “roughness” metric to describe surfaces,which has become prevalent in literature.53 exhibiting a wavelength of λ ≃2 μm, an amplitude of A ≃ 0.2 μm(and thus aspect ratio of A/λ ≃ 0.1),and a roughness of Ra ≃ 0.07 μmand Rq ≃ 0.08 μm (detailedbelow). We select three bacterial strains: spheroidal S. aureus and rodlike P. aeruginosa and E. coli K12, of dimensions commensurate with the micrometer-scale patternwavelength; for comparison, we include a spheroidal fungus C. albicans, several times larger than the pattern features,illustrated in In this work, we examine the impact of distincttopographies, withapproximately the same “roughness”, on microbial responseand proliferation, illustrated in Polydimethylsiloxane  was prepared at a 10:1 prepolymer/cross-linker ratioby mass, stirred, degassed under vacuum, deposited onto a glass plate,cured at 75 °C in a convection oven for 1 h, and cut into couponsof length, width, and thickness of 3 cm × 2.5 cm × 2.5 mm.PDMS has approximate surface energy of 18–20 mN/m. Wrinkledsurfaces were fabricated by plasma oxidation under strain, followedby strain release and replication onto fresh PDMS ,as illustrated in L1 – L0)/L0 at a speed of 0.02 mm/s with a precision of ±0.001mm; L0 and L1 are, respectively, the initial and final distances between the clamps.Surface plasma oxidation was carried out using a 40 kHz Diener Plasma(Femto), operated at power p = 20 W, oxygen gas  pressure P = 1 mbar , for a duration τ = 120 s. A prestrainϵ = 20% was imposed during oxidation and was released at a controlledspeed of 0.2 mm/s (to minimize surface cracking), resulting in 1Dsinusoidal wrinkles, with periodicity λ and amplitude A, determined by p, P,τ, and ϵprestrain, as previously reported.55Sinusoidal patterns (1D) were fabricated by uniaxialstrain of a PDMS coupon, using a strain stage controlled by a linearactuator and imposing the required prestrain ϵ =  and plasma-oxidized at p = 20 W for τ= 120 s, followed by strain release.Herringbone (H), or chevron, patterns58 A first generation of 1D wrinkles (master) is obtained at p = 20 W and τ = 120 s and then replicated onto afresh PDMS coupon in order to yield a fresh, stress-free PDMS surface,eliminating the brittle glassy layer resulting from the plasma oxidationstep. The replica is fabricated by casting liquid PDMS onto the 1Dmaster, previously coated with octadecyl trichlorosilane  by adsorption from the vapor phase in a desiccatorfor 30 min, and cross-linking at 75 °C for 1 h, before peeledoff from the master. The replica is then uniaxially stretched in theperpendicular direction with respect to the first generation and plasmatreated, using the same p and τ, to obtainonce releasing the prestrain, a secondary wrinkling generation. Theorthogonal wave superposition of the two wrinkling generations yieldsa symmetric checkerboard with appropriate prestrains ϵ1,2.Symmetric checkerboard(C), or “egg-tray”, patternswere fabricated by a sequential 2D wrinkling procedure, reported previously.A were computed from peak-to-peak distances and half of the peak-to-valleyheights, respectively. The surface roughness is normally reportedin terms of the arithmetical mean deviation of the profile, Ra(1D) = 0.075 μm and Rq(1D) = 0.090 μm; Ra(H) = 0.069μm and Rq(H) = 0.085 μm; Ra(C) = 0.065 μm and Rq(C) = 0.080 μm; Ra(F)= 0.020 μm and Rq(F) = 0.030 μm,with an uncertainty of approximately δR ≈0.005 μm. Water contact angle measurements  reveal that the 2D samples exhibit a slightly lowercontact angle, namely 85° for C and 98° for H surfaces,than 1D (105°) and flat F (106°) surfaces. Since the patterned(plasma-exposed) samples were replicated into virgin PDMS, these (modest)changes are ascribed solely to pattern topography since the surfacechemistry is identical .Surface topographies werecharacterized using atomic force microscopy (AFM) using a Bruker Innovamicroscope, in tapping mode at 0.2 Hz, equipped with Al-coated Sitips . AFM data analysis was carried out usingthe Gwyddion software, averaging wavelengths and amplitudes over aseries of three measurements recorded over different 100 μm× 100 μm scanned images; λ and Staphylococcus aureus (DSM346) and two Gram-negative Pseudomonas aeruginosa  and Escherichiacoli K12 (DSM498), and fungus Candida albicans (ATCC10231). The microorganisms were first imaged via scanning electronmicroscopy (SEM) to characterize their shape and dimensions, usinga LEO Gemini 1525 FEGSEM microscope (gold sputtering was employedto reduce surface charging).Three bacterial andone fungal strains were employed in the experiments: bacteria, Gram-positive 7), followedby incubation for 24 h at 32.5 °C, allowing an uniform incubationof samples with the same amount of CFUs.Precultures were started by transferringthe strains in tryptic soy broth  mediumin aerobic conditions at 32.5 °C. After 72 h, the strains aretransferred into a broth medium constituted by 5 mL of TSB medium+ 50 μL of bacteria medium from the initial culture and incubatedovernight under aerobic conditions at 32.5 °C. Serial dilutionof liquid culture and plating on TSB plates was carried out to determinecolony forming units (CFU)  with thesurface facing up, and inoculated with 1000 μL of liquid cultureunder aerobic conditions at 32.5 v/v technical-grade ethanol and incubated at roomtemperature for 15 min. This dehydration step was repeated for anadditional three times at 50, 75, and 96% v/v, respectively. After the last dehydration step, the sampleswere air-dried at room temperature. Surface characterization was carriedout using contact mode AFM (Bruker Innova microscope) at 0.1 Hz withAu-coated Si3N4 tips (MLCT), acquiring micrographsat different windows of 20 × 20, 50 × 50, and 100 ×100 μm2. Microbial distribution and orientation mapswere computed using ImageJ software.Prior to AFMand SEM imaging, microorganisms attached to the wrinkled structureswere dehydrated and immobilized. Each sample was put into a new wellof a 48-well plate containing 1000 uL of 2% glutardialdehyde at roomtemperature for 60 min. Glutardialdehyde was removed and replacedby 25% a, length b, aspect ratio b/a, and Gram stain status, as summarized in a ≈ b, and thus b/a ≈ 1, S. aureus and C.albicans are “spheroidal”. With b > a and b/a >1, P. aeruginosa and E. coli K12are termed ellipsoidal, or “rodlike” .The surfacepattern dimensions were designed 2a to be 59 The glassy skin thickness h induced by oxygen plasmaexposure of PDMS evolves through a frontal propagation process andcan be tuned by plasma power, oxygen pressure, and exposure time.55 In the low deformation limit, the wavelength λ and amplitude A of a wrinkled bilayer under strain is given by60h is the film thickness, E̅f and E̅s are, respectively, are the in-plane strain moduli of the film andsubstrate, given by E̅ = E/(1 – ν2), where E is theYoung’s modulus, ν is the Poisson ratio (≃ 0.5for PDMS), and ϵc is the critical strainA ≃ 0.2 μm, as shown by the star(★) symbols in C. albicans was chosenas a model for larger dimensions (∼2–3λ). Forcomparison, a schematic of microbial size and sinusoidal pattern isshown in constant roughness (threepatterns and planar reference), on the initial attachment and proliferationof , the microbes are organized into a “colony”regime, characterized by cells stacked on different layers and extremelyinterconnected. This kind of disposition favors the formation of maturebiofilms and is influenced by substrate surface energy and chargeand nature of the bacterial cell membrane.64 On 1D wrinkled surfaces (1D),the bacterial species are ordered according to the pattern geometryin a single direction, whereas on 2D checkerboard (C) surfaces microorganismswere oriented in two different directions. On 2D herringbone (H) surfaces,an alignment similar to 1D is observed, even though the bacterialorientation follows the pattern structure. As expected, commensuratesurface patterns induce specific bacterial arrangements, accordingto their geometry, confining bacteria into pattern depressions, andthese are capable of reducing their interconnection. In order to generatemultilayered aggregates and initiate colonization, bacteria must firstfill the wrinkling valleys to be able to occupy the available surface.However, the quantitative interplay between additional surface areadue to wrinkling, surface pattern tortuosity, and bacterial arrangementare not obvious and must be examined further. By contrast, the larger C. albicans do not appear to be significantly influencedby the surface patterns, beyond a modest deformation of individualcells along the 1D pattern direction.Afterinoculation and incubation, the cells on the surfaces wereimmobilized as described above thus providing stable surfaces forAFM and SEM imaging. x and y axis follow, respectively, thesmaller and longer axis of the bacteria, while the red and blue tracesindicate the F and 1D profiles. The spheroidal S. aureus attached onto a flat surface exhibits an average cell size (x and y cuts) of ≈1.4 μm.The 1D confinement reduces the average size  to ≈1μm. For rodlike bacteria E. coli K12 and P. aeruginosa on 1D surfaces, a similar effect is observedalong the y cut, where the cell size is shaped bythe confinement in the wrinkling valleys and a shrinkage is observed.By contrast, on F surfaces, the rodlike bacteria freely spread uponattachment, forming multilayered aggregates. C. albicans cells span over four wrinkling wavelengths, and their organizationand size is on average not influenced by the surface topography, althougha slight cross-sectional shrinkage and deformation is induced alongthe 1D pattern and appears to limit spreading and connectivity comparedto F surfaces.By extracting the cross-sectionaland longitudinal line profilesfrom the AFM images, it is possible to resolve the effect of curvatureof the pattern valley on the microbes, compared to a flat surface,as shown in 2 AFM images.In order to account for intrinsic cell variability in size and shape,cross-sectional deformations were further estimated with over 200cells for all the different strains and patterned surfaces, as reportedin Figures S2 and S3. Furthermore, relativelylarge area and distinct area sampling is needed to mitigate the statisticaleffects of local heterogeneity and the microscale. The confinementand retention imposed by the wrinkling valleys is manifested on thebacteria, whose dimensions are commensurate with pattern wavelengthand amplitude , as observed previously on stainless-steel surfaces,65 of broad roughness spectrum, and in particularalong grain boundaries. The fungus C. albicans, however,being double in size, can not be contained in the wrinkling valley,but upon adhesion, the cell membrane is also deformed by the patternfeatures as shown in P. aeruginosa and E. coli K12, whose orientation analysis is reported in P. aeruginosa also explore angles between 60 and75°. For E. coli K12, however, the alignmentto 0° is higher, which we associate with their smaller crosssection, and thus greater constrain within the wrinkling valleys.On C surfaces, the bacteria are oriented along two well-defined anglesset by the two generations of the orthogonal superposing pattern,i.e., along 0 and 90°. For P. aeruginosa, thedistribution is weighted toward 0°, ascribed to the slightlyhigher local curvature of the second  generation wrinkling.Evidently, bacterial alignment is very sensitive to the local wrinklingamplitude. E. coli K12 cells are equally distributedalong the two directions, corresponding to the matching amplitudesof the two wrinkling generations in this specimen. H surfaces arecharacterized by repetitive chevronlike domains, with an average angleof 90° associated with the simultaneous biaxial strain. Dependingon the homogeneity of the applied strain, the kink angle can havea distribution, thus leading to a range of orientations. Providedthat each pattern length is larger than the long axis of bacteria,H patterns can be construed as a sequence of local 1D patterns alternatingin orientation. For this reason, the integration window of the azimuthalFFT average was shifted to −45° to 45° to align accordingto the herringbone main direction. For P. aeruginosa, the average kink was measured at 45°, and the bacterial cellswere oriented accordingly. However, for E. coli K12,the average angle was 80°, much closer to the expected kink angle.The orientation mechanism appears analogous to that of 1D surfaces,whereby the bacteria follow the local director, and somewhat differentfor H surfaces, are characterized by an abrupt change in direction.While the elongated shape of rodlike bacteria can be expected to favoran orientation along their long axis, the impact of surface patternson spheroidal bacteria, lacking intrinsic shape anisotropy, appearsless clear.Wenext examine the degree of orientation imparted to the different microbialspecies by the model patterned surfaces. We first consider rodlikebacteria, S. aureus are reported in 66 associated with the fact that the second buckling event is excitedonto a (previously) undulated surface. The resulting 2D pattern amplitudehas been found to be reduced by a factor equal to the amplitude ratio A2/A1 of the twogenerations.67 Our data indicate that rodlikebacteria, with an intrinsic elongated shape, appear more efficientlyconstrained and directed by the wrinkling pattern, even when subjectedto a lower amplitude. Spheroidal S. aureus, whosesize is commensurate to the pattern scale, exhibits lower alignmenton C surfaces, with a lower amplitude, compared to that on 1D patterns;therefore, additional correlation angles, beyond 0 and 90°, areobserved. For H surfaces, the pattern amplitude is much closer tothat of the 1D surface, and the pattern characteristic orientationsare selected (0 and 45°) even though the number of cells alignedon average appears lower than that observed for rodlike bacteria.Orientation distributions for spheroidal C. albicans, with dimensionsmuch larger than the patterned length scales, areshown in For reference, the orientationmaps for spheroidal P. aeruginosa, based on AFM imaging of the four surface patterns F, 1D, H, andC. In order to define an objective surface area metric for comparison,one must consider that the undulations caused by wrinkling increase the area (3D), per unit of footprint area (2D).In practice, such an increase in surface area, on which bacteria canproliferate, could evidently be detrimental to bacterial proliferation,thus defeating the intended antimicrobial purpose. In experimentalterms, an AFM scan area of L2 (referredto as the “footprint” or “projected” area)corresponds to the same L2 area for aflat surface, but a greater surface area for undulated surfaces, whoseexact value depends on topography.We next examine the spatiotemporalevolution of a selected bacterial strain, Lx1 – Lx0)/Lx0 and thus Lx1 = (ϵ + 1)Lx0. The undulated length for ϵ = 20%,for instance, should be 20% larger than its original length. Estimatingthe excess surface area is more complex, as stretching in the x-direction is accompanied by a compression in the y-direction. Since PDMS is incompressible (V ≡ LxLyLz constant) and assuming constant thickness (Lz0 ≃ Lz1), the lateral compression could beestimated (as Ly1 ≈ Lx0/(ϵ + 1)), althoughis does not take into account the actual geometry of the strain fieldand the finite compression imposed by the stage clamps. We thus considerestimating the excess surface area by evaluating the line integralof a sine wave with amplitude A and wavelength λ,which can be experimentally measured, asx1 and x2 are integration limits. Taking the measured A =0.2 μm and λ = 2 μm (and referenceAFM line scan of Δx ≡ x2 – x1 = 20 μm),the perimeter of the sinusoidal pattern can be readily calculatedand compared to the projected length, to yield 68 we opt for a direct measurement of surface areabased on the AFM data of the various surfaces, which we find to bereproducible and in broad agreement with geometric estimates.Considering a 1D sinusoidalprofile, the excess length can be trivially estimated from strainas ϵ ≡  of the pattern, i.e., the valleys, are effectively“available” to be occupied by bacteria. As a result,an “available” surface area to bacteria, SA, might thus be estimated by the product (surface area× its available fraction). Considering an increased area of 110%due to wrinkling and an available fraction of 50% valleys, for a 1Dpattern, the available SA is ∼55%.Experimentally, we obtain ≈67% of the overall surface (slightlygreater than expected), rationalized in terms of the growing asymmetryof the sinusoidal profile toward higher deformations.68We next consider that in the initial stages of proliferation thebacteria preferentially colonize depressions of surface pattern, suchas grooves, valleys or wells.z = 0 planeand integrating the area fraction of negative amplitude . The green color indicates z ∼ 0, and the variation in amplitude is definedas positive  and negative .For the reference F surface, 100% of the area is considered available.The C and H surfaces display SA of ≈50and ≈48%, respectively, associated with their increased topographicalcomplexity. The sequential wave superposition in C surfaces generatesan array of “pits” and “dimples”, and SA is reduced by depressions that bacteria mustovercome along the z-axis; the simultaneous waveinterference in H surfaces generates zigzag patterns, which bacteriamust negotiate within the xy-plane.A series of calculated F, 1D, C, and H patterns andexperimentallymeasured available surface areas are reported in P. aeruginosa byAFM, at prescribed incubation times , collecting population statistics on three separatespecimens of the same surface type, and three distinct surface locations(100 × 100 μm2) of each of the different wrinkledtopographies . Furthermore,to quantify proliferation over larger and distinct sample areas, opticalmicroscopy was also carried out . Coverage was estimated by using thesame method reported in 2 AFM details of the temporal evolution of P. aeruginosa over the wrinkled topographies and P. aeruginosa are randomly distributedand progressively aggregate into multilayered lumps, indicative ofcolony behavior, leading to biofilm development, favoring interconnectionsand strengthening adhesion. This coverage appears linear in time.For comparison, the same coverage analysis was carried out on theoptical microscopy data and the results, in line with those obtainedby AFM, are reported in Figure S10. Inorder to account for the statistical sample variation and local heterogeneitywithin samples, coverage was independently calculated (as the percentagearea occupied by bacteria) at three or four distinct locations perspecimen for three specimens and subsequently averaged as shown in Figure S10, obtained from complementaryoptical microscopy analysis. In general, AFM imaging enables a moreprecise thresholding, and the data are thus selected for further analysis.Proliferation was quantifiedin terms of the areal coverage of graphies S5. Furthgraphies S5. Furthgraphies S5. Furthgraphies S5. Furthgraphies S5. Furth27 the patternsconsidered in this work thus yield Wenzel behavior, as bacteria aregenerally found to reside in pattern depressions. Furthermore, thetrend between areal coverage and contact angle is nonmonotonic, beingreversed for H and C surfaces; finally, the vanishingly small difference(∼1°) in contact angle between F and 1D results in a significantchange in coverage.While the water contact angles, respectively, 106, 105, 98, and85° for F, 1D, H, and C, vary somewhat for the patterned surfaces,we believe these modest changes cannot account for the experimentalobservations. These contact angles are considerably lower than thosecharacteristic of superhydrophobic surfaces (∼150°) achievedin certain high aspect ratio hierarchical wrinkled surfaces, and resultingin Cassie–Baxter behavior;SA appears to displace bacteria toward valleys,hence, ordering them according to predetermined pathways. In turn,the specific topography can exert mechanical frustration that impactsthe onset of colony behavior. On 1D surfaces, pattern depressionsappear to be able to contain bacteria within valleys at 2 h; however,the accumulation of bacteria in valleys appears to effectively “evenout” the surface perceived by subsequently approaching cells,which can freely adhere with no topographical constrains. The overallcoverage is nonetheless lower than that for F surfaces at all times.By contrast, both C and H patterns  nontrivially affect theonset of bacterial proliferation, with only residual coverage observedat 2 h and significantly reduced coverage thereafter.Instead, wrinkling appears to impact bacterialspreading in otherways; the reduction in “available” surface area SA. The data collapse, within measurementuncertainty, of the coverage results for distinct surfaces, at the same time demonstrates the merit of the SA estimates detailed above. In a first approximation,coverage thus appears linearly proportional to SA, and C and H surfaces offer considerable benefits comparedto 1D (and F) surfaces, which we associate with their tortuosity.Moreover, we observe a significant deviation for C and H data at theearly stages of proliferation (up to 2 h): The data deviate from linearity,and the onset of proliferation appears to be effectively suppressed,or delayed by a few hours, on these 2D topographies. For smaller patterndimensions with respect to microbial dimensions, an analysis in termsof specific contact points of attachment is often employed.46 In the present case, pattern curvatures arecommensurate with those of bacteria, and pattern amplitudes are smallerthan microbial dimensions. As such, the accessible surface area analysisappears appropriate. Finally, while proliferation is influenced by SA, its onset is additionally impacted by specific,local pattern topography which likely frustrates the bacterial spatialarrangement, connectivity, and motility, hindering the formation ofmultilayered aggregates.In orderto rationalize the coverage data in terms of the “available”surface area arguments above, Ra ≃ 0.02 μm), we selected uniaxial sinusoidal (1D), biaxialherringbone (H), and checkerboard (C) with λ ≃ 2 μmand A ≃ 2 μm and similar roughness, Ra ≃ 0.07 μm. We select three modelbacterial strains, namely, S. aureus, P.aeruginosa, and E. coli K12, and one fungus, C albicans, of various characteristic shape , Gram stain, and size. Our experimental results establishthat bacteria commensurate with surface pattern length scales alignclosely with the local pattern directors and, at early stages, withinvalleys or depressions in the topography. As expected, incommensurate C albicans is comparatively less affected by the surfacepatterns, beyond a modest alignment at individual cell level.In this paper, we experimentallyexamine the effect of microscalesurface patterning on microbial attachment and proliferation. Significantly,we investigate surfaces with comparable roughness, in order to decouplespecific effects of topography on model bacteria and a fungus. Inaddition to a flat (F) control surface . We associate this frustrationto the tortuosity of C and H patterns, in either the z- or xy-planes, imposing prescribed relative orientationalchanges commensurate with bacterial dimensions and thereby hinderingtheir proliferation.Bacterial proliferation, specifically of A, (ii) the role of surface chemistryand surface energy, including its differential impact in Gram positiveor negative bacteria, and the generality of the findings, (iii) thepossible role of detailed topographic profiles ,as well as in-plane features, such as the segment zigzag length ofcheckerboard (C) surfaces, (iv) the continuum toward nanoscale patterning,whose mode(s) of action differs from those at the microscale, and(v) the development of a robust and simple surface metric, or a smallset of metrics, with predictive ability for antimicrobial action.Overall, we believe that this method ofexploiting surface wrinklinginstabilities of bilayer soft materials (or surface replicas) providesa straightforward and economical route to patterning large areas ofsurfaces with consequential antimicrobial action. We show that roughnessmetrics alone are not sufficient to describe surface topography andthat pattern tortuosity plays a significant role in microbial attachmentand proliferation. Multiaxial patterning, either by simultaneous orsequential application of strain, is shown to provide a facile meansto augmenting antimicrobial action. Evidently, our study also opensmany lines of enquiry, regarding (i) the quantitative impacts of patternλ and


Brassica rapa L.) is an important oilseed crop in northwest China. Freezing stress severely limits its production and geographical distribution, and frequent extreme freezing events caused by climate change are increasing the chances of winter freeze-injury. However, the underlying mechanism of B. rapa response to freezing stress remains elusive. Here, B. rapa genome (v3.0) was used as a reference for the comparative transcriptomic analysis of Longyou 6 and Tianyou 2  under different freezing stress. Before and after freezing stress, 5,982 and 11,630 unique differentially expressed genes (DEGs) between two cultivars were identified, respectively. After freezing stress, the GO terms in Tianyou 2 were mainly involved in “macromolecule biosynthetic process”, and those in Longyou 6 were involved in “response to stimulus” and “oxidoreductase activity”. Morphological and physiological results indicated that Longyou 6 retained a higher basal freezing resistance than Tinayou 2, and that cold acclimation could strengthen the basal freezing resistance. Freezing stress could activate the MAPK signal cascades, and the phosphorylation level of Longyou 6 showed a higher increase in response to freezing treatment than Tianyou 2. Based on our findings, it was speculated that the cell membrane of B. rapa perceives external signals under freezing stress, which are then transmitted to the nucleus through the cold-activated MAPK cascades and Ca2+-related protein kinase pathway, thus leading to activation of downstream target genes to enhance the freezing resistance of B. rapa.Winter rapeseed ( Low temperature is an important environmental factor that negatively affects plant growth, development, crop productivity, quality, and geographic distribution . HoweverZea mays), rice (Oryza sativa), and potato (Solanum tuberosum) originate from tropical and subtropical regions and are chilling-sensitive, whereas plants that originate from temperate climates, such as wheat (Triticum aestivum), rye , and barley (Hordeum vulgare), have evolved sophisticated cold acclimation mechanisms to adapt to low temperatures and survive freezing temperatures (Cold stress includes chilling stress (0–15°C) and freezing stress (<0°C) . Maize  of various plants under cold stress, such as me level . The freme level . For insolerance . Over thBrassica rapa L.) is an important cruciferous oilseed crop in northwest China. Brassica species are closely related to the model plant Arabidopsis , therefore, B. rapa might be a “perfect” model crop for uncovering cold signaling in crops. Unfortunately, the molecular mechanisms by which B. rapa adapts to freezing stress remain poorly understood. Several studies have conducted transcriptome analysis of B. rapa under cold stress using a low accuracy B. rapa genome v1.5, v2.0, or v2.5 as the reference genome based on 4°C cold stress transcriptome analysis  and Tianyou 2 (weak cold tolerance) under short periods of sub-zero freezing stress using the newly assembled, high accuracy B. rapa genome v3.0 as the reference genome, which is based on single-molecule sequencing technology , Tianyou 2 (weak cold tolerance), and Westar were obtained from Gansu Agriculture University and Gansu Academy of Agriculture Sciences. B. rapa seeds were maintained at 4°C in ddH2O for 3 days for low-temperature vernalization, and the seeds were then grown on soil in a growth chamber at 22°C for 14 days under a 16 h light/8 h dark photoperiod. For cold-acclimated (CA) treatment, 14-day-old seedlings were treated at 4°C for 72 h. The seedings of CA and NA (non-acclimated) were transferred to a freezing chamber for freezing assays at 0, −4, and −8°C for 3 h, respectively. The freezing program began at 0°C, with the temperature decreasing by 1°C per hour until the temperatures described above were reached. After freezing treatment, the seedlings were shifted to 4°C and kept in darkness for 12 h and then transferred to normal conditions (16 h light/8 h dark) at 22°C for another 72 h of recovery. The seedlings’ phenotype at each freezing temperature were photographed, and the survival rates  Biological Company for RNA isolation, detection, and sequencing. Illumina HiSeq was used for transcriptome sequencing.This study used SOAPnuke (v1.4.0) software to count the original sequencing data and Trimmomatic (v 0.36) software to filter the data. The specific steps were as follows: (1) remove reads containing linkers (linker contamination); (2) remove reads with an unknown base N content greater than 5%, and (3) remove low-quality reads .B. rapa genome v3.0 (http://brassicadb.cn/#/Download/) was used as the reference for subsequent transcriptome data analysis. Bowtie 2 (v2.2.5) software was used to align clean reads to the reference gene sequence, and RSEM was used to calculate the expression levels of genes and transcripts. After obtaining the clean reads, we used Hierarchical Indexing for Spliced Alignment of Transcripts  to align the clean reads to the reference genome sequence. StringTie (v1.0.4) was used to reconstruct the transcript of each sample, and Cufflinks (v2.2.1) was used to compare the integrated transcript with the reference annotation information and define the transcript with the following characteristics as a new transcript: (1) unknown but located in intergenic regions; (2) located in the intron regions of known genes; (3) transcripts that have a particular intersection with the exons of known genes; and (4) potential new transcripts or transcription fragments .http://planttfdb.gao-lab.org/index.php).Getorf (EMBOSS:6.5.7.0) was used to detect the open reading frame (ORF) of Unigene, and Hmmsearch (v3.0) was used to align the ORF with the transcription factor protein domain, which was identified based on characteristics of the transcription factor family described by PlantTFDB  was used to design gene-specific primers, which are listed in −ΔCt, and BraActin (BraA03g004000.3C) was used as an internal reference gene, with three biological replicates were used for each sample.To confirm the validity of the RNA sequencing data, we randomly selected 16 cold-response DEGs and 20 differentially expressed transcription factors for qRT-PCR analysis. Total RNA was extracted from the stems and leaves using the TRIzol method, and cDNA was synthesized using a reverse transcription kit . Primer3 Plus  for 14 days and exposed to the freezing treatment. The freezing treatment protocol is similar to those in the plant materials and growth conditions section mentioned above. Materials were collected from the NA treatment and CA treatment groups (pretreated at 4°C for 72 h) at −4°C (3 h) and −8°C (3 h) to extract total proteins with a protein extraction buffer . The protein concentration of each sample was adjusted to the same level using Quick Start Bradford Dye Reagent . Total proteins were separated via 10% SDS/PAGE and transferred onto PVDF membranes (Bio-Rad). Immunoblotting was performed using anti–Phospho-p44/p42 MAPK (anti-pTEpY) .To evaluate the resistance of Longyou 6 and Tianyou 2 to freezing stress, the phenotypic and physiological indicators of 14-day-old non-acclimated (NA) and cold-acclimated (CA) seedlings were examined under different freezing treatments. The results showed that the phenotypes of Longyou 6 and Tianyou 2 did not differ significantly from those of the control (Westar) at 22°C . HoweverAs a series of comprehensive physiological and biochemical events occur under cold stress, we examined the changes in physiological indicators of NA and CA seedlings under freezing treatments. Our data showed that the ion leakage from Westar and Tianyou 2 was much higher than that from Longyou 6 under both NA and CA treatments , indicatArabidopsis cold-responsive transcriptome study shown that 3.9% of Arabidopsis genes are regulated by cold stress. Among these, 74% of cold-regulated genes were regulated by 24 h of cold treatment at 0°C; however, only 26% were regulated by 3 and 6 h of cold treatment at 0°C , and after the freezing treatment, it reached an astonishing 11,630 (SLD vs. STD)  had 15,284 DEGs after freezing treatment, among which the numbers of upregulated and downregulated genes were 5,043 and 10,241, respectively. Although Longyou 6  had more DEGs  after the freezing treatment, the number of upregulated and downregulated genes was similar, at 5,416 and 6,115, respectively. Concurrently, a comparison of the two varieties revealed that before the freezing treatment, the number of DEGs between the two varieties was 5,982 (CKL vs. STD) .Statistical analysis was conducted on the reference genome for single nucleotide polymorphisms, alternative splicing, and InDel sites in each sample. The single nucleotide polymorphisms (SNP) mainly included the substitution of A-G and C-T and were concentrated in the exon region, which is also the central location for InDel sites. In addition, five main types of alternative splicing were found in all samples, and the three types known as skipped exon (SE), alternative 3′ splicing site (A3SS), and retained intron (RI) had the most significant number .p value, 24,502 and 35,507 DEGs in Longyou 6 (CKL vs. SLD) and Tianyou 2 (CKT vs. STD) were divided into 35 GO terms (vs. CKT) that belonged to molecular functions, cellular components, and biological processes, respectively. After freezing treatment (SLD vs. STD), these values were 7,269; 9,613; and 8,010.GO annotation analysis was performed on the DEGs detected before and after the freezing treatment of the two varieties. According to the revised GO terms . DEGs beBased on these results, we further verified GO terms with a high degree of integration (DEGs greater than 10) . Before vs. SLD) and Tianyou 2 (CKT vs. STD) were mainly allocated to 19 KEGG pathways. Most of the pathways were classified as “metabolism”, which included “carbohydrate metabolism”, “biosynthesis of secondary metabolites”, “amino acid metabolism”, and “lipid metabolism”, and the remaining were classified as “genetic information processing” and “environmental information processing”. The statistical results of the KEGG channel between the two varieties (CKL vs. CKT and SLD vs. STD) before and after the freezing treatment were consistent with the above results  included “plant hormone signal transduction” (Ko04075), “MAPK signaling pathway-plant” (Ko04016), “plant–pathogen interaction” (Ko04626), and “phenylpropanoid biosynthesis” (Ko00940), and the most significant enrichment pathways in Tianyou 2 (CKT vs. STD) were “plant hormone signal transduction”, “ribosome “(Ko03010), and “MAPK signaling pathway-plant” (vs. CKT) were “phenylpropanoid biosynthesis”, “MAPK signaling pathway-plant”, “plant–pathogen interaction”, and “plant hormone signal transduction”  and Tianyou 2 (CKT vs. STD) were similar, mainly MYB, bHLH, AP2-EREBP, NAC, and WRKY. However, the number of differentially expressed transcription factors in Tianyou 2 was significantly higher than that in Longyou 6, and the number of downregulated transcription factors in Tianyou 2 was generally significantly greater than the number of upregulated ones (vs. CKT) was relatively low; however, the difference became particularly apparent after freezing treatment (SLD vs. STD), mainly reflected in MYB, bHLH, AP2-EREBP, NAC, and WRKY , which can recognize the phosphorylated/active form of MPK3, MPK4, and MPK6 . In order to corroborate whether freezing treatment can activate the MAPK cascades in and MPK6 . Immunoband MPK6 , and theand MPK6 . FurtherArabidopsis, approximately 3.9% of genes are regulated by cold stress  were mainly involved in the “biosynthesis of substances”. In contrast, the DEGs in Longyou 6 (CKL vs. SLD) were primarily related to the “stimulation response” and “oxidoreductase activity”. Relevant studies have shown that oxidoreductase can protect rice chloroplasts from low-temperature damage  were mainly related to the “membrane composition”, suggesting that the two varieties may have significant differences in the transmission of cold signals.The response of plants to cold stress can be divided into three stages. The first stage is the perception of cold stress by a different cold sensor, the second is the transduction of the low-temperature signal to the nucleus, and the third is the regulation of related downstream gene response to cold signals. Currently, the generally accepted view is that a change in cell membrane fluidity initiates low-temperature signals. This theory suggests that low temperatures can decrease the fluidity of cell membranes, and the hardening of cell membranes is accompanied by the rearrangement of the cytoskeleton, which stimulates the response of plants to low temperatures . Accordie damage , which iB. napus, cold activation of BN115 (an ortholog of the Arabidopsis COR15 gene) requires membrane rigidification  after freezing treatment (SLD vs. STD) were also mainly related to membrane composition, suggesting that membrane rigidification may be critical for the response of Brassica crops to freezing stress.The changes in DEGs before and after freezing treatment in the two varieties are consistent with the results of several previous studies. A study of rice showed that Nipponbare, a variety with a high chilling tolerance, accumulated ROS early and more rapidly than the low-chilling-tolerance 93–11 variety under chilling stress, revealing a ROS-dominated adaptation mechanism to chilling stress in the Nipponbare variety . Consistfication . This suvs. CKT), mainly reflected in the MAPK signal transmission, hormone signal transduction, and carbohydrate metabolism pathways. After freezing treatment, these pathways were enriched in the two varieties (SLD vs. STD); the MAPK and hormone signal transduction pathways were more enriched than the carbohydrate metabolism pathway. Similarly, the pathways enriched in Longyou 6 (CKL vs. SLD) and Tianyou 2 (CKT vs. STD) before and after freezing treatment were roughly the same, suggesting that the carbohydrate metabolism pathway may affect the cold tolerance of Longyou 6. However, this effect was far less potent than the MAPK and hormone signaling pathways  and Tianyou 2 (CKT vs. STD) before and after the freezing process showed that after freezing treatment, the number of upregulated genes of MYB and bHLH transcription factor families in Longyou 6 was significantly higher than the number of downregulated ones  and Tianyou 2 (weak cold tolerance) under different freezing stress. Phenotypic and physiological results confirmed that Longyou 6 may have a higher basal freezing resistance than Tinayou 2, and cold-acclimation can strengthen the freezing resistance of Longyou 6, while freezing stress can rapidly enhance the kinase activity of the MAPK cascades. Our results provide new insights into the response mechanism of