N6,2′-O-dimethyladenosine (m6A) reader, YTH N6-methyladenosine RNA binding protein 2 (YTHDF2), in regulating the malignant progression of gliomas.The prognosis for diffuse gliomas is very poor and the mechanism underlying their malignant progression remains unclear. Here, we aimed to elucidate the role and mechanism of the RNA YTHDF2 mRNA levels and functions were assessed using several independent datasets. Western blotting, quantitative polymerase chain reaction, and immunohistochemistry were used to evaluate the expression levels of YTHDF2 and other molecules in human and mouse tumor tissues and cells. Knockdown and overexpression were used to evaluate the effects of YTHDF2, methyltransferase-like 3 (METTL3), and UBX domain protein 1 (UBXN1) on glioma malignancy in cell and orthotopic xenograft models. RNA immunoprecipitation (RIP), methylated RIP, and RNA stability experiments were performed to study the mechanisms underlying the oncogenic role of YTHDF2.UBXN1 mRNA degradation via METTL3-mediated m6A, which, in turn, promoted NF-κB activation. We further revealed that UBXN1 overexpression attenuated the oncogenic effect of YTHDF2 overexpression and was associated with better survival in patients with elevated YTHDF2 expression.YTHDF2 expression was positively associated with a higher malignant grade and molecular subtype of glioma and poorer prognosis. YTHDF2 promoted the malignant progression of gliomas in both in vitro and in vivo models. Mechanistically, YTHDF2 accelerated 6A modification.Our findings confirmed that YTHDF2 promotes the malignant progression of gliomas and revealed important insight into the upstream regulatory mechanism of NF-κB activation via UBXN1 with a primary focus on mThe online version contains supplementary material available at 10.1186/s13045-021-01124-z. IDH)-mutant and 1p/19q co-deletion status according to the latest World Health Organization classification of central nervous system tumors   modification have provided new insights into the molecular mechanism of malignant progression of diffuse gliomas [6A is dynamically regulated by methyltransferases (‘writers’) and demethylases (‘erasers’) [6A can influence the entire RNA life cycle, including alternative polyadenylation, pre-RNA processing, RNA export from the nucleus to the cytoplasm, RNA translation, and RNA decay, through binding with different ‘readers’ of m6A [6A writers , erasers (FTO and ALKBH5), and readers  in diffuse gliomas and have revealed that the expression level of YTHDF2 (YTH N6-methyladenosine RNA binding protein 2) is significantly correlated with increased malignancy [Recent advances in understanding RNA  gliomas –9. Similrasers’) , 11. m6A’ of m6A –14. We h6A-modified RNA via serval different mechanisms, including de-adenylation by the CCR4–NOT complex, and endoribonucleolytic cleavage by HRSP12-RNase P/MRP [6A modification remains unclear.YTHDF2 destabilizes mse P/MRP –17. YTHDse P/MRP  and is ise P/MRP . These fse P/MRP , 20, 21.UBXN1 mRNA degradation by recognizing m6A modification mediated by the m6A writer, METTL3. Finally, we also validate the role of YTHDF2 in promoting tumor progression and activating NF-κB signaling in orthotopic xenograft models.Here, we investigated the expression levels of YTHDF2 mRNA and protein in gliomas and observed that YTHDF2 expression was positively correlated with increased glioma malignancy. Elevated YTHDF2 expression was associated with decreased survival of all diffuse glioma and GBM patients in both the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets. We demonstrate that YTHDF2 can promote cell proliferation and migration in several glioma models. Additionally, we show that elevated YTHDF2 can promote nuclear factor kappa B (NF-κB) activation by suppressing UBX domain protein 1 (UBXN1) expression. Mechanistically, we show that YTHDF2 can accelerate http://www.cgga.org.cn/) and the TCGA database (https://tcga-data.nci.nih.gov/tcga/tcgaDownload.jsp) [We obtained transcriptional and clinical data from the CGGA official website (oad.jsp) , 23. The2.All cells were cultured as previously reported , 24. Bri3 cells/well were seeded into six pairs of duplicate wells of a 96-well plate and incubated at 37 °C for 1–6 days. At the predesigned time point, the medium for six-repeated wells was changed, and 10 μl CCK-8 reagent was added to the 100 μl fresh culture medium. Absorbance was detected daily at 450 nm for 1–6 days.Cell proliferation was measured using the Cell Counting Kit-8 . Specifically, 1.5 × 105) in serum-free DMEM were added to the Transwell upper chamber, with the lower chamber containing DMEM with 10% FBS. After incubation for 6 h at 37 °C, the migrated cells present on the underside of the Transwell membrane were stained with crystal violet and counted under a microscope .For migration assays, GBM cells  following the manufacturer’s protocol and quantified samples with a NanoDrop 2000. cDNA was generated from 1 µg RNA and real-time qPCR was performed on a 7500 Fast Real-Time PCR system to determine target RNA levels. The primer sequences used are listed in Additional file Western blotting was performed as previously described . After wScrambled siRNA and YTHDF2 siRNA were purchased from GenePharma . The siRNAs were transfected into cells using PolyPlus-transfection according to the manufacturer’s instructions . The growth medium was changed 8 h after transfection. Target RNAs and proteins were measured by qRT-PCR and Western blotting, respectively, 48 h after transfection. Two non-overlapping siRNAs that effectively suppressed target gene expression were selected for subsequent experiments. The sequences of siRNAs used are presented in Additional file Lentiviruses expressing YTHDF2, METTL3, UBXN1, METTL3-shRNA (target sequence: gc CTT AAC ATT GCC CAC TGA T), YTHDF2-shRNA , scrambled shRNA, or empty vector were purchased from Beijing Syngentech Company and Shanghai Genechem Company.4 cells were plated into a 6-well plate and transfected with the indicated lentivirus using HitransG P (Genechem) in accordance with the manufacturer’s instructions. Infected cells were selected using 2 μg/ml puromycin  for ≥ 1 week, and the transfection efficiency was determined by RT-qPCR and Western blotting analysis.U87, LN229, and N33 cells were selected to establish stable YTHDF2 overexpression cell lines. U87 cells were used to establish METTL3 knockdown and overexpression cell lines. UBXN1 was overexpressed in YTHDF2-overexpressing U87 cells. Transfection was performed as previously reported . Briefly6A antibody (Abcam) was incubated with RNA for 1 h at 4 °C. Prewashed Pierce™ Protein A/G Magnetic Beads  were mixed with the antibody-treated RNA in immunoprecipitation buffer at 4 °C overnight. Finally, the methylated RNA was purified for sequencing or PCR.After extracting total RNA (200 µg) using TRIzol reagent, ribosomal RNA was removed using a RiboMinus™ Eukaryote Kit v2 . Then, the RNA was sheared into approximately 100-nucleotide fragments using RNA Fragmentation Reagents . Next, a portion of the RNA solution was stored at − 80 °C as input for RNA sequencing or PCR. An anti-mRIP assays were performed using the Magna RIP RNA-Binding Protein Immunoprecipitation Kit  according to the manufacturer’s instructions. IP lysis buffer was used to lyse U87 cells and the cell lysate was divided into anti-SNRNP70, anti-METTL3, anti-YTHDF2, anti-IgG, and input samples. The cell lysates were incubated with magnetic beads coated with 5 µg of specific antibodies overnight at 4 °C. After washing, the lysates were digested with Proteinase K, and the RNA bound to immunoprecipitated proteins was purified. qPCR was performed to measure the target RNA levels.Cells were seeded in 6-well plates, and siRNAs were used to knockdown specific expression for 2 days if necessary. Then, cells were treated with Actinomycin D  for 0, 1, 2, and 4 h or longer. RNA with added bacterial 16 s RNA was then extracted by TRIzol. The target RNA was measured by qPCR, and the bacterial 16 s RNA was used as a reference.5) transfected with an empty vector, YTHDF2 overexpression, or METTL3 overexpression vectors were suspended in PBS and injected into the right frontal node of nude mice. The inoculation position was 2 mm lateral and 2 mm posterior to the anterior fontanel. Tumor size was estimated from luciferase volume measurements and MRI. The mice were sacrificed when they exhibited disturbed activity or convulsion. The brain was then harvested and embedded in paraffin.All experiments were approved by Beijing Neurosurgical Institute following the U.K. Animals (Scientific Procedures) Act, 1986 and associated guidelines. Five-week-old female BALB/c nude mice  were selected for the experiments. U87 cells  and Prism 7 . A value of P < 0.05 was considered statistically significant.All statistical analyses were conducted using R v3.4.1  of CGGA. YTHDF2 mRNA levels were also increased in molecular subgroups of primary gliomas with higher malignancy   and TCGA RNA-seq (n = 595) cohorts used in our previous study [The above findings suggest that the elevated expression of YTHDF2 may play an oncogenic role in glioma. To explore potential effects of YTHDF2 expression on glioma functions, we identified the genes that positively correlated with YTHDF2 expression in all primary gliomas of four different datasets, including the CGGA RNA-seq  enriched pathway  and TCGA RNA-seq (n = 595) cohorts. METTL3 expression was significantly negatively correlated  with UBXN1 expression in CGGA cohort  to UBXN1 expression in TCGA cohort  and TCGA  datasets  in several datasets  [6A modification, it is still required to carefully verify the expression, prognostic value, potential biological functions, and underlying mechanisms of m6A ‘writers,’ ‘erasers,’ and ‘readers’ in different cohorts of gliomas. Here, based on our previous work, we investigated the expression levels and prognostic values of m6A reader YTHDF2 in additional two large cohorts of CGGA. We also verified the protein expression of YTHDF2 in gliomas with different histological grade and IDH-mutant status. We showed that both the mRNA and protein levels of YTHDF2 are positively correlated with increased grade of glioma malignancy and poor prognosis. Based on this, we showed that the elevated expression of YTHDF2 can promote the proliferation and migration of differentiated GBM cells by promoting NF-κB activation both in vitro and in vivo. Mechanistically, YTHDF2 accelerated m6A-dependent degradation of UBXN1 mRNA, which is a negative regulator of NF-κB activation.RNA mf glioma , 27, 32. cohorts . Howevers (GSCs) , 33, 34.IDH-mutant status can also dramatically impact the expression levels, prognostic values, and functions of m6A ‘writers,’ ‘erasers,’ and ‘readers’ in gliomas. IDH mutation can inhibit the activity of m6A ‘eraser’ FTO in leukemia and glioma cells by producing R-2-hydroxyglutarate [IDH-wildtype glioma, and METTL3 expression is positively associated with a higher malignant grade and poorer prognosis of IDH-wildtype gliomas but not IDH-mutant gliomas [IDH-mutant status, WHO 2016 classifications, and primary/recurrent status in this study. Unlike METTL3, which only increases with grade in IDH-wildtype gliomas, we demonstrated that YTHDF2 increased along with increasing WHO grades in both IDH-mutant and IDH-wildtype gliomas. Furthermore, we revealed that the expression of YTHDF2 significantly increased in recurrent gliomas compared with primary gliomas. We also showed that the expression of YTHDF2 has prognostic value in GBM, excluding the potential impact of histological grade on survival analysis in previous studies. These findings provide more detailed and reliable information to further understand the roles of YTHDF2 in gliomas.Consistent with our previous report, a recent study also showed that YTHDF2 expression is increased in GBM and has prognostic value in pan-gliomas , 19. Howlutarate , 36. Our gliomas . Thus, wThe specific biological functions and their mechanisms of YTHDF2 in glioma have not been reported until recently, mainly focusing on the studies of GSCs , 19. YTH6A-dependent mRNA clearance, which is required for GSC growth, invasion, and tumor formation in vivo [6A-dependent mRNA decay. UBXN1, a UBX domain-containing protein, suppresses NF-κB activation by maintaining the expression of IκB [YTHDF2 has been shown to accelerate the degradation of more than 3000 RNA targets, most of which are mRNAs, but which also include non-coding RNAs . In glio in vivo . Here, wn of IκB , 31. Addn of IκB , 40. HIVn of IκB . CollectRecently, downregulation of UBXN1 expression was also reported to be involved in the mechanism by which EGFR promotes NF-κB activation and the mechanism through which the long non-coding RNA, PRADX, activates NF-κB , 21. Mea6A writer, METTL3, plays a key role in the maintenance of GBM stem cell characteristics by regulating m6A enrichment in ADAM19, SOX2, and SRSF genes [6A enrichment in UBXN1 mRNA, and is therefore involved in modulating NF-κB activation. Overall, these findings indicate that m6A has the potential to modulate NF-κB in multiple ways, which should be clarified by further studies.Activated NF-κB can mediate the activation of STAT3, CEBPB, and TAZ by upregulating the expression of CD44, vimentin, and N-cadherin, thereby promoting the invasion, angiogenesis, and therapeutic resistance of glioma –45. The SF genes . RecentlSF genes . Here, oUBXN1 mRNA decay in gliomas by recognizing the m6A modification mediated by METTL3. This, in turn, enhances NF-κB activation (Fig. 6A modification.In conclusion, our findings demonstrate that YTHDF2 expression in diffuse gliomas increases with greater histological or molecular features of malignancy. Elevated YTHDF2 promotes malignancy of gliomas in both in vitro and in vivo models. Mechanistically, YTHDF2 accelerates ion Fig. E. Taken Additional file 1. The primers and supplementary figures for this manuscript.Additional file 2. Identified genes that were negatively correlated with YTHDF2 expression.Additional file 3. The raw gel figures used in this manuscript.


Colaptes auratus, is a widely distributed North American woodpecker and a long-standing focal species for the study of ecology, behavior, phenotypic differentiation, and hybridization. We present here a highly contiguous de novo genome assembly of C. auratus, the first such assembly for the species and the first published chromosome-level assembly for woodpeckers (Picidae). The assembly was generated using a combination of short-read Chromium 10× and long-read PacBio sequencing, and further scaffolded with chromatin conformation capture (Hi-C) reads. The resulting genome assembly is 1.378 Gb in size, with a scaffold N50 of 11  and a scaffold L50 of 43.948 Mb. This assembly contains 87.4–91.7% of genes present across four sets of universal single-copy orthologs found in tetrapods and birds. We annotated the assembly both for genes and repetitive content, identifying 18,745 genes and a prevalence of ∼28.0% repetitive elements. Lastly, we used fourfold degenerate sites from neutrally evolving genes to estimate a mutation rate for C. auratus, which we estimated to be 4.007 × 10−9 substitutions/site/year, about 1.5× times faster than an earlier mutation rate estimate of the family. The highly contiguous assembly and annotations we report will serve as a resource for future studies on the genomics of C. auratus and comparative evolution of woodpeckers.The northern flicker,  Colaptes auratus is a polytypic North American woodpecker with a distribution spanning from Alaska to northern Nicaragua, Cuba, and the Cayman Islands. Colaptes auratus consists of up to 13 described subspecies  to assemble the first published chromosome-level genome for C. auratus and Picidae. As whole-genome sequencing becomes more feasible and prevalent, high-quality reference genomes will undoubtedly serve as essential resources. We expect the chromosome-level assembly presented here will be of great use to those interested in the genomic evolution of woodpeckers and birds, at large.Here, we describe Caur_TTU_1.0, a C. auratus specimen (MSB 48083) deposited at the Museum of Southwestern Biology (MSB). The specimen was a wild female collected on July 11, 2017 in Cibola County, New Mexico (see MSB database for complete specimen details) and exhibited the ‘red-shafted’ morphology associated with C. auratus populations of western North America. We used a combination of 10× Chromium, PacBio, and Hi-C sequencing data for genome assembly. 10× Chromium library sequencing was carried out by the HudsonAlpha Institute for Biotechnology . They performed high-molecular weight DNA isolation, quality control, library preparation, and shotgun sequencing on one lane of an Illumina HiSeqX. For long-read PacBio sequencing, we used the services of RTL Genomics . They performed high-molecular weight DNA isolation using Qiagen  high-molecular weight DNA extraction kits, PacBio SMRTbell library preparation, size selection using a Blue Pippin (Sage Science), and sequencing on six Pacific Biosciences Sequel SMRTcells 1M v2 with Sequencing 2.1 reagents. Hi-C library preparation was performed with an Arima Genomics Hi-C kit  by the Texas A&M University Core facility. The Hi-C library was then sequenced on a partial lane of an Illumina NovaSeq S1 flow cell at the Texas Tech University Center for Biotechnology and Genomics.We obtained breast muscle tissue from a vouchered Colaptes  using the nucmer module of MUMMER v 4.0.0b2  and other in-progress lab genome assembly projects in songbirds (Supplementary File S15).We annotated TEs and repetitive content in the Caur_TTU_1.0 assembly using a custom de novo repeat library, we used RepeatMasker v 1.332 , (2) Merops nubicus (GCF_000691845.1_ASM69184v1_Merops_nubicus_protein.faa), (3) Apaloderma vittatum , (4) and Buceros rhinoceros (GCF_000710305.1_ASM71030v1_Buceros_rhinoceros_protein.faa) . We performed a reciprocal BLAST of all species versus C. auratus using blastn to identify putative homologs across all four species (Supplementary File S19).We extracted the putative CDS (Supplementary File S14) from the Caur_TTU_1.0 assembly using the final MAKER output and bedtools. In addition, we downloaded the CDS for To put the evolution of the CDS regions in a timed evolutionary context, we downloaded a phylogenetic tree comprising all orders of Neoaves  and used jModelTest2 . All associated raw sequencing data, PacBio (SRR12364887), Chromium 10x (SRR12363123), and Hi-C (SRR12363461) are available from NCBI SRA. Scripts, associated files, and workflows used for this project are available on GitHub (github.com/jphruska/Colaptes_genome). Outputs from BUSCO, Maker, RepeatMasker along with the custom repeat library, a tab-delimited text file including information on mummer alignments, and a text file including information on the homologs used for mutation rate estimation are deposited as supplemental files in figshare: 6 Pacific Biosciences (PacBio) long-reads (∼34× coverage), 4.47 × 108 10× Chromium paired-end reads (∼58× raw coverage), and 3.25 × 108 Hi-C paired-end reads . The final assembly had an L50 of 43.938 Mbp scaffolds and an N50 of 11  of the assembly. When compared to other vertebrates, avian genomes contain comparatively low repetitive content  and 149,433 exons (mean length: 161.351 bp) (Supplementary File S13). The quantity of genes and exons recovered is in line with previously annotated bird genomes .C. auratus is 4.007 × 10−9 substitutions/site/year; with a 95% credible interval = 3.525 × 10−9–4.976 × 10−9. This rate is ∼1.5× higher than a previous estimate of the Downy Woodpecker Dryobates pubescens (2.42 × 10−9; The mutation rate analysis of fourfold degenerate sites from neutrally evolving genes suggests that the mean rate in


Bacillus cereus (UFRABC40) was selected, as it resulted in the most significant gain in growth variables. In a commercial coconut tree nursery, we subjected seedlings to two treatments, both with seven replications: control 100% NPK chemical fertilizer (CF) and B. cereus + 50% NPK CF. The results indicated that the inoculation increased phytohormone levels  and leaf gas exchange  in leaves. Furthermore, growth parameters  and macro- and micronutrient levels  were improved. Our results show the potential ability of strain Bacillus cereus UFRABC40 to promote the growth performance of coconut seedlings under decreased application of inorganic fertilizers. The application of microbial-based products in coconut seedling production systems improves plants’ physiological performance and the efficiency of nutrient use.Coconut production in the Amazon requires the knowledge and development of sustainable technologies to alleviate the detrimental effects of inorganic chemical fertilizers and intensive farming practices. In this study, we investigated the effects of plant growth-promoting rhizobacteria (PGPR) isolated from coconut seedlings on nutrient use efficiency (NUE) and physiological mechanisms related to biomass accumulation of seedlings grown with reduced inorganic fertilizer levels. Of the 96 PGPR isolates tested on rice plants, the isolate  The cultivation of coconut trees is of great economic and social importance due to the value generated by coconut production. According to The global demand for food has resulted in large use of CFs to attain maximum agricultural efficiency. According to Pseudomonas sp. and Bacillus sp. have been shown to promote the growth of coconut seedlings  inoculation on coconut seedlings growth by investigating the physiological and nutritional mechanisms in seedlings grown under low CF conditions.The experiment was conducted in a coconut tree seedling nursery, Santa Isabel do Pará-Brazil .–3). Next, an aliquot of 50 μL was separated from the concentrated suspension and seeded into three 9-mm Petri plates containing 10 mL of culture medium  (Six soil samples containing roots (100 g) were collected from an 8-year-old dwarf coconut plantation in Brazil. Each sample was divided into portions of 10 g to obtain the isolates. Each soil sample (10 g) was diluted in 50 mL of sterile distilled water and agitated for 30 min. An aliquot of 20 μL was then separated from the original suspension and diluted in 80 μL (10of agar) . Plates of agar) . These pThe selection of growth-promoting isolates was carried out on rice plants, and subsequently, their interaction with coconut seedlings was tested according to the method described by 8 CFU⋅mL–1 (p < 0.05). The R40 isolate  were inoculated with 20 mL of bacterial suspension obtained from the culture growth in liquid medium to 10CFU⋅mL–1  and keptCFU⋅mL–1 . The exp isolate  resulted–1 Taq DNA polymerase, 4 mM MgCl2 reaction buffer, 0.4 mM of each DNTP; Promega Corporation, Madison, WI, United States), 10 μM of each primer, and 50 ng DNA. Amplification of the 16S rDNA region was performed in a thermal cycler  with the following steps: initial denaturation at 94°C for 4 min; 25 cycles of 94°C for 1 min, 55°C for 1 min, and 72°C for 1 min; and a final extension at 72°C for 7 min. Reactions were purified using 5 μL of PCR product plus 2 μL Exo-SAP enzyme (Exonuclease). Samples were purified via a thermal cycler, performed at 37°C for 4 min, followed by an incubation period at 80°C for 1 min to inactivate both enzymes irreversibly. After the purification reaction, sequencing was carried out in an automated sequencer (ABI3730) at the Laboratory of Bioinformatics and Evolutionary Biology, Federal University of Pernambuco (LABBE-UFPE).The R40 isolate was cultured in culture medium 523  for 24 h1. Afterward, all sequences were aligned (MEGA). Bayesian inference (IB) analysis was performed by means of Mr. Bayes v. 3.2.6 (2 using the best nucleotide replacement model. This was selected according to Aikake’s Information Criterion (AIC) through Mr. Modeltest 2.3  medium under 100 rpm agitation and incubated at 28°C for 78 h. Subsequently, 3 mL of the suspension was centrifuged at 4°C for 10 min at 4,000 rpm . Then, 9The R40 isolate was inoculated into test tubes containing a 10 mL Tryptic Soybean Broth (TSB) (1:10 diluted) medium  and incubated at 28°C under agitation at 114 rpm for 24 h. Subsequently, tubes containing the bacterial suspension were centrifuged for 10 min at 12,000 rpm. Then, 1 mL of the supernatant was transferred into another tube containing 1 mL of the blue chrome S (BCS) solution. Fifteen minutes after mixing, if siderophores were produced, the dark blue mixture turned yellow .3(PO4), 25 g MgCl2W6H2O, 0.25 g MgSO4W7H2O, 0.2 g KCl, and 0.1 g (NH4)2SO4 2SO4 , at a pH®) was as follows: 0.086 g kg–1 N, 0.264 g kg–1 P, 0.580 g kg–1 K, 0.128 mg kg–1 Ca, 0.447 mg kg–1 MgO, 272.86 mg kg–1 S, 42.25 mg Na, 0.703 mg L–1 B, 0.12 g kg–1, copper Cu, 0.5 mg kg–1 Fe, 0.6 mg kg–1 Mn, 0.78 mg kg–1 Zn, and 92.43% organic matter (OM).In a coconut tree nursery, the coconut seeds were sown in wooden boxes  containing coconut fiber and moistened daily for 90 days. The chemical characterization of the coconut fiber substrate (Golden Mix type 4 – AMAFIBRA2O5), 10 g potassium chloride (60% K2O), and 5 g magnesium oxide (30% Mg)  containing 7.5 kg/bag of coconut fiber (50% moist). Chemical fertilization was performed 30 days after transplanting (DAT) with 3 g urea, 40 g simple superphosphate (18% P(30% Mg) .8 CFU) + 50% standard CF at 90 DAT. The bacterial strain was inoculated by applying 300 mL plant–1 of a suspension at 108CFU⋅mL–1 through watering at 40 and 70 DAT. Biometrics, gas exchange, hormone levels, and nutrient levels were evaluated at 160 DAT. The experimental design was completely randomized, with 10 replications and 2 treatments.For the establishment of treatments, the recommendation of commercial fertilization was followed. Thus, the control treatment is characterized as 100% chemical fertilization (CF) and without the use of bioinoculant. The control treatment used standard CF (as described in the previous section) applied at 90 and 150 DAT. The treatment with rhizobacteria comprised inoculation with a suspension of R40 isolate , stomatal conductance to water vapor (gs), transpiration rate (E), and instantaneous carboxylation efficiency (A/Ci) were estimated between 08:00 and 11:00 am using a portable open-flow gas-exchange system  under an external CO2 concentration of 400 μmol mol–1 of air and artificial photosynthetically active radiation (PAR) of 900 μmol of photons m–2 s–1.Gas exchange parameters were estimated using the first physiologically mature, fully expanded leaf, from apex to base, at 3 months of age. The net assimilation of CO3 and GA4) hormone levels were determined according to –1 coupled to a triple quadrupole QQQ mass spectrometer (Agilent Technologies). Mass spectra were alternately negative/positive operated according to the retention time for each hormone. The generated mass spectra were processed using the MassHunter software to obtain the extracted ion chromatograms (XIC) for each transition and area values, indicating the abundance of each hormone. A curve pattern for each hormone over a concentration range from 0.1 to 300 ng mL–1 was used to convert the XIC area values into ng g–1 of plant tissue. Molecular mass spectra analysis was conducted using the Skyline software.Indole acetic acid (IAA) and gibberellic acid (GA–1) = aerial dry weight (g)/plant applied nutrient (g) (Leaf samples dried in an oven with forced air circulation at 60°C were ground. The samples were submitted to sulfuric and nitroperchloric digestion. The determination of nitrogen (N) was by distillation in Microdistillator Kjeldhal, phosphorus (P) by visible ultraviolet spectrophotometry (UV-VIS), and potassium (K), calcium (Ca), and iron (Fe) by absorption spectrometry atomic, flame modality (EAA/cham). Analysis of boron (B) was undertaken after dry digestion of the samples using the method described by Azometrinah . The nutient (g) .t-test (p < 0.05). All data were analyzed using the R software . Based on the construction of the phylogenetic tree from 29 accesses, it was possible to identify the isolate as B. cereus. The sequence was deposited in GenBank as B. cereus (UFRABC40) with accession number MN393059 , root dry weight (122%), total dry weight (35%), height (26%), and diameter (30%) compared with the control treatment  and then to NO3– ions (nitrification). In a study with Triticum aestivum, the inoculation of Bacillus megaterium SNji (BmeSNji) and Azospirillum brasilense 65B (Abr65B) provided greater availability of nutrients to plants, such as N, from decomposition of organic matter resulting in greater accumulation of biomass in plants  inoculation increased P production by 18% and its total absorption by 46%. This was due to the increased activity of enzymes such as dehydrogenases and phytases , reducing Fe3+ to Fe2+  catabolism was demonstrated, releasing B as orthoboric acid [B(OH)3] 3] . Rhizoba[B(OH)3] . Concomi[B(OH)3] . Therefo[B(OH)3] . In the ive soil . These reedlings . The incB. cereus provided coconut seedlings with a greater possibility of absorption and translocation of nutrients, contributing to a greater growth in the aerial part  plants, where the use of Bacillus sp. enhanced the production of several isomers of gibberellins  that were responsible for leaf area expansion and increased leaf emission rate  can be found below: AC was present in all study assays and was responsible for fieldwork, statistical analysis of data, interpretation of results, and writing the manuscript. SC was present in all study assays and was responsible for fieldwork. DS, AM, EA, and PL were responsible for statistical analysis, analysis of economic indicators, and interpretation of results and assisted in writing and revising the manuscript. GS was responsible for project management, guiding the students through all stages of the process and also responsible for the verification and monitoring of field data and writing the manuscript. All authors contributed to the article and approved the submitted version.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.


The M andP ligands have similar affinities, whereas the O ligand has 3-foldlower affinity, reflecting differences in binding enthalpy and entropy.The results reveal surprising differences in conformational and solvationentropy among the three complexes. NMR backbone order parameters showthat the O-bound protein has reduced conformational entropy comparedto the M and P complexes. By contrast, the bound ligand is more flexiblein the O complex, as determined by 19F NMR relaxation,ensemble-refined X-ray diffraction data, and MD simulations. Furthermore,GIST calculations indicate that the O-bound complexhas less unfavorable solvation entropy compared to the other two complexes.Thus, the results indicate compensatory effects from ligand conformationalentropy and water entropy, on the one hand, and protein conformationalentropy, on the other hand. Taken together, these different contributionsamount to entropy–entropy compensation among the system componentsinvolved in ligand binding to a target protein.Molecular recognitionis fundamental to biological signaling. Acentral question is how individual interactions between molecularmoieties affect the thermodynamics of ligand binding to proteins andhow these effects might propagate beyond the immediate neighborhoodof the binding site. Here, we investigate this question by introducingminor changes in ligand structure and characterizing the effects ofthese on ligand affinity to the carbohydrate recognition domain ofgalectin-3, using a combination of isothermal titration calorimetry,X-ray crystallography, NMR relaxation, and computational approachesincluding molecular dynamics (MD) simulations and grid inhomogeneoussolvation theory (GIST). We studied a congeneric series of ligandswith a fluorophenyl-triazole moiety, where the fluorine substituentvaries between the  Rational structure-based design of effectors bycomputational approaches has advanced immensely in recent decadesyet remains an extremely challenging endeavor. The main challengeinvolves the fact that the free energy of binding is manifested asa small difference between large numbers originating from a vast numberof interactions between the protein, ligand, other solutes, and solventmolecules. The interaction energies depend sharply on interatomicdistances and orientations, which vary in a time-dependent mannerwith the conformational dynamics. Furthermore, changes in conformationalfluctuations in proteins upon ligand binding can give rise to significantentropic contributions to affinity.8 and solvation in ligand binding.13 Galectin-3is an interesting model system in this regard, because its bindingsite is relatively solvent-accessible, being located in a shallowgroove across one of the two β-sheets. Several water moleculesform an integral part of the ligand environment by forming bridginghydrogen bonds between the ligand and protein.14 Galectin-3 is a member of the galectin family of mammalian lectins,which is defined by a conserved sequence motif that confers affinityfor β-galactoside-containing glycans.16 Galectins play important roles in cell growth, cell differentiation,cell cycle regulation, signaling, and apoptosis, making them targetsfor medicinal chemistry programs to treat inflammation and cancer,18 with several cases reported for galectin-3.21 Recently, ithas also been suggested that galectin-3 plays a role in the pathologyof Alzheimer’s disease.22We have studied the carbohydrate recognition domain of galectin-3 to address specificallythe roles of conformational entropyortho, meta, or para position; henceforth, the ligands are denoted O, M,and P. The advantage of using ligands with minimal structural differencesis that differences in binding thermodynamics are dominated by theproperties of the ligand–protein complexes, while the chemicalpotential of the free ligands contributes relatively less in the comparativeanalysis. On the other hand, the minor structural differences betweenthe ligands in this series lead us to expect limited differences inthe structures of the complexes and in their binding thermodynamics.Thus, this study focuses on the thermodynamic effects of minute changesin protein–ligand structure and aims at characterizing theresponse of the entire system, including the solvent. In particular,we address how the various entropic contributions to binding mightvary between slightly different protein–ligand complexes.Here, we investigate the effects of minute changes in ligand structureon the binding enthalpy and entropy, broken down into separate contributionsfrom various interactions or degrees of freedom. We report a comparativeanalysis of galectin-3C in complex with three congeneric fluorophenyltriazolyl-thiogalactosidesthat differ by the position of the fluorine substituent, which isplaced in the We used a combination of experimental and computational approaches,including isothermal titration calorimetry (ITC), competitive fluorescencepolarization, X-ray crystallography, NMR relaxation, and moleculardynamics (MD) simulations followed by conformational entropy and gridinhomogeneous solvation theory (GIST) calculations. Our results showthat minor differences between protein–ligand complexes intheir overall binding thermodynamics might encompass greater differencesamong individual contributions, including a case of entropy–entropycompensation between the protein, ligand, and solvent degrees of freedom.23 1,3-Dipolarcycloaddition of 3-fluoro- and 4-fluorophenylacetylene to 3′-azido-3′-deoxy-β-d-galactopyranosyl-1-thio-β-d-glucopyranosideunder Cu(I) catalysis afforded ligands M and P, respectively. Proceduresand physical data for M and P are described in the Supporting Information.The synthesis of ligands M and P wasperformed as described for O.5 to produce 15N-, 15N/13C-, and 15N/13C/2H-labeled protein as well asunlabeled protein. The protein stock solution was 16 mg/mL in buffer,consisting of 10 mM Na2HPO4, 1.8 mM KH2PO4, 140 mM NaCl, 2.7 mM KCl, pH 7.3, 2 mM ethylenediaminetetraaceticacid (EDTA), 4 mM tris(2-carboxyethyl)phosphine hydrochloride (TCEP),and 150 mM lactose. The protein stock solution was stored at 278 K.Galectin-3C wasexpressed and purified following published protocols15N-labeledgalectin-3C samples were prepared by extensive dialysis  against 5 mM 4-(2-hydroxyethyl)-1-piperazinethanesulfonicacid (HEPES) buffer to remove all lactose, followed by centrifugationat 14 000 rpm for 5 min to remove any aggregates. The proteinconcentration was determined by UV absorption at 280 nm, as describedbefore.4 The ligands are water-solubleand do not require addition of DMSO. The three ligands were dissolvedinto 7–10 mM stock solutions using the same HEPES buffer asused in the protein samples and sonicated immediately prior to experiments.ITC experiments were performed on a MicroCal PEAQ–ITC instrument at pH 7.3 and a temperature of 301.1 K by titratingthe protein at a concentration of 100 ± 4 μM into the cellcontaining the ligand at a concentration of 1000 ± 10 μM.Two replicate experiments were performed for each complex. The timebetween injections was set to 4 min. Peak integration was done usingNITPIC,24 with individual error bars assignedto each injection. A single-site binding model was fitted simultaneouslyto the two titration curves to yield the binding enthalpy (ΔH°), fraction of binding-competent protein (n), and dissociation constant (Kd), using ITCsy.25 The heat released orabsorbed during the ith injection is given by26Vi is the volume of the ith injection, V0 is the cell volume, Qoff is an offset parameter accounting for theheat of mixing,and Qi is the heat followingthe ith injectionnMi + Xi + Kd and Mi and Xi are the total concentrationsof protein and ligand, respectively,in the cell at any given point of the titration.G° = RT ln(Kd) and −TΔS° = ΔG° – ΔH°. The statistical uncertainty is greater for −TΔS° than for ΔG° or ΔH°, since it is derived from twoparameters. ITCsy/SEDPHAT produces asymmetric error estimates, butin the present case, the errors are nearly symmetric, and we reportthe errors as the average of upper and lower errors.The free energyand entropy of binding were determined via the equations Δ27 For each complex, 3600diffraction images were collected with 0.1° rotation and 0.01s exposure time. All data were integrated using XDS.28Small crystals of lactose-boundgalectin-3C were grown with the hanging drop method in NeXtal plates(Qiagen) and with the following reservoir condition: 20% (w/v) PEG4000, Tris-HCl pH 7.5, 0.4 M NaSCN, 10 mM β-mercaptoethanol.The drop volume was 2 μL, and the protein solution/reservoirratio was 1:1. Small crystals were then moved to drops containingthe same reservoir with the addition of 10 mM of the ligand , from a 100 mM stock solution in PBS buffer. Soaking lastedfor 12–15 h. Before data collection, crystals were placed fora couple of seconds in a drop containing 1 volume of 100% PEG 400and 3 volumes of crystallization solution to prevent ice formationduring cryo-cooling. Data were collected on EMBL beamline P13 at PETRA-III,DESY, Hamburg.29 Cross-validation was based on 10% of the reflections.Molecular replacement was done using Phaser in the Phenix suite version1.14,31 using as template the lactose–galectin-3Cstructure13 with lactose and water moleculesremoved. Ligands and their crystallographic restraints were generatedthrough phenix.eLBOW.32 Restrained refinementwas then performed using phenix.refine33 using all remaining reflections. Manual rebuilding, including additionof water molecules, was done using Coot.34 Data collection and refinement statistics for analysis of X-raycrystallography data are found in Table S1.Scaling and merging were done with Aimless.35 in the Phenix1.14 software suite. The X-ray crystal structures of the M, P, orO complexes were used as starting structures. The crystallographicwater molecules were kept, and hydrogen atoms and missing atoms inthe protein were added using the phenix.ready_set module. Ligand restraintsand coordinates were the same as those used in the original refinement.Ensemblerefinement of the X-ray diffraction data was performed using the modulephenix.ensemble_refinementpTLS) was optimized by testing five different values  and choosing the one that yielded the lowest Rfree, which was pTLS = 0.6 for all three protein–ligand complexes. An ensembleof structures was then generated by running MD simulations, in whichthe model was restrained by a time-averaged X-ray maximum-likelihoodtarget function. Three X-ray weight-coupled temperature bath offsetswere tested , with the default value of 5 Kgiving the lowest Rfree. A 1.25 ps relaxationtime of the time-averaged restraints was used, resulting in 25 pslong MD simulations, with structures stored every 0.05 ps. All structuresgenerated by ensemble refinement were kept, resulting in 500 differentstructures in each ensemble. Atomic fluctuations were calculated usingthe cpptraj module of Amber after removal of thewater molecules.36The collective dynamics of the protein was described using a TLS(translation–libration–screw) model with a single group,which included both the protein and the ligand atoms. The percentageof atoms included in the TLS fitting  spectrum at each titration point.The resulting saturation levels were 98.0–98.5%, as calculatedfrom the value of Kd determined by ITCmeasurements. In addition, 15N-labeled samples of eachcomplex were obtained by pooling the cell contents from the two replicateITC experiments, resulting in a galectin-3C concentration of 0.08mM. 15N-HSQC spectra were acquired on these samples toconfirm that the samples resulting from the ITC titrations are essentiallyidentical to those used in the NMR relaxation experiments.Samples of 37 and CBCA(CO)NH)38 spectra andprevious assignments for various galectin-3C complexes.5 The ligands were titrated into the NMR tubesin five to seven steps so as to follow the movement of individualresonances from the apo state to the ligand-bound state. Methyl groupswere assigned using 13C-HSQC, CCH-TOCSY, and HCCH-TOCSYexperiments40 and previous assignments.5 All spectra were processed using NMRPipe,41 employing a solvent filter, squared cosine apodization,linear prediction, and zero filling to twice the number of pointsin all dimensions. Resonance assignments were done using the CcpNmrprogram suite.42 Chemical shift perturbationswere calculated as ([Δδ(1H)]2 +[0.16Δδ(15N)]2)1/2 forbackbone amides and ([Δδ(1H)]2 +[0.25Δδ(13C)]2)1/2 formethyl-bearing side chains.Backbonechemical shift assignments were based on HNCACB15N R1, R2, and {1H}–15N nuclear Overhausereffect (NOE) experiments were performed at static magnetic field strengthsof 11.7, 14.1, and 18.8 T and a temperature of 301 K. Temperaturecalibration was done prior to each relaxation series experiment usinga type-T copper-constantan thermocouple element with one electrodein an ice–water bath and the other in an NMR tube in water,positioned at the sample location inside the magnet. Experiments at11.7 and 14.1 T were performed on Varian/Agilent VNMRS DirectDrivespectrometers, and experiments at 18.8 T were performed on a BrukerAvance III HD spectrometer. The spectral widths were 14–16and 28–30 ppm for 1H and 15N, respectively,covered by 1024 and 128 points. Relaxation decays were recorded with10 relaxation delays ranging between 0–1 s for R1 and 0–0.2 s for R2 with a 1.2 ms delay between refocusing pulses. The NOE experimentsat 11.7, 14.1, and 18.8 T all used the same 1H saturationtime of 7 s, and the recycle delay between experiments was 3, 7, and3 s, respectively. The reference experiment (without 1Hsaturation) was acquired using a recycle delay of 10, 14, and 10 sat 11.7, 14.1, and 18.8 T, respectively. All spectra were processedas described above. Relaxation rates were extracted using PINT, whichemploys line-shape fitting to resolve overlapped peaks.43 Peak intensities were evaluated using a weightedsum of Lorentzian and Gaussian line shapes. Monoexponential functionswere fitted to the R1 and R2 relaxation decays using a jackknife error estimation.44 NOEs were calculated as the ratio of the peakintensities in the saturated and reference experiments, and the standarddeviations were determined by propagating the errors of intensitiesestimated from the baseplane noise. Error estimates are reported as1 standard deviation (SD).Backboneamide 2H relaxation experiments45 measuring R1(DZ), R(3ZD2 – 2), R2(D+), and R(D+DZ + DZD+) were recorded at 11.7 and 14.1 T. Spectral widths were16 and 20 ppm for 1H and 13C, respectively,covered by 1024 points in the 1H dimension at both fieldstrengths and by 70 and 84 points for 13C at 11.7 and 14.1T, respectively. Relaxation decays were sampled by 9 points covering0–0.1 s for R1(DZ) and R(3DZ2–2) and 0–20 msfor R2(D+) and R(D+DZ+DZD+). The recycledelay was 1.8–2 s. Peak intensities were evaluated as describedabove.Methyl 19F relaxation R2 experimentswere recorded at 470 and 659 MHz on Agilent/Varian VNMRS and Brukerspectrometers, respectively. The temperature was calibrated to 301± 0.1 K using a methanol reference sample. 1H decouplingduring the relaxation period was achieved using a 180° pulseor WALTZ train. The experiment at 470 MHz utilized a spectral widthof 4596 Hz, 3584 transients, relaxation delays of 0, 4, 8 (×2),12, 16, 20, 28, and 40 ms, and a recycle delay of 4 s. The experimentat 659 MHz utilized a spectral width of 13 158 Hz, 512 transients,relaxation delays of 0, 2, 4 (×2), 6, 8 (×2), 12, 16, 20(×2), 28, 32, and 40 ms, and a recycle delay of 5 s. Both experimentswere run in an interleaved manner. All 19F spectra wereprocessed using NMRPipe41 with zero fillingand matched filter apodization. Peak intensities were evaluated usingin-house MATLAB scripts. Errors in peak intensities were estimatedfrom the baseline noise. Monoexponential functions were fitted tothe R2 decays using least-squares optimizationfunctions available in MATLAB, with parameter uncertainties estimatedfrom 1000 Monte Carlo simulations.4648 were fitted using the relax software package (v 4.0.2),51 with an NHbond length of 1.02 Å and a 15N chemical shift anisotropyof −172 ppm. Hydrogen atoms were added to the PDB structuresusing UCSF Chimera.52 The backbone optimizationin relax was restricted to the four most basic modelsdefined by the parameter sets: {O2}, {O2, τe}, {O2, Rex}, or {O2, τe, Rex}, where O2 denotes the generalized orderparameter, τe denotes the effective correlation timefor the internal motion, and Rex denotesexchange contributions to R2; in addition,the correlation time for overall rotational diffusion τc, was fitted as well as the diffusion tensor.53 The definitions of anisotropy (ζ) and rhombicity(η) of the diffusion tensor54 areζ = 2DZZ/(DXX + DYY) and η = 1.5(DYY – DXX)/[DZZ – 0.5(DXX + DYY)], where {DXX, DYY, DZZ} are theprincipal components of the molecular rotational diffusion tensor.BackboneNH model-free parameters51 Three models were fitted using two {O2, τf}, three {O2, τf, τeff}, or four{Of2, Os2, τf, τeff}parameters, where τf is associated with fast motions,τeff = (1/τc + 1/τs)−1, and τs denotes the correlationtime for slow internal motions on par with τc.55 The global correlation time and diffusion tensorwere fixed to values obtained from the 15N backbone model-freeoptimization in relax. Model selection was performedusing an F test at the level α = 0.95 (p < 0.05).56 Error estimates,reported as 1 SD, were based on Monte Carlo simulations using 500samples.46Side-chain methyl-axis model-free optimization was performed usingin-house MATLAB routines employing the fmincon function to find theminimum of a constrained nonlinear multivariable function. Numericallystabilized model-free equations suggested previously were used.P(A) = P(A) – (P(B) + P(C))/2. The values of ΔP resulting from thisintercomplex comparison are a factor of 1.5 greater than differencescalculated relative to the average value of all three complexes. Thesum of all three ΔP(i) is zero.We report the comparisonsof the three complexes as the difference between the parameter valueof a certain property of complex A minus the average value for theother two complexes, B and C, i.e., Δ57R is the gas constantand the sum runs over all residues k. Using the intercomplexcomparison described above, the equation becomesThe change in backboneconformational entropy, comparing two states,was estimated from the NMR order parameters using the relationship57Cm is a parameter that depends on theresidue type: Cm = 1.32for Val and Thr, 3.1 for Ileand Leu, and 2.31 for Met.57 The sums runover all residues n of type m. Since The conformational entropy change ofthe side-chain methyl axis was calculated asThe radialdistribution of protein conformational entropy around the bound ligandwas calculated by averaging the backbone entropy of residues locatedin 1–2 Å thick shells surrounding the ligand. The distancefrom the ligand was evaluated as the shortest distance between anyligand atom and any atom of a given residue in the corresponding X-raycrystal structure. The residues were sorted into shells with a thicknessof 1 Å (2–9 Å) or 2 Å (9–27 Å);the thinner shells were used to improve the resolution of the dataclose to the ligand. The lowest entropy value for each complex wastaken as the reference and set to 0. The cumulative average entropyvalues were also computed as a function of the distance from the ligand.58 The X-ray crystal structures of the three complexes wereused as the starting points for MD simulations. All crystal-watermolecules were kept in the simulations. Each galectin-3C complex wassolvated in an octahedral box of water molecules extending at least10 Å from the protein using the tleap module.The simulations were set up in the same way as in our previous studiesof galectin-3C.60 All Glu and Asp residues were assumed tobe negatively charged, and all Lys and Arg residues positively charged,whereas the other residues were neutral. The binding-site residueHis158 was protonated on the ND1 atom, whereas the other three Hisresidues were protonated on the NE2 atom, in accordance with the neutronstructure and NMR measurements14 as wellas previous extensive test calculations with MD.61 This resulted in a net charge of +4 for the protein. Nocounterions were used in the simulations.All MDsimulations were run with the Amber software suite.62 watermolecules with the TIP4P-Ewald model,63 whereas the ligands were treated with the general Amber force field(GAFF).64 Charges for the ligands wereobtained with the restrained electrostatic potential method.65 The ligands were optimized with the semiempiricalAM1 method, followed by a single-point calculation at the Hartree–Fock/6-31G*level to obtain the electrostatic potentials, sampled with the Merz–Kollmanscheme.66 These calculations were performedwith the Gaussian 09 software.67 The potentialswere then used by antechamber to calculate the charges. A few missingparameters were taken from a previous study.8The protein was describedby the Amber ff14SB force field,2. Finally, the system was equilibrated for 1 ns, followedby 100 ns of production simulation, both performed with constant pressureand without any restraints. For each protein–ligand complex,10 independent simulations were run.68 Consequently,the total simulation time for each complex was 1 μs. All bondsinvolving hydrogen atoms were constrained to the equilibrium valueusing the SHAKE algorithm,69 allowing fora time step of 2 fs. The temperature was kept constant at 301 K usingLangevin dynamics,70 with a collision frequencyof 2 ps–1. The pressure was kept constant at 1 atmusing a weak-coupling isotropic algorithm71 with a relaxation time of 1 ps. Long-range electrostatics were handledby particle-mesh Ewald (PME) summation72 with a fourth-order B spline interpolation and a tolerance of 10–5. The cutoff radius for Lennard–Jones interactionsbetween atoms of neighboring boxes was set to 8 Å. The snapshotswere analyzed with the cpptraj module.36 We also performed simulations of the three ligandsfree in solution, using the same setup.For each complex, 1000 steps of minimization were used, followedby a 20 ps constant-volume equilibration and 20 ps constant-pressureequilibration, all performed with heavy nonwater atoms restrainedtoward the starting structure with a force constant of 4184 kJ/mol/Å73 The covariance matrix of the NH bond vectors was obtainedfrom the trajectories by the cpptraj module36 in the Amber software.58We calculated NH order parametersfrom the MD trajectories usingisotropic reorientational eigenmode dynamic analysis.75 The Cartesian coordinates from the trajectorieswere transformed to internal coordinates, and the entropies were thencalculated from probability distributions over all possible statesof these coordinates using a bin size of 5° . Entropies were normalized to that of a free rotor.4 All entropies are reported as −TΔS at 301 K. Protein-backbone entropieswere calculated from two-dimensional distributions of the ϕand ψ dihedral angles. In our previous study, we showed thatthe effect of correlated motions is minimal for entropy estimatesof galectin-3C, less than 1 kJ/mol.8Conformational entropies were calculated from the ensembleof configurations of the protein and ligands by analyzing the dihedralangle fluctuations.60 The reported uncertainties are standard errorsover these 200 simulations.A total of 100 000 snapshots with a 10 ps sampling frequencywere used for entropy and order parameter estimates, employing separatesimulations for the complexes, for free galectin-3C, and for the solvatedligands. Entropies and order parameters were calculated as averagesover 200 simulations of 5 ns .The 5 ns time window is similar to the rotational correlation timeof the protein (∼7 ns). This procedure yields more stable entropyestimates by restricting the dependence on rare events.77 implemented in the cpptraj module of the Amber software. The method requires snapshots fromMD simulations in which the solute is kept restrained. Therefore,we performed 10 independent 100 ns long MD simulations, in which thesolute was kept restrained toward the starting crystal structure .We identifiedwater sitesand analyzed the thermodynamics of the solvent around the galectin-3Ccomplexes, using GIST,Eww, and solute–water interactionenergy, Esw, as well as translational, Strans, and rotational, Srot, entropy contributions were calculated for a rectangulargrid of dimensions 24 × 14 × 14.5 Å, centered on theligand and extended at least 3 Å on each side of the ligand;for the free ligands, the dimensions were 16.5 × 18 × 21Å. The grid was divided into cubic boxes (0.5 × 0.5 ×0.5 Å), for which the thermodynamic properties were calculated.The sum of these properties over the entire region reveals the changesin the hydration thermodynamics of the region for each cluster, relativeto bulk water.For each protein–ligand system, the water–water interactionenergy, 79 real-solvent (COSMO-RS) approach81 using theCOSMOTHERM software.82 This is one of themost accurate methods to estimate solvation free energies of smallmolecules.84 The calculations were based ontwo single-point density-functional theory calculations with the BP86method86 and the TZVP basis set,87 one performed in a vacuum and the other in the COSMO solventwith an infinite dielectric constant, as required by method. The structureswere taken from unrestrained MD simulations of the free ligands, 110snapshots from each ligand.Solvation free energies for the free ligandsin water solutionwere calculated with the conductor-like solvent model (COSMO)o-, m-, and p-fluoro-phenyltriazolyl-galactosylthioglucosides = 2.0 ± 0.3 μM, Kd(P) = 2.5 ± 0.2 μM, and Kd(O) = 7.2 ± 1.3 μM. Similar to our previous observationsfor other ligand series,8 the relative affinitiescorrelate well with results from competitive fluorescence polarizationexperiments. The ΔH° values for M–and P–galectin-3C are −50.4 ± 1.2 and −49.1± 0.8 kJ/mol, hence equal within error, while O–galectin-3Chas a smaller enthalpic contribution of −45.5 ± 1.9. Theentropies (−TΔS°)are 17.5 ± 1.2, 16.7 ± 0.8, and 15.8 ± 1.9 kJ/mol forthe complexes with M, P, and O, respectively. The data follow a trendsuggesting enthalpy–entropy compensation . Takentogether, the ITC experiments reveal that the complexes have similarthermodynamic signatures overall, which might be expected from thesimilar structure of the ligands. The samples resulting from the ITCtitrations were characterized by 1H–15N HSQC spectroscopy and compared to the separately prepared NMR samples,to make sure that the protein chemical shifts were identical in thetwo samples, thereby verifying that the sample conditions and proteincharacteristics were virtually identical in the two types of experiments.Based on the determined Kd values andreactant concentrations, the saturation levels of the complexes afterthe last ITC injection were 97.6, 97.0, and 92.4% for M, P, and O,respectively.We characterized theoverall macroscopic thermodynamics for the binding of the three ligandsto galectin-3C using ITC. We performed two replicate titrations witheach of the ligands, followed by a combined fit of the duplicate data2A–C. Tabensation 2E, as ofTable S1). The ligands are very well-definedby the electron density . Theoverall binding mode of the ligands  interaction with the phenyl group.89 The orientation of the phenyl ring differs subtlywith the position of the fluorine , which likely is due to increasedmobility. Possibly, the higher mobility of O could be related to thefluorine being coordinated by a side chain rather than a backbonecarbonyl as in the case of M and P. The positions and occupanciesof water molecules around the binding site are quite similar for allthree ligands. Complete data collection and refinement statisticsare listed in Table S1 of the SupportingInformation.Very high-resolutioncrystal structures were obtained for galectin-3Cin complex with the three ligands . The resultingensembles show what conformations are compatible with the crystallographicraw data and may point out groups that show larger movements thanindicated by the crystallographic Differencesin chemical shifts between protein–ligand complexes providea sensitive indication of differences in structure and dynamics. Bymapping chemical shift changes in the protein upon ligand binding,we verify that the ligands bind to the protein in solution in thesame pose as that observed in the crystal structures. In addition,chemical shift differences between complexes serve to validate resultsof the ensemble-refined X-ray structures and can further identifysubtle differences between the complexes in their structure and dynamics.8 Superimposedspectra of the three complexes are shown in the SI . In keeping with previous results,8 ligand binding leads to chemical shift differences for the backboneamides throughout the protein. The chemical shifts of 115 backboneamides were used to monitor ligand binding . Significant chemical shift differencesin the 1H–15N HSQC spectra between thethree complexes are observed primarily in regions close to the ligand, includingresidues 144–147, 158–165, 173–175, 184, and235–239 but also for residues further away, viz., residues116, 139, and 243.We assigned the chemical shifts of O–, M–, and P–galectin-3Cbased on HNCACB and CACB(CO)NH experiments and assignments reportedpreviously.n the SI S2 and S3In several cases, the observed chemical shift differences amongthe three complexes can be rationalized in terms of structural differencesobserved in the crystal structures. Starting with residues to theleft in the binding site as presented in A number of other residuesshow NH chemical shift differences betweencomplexes that cannot be explained by the static structures, whichare virtually identical in these regions. Most likely, these shiftchanges report on differential dynamic averaging of the chemical shifts.Prominent examples include Arg162 and Glu165, the side chains of whichpack together and interact with the glucose moiety, as seen in Figure S4B), but there are few methyl-bearingside chains close to binding site. Notably, Ala146 Cβ showsthe largest perturbation by far. The methyl group of Ala146 is locateddirectly beneath the fluorophenyl ring  are 7.12, 6.97, and 6.95ns for M, P, and O. 15N relaxation data back-calculatedfrom the optimized model-free parameters show good agreement withthe experimental values for all three complexes .We interpreted the relaxation data using the model-freeformalism.19F R2 relaxation experiments. The results reveal that the 19F R2 is significantly enhancedfor the O ligand compared to the other two ligands, indicating thatthe O ligand undergoes conformational exchange on a fast time scale. In contrast, the M and P ligandsboth appear to populate a single conformation in the complex. Notably,these results are in perfect agreement with the ensemble-refined X-raycrystal structures . The subtle differences in O2 revealed by the intercomplex comparisons  might be expected based on the smallstructural differences between the complexes. It is clear that M–and P–galectin-3C have more flexible residues close to thebinding site than does O–galectin-3C, which is also reflectedin the slightly higher <O2> fortheO complex. These results are in qualitative agreement with RMSF valuesdetermined from the ensemble-refined X-ray structures . Among these, subtle differences in O2 are observed in the segment 159–165,which includes several residues that are either in direct contactwith the ligand (Asn160 and Arg162) or participating in the hydrogen-bondingnetwork around the ligand (Glu165). Most likely, variation in side-chaininteractions with the ligand among the different complexes affectsthe backbone fluctuations. Further, residues Thr175, Asn179, Trp181,Arg186, and Ser237 also show differences in O2. Trp181 and Arg186 contact the ligand with their side chains,while Thr175 forms a hydrogen bond with Gly182. The carbonyl of Ser237is involved in a fluorine–amide interaction in the P complex,which shows the highest O2, whereas O2 is lowest in the O complex for this residue.The largest differences are observed for Arg183, Phe190, and Glu193,none of which are located within 6 Å from the ligand. All threebackbone amides lack hydrogen bonding partners and appear in nonregularsecondary structure elements. Despite being located in a more remoteβ-turn, residues 150–154 also show minor differencesthat again might arise from variations in ligand interactions relayedfrom the binding site via a network of side chains, see further below.The high degree of similarityamong the three complexes in O2 ≤ −0.025) inthe given complex compared to the other two complexes, while cyan,thin tubes indicate residues that are less flexible (ΔO2 ≥ 0.025). Residues that have greaterflexibility in M–galectin-3C than in the other two complexesare found very close to the binding site (<3 Å away), see O2 ≤ −0.02  for M–, P–, and O–galectin-3C, respectively.As in the case of the backbone order parameters, one-way ANOVA doesnot reject the null hypothesis of equal means. O2 as well as the intercomplexdifferences, ΔO2, while Figure S11 shows the pairwise comparisons.The sidechains are naturally more flexible than the backbone, as reflectedby their generally lower order parameters. The order parameters arelisted in 2H relaxation data are available forfour of these residues, namely Ile145, Ala146, Leu147, and Val172.Among these, Ala146 shows the greatest variation with O2 values of 0.92, 0.93, and 0.60 for the M, P, and O complexes,respectively. Interestingly, Ala146 interacts directly with the fluorinatedphenyl ring of the ligand. The higher flexibility of Ala146 in theO complex is in agreement with the ensemble-refined crystal structureand the 19F relaxation data, which both indicate significantlygreater fluctuations of the fluorophenyl ring in the O complex thanin the other two complexes . Ile145 Cδ2 shows O2 values of 0.67, 0.76, and 0.72 for the M,P, and O complexes. Leu147 does not show any significant differencesin O2, and Val172 shows only very minordifferences.We investigated the detailed differencesobserved for individualresidues between the complexes. There are only 5 methyl-containingresidues among the 28 residues within 6 Å of the ligand, wherethe greatest variations among complexes in backbone order parametersare seen. Side-chain xes cf., 4. ClearlO2 of 0.41,0.77, and 0.78 for the M, P, and O complexes . Ala146 and Val116 arelocated on either side of Ser237, the backbone of which responds todifferences in the fluorine position (as described above). Further,Val155 Cγ1 and Ala156 show minor ΔO2, as was also observed for the backbone of residues 150–154.The side chain of Val225, which packs against Ala156, also exhibitsa difference between complexes with Cγ2 having a higher O2 for the P complex. Leu120, Leu234, and Ala245form a cluster of interacting side chains that all show minor ΔO2. Both Leu120 Cδ2 and Ala245 have thelowest O2 in the P complex, but theseresults cannot be explained by inspecting the crystal structures.Further away from the binding site, Val126 Cγ1 shows the oppositebehavior compared with Leu120 and Ala 245. Finally, Val170 Cγ1,which packs against V172, has O2 valuesof 0.39, 0.58, and 0.68 for the M, P, and O complexes, respectively.Similar to previous results for a number of galectin-3C complexes,8 differences in order parameters do not generally correspond to obviousstructural differences between the complexes. This finding reconfirmsthat subtle but statistically significant differences in dynamicscan arise among structurally similar complexes.Outside of the immediate binding site, Val116 Cγ1also showssignificant differences with O2, lead to significant differences in entropy betweencomplexes, despite <O2> not showingsignificant variation among M–, P–, and O–galectin-3C.We estimated the differences between complexes in their conformationalentropy contributions to ligand binding, using TΔΔSbb,M = −5.7 ± 0.6 kJ/mol and −TΔΔSbb,P = −6.0± 0.8 kJ/mol, whereas O–galectin-3C has a lower backboneentropy yielding −TΔΔSbb,O = 11.7 ± 0.8 kJ/mol. Hence, the backbone conformationalentropy favors the binding of M and P, whereas the binding of O isdisfavored in the intercomplex comparison. The side-chain entropydifferences are less pronounced and have greater uncertainty: −TΔΔSsc,M = −0.4± 2 kJ/mol, −TΔΔSsc,P = 4 ± 2 kJ/mol, and −TΔΔSsc,O = −4 ±2 kJ/mol. The empirically calibrated “entropy meter”,proposed by Wand and co-workers,95 is basedon the difference between complexes in their average order parameterof the methyl axes and consequently does not capture the minor effectsobserved here, because <O2> is notsignificantly different between the M, P, and O complexes.The nonlinear dependenceof entropy on the backbone order parameter,together with residue-specific variations in ΔTΔΔSbb+sc,M = −6 ±2 kJ/mol, −TΔΔSbb+sc,P = −2 ± 2 kJ/mol, and −TΔΔSbb+sc,O = 8± 2 kJ/mol. The differences between M and P are not significant,while O differs significantly from the other two. This suggests thatthe conformational entropy of the backbone and methyl-bearing sidechains determined by NMR favors binding of M and, to a minor extent,also P, whereas it disfavors binding of O.Summing up the conformational entropy of the backboneand the sidechains results in intercomplex differences: −MD simulations can provide information on the totalconformational entropy of the protein and ligand. Therefore, we performedMD simulations to complement the entropies estimated from the NMRexperiments, which are limited to sampling the backbone and methyl-bearingside chains.The coordinate RMSFs from the MD simulations providea first overview of differences between the complexes. The MD simulationsconfirm the results from the ensemble-refined crystal structures andthe NMR data, showing that the RMSF is largest for the O ligand inthe complex (1.16 ± 0.08 Å) and smallest for P (0.90 ±0.09 Å). However, the RMSF for the protein barely differs betweencomplexes, where P–galectin-3C has 0.84 ± 0.01 Åcompared to 0.85–0.86 ± 0.01 Å for the M and O complexes.96 The simulated order parameters are modestly higherthan the experimental parameters on average, with <O2>MD – <O2>NMR = 0.006, 0.013, and 0.007 for the M, P,and O complexes,respectively. However, there are clear discrepancies in the details. Clearly, it is a great challengefor MD simulations to reproduce the variations in dynamics based onthe minor differences in structure among the three complexes.To further validate the simulations against experimental data,we calculated order parameters for the backbone and compared withthose measured by NMR. The simulated order parameters reproduce theexperimental ones with a pairwise RMSD between 0.043 and 0.056, whichis in line with previous results.Table S4) but suggestthat it is slightly higher in the M complex, in line with the NMRresults. However, the MD simulations do indicate a significant differencein the conformational entropy of the ligands within the complexes,where O deviates from M and P (Table S4). The ligand contributions to the total entropy change are 16.1± 0.4 kJ/mol for O, 19.6 ± 0.4 kJ/mol for M, and 20.4 ±0.3 kJ/mol for P. The less unfavorable entropy contribution from theO ligand primarily reflects a higher degree of flexibility in thebound state, which agrees with the ensemble-refined crystal structures,and the lower order parameters of methyl-bearing side chains locatedin direct proximity to the ligand, mentioned above. By contrast, theconformational entropies of the free ligands agree within 1 kJ/mol(Table S5). Similar, but less reliable,results are obtained by molecular mechanics with generalized Bornand surface area solvation (MM/GBSA) and interaction entropy calculations(Table S6); see the Supporting Informationfor details.We estimated the conformational entropy from dihedral angle distributionsin the MD trajectories. The MD simulations do not yield any clearlysignificant difference between the three complexes in the conformationalentropy of the protein , Trp181, Arg186, Asn160, andVal172 show similar trends with O2 progressivelyincreasing in the order M–, P–, and O–galectin-3C.The distance dependence of thebackbone entropy mirrors the greaterbackbone fluctuations in the binding sites of M– and P–galectin-3Ccompared to O–galectin-3C cf., 7. Since residues 9B. In th77 The results for the O, M, and P complexes revealdifferences in their hydration thermodynamics, relative to bulk water. Moreover, the total solvation entropies,calculated by GIST, for the free ligands agree within 0.7 kJ/mol (Table S5). Thus, the differences in total solvationentropy of binding among the three systems is governed by the observeddifferences in hydration of the complexes.Solvation free energies of the free ligands inwater solution,obtained by COSMO-RS calculations, are identical within errors . Hence, the difference in binding thermodynamicscan be related primarily to differences between the ligand–galectin-3Ccomplexes. As might be expected from their structural similarity,the three ligands bind to galectin-3C with similar free energy. However,the O complex deviates from the other two in having less favorablebinding enthalpy and less unfavorable binding entropy. The differencein binding enthalpy can ostensibly be rationalized in a straightforwardmanner by a reduced number of interactions involving the fluorineatom and surrounding protein in the O complex, compared to the M andP complexes. The difference in total entropy of binding arises asthe net sum of opposing contributions from different components. Theexperimental results from NMR relaxation and ensemble-refined X-raydiffraction data suggest that the backbone of galectin-3C has reducedentropy when bound to O, especially for residues close to the bindingsite.  One can speculate that the less favorable interactionsof the ortho-fluorine atom with the protein createstrain and restrict the flexibility of the protein backbone. However,this unfavorable entropic contribution to binding is apparently offsetin the O complex, but not in the other two complexes, by increasedentropy of the ligand and protein side-chains in immediate proximitywith the ligand. The higher degree of flexibility of O can intuitivelybe associated with the fewer favorable protein–ligand interactionsin this complex. Finally, the three complexes differ markedly in solvationentropy: A large difference in water entropy for the protein–ligandcomplexes favors the O complex.The minor structural difference between the ligands renderstheirchemical potentials in the free state highly similar, as confirmedby calculations of conformational entropies, GIST solvation entropies,and COSMO-RS solvation free energies; the results do not differ, withinthe calculated uncertainties, between the three ligands (entropy–entropy compensation between the conformationaldegrees of freedom of the protein, which disfavor binding of O overM or P, and the ligand and solvation entropies, which favor bindingof O. These results highlight intricate details of binding thermodynamicsand the importance of accounting for all components of the systemin order to advance prospects for rational drug design.Thus, the present results indicatean interesting case of


We found that ginkgetin (1) blocks TRPV4-elicited Ca2+ influx into macrophages, (2) inhibits oxidized low-density lipoprotein (oxLDL)-induced foam cell formation by suppressing the uptake but not the binding of oxLDL in macrophages, and (3) attenuates oxLDL-induced phosphorylation of JNK2, expression of TRPV4 proteins, and induction of inflammatory mRNAs. Considered all together, the results of this study show that ginkgetin inhibits proatherogenic/inflammatory macrophage function in a TRPV4-dependent manner, thus strengthening the rationale for the use of natural compounds for developing therapeutic and/or chemopreventive molecules.Atherosclerosis, a chronic inflammatory disease of large arteries, is the major contributor to the growing burden of cardiovascular disease-related mortality and morbidity. During early atherogenesis, as a result of inflammation and endothelial dysfunction, monocytes transmigrate into the aortic intimal areas, and differentiate into lipid-laden foam cells, a critical process in atherosclerosis. Numerous natural compounds such as flavonoids and polyphenols are known to have anti-inflammatory and anti-atherogenic properties. Herein, using a fluorometric imaging plate reader-supported Ca The pivotal initiating event that leads to atherosclerosis involves retention of modified low-density lipoprotein (oxLDL) particles in the subendothelial aortic intimal spaces primarily in arterial branch points2. During early atherogenesis, following endothelial dysfunction, circulating blood monocytes transmigrate into the intima areas, and differentiate into tissue macrophages3. In aortic intimal spaces, binding and uptake of oxLDL by macrophages aided by scavenger receptors like SRA and CD36 creates a feedforward athero-inflammatory loop resulting in development of lipid-laden foam cells, a critical process in atherosclerosis8. Cascades of athero-inflammatory events provoked by foam cells lead to the formation of an early fatty streak and eventually the appearance of advanced atherosclerosis lesions characterized by the presence of fibrous cap, calcified tissue, immune cells, matrix proteins, and lipids10.Atherosclerosis, a progressive inflammatory disease of large arteries, constitutes the foremost contributor to the growing burden of cardiovascular disease-related mortality and morbidity globally2+ is ubiquitously expressed in various cell types including macrophages24. Previously known as an osmosensor, TRPV4 is now known to respond to multifarious biochemical and biomechanical stimuli including heat, matrix stiffness, osmolarity, growth factors, pH, and 4α phorbol esters24. TRPV4 is reported to mediate several physiological functions like pain sensation in inflammation and neuropathies23, myofibroblast differentiation and migration 26, and osteoclast differentiation in the bone27. TRPV4 mutations have been associated with several channelopathies31. Recent studies by our group and others have implicated a possible role of this channel in myriad pathological conditions like lung injuries32, foam cell formation16, tissue fibrosis33, foreign body response13, and cancer35. Previously, an atheroprotective role of TRPV4 was shown, in which chemical agonist-induced TRPV4 function in endothelial cells was shown to be associated with activation of eNOS and inhibition of monocyte adhesion to endothelial cells36. In contrast, deficiencies in TRPV4 functions have been linked to numerous atheroinflammatory responses including endothelial dysfunction, reduced macrophage foam cell generation, and vascular diseases39. Our laboratory has recently identified a proatherogenic role of TRPV4 whereby it regulates oxLDL-induced macrophage foam cell formation. Mechanistically, we showed that TRPV4 affects the uptake but not binding of oxLDL in macrophages in a CD36-independent manner14. In another study, we reported a TRPV4-dependent exacerbation of oxLDL-induced foam cell formation in the presence of lipopolysaccharide and increasing matrix stiffness, thus providing a link between mechanosensing and risk of atherosclerosis16. Considered all together, these findings suggest TRPV4 as an attractive target in atherosclerosis and other diseases, thus incentivizing the discovery of small molecule inhibitors of TRPV4 that can be translated into clinically effective therapeutics.Transient Receptor Potential Vanilloid 4 (TRPV4) of the TRPV subfamily, a non-selective, polymodal cation channel permeable to Ca45. Recent studies by multiple groups have identified two flavonoids, berberine and apigenin, as potential modulators of TRPV4 activity47. We have developed and used a semi high-throughput screening method to identify potential antagonists of TRPV4 from a library of natural compounds using a Fluorometric Imaging Plate Reader (FLIPR) -based Ca2+ influx assay. Herein, we report the identification and functional analysis of ginkgetin, a biflavone, as a novel inhibitor of TRPV4-dependent atherogenic processes in macrophages, thus laying a foundation for further studies of this natural compound as a natural anti-atherosclerotic small molecule compound. Ginkgetin has been reported to display neuroprotective, anti-carcinogenic, and anti-inflammatory roles49. We report the mechanism of action of ginkgetin against TRPV4 in the setting of macrophage foam cell formation using numerous biochemical and cellular assays.The uses of natural compounds in therapeutics have gained prominence, primarily driven by the need for cost effective and biocompatible compounds as compared to the currently existent synthetic drugs. Natural compounds such as flavonoids, alkaloids, polyphenols, and curcuminoids, affect cardiovascular diseases via diverse mechanisms such as inhibition of proinflammatory signals, insulin sensitization, and improving blood lipid profiles by targeting the AMPK, COX1/2, eNOS, and Nrf2 pathways16. A murine macrophage cell line (RAW264.7), and human dermal fibroblasts (HDF) were purchased from ATCC , and were cultured, respectively, with DMEM or MEM . Murine bone marrow derived macrophages (BMDMs) were harvested from 6- to 7-week old mice, as previously described51. Briefly, femurs from WT and TRPV4 KO mice were collected, and bone marrow of femurs was flushed out with complete DMEM media with 10% FBS and. The suspended bone marrow cells were filtered through a 70 µm strainer (BD bioscience), centrifuged, and maintained in DMEM medium supplemented with M-CSF (25 ng/mL) for 7–8 days at 37 °C to allow differentiation into macrophages.We purchased congenic C57BL/6 wild type (WT) mice from Charles River Laboratories . University of Maryland Animal Care and Use Committee guidelines were followed for all animal experiments and the authors complied with ARRIVE guidelines. For isolation of thioglycolate-induced murine resident macrophages (MRM), peritoneal lavage was collected after 4 days of intraperitoneal thioglycolate injection, and cells were maintained in 10% fecal bovine serum (FBS) containing DMEM4 for 12 h at 37 °C to prepare copper-oxidized LDL (oxLDL)16. Fluorescent dye-labeled, DiI-oxLDL, was purchased from Invitrogen , and MCSF from R&D . Antibodies for FACS analysis were: TRPV4 , CD36 , TLR4 and TLR6 . PE-conjugated secondary antibodies were from R&D , and PE-conjugated isotype controls were from BD . For quantitative polymerase chain reaction (qRT-PCR), we used RNeasy kit from QIAGEN . All primers were purchased from Bio-Rad . Cell culture media, cell culture related products, and western blot reagents were obtained from Thermo Fischer Scientific .Ginkgetin and GSK1016790A (GSK101) were purchased from Sigma-Aldrich , and FLIPR calcium 6 assay kit was obtained from Molecular Device . Human native LDL (nLDL) was purchased from Stemcell Technologies . We incubated nLDL with 5 µM CuSO17. We identified ginkgetin (GGT), as a novel antagonist to TRPV4. Primary and secondary screening was performed with RAW264.7, HDF, and BMDMs to assess the ability of ginkgetin and other candidates to inhibit TRPV4-elicited Ca2+ influx17. As controls, we used A23187, a calcium ionophore, TRPV4 null BMDMs, and GSK219, a selective TRPV4 antagonist17. After secondary screening, we identified six compounds showing more than threefold inhibition of TRPV4-mediated Ca2+ influx compared to vehicle control. Ginkgetin was identified as the most potent inhibitor of TRPV4 activity. BMDMs (5 × 104 cells/well) were seeded into collagen coated (10 µg/ml) 96-well clear bottom black plates, and incubated at 37 °C, 5% CO2. On the day of the experiment, cells were loaded with calcium 6 dye in HBSS, 20 mM HEPES, 2.5 mM probenecid and incubated for 45 min at 37 °C. After incubation, library compounds were added to each well to a final concentration of 2 µM. Plates were incubated for another 45 min at 37 °C. Ca2+ influx was induced by the addition of 10 nM of TRPV4 agonist GSK1016790A in vehicle- or compound-pretreated cells, and recorded by measuring ΔF/F (Max–Min), as previously described17. Data are shown as relative fluorescence units (RFU).We undertook a semi high-throughput screening for antagonists of Trpv4 from a library of 2000 natural compounds  using a fluorometric imaging plate reader (FLIPR)-based calcium influx assayThioglycolate-elicited peritoneal macrophages were seeded in 10% FBS containing DMEM and incubated overnight. Unadhered cells were washed off, and 1% bovine serum albumin (BSA) containing DMEM was added to the plate and incubated for 3 h before ginkgetin treatment. To determine expression of CD36, TRPV4, TLR2, TLR4, TLR6, and GAPDH proteins whole cell lysates were prepared from cells treated overnight with ginkgetin (1 and 5 µM) or vehicle in the presence or absence of oxLDL (25 µg/ml). To determine the expression levels of LPS-triggered p-JNK and JNK, cells were pretreated with ginkgetin (5 and 10 µM) for 3 h and then stimulated with E. coli LPS for 30 min. Whole cell lysates were prepared from twice-washed cells (ice cold PBS) using RIPA buffer with added protease-inhibitor cocktail . Blots were probed with rabbit anti-TRPV4 , anti-mouse CD36 , rabbit anti-TLR4, rabbit anti-TLR6, rabbit anti-JNK, and rabbit anti-p-JNK primary antibodies  followed by anti-rabbit and anti-mouse HRP-conjugated secondary antibodies . Blots were stripped and re-probed with anti-GAPDH IgG  for loading control.2 for 2 h. GGT (1 or 10 µM) was added to the cells, and after 3 h of incubation, oxLDL (50 µg/ml) was added, and cultures were incubated overnight at 37 °C. Native LDL (50 μg/ml) was added to control group wells and incubated overnight. Cells were fixed in 4% paraformaldehyde followed by Oil Red O staining to quantify foam cell generation.Thioglycolate-triggered MRM were seeded on glass coverslips in a 12 well plate with DMEM, 10% FBS and incubated at 37 °C, 5% CO10 pfu/ml), and adenovirus blank vector, Ad(RGD)-CMV-null , from Vector Biolabs, Malvern, PA. We used 1 × 108 pfu/ml for all experiments. For foam cell generation studies, mouse peritoneal macrophages were incubated with Ade-TRPV4 or Ade-Vec in DMEM media for 72 h before addition of oxLDL to generate macrophage foam cells.We collected Adenovirus constructs to express Ad(RGD)-mouse-TRPV4  of ginkgetin or vehicle for 3 h and incubated with DiI-oxLDL at 4 °C for one hour. After pre-treatment with ginkgetin or vehicle, cells were incubated with DiI-oxLDL at 37 °C for 30 min to asses uptake. Images were captured by fluorescence microscopy (63x), and Image J was used to quantify results.Thioglycolate-elicited peritoneal macrophages were cultured in DMEM, 10% FBS for 24 h. Unadhered cells were washed off, serum free medium was added, and cells were incubated for 2 h followed by addition of 5 µM GGT or vehicle, and incubation was continued for 3 h. Treated cells were scraped off the plate and resuspended in PBS, 2% BSA. Cells were incubated with primary antibodies for 45 min followed by 30 min incubation with PE-conjugated secondary antibody. A FACSCantoII flow cytometer  was used to acquire data. All data were processed using FlowJo software.T method described in the Bio-Rad qRT-PCR system user bulletin.Thioglycolate-induced macrophages were treated with 5 µM ginkgetin or vehicle for 3 h; 25 µg/ml of oxLDL were added to vehicle or ginkgetin-treated cells, and incubation was continued overnight. RNeasy Micro Kit  from QIAGEN was used to extract total RNA, and iTaq Universal SYBR Green One-Step Kit from Bio-Rad was used to reverse transcribe the RNA. A C1000 Touch Thermal Cycler (Bio-Rad) was used to acquire data. Expression of a gene was determined as the amount of gene-specific mRNA relative to GAPDH mRNA using the comparative CData are presented as mean ± SEM of biological triplicates. GraphPad Prism 7.0 software was used, and calculations were conducted using Student’s t-test for two groups or one-way ANOVA for multiple groups.All experiments on mice were conducted in accordance with the Institutional Animal Care and Use Committee (IACUC) guidelines, and were approved by the University of Maryland‐College Park Review Committee.2+ influx assay  inhibited GSK101-induced (a TRPV4 specific agonist) Ca2+ influx. We performed a secondary screening of the lead compounds using the same assay on RAW 264.7 cells. We further verified and selected 6 compounds which produced reproducible and significant  inhibition of TRPV4-elicited Ca2+ influx in BMDMs as compared to the vehicle control  was identified as one of the most potent inhibitors of TRPV4 in the screening. Since previously published reports showed that GGT has an anti-inflammatory and anti-adipogenic effect, we selected GGT for further functional analysis on TRPV4-dependent macrophage proatherogenic activity53.A library containing a collection of structurally and functionally known natural compounds was screened using a semi high-throughput FLIPR-based Casay Fig. A to inve2+ influx is involved in oxLDL-mediated foam cell formation 16. Therefore, we evaluated the possibility that foam cell formation was inhibited by GGT-mediated antagonization of TRPV4 activity. Wild-type murine peritoneal macrophages were incubated with oxLDL to induce foam cell formation in the presence or absence of GGT, and analyzed by Oil Red O staining. As expected, we found that generation of foam cells increased by fivefold in oxLDL treated macrophages in comparison to native LDL . We found similar expression levels of CD36, TRPV4, TLR6, and TLR4 as compared to the untreated control  remained unchanged  treatment compared to vehicle control  ginkgetin blocks TRPV4-mediated Ca2+ influx into macrophages, ii) ginkgetin blockade of TRPV4 function markedly decreased oxLDL-induced foam cell formation by suppressing the uptake of oxLDL in macrophages, and iii) ginkgetin blocks LPS- and oxLDL-induced phosphorylation of JNK1/2, expression of TRPV4 proteins, and inflammatory gene expression. In aggregate, the results of our study showed that ginkgetin inhibits proatherogenic/inflammatory macrophage function in a TRPV4-dependent manner.Natural compounds such as flavonoids and polyphenols are known to modulate cardiovascular responses via diverse mechanisms, such as inhibition of proinflammatory signals, insulin sensitization, oxidative status, and improving blood lipid profiles10. Since foam cell formation is a critical process in atherogenesis, understanding and manipulating foam cell formation can have therapeutic potential. Macrophages are known to express an array of membrane penetrating ion channels and pumps, including TRPV4, a mechanosensitive polymodal Ca2+ permeable ion channel, which is activated by both physical and biochemical stimuli24. Published studies by our laboratory and others have identified a role of TRPV4 in macrophage inflammation and oxLDL-induced foam cell formation26. TRPV4 can thus serve as a viable target for attenuation of proatherogenic processes.Atherosclerosis, an aortic disease, is initially fueled by inflammation and the engorgement of macrophages with oxLDL, inducing formation of macrophage foam cells, which subsequently progress to form atheroma61. However, the roles of natural compounds such as TRPV4 antagonists have not been investigated until recently. A study by Wang et al. reported that berberine, an alkaloid from Chinese herbs, mediates vasorelaxation by suppression of TRPV4 function46. Other groups have reported that plant cannabinoids can regulate the expression and activity of TRPV4 channels, and can have potential therapeutic applications in the gastrointestinal tract and other TRPV4-associated channelopathies62. Ma et al. recently proposed that apigenin, a plant-derived flavone, activates TRPV4 resulting in vascular dilation47. Herein, we report that ginkgetin, a biflavone, identified using semi high-throughput screening, blocks oxLDL-induced foam cell formation and inflammatory signals by targeting a TRPV4-evoked proatherogenic/inflammatory pathway. Ginkgetin, isolated from Gingko biloba, is a known anti-inflammatory and anti-carcinogenic compound49. Studies by Lian et al. have highlighted a role of ginkgetin in improving atherosclerosis by reducing MMP-2 and MMP-9 and increasing levels of NO and NOS in thoracic aortas of rat52. In the present study, we showed that ginkgetin blocks TRPV4-elicited Ca2+ influx in macrophages. Mechanistically, we showed that ginkgetin blocks the uptake of oxLDL, but not binding, in macrophages.Ample evidence suggests that natural compounds affect metabolic and inflammatory disorders like cardiovascular diseases by targeting fat and cholesterol synthesis, inhibiting prostaglandin synthesis, neutralizing reactive oxygen and nitrogen species, and impairing NF-κB-mediated release of proinflammatory cytokines7. It was shown that CD36 null mice that lack ApoE or LDLR are less prone to developing atherosclerotic lesions6. Previous studies from our group identified an essential role of the CD36-JNK signaling cascade in macrophage foam cell formation7. Toll like receptors TLR2, TLR4, and TLR6 act as coreceptors by interacting with CD36, and have been shown to be involved in atherogenesis63. We examined the possibility that lack of foam cell formation seen in ginkgetin-treated macrophages was due to decreased expression of CD36, TRPV4, TLR2, TLR4, or TLR6 proteins. Interestingly, ginkgetin treatment did not significantly decrease expression of CD36, TLR2, TLR4, or TLR6 proteins at basal levels or under oxLDL-stimulated conditions. However, ginkgetin specifically blocked oxLDL-induced upregulation of expression of TRPV4 proteins, while its expression at basal levels remained unchanged. Considered all together, these results suggest that ginkgetin blocks oxLDL-induced foam cell formation via a mechanism independent of CD36 and TLR expression, but dependent on TRPV4.Studies done in vivo and in vitro suggested a critical role of CD36 as the principal scavenger receptor in uptake of oxLDL and macrophage foam cell formation57. It was also reported that JNK is involved in the modulation of MMP-9 and MMP-13, which are overexpressed in atherosclerotic lesions68. Given the recognized link of JNK in atherogenic processes, we wanted to determine if ginkgetin could inhibit the activation of JNK. In accord with previous reports, our results also showed that ginkgetin blocks inflammatory stimulus-induced phosphorylation of JNK2 in macrophages. This result suggests a possible mechanism by which ginkgetin blocks foam cell formation. Furthermore, we found a significant downregulation in oxLDL-induced expression of TNFα, IL12, IL1β, and MCP1 mRNA in ginkgetin-treated cells compared to vehicle control, highlighting potential anti-inflammatory roles of ginkgetin in response to oxLDL. In summary, our results showed that ginkgetin inhibits proatherogenic and inflammatory macrophage function in a TRPV4-dependent manner. These findings thus strengthen the rationale for the use of natural compounds in development of potential therapeutics and/or chemopreventive reagents.Previous studies from our group and others showed that JNK2 activation is involved in foam cell formation and atherosclerotic lesion developmentSupplementary Information