CRPC-Lncs, that are highly expressed in CRPC tissues. Notably, silencing of two lncRNAs (CRPC-Lnc #6: PRKAG2-AS1 and #9: HOXC-AS1) alleviated CRPC tumor growth, showing repression of AR and AR variant expression. Mechanistically, subcellular localization of the splicing factor, U2AF2, with an essential role in AR splicing machinery was modulated dependent on the expression level of CRPC-Lnc #6. Thus, our investigation highlights a cluster of lncRNAs which could serve as AR regulators as well as potential biomarkers in CRPC.The molecular and cellular mechanisms of development of castration-resistant prostate cancer (CRPC) remain elusive. Here, we analyzed the comprehensive and unbiased expression profiles of both protein-coding and long non-coding RNAs (lncRNAs) using RNA-sequencing to reveal the clinically relevant molecular signatures in CRPC tissues. For protein-coding genes upregulated in CRPC, we found that mitochondria-associated pathway, androgen receptor (AR), and spliceosome associated genes were enriched. Moreover, we discovered AR-regulated lncRNAs,  By RNA sequencing of prostate cancer samples, Takayama et al identify a cluster of lncRNAs that are enriched in castration-resistant prostate cancer. This cluster is regulated by androgen receptor (AR) and associate with factors involved in AR splicing, suggesting a role for these lncRNAs in AR signalling and tumorigenesis. Advances in the treatment of prostate cancer by surgery and radiation have contributed to the favorable prognosis for patients2. In addition, androgen-deprivation therapy (ADT) has resulted in significantly prolonged survival of patients with metastatic prostate cancer. Drugs used in ADT including enzalutamide, bicalutamide, and abiraterone inhibit androgen signaling, which is the major growth-promoting pathway mediated by the androgen receptor (AR). Despite the initial success of the treatment, many aggressive cancers, termed castration-resistant prostate cancer (CRPC), grow even in castrate levels of androgen. The main cause of CRPC development is the reactivation of AR signaling3. Previous studies have discovered the underlying mechanisms for the enhanced AR downstream signaling, expression of AR, and its splice variants lacking the ligand-binding domain (ARVs) in CRPC4. The variant AR-V7 was shown to regulate distinct and androgen-independent activation of its downstream signals, which contributes to the development of CRPC6. Thus investigation of the molecular mechanisms of activation of AR, AR-V7, and its downstream signals would have a significant clinical impact in treating CRPC.Prostate cancer is the most common cancer in men and is the second leading cause of cancer death in the developed countries7, including prostate cancer cells8. Previous reports highlighted lncRNA-mediated association of RNA-binding proteins or transcription factors with specific genomic regions in prostate cancer progression9. We previously reported that an androgen-induced lncRNA, CTBP1-AS, in the antisense region of carboxyl terminal binding protein 1 (CTBP1) promotes castration-resistant tumor growth10. CTBP1-AS modulates the global epigenetic status to repress negative cell cycle regulators or AR corepressor, CTBP1, to promote tumor growth and activation of AR activity. Interestingly, recent reports showed that androgen-regulated lncRNAs are implicated in several processes in AR activation. Androgen-repressed HOTAIR increased AR expression through posttranslational stabilization of protein by blocking the E3 ligase, MDM2, targeting AR for ubiquitylation11. Androgen-induced ARLNC1 is highly expressed in CRPC tissues and stabilizes the AR mRNA by RNA–RNA hybridization to enhance AR expression level posttranscriptionally12. Thus lncRNAs, particularly the AR-regulated lncRNAs, form an important regulatory layer in global gene expression. Moreover, alterations of the lncRNA expression profile in CRPC are assumed to be one of driving forces for cellular transformation. However, the clinical relevance of lncRNA expression and associated molecular mechanisms in CRPC has not been fully understood.Long non-coding (lnc) RNAs have diverse functions such as epigenetic and gene regulation in cancer cells12. In the previous study, we measured the expression levels of targeted protein-coding genes using tumor samples from patients with metastasis and demonstrated that hormone-regulated and stem cell-related markers could predict survival of these patients13. Meanwhile, more comprehensive and unbiased analyses of gene expression are preferable in tumors to identify the clinical and molecular signatures responsible for the aggressiveness of prostate cancer. Here we performed directional RNA-sequencing (RNA-seq) using clinical samples obtained from localized prostate cancer and CRPC patients. For the protein-coding genes, we found a cluster of upregulated genes in CRPC. In addition, by integrating with AR chromatin immunoprecipitation-sequencing (ChIP-seq) data that we performed using several prostate cancer cell lines20, we found changes in the AR program in CRPC tissues. Furthermore, we discovered a cluster of CRPC-enriched lncRNAs (abbreviated as CRPC-Lncs), which are regulated by AR. Using functional analysis, we revealed that splicing factors with an essential role in AR splicing machinery associate with this cluster of lncRNAs, indicating that these RNAs can create the conditions for promoting AR splicing and overexpression. Taken together, our investigation identified a cluster of lncRNAs, CRPC-Lncs, which could serve as new biomarkers and therapeutic targets in CRPC.Transcriptional characterization of cancer tissues can reveal important molecular signatures associated with the disease progression18 RNA-seq data, we mapped the genome-wide expression profile of protein-coding genes and lncRNAs registered in RefSeq, GENCODE, and NONCOE databases. We then compared the gene expression levels of CRPC tissues with benign prostate and localized prostate cancer tissues, demonstrating that CRPC-associated alteration of signals in tumors occurred on a genome-wide scale  were expressed in each of these datasets. We found that most of them are similarly upregulated in the metastatic CRPC tissues based on these microarray data, in line with our RNA-seq analysis , which are assumed to be abundant in CRPC specifically Fig. . We thenAR3 Fig. . Thus thAR3 Fig. . Interessis Fig. .Next, we analyzed the molecular functions upregulated in CRPC tissues by analyzing Kyoto Encyclopedia of Genes and Genomes pathway. Among RefSeq genes in Type_A , we found remarkable enrichment of genes involved in biological activities, such as oxidative phosphorylation (OXPHOS), spliceosome, DNA replication, and cell cycle Fig. . Interes16. We used three AR ChIP-seq data to select AR-binding genes, which were the closest genes to AR-binding sites in androgen-dependent-type prostate cancer cells (LNCaP and VCaP) and CRPC model cells (22Rv1)  to investigate AR-dependent signals because 22Rv1 cells express ligand-independent AR variant (AR-V7) at a high level and increased binding of AR was observed without DHT treatment in ChIP-seq data16. Then we integrated the AR ChIP-seq data with RNA-seq to identify AR-regulated genes. A total of 1748 genes were found to be induced by AR in LNCaP cells, and around half of them were also AR-induced genes in VCaP cells  in prostate cancer tissues compared to benign prostate tissues was high (13.8%). However, most of them (10.2%) were downregulated in the CRPC tissues compared to the prostate cancer tissues  than that in LNCaP/VCaP (13.8%)  Fig. . In addills Fig. . In 22Rvlls Fig. . Among tues Fig. . In cont8%) Fig. , while t8%) Fig.  compared8%) Fig. , suggestlts Fig. . Within lts Fig. . We founlts Fig. .Fig. 2ARARLNC112, PCAT-121, and CTBP1-AS10, were also upregulated in CRPC compared to benign prostate or localized prostate cancer tissues . Basically, NR_ genes in RefSeq(reads per kilobase of exon per million mapped reads (RPKM) > 5), genes in GENCODE (RPKM > 1), and NONCODE (RPKM > 1) are supposed to be candidates for lncRNAs. However, we noticed that most of the lncRNAs registered in these databases are composed of exons of protein-coding genes and could not be determined to be protein-coding genes or lncRNAs. Therefore, we checked carefully whether the identified transcripts were bona fide lncRNAs or only reflected the detection of exons included in protein-coding genes. Totally, we obtained a total of 91 in Type_A and 72 in Type_B transcripts that were considered as “CRPC-related lncRNAs” Fig. . We founues Fig. .Fig. 3Id12. Therefore, we hypothesized that lncRNAs would be useful as novel biomarkers for CRPC diagnosis. Here we compared the sets of AR downstream signals in prostate cancer cells identified by RNA-seq with those obtained as CRPC-related lncRNAs  because they were highly expressed in CRPC tissues and their roles in prostate cancer had not been reported previously (Table HOXC-AS1 (CRPC-Lnc #9) located in the HOXC cluster in the vicinity of HOTAIR was included. We also observed uncategorized lncRNA expression in the intron of HOXC4-6 (HOXC-intron) to be also overexpressed in CRPC tissues  as well as ARLNC1 -5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, inner salt) and cell growth assays, we observed inhibition of cell proliferation following the silencing of these CRPC-Lncs in 22Rv1 and DU145 cells  treatment reduced 22Rv1 tumor growth after castration, suggesting that these lncRNAs have a role in CRPC tumor growth. Using western blot analysis, we showed that the expression levels of AR-V7 and full-length AR protein were drastically reduced following the treatments of these siCRPC-Lncs  cells15 derived from LNCaP  assay to analyze the interaction of CRPC-Lncs with the splicing factors. We observed the most significant interaction between CRPC-Lncs and U2 small nuclear RNA auxiliary factor 2 (U2AF2)32 among the three splicing factors 33, and polypyrimidine tract-binding protein-associated splicing factor (PSF))16 examined  and RNA-seq data  . We found that high expression levels of CRPC-Lncs are significantly more correlated with gene induction than repression, suggesting that CRPC-Lncs facilitate gene induction of other downstream targets as well as AR in prostate cancer tissues  and the University of Tokyo . The study was approved by the Human Genome, Gene Analysis Research Ethics Committee of the Jichi Medical University No. 18-15), Tokyo Metropolitan Institute of Gerontology (No. 1183), and University of Tokyo (G10044). Written informed consent was obtained from each patient before treatment. This study comprised of six benign prostate and eight localized prostate cancer tissues from eight Japanese patients with prostate cancer. We collected both prostate cancer and benign prostate tissues using laser capture microdissection from prostate tissue sections obtained by radical prostatectomy as described before8-15, Tok15 for 48 h. Following 24 h of treatment of cells with 10 nM DHT or vehicle treatment, cells were collected and total RNA was extracted for RNA-seq analysis. Total RNA was extracted from the benign prostate and prostate cancer tissues as well as CRPC tumor samples and assessed for integrity and concentration using an Agilent 2100 Bioanalyzer system. We confirmed that the RNA integrity numbers of all samples were >9.0. RNA-seq libraries were constructed using the TruSeq RNA Library Preparation Kit v2 . Sequencing was performed by Illumina HiSeq 250016. For removing rRNA sequences, we used Bowtie 2v. 2.2.6. Mapping to human genome (hg19) was performed using Tophat 2.1.0. Alignments were generated in the SAM format from given single-end reads. For expression analysis, the reads were mapped to the human RefSeq mRNA database or GENCODE/NONCODE database and then we subsequently calculated counts for annotated features, exons, transcripts, or genes. The expression levels of the mapped transcripts were normalized to RPKM to facilitate comparison among different samples. To detect genes highly expressed in CRPC samples, Mann–Whitney U test was conducted to identify significantly differentially expressed genes among the benign prostate, prostate cancer, and CRPC samples. Genes with P ≤ 0.05 were considered to be significantly upregulated in CRPC. Pathway enrichment in hierarchically clustered gene groups was evaluated using The Database for Annotation, Visualization, and Integrated Discovery (DAVID).22Rv1 cells were transfected with control siRNA (siControl) or siRNA (10 nM) targeting AR (siAR)2 atmosphere. Also, we routinely checked for mycoplasma contamination using a PCR-based kit, Mycoplasma Detection Kit . Additional viral infection was checked using PCR method . We maintained stocks of low-passage cells and restarted our cell culture with a fresh vial at least once a month. VCaP and 293T cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 50 U/mL penicillin, and 50 μg/mL streptomycin. 22Rv1 and LNCaP cells were cultured in RPMI medium supplemented with 10% FBS, 50 U/mL penicillin, and 50 μg/mL streptomycin. LTAD cells were cultured in phenol-red free RPMI medium supplemented with 10% charcoal–dextran-stripped FBS, 50 U/mL penicillin, and 50 μg/mL streptomycin10. For androgen treatment, cells were incubated in phenol-red-free RPMI/DMEM medium supplemented with 2.5% charcoal–dextran-stripped FBS for 3 days before androgen stimulation.The cell lines used in the present study were obtained from ATCC. Identities of the cells were confirmed by short tandem repeat analyses in 2019 and 2015 . All cell lines were grown at 37 °C in a 5% COCRPC-Lnc #6 RNA probe  and grown for 24–72 h. Cells were fixed using fixation buffer  for 10 min. After washing the cells twice with phosphate-buffered saline (PBS), cells were permeabilized by incubating them in 70% ethanol for at least 1 h at 4 °C. After washing twice with PBS, Stellaris RNA-FISH Wash buffer A  including 10% deionized formamide  was added to cells and incubated for 5 min. In all, 12.5 μM AR intron and/or DHT were purchased from Wako . Rabbit polyclonal anti-AR (ab108341) and anti-AR-V7 (ab198394) antibodies were purchased from Abcam . The rabbit polyclonal anti-H3 (4499) antibody was purchased from Cell Signaling Technology . The mouse monoclonal anti-β-actin (A2228) antibody was obtained from Sigma. The mouse monoclonal anti-U2AF2 (MC3), anti-hnRNAPA1 (4B10), and a goat polyclonal anti-GAPDH (V18) antibodies were purchased from Santa Cruz Biotechnology .CRPC-Lncs. The siRNAs transfected were as follows: #1: n312690, #2: n507220, #3: n272703, #4-1: n503418, #4-2: n503419, #5: n507156, #6-1: n509295, #6-2: 10620318, #7: n515266, #8: n507972, #9-1: n489630, #9-2: n489629, #10: n513639, #11-1: n508406, #11-2: n50847, and #12: n502900 . Two siARs were described before15. siAR#1 targets only AR full length. siAR#2 targets both full length and variants of AR15. The transfection reagent Lipofectamine RNAiMAX (Thermo Fisher) was used according to the manufacturer’s instructions. Cells were incubated for 48–72 h posttransfection to confirm the knockdown. To establish stable cells overexpressing CRPC-Lnc #6, we transfected CRPC-Lnc #6 pcDNA3.0 or empty vector into LNCaP cells using X-tremeGene HP DNA Transfection Reagent . Clones expressing CRPC-Lnc #6 were selected by incubating cells in the presence of G418  at the concentration of 500 μg/mL.Cells were transfected with control siRNA or siRNAs (10 nM) targeting 16. Whole-cell extracts were prepared in lysis buffer ). Obtained lysates were loaded on sodium dodecyl sulfate (SDS)–polyacrylamide gels for electrophoresis and subsequently transferred onto Immobilon-P Membranes . Membranes were incubated with the specific primary antibodies at 4 °C overnight. After incubation with secondary antibodies for 1 h, antibody–antigen complexes were visualized using Western Blotting Detection Reagents .Nuclear and cytoplasmic proteins were obtained by using hypotonic buffer  and RIPA buffer. Protein concentration was calculated by using the BCA Assay Kit . Western blot analysis was performed as described before16. Details of the PCR primers are listed in Supplementary Table Total RNA was isolated from benign prostate and tumor tissues, as well as from prostate cancer cells, using the Qiagen RNeasy Mini Kit . Complementary DNA was produced by PrimeScript . qPCR was performed in a StepOne PCR System (Thermo Fisher) using KAPA SYBR Green (Sigma). Gene expression was calculated by ΔΔCt method and normalized to GAPDH at the same condition16. Briefly, the cell lysates were incubated with splicing factor antibody coupled IgG magnetic beads at 4 °C overnight. The RNA/antibody complex was washed six times. The RNA was then extracted using ISOGEN and subjected to qRT-PCR. Primer sequences to detect AR intron have been described previously16.For RIP assay, we used the EZ-magna RIP RNA-binding Protein Immunoprecipitation Kit  according to the manufacturer’s protocol10 with some modifications. Biotin-labeled CRPC-Lnc #6 and antisense fragments were prepared using Biotin RNA Labeling Mix (Roche) and T7 RNA polymerase. Biotinylated RNAs were treated with RNase-free DNase (Qiagen), and 10 pmol biotinylated RNA was heated to 60 °C for 10 min and slowly cooled to 4 °C. The RNA was mixed with 100 μg of pre-cleared nuclear extract in RIP buffer supplemented with tRNA (0.1 μg/µL) and incubated at 4 °C for 8 h. A total of 60 µL of washed Streptavidin Agarose beads (Thermo Fisher) was added and incubated for an additional 1 h at 4 °C. The beads were washed five times with RIP buffer and boiled in SDS buffer, and the retrieved proteins were analyzed by western blot analysis.RNA pulldown was performed as described previously4 cells/well in phenol-red-free RPMI medium with 5% charcoal-stripped FBS. After 2 days incubation, the cells were co-transfected with luciferase reporter plasmid (MMTV and PSA-Luc), Renilla luciferase control, and Tk-pRL vector  as a reference using X-tremeGene HP DNA Transfection Reagent (Sigma). After 24 h of incubation, the cells were treated with 10 nM DHT or vehicle (0.1% ethanol) for further 24 h. Luciferase activity was measured using the Dual Luciferase Assay Kit (Promega). The firefly luciferase activity was normalized to the Renilla luciferase activity.LNCaP and 22Rv1 cells were plated in 24-well dishes at a density of 3 × 103) were plated and cultured in 96-well dishes for MTS assay. For cell growth assay, cells were cultured at 5 × 103 cells in 24-well plates. Cells were trypsinized and counted using the trypan blue exclusion method. The MTS and cell growth assay were performed in four biological replicates.CellTiter 96 Aqueous Kit (Promega), an MTS-based assay, was used to quantify cell vitality and cell growth rate. Cells  complexed with Opti-MEM and 15 μL Lipofectamine RNAiMAX (Thermo Fisher) was injected into the tumors 3 times/week for 2 weeks. Tumor volume was calculated using the formula 1/2 × r1 × r22 (r1 and r2 (r1 > r2) represent the length and width of the tumor, respectively). Tumors were measured with calipers twice a week. After monitoring the tumor size for 2 weeks, the mice were sacrificed, and the tumor samples were analyzed. Animal care was in accordance with the Tokyo Metropolitan Institute of Gerontology animal experiment guidelines. The ethics committee of animal experiments at the Tokyo Metropolitan Institute of Gerontology approved our study protocol. Tumors were homogenized in ISOGEN  and RIPA buffer with protease inhibitor cocktail  for qRT-PCR and western blot analysis17.Prostate cancer cells, 22Rv1, suspended in 100 μL medium were mixed with 100 µL of Matrigel  and subcutaneously injected into one side of 20 5-week-old male BALB/c nude mice (CLEA Japan) using a syringe fitted with a 26-G needle. After 7–10 days postinjection, the primary tumor measured approximately 100 mmN values represent technical replicates for qPCR analyses of cancer cell lines but biological and clinical independent samples for other experiments. The data are presented as mean ± SD. In most experiments using cell lines, we used the two-sided Student’s t test to determine the statistical significance between groups. Significance was defined as P < 0.05. qPCR analyses in cell lines were performed in technical replicates. Cell growth assay and luciferase assay were performed in biological replicates. For the assays of stable cell lines, we performed two-way analysis of variance to analyze the significance between the two groups. To determine correlations, we used Spearman’s correlation test. Other statistical tests are described in the figure legends. Excel  or GraphPad Prism software ver. 6.0  was used for the statistical analysis.We performed all experiments at least twice and confirmed similar results. Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting SummaryPeer Review File


The etiology of dental-supporting tissue diseases in children is multi factorial and not merely related to oral hygiene. Therefore, in the present study, we investigated the relationship between children <18 years old with allergic rhinitis (AR) and the risk of dental-supporting tissue diseases.Data from the National Health Insurance Research Database (NHIRD) of Taiwan were used to conduct a retrospective longitudinal cohort study. The study cohort comprised 378,160 patients with AR (AR group) and 378,160 patients without AR (non-AR group), who were selected through frequency matching based on age, sex, and the index year. The study patients were followed until dental-supporting tissue diseases occurrence, withdrawal from the National Health Insurance program, or December 31, 2013. Cox proportional hazards regression analysis was conducted to calculate the risk of dental-supporting tissue diseases in the AR group after adjustment for age, sex, and relative comorbidities.The adjusted HRs of periodontal, pulp, and periapical diseases in AR children were higher than those in the non-AR controls . The AR to non-AR HRs of these inflammatory dental diseases were particularly higher in children <6 years old and in boys. The HRs of periodontal, pulp, and periapical diseases were greatest in those with >5 AR-related medical visits/year .Children with AR had a greater risk of inflammatory dental-supporting tissue diseases, particularly those <6 years old with primary teeth, boys, and those with severe persistent AR. The present study is the first to utilize a large sample to investigate the association between childhood AR and inflammatory dental-supporting tissue diseases, including periodontal, pulp, and periapical diseases.Children with AR had a greater risk of inflammatory dental-supporting tissue diseases, particularly those < 6 years old with primary teeth, boys, and those with severe persistent AR.Identifying risk factors for inflammatory dental-supporting tissue diseases is important not only to prevent inflammation in the oral cavity but also to prevent multiple inflammation-related systemic diseases.1 The periodontal, pulp, and periapical tissues are closely associated with development, anatomy, and function. Pulp and periapical disease may affect the initiation and progression of periodontal disease. In contrast, patients with chronic periodontitis usually have pulpal tissue changes such as inflammation, edema, necrosis, fibrosis, and calcification. The exchange of immune cells and inflammatory mediators between periodontal, pulp, and periapical tissues is considered to happen through the vascular system in the apical foramen. Besides, a variety of systemic diseases are reported to interfere with periodontal, pulp, and periapical health.,5Periodontal, pulp, and periapical diseases, inflammatory dental-supporting tissue diseases, are common dental disorders in children.,5 Therefore, identifying risk factors for periodontal, pulp, and periapical diseases in the pediatric population is important not only to prevent inflammation in the oral cavity but also to prevent multiple inflammation-related systemic diseases. However, despite increasing research on the link between dental-supporting tissue diseases and systemic diseases over the past few decades, the fundamental biological mechanisms of the association are not fully elucidated.,5Chronic periodontitis and pulpitis may increase systemic inflammation, and are linked to multiple systemic diseases such as type 2 diabetes, metabolic disease, obesity, cardiovascular disease, fatty liver disease, and cancer.,7. Allergic rhinitis (AR) is the most common allergic disease. Children with AR are frequently observed to have gingival and periapical inflammation.,9 Interleukin (IL)-5,12,13, and IL-16, involved in the regulation of the onset and development of AR,,11 also play a role in the pathogenesis of periodontal diseases. Several studies have discussed the relationship between AR and dental caries in children.,13–15 Although some studies with inconsistent results have focused on adult patients,15 there is no study demonstrating the relationship between AR and periodontal, pulp, and periapical diseases in childhood.A few studies show the relationship between periodontal disease or salivary micro-flora change and allergic disease.Because there are 3 stages, including primary, transition, and permanent, of teeth development in children, whether AR is an independent risk factor for dental-supporting tissue diseases in childhood is of great interest. This large, population-based study aimed to explore how AR correlates with childhood inflammatory dental-supporting tissue disease, including periodontal, pulp, and periapical diseases.22.1 The NHIRD contains all the registry data of the insured, diagnostic codes of International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), as well as details of outpatient and inpatient visits, procedures, prescriptions, and medical expenditure. This study used a dataset from the NHIRD, containing a randomly selected sample of half of all insured children in Taiwan. Based on the Personal Information Protection Act, de-identification was performed before the release of the dataset to researchers; thus, informed consent was not required for this study. This study received approval from the institutional review board of China Medical University Hospital (CRREC-103-048).The National Health Insurance (NHI) program was established in Taiwan in 1995. This program has covered the reimbursement of medical care for approximately 23 million people, accounting for over 99% of the population of Taiwan.2.2This was a retrospective cohort study using the NHIRD. Between 2000 and 2012, patients <18 years old with newly diagnosed AR  code 477) were selected as the AR cohort. The comparison non-AR cohort with no AR diagnostic codes was selected by 1:1 matching based on a propensity score. The propensity score was calculated using a logistic regression model to estimate the probability of disease assignment based on baseline variables, which included age, sex, index year, urbanization, and the presence of the following comorbidities: chronic sinusitis (ICD-9-CM 473), obstructive sleep apnea (ICD-9-CM 327.23), asthma (ICD-9-CM 493), hypertrophy of the tonsils and adenoids (ICD-9-CM 474.1), and obesity (ICD-9-CM 278). Confounders such as age, sex, urbanization, and comorbidities were adjusted for in the analysis.2.3,17 Urbanization has been reported to be related to oral hygiene. The urbanization level, categorized by the population density of the residential area into 4 levels, with level 1 being the most urbanized and level 4 being the least urbanized, was adjusted in our study.Periodontal, pulp, and periapical diseases are multi factorial diseases. Well known risk factors for oral diseases include poor oral hygiene, frequent consumption of a sugary diet, female, and urbanization. and may be related to the frequent consumption of a sugary diet. There were no data on the frequency of sugary diet consumption. Therefore, obesity was used instead of frequency of sugar diet consumption. Chronic sinusitis and asthma are common comorbidities in children with AR. The etiology and medication, such as antibiotics and inhaled steroids, might be associated with oral disease.,21 There is some evidence on the association between periodontal disease and obstructive sleep apnea. These comorbidities are thought to be confounding factors that increase the risk of periodontal, pulp, and periapical diseases.Obesity has been reported to be associated with periodontal diseaseDental facial anomaly and children with disability could increase the risk of dental disease because of difficulty in maintaining dental health. Individuals with congenital anomalies (ICD-9-CM 740-759) and mental retardation (ICD-9-CM 317-319) were excluded. Individuals diagnosed with dental diseases and dentofacial anomalies before 2000 were also excluded.2.4The risk of periodontal, pulp, and periapical diseases was compared in the AR and non-AR cohorts between 2000 and 2013. All physician-diagnosed dental diseases were determined using diagnostic codes for periodontal (ICD-9-CM 523), pulp, and periapical diseases (ICD-9-CM 522).2.5P value <.001 was considered statistically significant.Baseline characteristics of AR and non-AR cohorts were compared using standardized mean difference. Values of standardized mean differences ≤0.01 indicated a negligible difference in mean values between the AR and non-AR cohorts. We used a Cox proportional hazard regression model to compare the risk of periodontal, pulp, and periapical diseases in AR and non-AR cohorts, adjusted for age, sex, urbanization, and comorbidities. We also compared the risk of development of these diseases given the average frequency of AR-related medical visits. The cumulative incidence curves of periodontal, pulp, and periapical diseases in the study cohort were estimated using Kaplan-Meier analyses. All analyses were performed using SAS software, version 9.1 , and a 3A total of 378,160 AR patients were identified; 45.9% of these patients were girls. The mean  age at AR diagnosis was 4.85 (3.82) years, and 73.6% were newly diagnosed before the age of 6 years Regarding the effect of comorbidities, a high HR for periodontal, pulp, and periapical diseases was noted even for AR children without comorbidities of chronic sinusitis, obstructive sleep apnea, asthma, and hypertrophy of tonsils and adenoids Table . Figure 4,2 Studies on the association between AR and inflammatory dental supporting tissue diseases have reported conflicting results.,22,23 The present study is the first to utilize a large sample to investigate the association between AR and periodontal, pulp, and periapical diseases in children. We found that AR children had a significantly high risk and higher cumulative incidences of periodontal, apical, and periapical diseases than those without AR. A particularly high risk for these inflammatory dental supporting tissue diseases were found in those below age of 6 years with primary teeth and boys. Further, children with more frequent medical visits for AR had higher risks for periodontal, pulp, and periapical diseases, indicating a dose-dependent effect.Chronic inflammatory diseases affecting dental tissues if not diagnosed promptly and treated appropriately may ultimately lead to tooth loss. and the other one in Korea revealed an inverse association between periodontal disease and AR in adults. In contrast, another 2 matched case-control studies in Taiwan showed a positive correlation between AR and periodontal disease in adults.,23 Moreover, Ho et al revealed that men, urban citizens, and low income people with AR had higher incidence of pulpitis. Our study found that AR children had a high risk of inflammatory dental supporting tissue diseases. Moreover, we also assessed the risks of these dental surrounding tissue diseases according to different stages of teeth development. Our results revealed that younger children with primary teeth had particularly higher risks for periodontal, pulp, and periapical diseases. Because chronic periodontitis and pulpitis may increase systemic inflammation and many systemic diseases, such as type 2 diabetes, metabolic disease, obesity, cardiovascular disease, fatty liver disease, and cancer, aggressive treatment of childhood AR may be beneficial for oral health improvement and for prevention of certain systemic diseases.,5There are few studies investigating the interaction between AR and periodontal disease and the findings are contradictory. One cross-sectional study in Germany,23 However, there are limited studies on the association between intranasal steroids and dental inflammatory disease. The mainstay treatment of AR includes oral or intranasal antihistamines and intranasal corticosteroids. The management of AR depends on its severity and duration. Those with persistent and severe AR are at a higher risk of using intranasal steroids. We speculate that nasopharyngeal deposition of steroids due to postnasal drip might induce the same oral diseases that inhaled steroids do, which explains our finding that AR children with more frequent medical visits have a higher risk of periodontal, pulp, and periapical diseases.Inhalation of corticosteroids has been reported to increase the incidence of caries and periodontitis because of a resultant change in oral pH, local deposition of steroids in the oral cavity, and their effect on oral mucosa1. Our study showed that AR children had a higher incidence and risk of dental soft tissue inflammation. Although the pathogenesis remains unclear, dry mouth due to mouth breathing and taking an oral antihistamine and inflammatory reactions due to AR may worsen dental health.,21 Mouth breathing may cause gingival surface dehydration, decreased epithelial resistance to bacterial plaques, and limit salivary auto-cleaning. Certain immunological and inflammatory reactions have been reported to interplay between AR and these dental diseases.,12,28 Mast cells play a crucial role in the pathogenesis of AR. Several studies demonstrate that mast cells may also play an important role in the development of pulpitis and periodontitis during its acute stages and its subsequent transition to chronic inflammation. Histamine, released from mast cells, acts as a strong vasodilator and mediator of vascular permeability and may play a role in initiating pulp, periapical, and periodontal inflammation.Chronic polymicrobial infection to the surrounding dental tissues and eliciting a host inflammatory immune response in susceptible individuals is a central feature of periodontal, pulp, and periapical diseases. Nonetheless, we report a positive and dose-dependent relationship between AR and development of periodontal, pulp, and periapical diseases. Second, the AR cohort was selected based on the diagnostic code of ICD-9-CM. Ideally, a skin prick test is necessary to confirm the AR diagnosis; however, the diagnoses for our study cohort were made by a licensed and well-trained physician. Third, surveillance bias should be taken into consideration for the AR cohort with a high risk of dental surrounding tissue diseases. Those with severe and persistent AR need more visits for medical care, which can trigger early screening for dental diseases. However, AR children with more medical care visits may have a higher chance to have oral health knowledge and surveillance, that may controvert this bias.There are several limitations of our study. First, the claims data do not include information on oral hygiene and the severity of periodontal, pulp, and periapical diseases. The urbanization used instead of oral hygiene due to association with oral hygiene behavior.5AR children had a higher risk for inflammation of dental supporting tissues, including periodontal, pulp, and periapical diseases. The risk was much higher in children aged less than 6 years with primary teeth, in boys, and in those with severe persistent AR. These findings highlight the importance of awareness and regular follow-up of dental conditions in AR children.∗Wan-Yu Lai and Chang-Ching Wei contributed equally.Wan-Yu Lai, Chen-Hao Mai, and Chang-Ching Wei conceptualized and designed the study. Wan-Yu Lai and Chang-Ching Wei drafted the initial manuscript. Cheng-Li Lin carried out the acquisition of data and analysis and interpretation of data. Lei Wan and critically reviewed and revised the manuscript. Chang-Ching Wei and Jeng-Dau Tsai coordinated and supervised data collection, critically reviewed the manuscript, and approved the final manuscript as submitted.Conceptualization: Chang-Ching Wei, Wan-Yu Lai.Data curation: Wan-Yu Lai.Formal analysis: Jeng-Dau Tsai.Funding acquisition: Jeng-Dau Tsai.Investigation: Chang-Ching Wei, Jeng-Dau Tsai, Lei Wan, Chen-Hao Mai.Methodology: Jeng-Dau Tsai, Lei Wan, Chen-Hao Mai, Cheng-Li Lin.Resources: Cheng-Li Lin.Software: Chen-Hao Mai, Cheng-Li Lin.Supervision: Chang-Ching Wei, Lei Wan.Validation: Chen-Hao Mai.Writing – original draft: Chang-Ching Wei.Writing – review & editing: Chang-Ching Wei.


Using a retrospective cohort design with patients in the Department of Veterans Affairs healthcare system, we use doubly robust regression and matching to estimate the association between baseline use of α1-AR antagonists and likelihood of death due to COVID-19 during hospitalization. Having an active prescription for any α1-AR antagonist  at the time of admission had a significant negative association with in-hospital mortality  and death within 28 days of admission . In a subset of patients on doxazosin specifically, an inhibitor of all three alpha-1 adrenergic receptors, we observed a relative risk reduction for death of 74%  compared to matched controls not on any α1-AR antagonist at the time of admission. These findings suggest that use of α1-AR antagonists may reduce mortality in COVID-19, supporting the need for randomized, placebo-controlled clinical trials in patients with early symptomatic infection.Effective therapies for coronavirus disease 2019 (COVID-19) are urgently needed, and preclinical data suggest alpha-1 adrenergic receptor antagonists (α While dde novo and signal in an autocrine/paracrine self-regulatory fashion  (1-AR) antagonist that inhibits all three receptor subtypes  can prevent cytokine storm and death by blocking deleterious catecholamine signaling and immune responses  compared to non-users  are monoamine hormones that signal through adrenergic receptors (ARs) expressed on tissues including cells of the immune system –10. Cell fashion ,11. Beyo, MIP-2) ,10–12. Ion-users . Similarammation . We thernfection , but dat1-AR antagonists with in-hospital mortality in patients with COVID-19. Here, we analyzed a large cohort of patients hospitalized at Veterans Health Administration (VA) hospitals, in whom α1-AR antagonists are commonly used to treat unrelated diseases such as benign prostatic hyperplasia (BPH), post-traumatic stress disorder (PTSD), or arterial hypertension  criteria .1-AR antagonists at the time of admission for the index hospitalization. Active prescriptions of α1-AR antagonists  were identified and defined by the patient having medication on hand on the day of the index admission, regardless of dosage. Secondary analyses examined the effect of tamsulosin  and doxazosin  individually. Finally, with in-hospital therapies evolving during the pandemic, we repeated the analysis by week and VA hospital to ensure results were not driven by any particular time or location.The primary outcomes were death during the index hospitalization and death within 28 days of admission. The primary exposure variable was the use of αWe obtained data on patient demographics, vital signs, and prescription drugs from the VA’s corporate data warehouse (CDW). Patient comorbidities were captured based on the International Classification of Diseases, Version 10 codes from VA care in the year prior to index admission. Other physiologic variables, including oxygen saturation and temperature, were defined at time of inpatient admission.2.21-AR antagonist prescriptions to all other patients with COVID-19 using Fisher’s exact test. We then estimated propensity scores and trimmed the sample to ensure overlap in the propensity score distributions of the exposed and unexposed groups. On this reduced sample, the adjusted analysis used inverse propensity-weighted logistic regression adjusting for patient age at admission , calendar week, location of hospitalization, and comorbidities diagnosed any time in the two years prior to the index inpatient stay. This approach is “doubly robust” in that it uses the observed confounders in both the calculation of the propensity score and the odds ratios. Comorbidities included in the matching procedure were diabetes mellitus, arterial hypertension, heart failure, ischemic heart disease, acute myocardial infarction, chronic obstructive pulmonary disease (COPD), end-stage renal disease (ESRD), and PTSD. We also included an indicator variable for oxygen saturation under 94 percent on the day of admission.Analyses followed the methodology of a companion paper examining patients with acute respiratory distress and pneumonia . UnadjusAll of the control variables reflect information on patients prior to admission with COVID-19. As noted above, we controlled for secular changes in COVID-19 care using calendar week, starting with February 20, 2020. We chose not to examine endpoints during the hospital stay, such as use of a ventilator or admission to the ICU, given this is based on physician coding or data structures that we cannot assure were handled uniformly, especially during surges. We also chose not to control for processes of care during the stay given this could introduce bias in the analysis.We then conducted a 5:1 matched analysis using the same covariates as the adjusted model . This ap33.11-AR antagonist at time of admission. Of those taking α1-AR antagonists, 73% of patients were on tamsulosin , 12% on terazosin (n=679), 10% on prazosin (n=581), 4% on doxazosin (n=215), 3% on alfuzosin (n=186), and less than 1% were on silodosin (n=5)  . 177 pat3.21-AR antagonist, compared to non-users, had an 18% relative risk reduction for death during their hospitalization , in-hospital mortality was 6% overall and 5.5% in our sample. Patients with confirmed COVID-19 taking any α1-AR antagonists, while the lighter green represent doxazosin. Results were similar for the suspected COVID-19 sample. Patients taking any α1-AR antagonists, compared to non-users, had an 20% relative risk reduction for death (p ≤ 0.001) in this cohort , than for a pooled population of patients treated with any α1-AR antagonist in whom tamsulosin was the most common drug (72%). This was similarly true for patients treated exclusively with tamsulosin, a “uroselective” α1-AR antagonist on α1A- and α1D-ARs without clinically relevant inhibition of α1B-ARs expressed by immune cells and the peripheral vasculature  may be required to effectively interrupt autocrine and paracrine catecholamine signaling in monocytes and other immune cells that enhance inflammatory injury  may therefore be consistent with a redundancy in catecholamine signaling pathways which are globally inhibited by doxazosin, whereas tamsulosin allows for continued signaling through the α1B-AR. These findings have practical implications for the selection of α1-AR antagonists for the prevention of inflammatory injury and suggest that the immunomodulatory benefits may not be uncoupled from inhibition of α1B-ARs expressed on the peripheral vasculature.Immune cells can induce expression of all three α α1B-ARs , and cat α1B-ARs . As suchy injury ,20. Indesyndrome . The mar1-AR blockade in the prevention of inflammatory and autoimmune injury. In a model of encephalitis, early α1-AR inhibition reversed neutrophil infiltration in lungs and prevented hemorrhagic pulmonary edema  may reduce in-hospital and 28-day mortality in patients with COVID-19 and highlight the need for randomized placebo-controlled clinical trials to examine the efficacy of α1-AR antagonists for improving survival and preventing adverse outcomes from COVID-19. Importantly, α1-AR antagonists are inexpensive, administered orally, do not require refrigeration, and have a well-established safety profile. Thus, if trials confirm these results, α1-AR antagonists could be widely deployed to reduce mortality from inflammatory injury. Importantly, α1-AR antagonists are immunomodulatory, but not immunosuppressive drugs. Long-term use of doxazosin does not appear to be associated with the development of opportunistic infection in human studies  which confer an increased risk of opportunistic infection.Our results suggest that inhibition of catecholamine signaling with doxazosin  at the time of admission. Among different α1-AR antagonists, doxazosin was associated with a 74% relative risk reduction for death, while tamsulosin had a more modest 18% relative risk reduction for death. A clinical trial testing the efficacy and safety of α1-AR antagonists such as doxazosin to prevent hyperinflammation and reduce mortality in COVID-19 would appear warranted.In summary, patients hospitalized with COVID-19 had lower odds of in-hospital and 28-day death if they had an active prescription for any α1


Sir,The 2020 update position paper on secondary prevention and cardiovascular rehabilitation from the Secondary Prevention and Rehabilitation Section of the European Association of Preventive Cardiology is welcome. However, the paper is wanting for the lack of a firm recommendation for home-based cardiac rehabilitation especially as these programmes are highly relevant in the era of coronavirus disease 2019–COVID-19.3 and some of these approaches have been reiterated in the position paper.7 The British Heart Foundation has just awarded a grant to co-develop a digital version of REACH HF to improve access to rehabilitation to patients with heart failure in the UK.Traditionally, the majority of patients receive cardiac rehabilitation in supervised, group-based classes conducted in hospital outpatient departments or community centres, ranging in frequency, intensity and duration of exercise and self-help guidance over several weeks. Participation rates in centre-based cardiac rehabilitation programmes remain suboptimal, despite current national and international recommendations.The impact of the COVID-19 pandemic on health and healthcare systems in most European countries and North America has meant that the general public have been advised to abide by social distancing guidance. This can be achieved by staying at home, to prevent the spread of the virus. Patients with heart disease are at high risk of severe complications from COVID-19 infection, and, therefore, have been advised to self-isolate to lower the risk of contracting the virus. For these individuals, the ability to exercise and follow a self-care rehabilitation intervention without leaving the home has never been more pertinent.www.theheartmanual.com).Home-based cardiac rehabilitation programmes can also be delivered digitally and remotely.The COVID-19 crisis is moving us in a direction where self-managed, home-based interventions are being encouraged, and are likely to remain with us beyond the current pandemic. Research on the implementation of novel cardiac rehabilitation approaches is urgently needed to help guide changes in future cardiac rehabilitation services and maintain provision of patient care.http://www.aacvpr.org/covid19). However, caution is required when introducing innovative modes of delivery, and consideration should be given both to ‘patient-related barriers for digital health deployment’In response to the outbreak of COVID-19, the American Association of Cardiovascular and Pulmonary Rehabilitation has established the Innovative Delivery Model Collaborative to facilitate digital approaches and encourage home-based delivery of cardiac rehabilitation, supported by several providers, including the Mayo Clinic (9The urgency of maintaining access to evidence-based cardiac rehabilitation services from the safety of home, particularly during the current global pandemic, could not be greater. We need to act now.


Expression of NRP1 in perivascular tumor cells was explored as a marker for RCC survival in the two RCC cohorts. Results were further validated using a publicly available gene expression dataset of clear cell RCC (ccRCC). We found that VEGFR2/NRP1 trans complexes were detected in 75% of the patient samples. The presence of trans VEGFR2/NRP1 complexes or perivascular NRP1 expression was associated with a reduced tumor vessel density and size. When exploring NRP1 as a biomarker for RCC prognosis, perivascular NRP1 and general tumor cell NRP1 protein expression correlated with improved survival in the two independent cohorts, and significant results were obtained also at the mRNA level using the publicly available ccRCC gene expression dataset. Only perivascular NRP1 expression remained significant in multivariable analysis. Our work shows that perivascular NRP1 expression is an independent marker of improved survival in RCC patients, and reduces tumor vascularization by forming complexes in trans with VEGFR2 in the tumor endothelium. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.Renal cell carcinoma (RCC) treatment has improved in the last decade with the introduction of drugs targeting tumor angiogenesis. However, the 5‐year survival of metastatic disease is still only 10–15%. Here, we explored the prognostic significance of compartment‐specific expression of Neuropilin 1 (NRP1), a co‐receptor for vascular endothelial growth factor (VEGF). NRP1 expression was analyzed in RCC tumor vessels, in perivascular tumor cells, and generally in the tumor cell compartment. Moreover, complex formation between NRP1 and the main VEGF receptor, VEGFR2, was determined. Two RCC tissue microarrays were used; a discovery cohort consisting of 64 patients and a validation cohort of 314 patients. VEGFR2/NRP1 complex formation in  Renal cell carcinoma (RCC) represents approximately 3% of all adult cancers worldwide, with an incidence of 337 860 new cases per year and 143 406 deaths in 2012 VHL) VHL encodes the von Hippel–Lindau protein, which is critical in targeting the transcription factor hypoxia inducible factor (HIF) for degradation. In tumors with VHL mutations, HIF is constitutively stable, promoting the expression of a wide range of genes. These include vascular endothelial growth factor (VEGF), a potent inducer of tumor angiogenesis through binding to its receptor (VEGFR2) There are two main RCC tumor types; clear cell and papillary. Clear cell RCC (ccRCC) represents about 70% of all cases cis, while expression on adjacent cells results in formation of complexes in trans, between the cells Neuropilin 1 (NRP1) is a nonenzymatic transmembrane glycoprotein that binds VEGF to form a ternary complex with VEGFR2 on endothelial cells, potentiating downstream signaling to induce proliferation and migration trans arrests the receptor on the cell surface, suppressing tumor angiogenesis and growth in vivotrans complexes was identified as an independent marker of improved overall survival We have shown previously that the interaction of tumor cell NRP1 with endothelial VEGFR2 in All participating patients gave their written informed consent, and sample collection was made with the approval of the regional research ethics board of Stockholm (Dnr. 2010/1339 32) and Lund (Dnr 282/2007). The studies were performed in compliance with the 1975 Declaration of Helsinki, as revised in 1983.Two tissue microarrays (TMAs) were used, referred to as the discovery and validation cohorts. The discovery cohort consists of tumor biopsies from 64 patients diagnosed with RCC between 1997 and 2005 at Karolinska University Hospital, Stockholm, Sweden. Tumors were evaluated histologically by a pathologist, and representative tumor parts were chosen when establishing the TMA. Each tumor is represented by two 1‐mm diameter core punch biopsies. All patients developed metastatic disease and were treated with sunitinib as first line treatment. Patients were classified according to the TNM classification system for malignant tumors. T stage describes tumor size and tissue invasion, N stage describes the spread to regional lymph nodes, and M stage describes metastatic spread to distant organs. M stage in this cohort describes the presence of metastasis at diagnosis The validation cohort consists of 314 RCC patients diagnosed between 1978 and 1996 at Skåne University Hospital Malmö, Sweden. The patients did not receive adjuvant therapy. Data for patients included sex, age, tumor stage, Fuhrman grade, metastasis present at diagnosis, pathological classification, and survival time. Primary tumor sections were revised by a certified pathologist, confirming the RCC diagnosis and selecting representative tumor areas of two 1‐mm diameter core biopsies to be included in the TMA. Data of patient characteristics, treatment, and overall survival were collected in clinical registries. Details about the cohorts are presented in Table NRP1 mRNA data from 12 cancer types and normal control tissues publicly available transcriptome data generated by The Cancer Genome Atlas (TCGA) Research Network (http://cancergenome.nih.gov/) and the genotype tissue expression (GTEx) project  were analyzed. Data were accessed through the web‐based tool Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn) 2 fold‐change of 1 and a p‐value <0.05. Differences between cancer types were not analyzed. The correlation between NRP1 transcript levels and survival was analyzed in a ccRCC patient cohort with publicly available transcriptome data (KIRC), generated by TCGA (http://cancergenome.nih.gov/). The dataset consists of gene expression data from 534 patients with ccRCC. Dichotomization of the KIRC gene expression dataset was performed using the same cut‐off as in the RCC cohort, where the highest three quartiles were considered positive for NRP1 expression.in situ proximity ligation assay (PLA). Anti‐VEGFR2  was used for immunofluorescent staining (IF) at a 1:150 dilution. Anti‐NRP1 antibody  was used for PLA at a 1:100 dilution. Anti‐NRP1  at a 1:100 dilution was used for IF. Dylight‐650 conjugated anti‐CD34  was used for in situ PLA at a 1:100 dilution. Anti‐CD34, clone QBEnd10  was used for IF at a 1:100 dilution. Secondary antibodies, Alexa Fluor 488 donkey anti‐mouse, Alexa Fluor 647 donkey anti‐mouse, Alexa Fluor 555 donkey anti‐rabbit, Alexa Fluor 488 donkey anti‐goat , and Alexa Fluor 647 donkey anti‐goat  were used for IF at a 1:400 dilution,  was used at a 1:100 dilution in an Formalin‐fixed paraffin‐embedded (FFPE) TMA slides were deparaffinized using an EtOH gradient  followed by epitope retrieval using a microwave at 2 × 5 min in target retrieval buffer, pH 9 (Agilent Technologies). Slides were washed in Tris‐buffered saline (TBS) supplemented with 0.1% Tween 20, before blocking in 10% normal donkey serum supplemented with 1% bovine serum albumin (BSA) in TBS. Slides were incubated with primary antibodies overnight at 4 °C and with secondary antibodies for 1 hour at room temperature, thereafter with Hoechst 33342 before mounting with Fluoromount mounting medium . The specificity and sensitivity of antibodies against NRP1, VEGFR2, and CD34 were validated by staining of porcine aortic endothelial (PAE) cells stably overexpressing human VEGFR2 and NRP1  followed by epitope retrieval using a microwave at 2 × 5 min in target retrieval buffer, pH 9 (Agilent Technologies). Slides were subjected to in situ PLA according to manufacturer's instructions . In brief, sections were blocked in Duolink blocking buffer, before incubation with primary antibodies overnight. After washing, appropriate PLUS and MINUS probes were applied, and ligation, rolling circle amplification, and detection with fluorescent probes were performed. Upon completion of the in situ PLA protocol, cells were counterstained using a DyLight650‐conjugated CD34 antibody and Hoechst 33342, before mounting with Fluoromount mounting medium (Merck). As technical control for each experiment, the same procedure was performed with the omission of either of the primary antibodies.Sections for Images of the discovery TMA were acquired using a Leica confocal microscope SP8 with the Leica Application Suite X software  using HC PL CS2 20x and 40x objectives with 0.75 NA and 1.3 NA, respectively. The lasers used were diode 405, OPSL 488, OPSL 552, and diode 638 with 1 AU pinhole. The xy pixel size was 0.568 μm and 0.388 μm for the 20x and 40x objectives, respectively. Multispectral images of the validation TMA were acquired using the Vectra Polaris Automated Quantitative Pathology Imaging System automated scanning system  using a 20x PL‐APO objective with NA 0.45. The gain, offset, and exposure time for each fluorophore were kept the same for all biopsies.trans or cis based on their position relative to the CD34 positive blood vessel. Cis was defined as complexes localized within the CD34‐positive area, whereas trans was defined as complexes adjacent but not overlapping with the CD34 staining (maximum one nucleus away), as described previously cis and trans were scored as either present or absent. NRP1 IF staining was scored within three compartments; in endothelial cells (overlapping with CD34), in perivascular tumor cells (tumor cells adjacent to CD34 positive vessels), and general tumor cell expression. Each compartment was scored negative or positive for NRP1 expression. All scoring was performed independently by two investigators (EM and ES), blinded with regard to clinicopathological characteristics and outcome.Processing of images and automated analysis was performed using the Cell Profiler software t‐test was performed as indicated and p‐values < 0.05 were considered statistically significant. Association of NRP1 levels with clinicopathological parameters was analyzed with Fisher's exact test or Pearson's chi‐square test. Probabilities of survival were estimated using the Kaplan–Meier method and log‐rank test. The correlation of patient survival time with VEGFR2/NRP1 trans complexes, NRP1 levels in tumor cells or NRP1 levels in perivascular tumor cells was evaluated using Cox proportional hazards regression model in uni‐ and multivariable analyses. The SPSS software package 21.0  was used for statistical analysis, and p‐values < 0.05 were considered statistically significant.For comparison of mean values, an unpaired two‐tailed Student's trans requires expression of VEGFR2 on endothelial cells and NRP1 by perivascular tumor cells  dataset showed approximately 10‐ to 50‐fold elevated levels of NRP1 mRNA, as compared to normal kidney tissue  consisting of samples from 64 RCC patients . NRP1 protein expression was detected mainly in cancer cells but also in endothelial cells. NRP1 expression was scored in three compartments: (1) generally in tumor cells, (2) in perivascular tumor cells . Of the 63 patients analyzed, 47 (75%) displayed trans complexes and 17 (27%) displayed cis complexes. As technical control for each experiment, the same procedure was performed with the omission of either of the primary antibodies  trans, as compared to samples lacking trans complexes  was associated with general NRP1 expression in tumor cells (identified by IF staining), and in particular with expression in perivascular tumor cells, chi‐square tests were performed. There was a significant association between the presence of trans complexes and NRP1 expression in perivascular tumor cells , and between trans complexes and general NRP1 expression in tumor cells . The marked association of trans complexes with perivascular NRP1 expression prompted the question whether NRP1 expression, based on IF staining, also associated with vessel characteristics. As shown in Figure p‐value = 0.059) and a significantly smaller vessel size, compared to samples lacking NRP1 expression.To determine if the level of trans complexes. Moreover, samples with VEGFR2/NRP1 trans complexes, detected by PLA, or alternatively, NRP1 expression on perivascular or tumor cells, detected by IF staining, exhibited decreased overall vessel area and reduced vessel size.In summary, patients with high NRP1 expression in tumor cells and in particular in perivascular tumor cells, showed a preferential presence of trans complexes in RCC and the associated vessel parameters, staining for NRP1, VEGFR2, and CD34 was performed on an independent validation cohort of 314 RCC patients . Similar to the discovery cohort, patients were scored for general tumor cell‐, perivascular‐, and endothelial NRP1 expression. Among the 314 patients included in the TMA, 297 yielded informative staining; 224 (75%) were positive for tumor cell‐NRP1, 208 (70%) for perivascular NRP1, and 93 (31%) for endothelial cell‐NRP1.To explore the clinical relevance of VEGFR2/NRP1 p‐value = 0.002, validation cohort; Log rank test, p‐value <0.001).Next, perivascular NRP1 expression in the discovery and validation cohorts was analyzed for association with clinicopathological characteristics and patient survival. There was no association between positive NRP1 staining and relevant RCC clinicopathological characteristics including sex, age, histology, MSKCC‐score, tumor stage (T stage), and presence of metastasis at diagnosis (M stage) in the discovery cohort Table A,B showep‐value = 0.029) and validation cohort .General tumor cell NRP1 expression was similarly analyzed with respect to clinicopathological characteristics and outcome in both RCC cohorts. In the discovery cohort, general expression of NRP1 in tumor cells was not associated with clinicopathological characteristics .To explore the impact of NRP1 in an additional independent RCC patient cohort, cis with endothelial‐expressed VEGFR2 . Similar observations were made in the validation cohort . This observation of worse overall survival for patients with NRP1 expression in the tumor vessels is supported by previous findings that VEGFR2/NRP1 cis‐complexes promote tumor angiogenesis in mouse models trans configuration dominates the positive cis effects in regulating angiogenesis, in congruence with mechanistic data from murine tumor models NRP1 expression in endothelial cells also allowed complexes to form in In conclusion, NRP1 expression in the tumor cell compartment correlated with improved survival in two independent RCC cohorts, which was further supported by results from the KIRC ccRCC dataset. A more in‐depth analysis of the expression pattern underscored the clinical relevance of the interaction of perivascular NRP1 with endothelial VEGFR2.p‐value = 0.03). Similar results were shown for NRP1 expression in perivascular tumor cells . In addition, for the validation cohort, there was decreased risk of cancer‐related death with NRP1 expression in tumor cells  and perivascular NRP1 . Multivariable analysis was performed on both cohorts including perivascular , and in the validation cohort    Table . MultivaTogether, these data identified NRP1 expression in perivascular tumor cells as a novel independent marker for RCC patient prognosis.trans were not explored in these studies. Findings from our previous work, supported by the present study, suggest that the NRP1 expression pattern, not only the overall expression levels, is of critical importance for tumor progression and patient prognosis trans complexes with VEGFR2, preventing VEGFR2 internalization and productive downstream signaling  in human RCC tumors, and halts tumor angiogenesis, thereby improving patient survival. Perivascular NRP1 expression serves as a novel independent marker of improved survival. Future studies on larger population‐based RCC cohorts should be performed to confirm the application of NRP1 status as a clinical biomarker for survival, and further explore NRP1 as a predictive marker for anti‐angiogenic therapy. Our work also encourages additional studies to explore the compartment‐specific expression of biomarkers in tumors and the clinical relevance of receptor complexes in different configurations.In summary, we show for the first time that tumor cell–expressed NRP1 forms complexes with VEGFR2 expressed by the endothelium  mRNA expression in solid tumors and normal tissue controlsClick here for additional data file.Figure S2. Tumor NRP1 expressionClick here for additional data file.Figure S3. Immunofluorescence (IF) isotype controls and in situ proximity ligation assay (PLA)‐negative controlsClick here for additional data file.Figure S4. Correlation between overall survival and general tumor cell NRP1 expression or compartment specific expression of NRP1Click here for additional data file.Table S1. Primary and secondary antibodies for immunofluorescence (IF)Table S2. Association of clinicopathological characteristics with tumor cell NRP1 expression status in the discovery and validation cohorts and NRP1 mRNA expression level in the publicly available gene expression dataset of clear cell renal cell carcinoma, denoted KIRC.Table S3. Multivariable analysis of overall survival in the discovery, validation, and KIRC cohortsClick here for additional data file.


Biogenic CBM is an important component of detected CBM, which is formed by coal biodegradation and can be regenerated by anaerobic microorganisms. One of the rate-limiting factors for microbial degradation is the bioavailability of coal molecules, especially for anthracite which is more condense and has higher aromaticity compared with low-rank coal. In this paper, NaOH solution with different concentrations and treating time was employed to pretreat anthracite from Qinshui Basin to alter the coal structure and facilitate the biodegradation. The results showed that the optimal pretreatment conditions were 1.5 M NaOH treating for 12 h, under which the biomethane production was increased by 17.65% compared with untreated coal. The results of FTIR and XRD showed that NaOH pretreatment mainly reduced the multi-substituted aromatics, increased the C-O in alcohols and aromatic ethers and the branching degree of aliphatic chain, and decreased the aromatic ring structure, resulting in the improvement of coal bioavailability and enhancement of biomethane yield. And some organics with potential to generate methane were released to filtrate as revealed by GC-MS. Our results suggested that NaOH was an effective solution for pretreating coal to enhance biogenic methane production, and anthracite after treating with NaOH could be the better substrate for methanogenesis. The exploitation and utilization of coalbed methane (CBM) can improve the energy structure worldwide ensuring the sustainable development of energy. It also can greatly reduce the cost of coal mining as methane is the main hazard in the coal mine. Biogenic CBM is an important part of detected CBM, which has been found in many CBM fields around the world using isotope techniques ,2. It isUntil now, a number of studies have demonstrated that coal in different ranks can be degraded to produce methane by indigenous and exogenous microorganisms ,4. Livin4) [2O2) [3) [4 [Coal biodegradation is mainly carried out by anaerobic microbes which transformed macromolecular organic compounds in coal into smaller organic molecules and then further converted to methane ,8. Howev4) , hydroge4) [2O2) –17, nitr2O2) [3) ,19, sodi2O2) [3) ,21, and ) [3) [4 .Actinomyces and the amount of liquefied coal increased with the increase of the culture media pH which is related to the amount of polypeptides and polyamines contained in it [During the growth of microorganisms, some alkaline substances, such as ammonium ions and biogenic amines, could be secreted or produced by utilizing certain compounds in the environment. These alkaline substances might participate in the degradation of coal. Strandberg reported in l987 that low-ranked coal could be solubilized by an extracellular substance produced by ed in it . In 1989ed in it . In the ed in it . Moreoveed in it . And NaOed in it . Therefoed in it ,28.In fact, NaOH pretreatment has been reported to increase the solubility of coal. Huang et al. treated subbitumious with NaOH, and found the enhancement of solubility of organic carbon and the releasement of aromatic fragments with low molecular weight ,21. Mostad, Ad, Vdaf) and ultimate analysis  are performed respectively according to national standards of GBT212-2008 and GB476-91. The corresponding results are as follows: moisture, 1.90%; ash, 10.41%; volatiles, 8.82%; C, 83.01%; H, 3.24%; O, 1.76%; N, 1.27%; S, 0.31%.The coal sample was collected from Sihe coal mine in Qinshui basin, south-central Shanxi province, where biogenic CBM has been found and diverse microbial communities have been detected in the produced water . We statThe microflora QSB-1 used in the experiment was enriched from produced water obtained from the same site as the coal sample. It is capable of biodegrading coal to produce methane under anaerobic conditions with optimum growth temperature at 35 °C.Three concentrations of NaOH including 0.1 M, 0.5 M, and 1.5 M were selected for pretreating the coal. The dried coal sample (g) and NaOH solution (ml) were mixed at a ratio of 1:5 and stirred by a magnetic stirrer for 6 h. Then, the mixtures were filtered using a 0.22 μm filter , and washed with sterilized water until the filtrate became neutral. The residual coal samples on the filter were dried at 45°Cfor 6h and weighed before anaerobic cultivation.Based on the results of methane production in cultivations with pretreated coal samples, a proper concentration of NaOH would be determined. Under the selected concentration of NaOH, different pretreating time including 4 h, 8 h, 12 h, and 16 h was chosen to determine the optimal treating time. The detailed procedures were the same as the previous experiment of different concentrations.2S (15%), and 23.1 mL sterile water. The composition and content of the basic medium, trace mineral solution, and vitamin solution were prepared according to our previous work [The cultivations were carried out in 140 ml culture bottles. 1.0 g pretreated coal or raw coal was loaded in each bottle with 27 ml anaerobic medium and 3 ml microflora QSB-1. The anaerobic medium included 3 mL basic medium, 0.15 mL trace metals solution, 0.15 mL vitamin solution, 0.6 mL cysteine (15%)—Naous work . The cul-1. The prepared pellets were detected by the spectrometer, and the measured wavenumbers region ranged from 400 to 4000 cm-1. The correlative data were obtained with the built-in operation software OPUS 5.5 and analyzed using Origin 8.0 software. And curve-fitting analysis was further performed using the Peakfit 4.12 software in the selected region of obtained spectrograms to obtain the detailed peak positions, intensities, widths, and areas.FTIR was employed to analyze the changes of functional groups after NaOH treatment. FTIR analysis was performed according to the description in previous literature . 0.001 gThe microcrystalline structure of the coal sample was determined by XRD. A TD-3500 X-ray diffractometer was adopted to collect XRD data from the obtained coal samples with Cu Ka radiation under the condition of 30 kV and 20 mA. The continuous scanning mode was used in a range of 5 to 90° with a 0.05° step interval and a 6°/min scanning rate. Jade software was used to fit the diffraction patterns in the two regions of 16–34° and 39–49°. The broad hump in the region of 16–49° was fitted to two Gaussian peaks around 26° and 43°, representing the peak 002 and 100, respectively . The posa and Lc was determined according to conventional Scherrer equations [a = 1.84λ/(βa cos φa) and Lc = 0.94λ/(βc cos φc), where λ is the wavelength of X-ray; βa and βc are the half width of peak 100 and peak 002; φa and φc are the corresponding scattering angles, respectively. The aspect ratio of the stacking aromatic layer is equal to the ratio of La to Lc. The stacking layer number (N) of aromatic carbon corresponds to the ratio of Lc to d002, where d002 can be computed by λ/2sinφc.The microcrystalline structure parameter Lquations ,35: La =2Cl2 for three times at neutral, alkaline, and acidic conditions, respectively. All the separated liquid was mixed. Excess anhydrous sodium sulfate was added in the mixed liquid, and the sealed bottle was placed at 4 °C for 12 h before rotary evaporation at a temperature of 40 °C. The obtained solution was evaporated to about 5 mL and concentrated to about 1 mL by nitrogen purge. Then, the organics in the extracted samples were analyzed by GC-MS system . The GC column was operated in a temperature programmed mode by maintaining the temperature at 60 °C for 3 min, then increasing to 150 °C with an increment of 20 °C/min, finally increasing to 230 °C with an increment of 5 °C/min and then maintaining for 5 min. The identification of the organic compounds was undertaken with reference of National Institute Standard and Technology (NIST 14L).The filtrate obtained after pretreatment with 1.5 M NaOH for 12 h was extracted using 10 mL CHIn order to study the effect of NaOH concentration on biomethane production, the pretreatment of coal was implemented by NaOH with different concentrations. After about 40 days of cultivation, significant methane was produced from both raw coal and pretreated coal . After tThe methane production rates in cultivations with coal pretreated by different concentrations of NaOH have the same trend, which can be briefly divided into three periods. The first 10 days is a slow growth period (lag phase) when methane production is relatively slow, which may be due to the low abundance of microflora. The next 15 days is a rapid growth period  when methane production increased sharply. Then it gradually stabilized in the last period (plateau phase). However, methane production from pretreated coal increased rapidly after 20 days of cultivation when methane production from raw coal was almost stable, resulting in higher methane production. It was more likely that the bioavailability of coal increased after NaOH treatment mainly by converting previously refractory compounds in coal into biousable organic matter instead of generating new small molecular compounds that was easy to biodegrade, because the methane production curves in the earlier stages before and after treatment overlapped.2O2 treatment which would resulted in less biodegradable compounds left in coal by over oxidation.The effects of pretreating time on methane production were analyzed under 1.5M NaOH which showed the most promising treatment concentration on methane production. The methane production from coal treated for 0 h, 4 h, 8 h, 12 h, and 16 h was shown in th day). Then, these treatments divided into two groups based on methane production. One included whose treating time was longer than 8 h and methane production was higher. The other was those treating time was shorter than 8 h. As reported previously that alkali treatment could break, disrupt, and reform various interaction forces and entanglements between coal macromolecules [Same as the observation in the experiment of different concentrations, the methane production curves for different NaOH treating time almost overlapped in the early stage , 1000–1800 cm-1 , and 2800–3000 cm-1  [2 of samples are all over 0.99.The FTIR spectrograms of coal samples untreated and treated with 1.5 M NaOH for 12 h were shown in l group) . The fit-1 , diameter (La), stack height (Lc) of crystalline structure, the number of stacking layer of aromatic carbon (N), and the aspect ratio (La/Lc).The XRD diffraction diagrams of the raw coal and residual coal pretreated with 1.5 M NaOH for 12 h were shown in 002) increased from 0.35 to 0.36 Å. The La and Lc of aromatic crystal decreased from 9.45 to 5.61 Å and from 1.80 to 1.66 Å, respectively. The number of stacking layer of aromatic carbon in coal pretreated with NaOH decreased from 4.66 compared with raw coal at 5.11, suggesting the crystalline layers became thinner. And the aspect ratio reduced from 5.25 to 3.38 Å, indicating that the decreased rate of La was greater than the decreased rate of Lc. The d002 value of pretreated coal sample was higher than that of raw coal. Thus, NaOH pretreatment facilitated coal biodegradation by decreasing the microcrystalline structure of coal which was in accordance with methane production and FTIR spectroscopy analysis.After treated with NaOH, the interlayer spacing  increased by 17.65%. NaOH mainly reduced the carbonyl groups and microcrystalline structure of coal, increased the C-O in alcohols and aromatic ethers and branching degree of aliphatic chain by removing some organics from coal and disrupting the association between organics in coal. These changes facilitated the interaction between coal and microbe and improved the bioavailability of coal, thus enhancing the methane production. These results suggested that NaOH was an effective solution for pretreating coal to enhance biogenic methane production.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Fusarium oxysporum f. sp. cubense (Foc) race 1, is a major disease of bananas in East Africa. Triploid East African Highland (Matooke) bananas are resistant to Foc race 1, but the response of diploid (Mchare and Muraru) bananas to the fungus is largely unknown. A breeding project was initiated in 2014 to increase crop yield and improve disease and pest resistance of diploid and triploid East African Highland bananas. In this study, eight Mchare cultivars were evaluated for resistance to Foc race 1 in the field in Arusha, Tanzania. In addition, the same eight Mchare cultivars, as well as eight Muraru cultivars, 27 Mchare hybrids, 60 Matooke hybrids and 19 NARITA hybrids were also screened in pot trials. The diploid Mchare and Muraru cultivars were susceptible to Foc race 1, whereas the responses of Mchare, NARITAs and Matooke hybrids ranged from susceptible to resistant. The Mchare and Matooke hybrids resistant to Foc race 1 can potentially replace susceptible cultivars in production areas severely affected by the fungus. Some newly bred Matooke hybrids became susceptible following conventional breeding, suggesting that new hybrids need to be screened for resistance to all Foc variants.Fusarium wilt, caused by the soil-borne fungus  Banana cultivation in East and Central Africa (ECA) is dominated by a group of cooking bananas commonly referred to as East African Highland bananas (EAHB). These bananas were introduced into the region from Asia about 2500–3000 years ago, where after secondary evolution took place in the highlands of Uganda and Tanzania ,2. TodayEAHBs comprise diploid and triploid bananas ,7,8,9,10Fusarium oxysporum f. sp. cubense (Foc), is present in most banana-growing regions of ECA [Fusarium wilt of banana, a disease caused by the soil-borne fungus s of ECA . Foc infs of ECA ,17. Thiss of ECA . Foc pros of ECA ,17,18. Ts of ECA ,21,22,23s of ECA .Foc is diverse and comprises three races based on their pathogenicity to a group of differential cultivars, with Foc races 1, 2 and 4 causing disease to Gros Michel, Bluggoe and Cavendish bananas, respectively ,26. All Bananas can be evaluated for Fusarium wilt resistance in pot assays under screen house and greenhouse conditions, or in the field. Results obtained from pot and field screenings are not always similar. Field screenings provide an evaluation of plant response under prevailing environmental and geographic conditions, but depend on the distribution of inoculum in the soil and the Foc strain present . Pot assIn 2014, a project to breed EAHB for higher yields and for disease and pest resistance started in Uganda and Tanzania. Mchare hybrids were derived from crosses between Mchare cultivars and diploid bananas resistant to Foc race 1 . NineteeAll Mchare cultivars and Sukari Ndizi, the susceptible control, developed internal rhizome symptoms of Fusarium wilt in the screen house 10 weeks after inoculation with Foc race 1 with disease incidence between 88.9–100% . The disMakyughu II, Mchare Laini, Akondro Mainty, Mchare Mlelembo, Huti Green Bell and Ijihu Inkundu developed internal rhizome discolouration in the plant crop at Arusha, with a RDI that did not differ significantly from that of the susceptible control, Sukari Ndizi . Huti WhThe RDI rating obtained for the screen house and field evaluation of Mchare cultivars was significantly positively correlated for the first ratoon (r = 0.76) . This inThe Muraru cultivars were all susceptible to Foc race 1 with disease incidence between 88.9–100% and disease severity ranging from 3.7 for Muraru M3 to 5.8 for Njuru, compared to the 6.0 for the susceptible Sukari Ndizi . DiseaseNineteen NARITA hybrids were evaluated in the screen house. Of these, only NARITA 16 and NARITA 12 developed Fusarium wilt symptoms (disease incidence between 55.6–88.9%), with disease severity significantly higher than those in Mbwazirume, the resistant control, but also significantly less than those in Sukari Ndizi, the susceptible control . NARITA Sixty Matooke hybrids were evaluated for resistance against Foc race 1. Thirty-eight of these were considered resistant, as their disease severity did not differ from that of Mbwazirume, the resistant control. Among the 38 resistant hybrids, 25 hybrids did not develop any Fusarium wilt symptoms (disease incidence 0%), whereas 13 hybrids had a few plants displaying internal spots in the inner rhizomes (disease incidence between 11.1–83.3%), with an average RDI below 2 on a scale of 6 . SixteenUntil recently, a limited amount of information was available on the response of diploid EAHB bananas to Foc race 1, despite the seasonal appearance of Fusarium wilt on Mchare and Muraru bananas in Tanzania and Kenya, respectively ,34. ThisThe results obtained from pot and field evaluations were well correlated, indicating that the pot assay can be used for the mass screening of banana varieties. Results obtained from pot trials when using the millet seed technique reflected those from field evaluation. The millet seed technique has also been widely used for the mass screening of banana varieties for Fusarium wilt resistance ,43,44. F1 offspring. An advanced screening of F2 population could confirm the nature of the inheritance of resistance. Among the Mchare hybrids tested, hybrids obtained from the same parents  did not produce the same results. T.2274-6 and T.2273-2, for instance, were resistant, while T.2269-2 and T.2691-15 were susceptible to Foc race 1. Calcutta 4 is resistant to Foc race 1  and HutiMatooke bananas are resistant to Foc race 1 ,32. NARIThe disease development in the Mchare cultivars Kahuti and Huti White at the field site in Arusha was slow compared to other Mchare cultivars. Makyughu II, Mchare Mlelembo, Akondro Mainty, Huti Green Bell and Ijihu Inkundu developed Fusarium wilt in the plant crop, but Kahuti and Huti White only showed internal rhizome symptoms during the first ratoon. It is unlikely that these varieties escaped the disease in the first production cycle, as diseased plants had been spread in the field during trial preparation. The delayed disease development might, however, be attributed to low inoculum levels or the uneven distribution of inoculum in the soil. If the cultivars were not evaluated for at least two production cycles, they would have been ranked as resistant. It is therefore important to consider increasing the inoculum in the field and/or to evaluate two production cycles when evaluating banana for disease resistance in the field. The evaluation of internal rhizome symptoms of Fusarium wilt near harvest proved to be of greater value than the rating of external leaf symptoms. Kahuti and Huti White varieties displayed yellowing of leaves that were rated as Fusarium wilt symptoms in the plant crop, but transverse cutting of rhizomes near harvest time showed no disease. This suggests that leaf yellowing may have been caused by other stresses. The rating of internal rhizome symptoms to assess Fusarium wilt is therefore needed when trial material presents little or no external symptoms. The leaf symptoms of Huti White and Kahuti that were visible in plant crop were probably caused by other stresses such as drought, deficiencies or nematodes, as the resistant control Nakitengwa also displayed a level of leaf yellowing.In future, Mchare and Matooke hybrids with Foc race 1 resistance should be evaluated for agronomic performances and resistance to other important banana pests and diseases. Resistant diploids can also be used in banana breeding programs as sources of Foc race 1 resistance. Foc race 1-resistant EAHB hybrids should also be screened for resistance against Foc TR4. Foc TR4 has first been detected in northern Mozambique in 2013 , and poses a serious threat to banana production in neighbouring Tanzania. It is known that banana cultivars respond differently to Foc race 1 and TR4, and that Foc TR4 has a larger host range ,43,49,50Eight Mchare cultivars and eight Muraru cultivars were evaluated for resistance to Foc race 1 in Arusha , togetheFoc isolates CAV 3733 and CAV 3856, collected from infected bananas in Kawanda and Arusha, respectively, were used in Uganda and Tanzania as inoculum. The isolates were identified with Foc Lineage VI-specific DNA markers and by vegetative compatibility group (VCG) analysis. The Foc isolates were identified as Foc VCG complexes 0124/5/8/22 (CAV 3733) and 0124/22 (CAV 3856), respectively, which both belong to Foc race 1. The isolates are stored at the culture collection of Stellenbosch University’s Department of Plant Pathology. Eleusine corocana) seeds were washed and soaked in tap water for 6 h, the water was drained and the millet seeds autoclaved in 415 × 600 mm autoclavable bags  on two consecutive days for 20 min at 120 °C. Twenty to 30 mycelial plugs (10 mm × 10 mm) from a Foc race 1 isolate, grown on PDA for 4–5 days at 25 °C, were then used to inoculate 3 kg of sterile millet seeds in the autoclaved bags. The bags were incubated in the dark at 25 °C for 10–14 days and shaken every third day to ensure that the fungus properly colonised the millet seeds. A millet seed technique was used to inoculate banana plantlets in screen house trials . Finger For screen house inoculations, 2-month-old banana plants were inoculated with Foc-colonised milled seed mixed into the potting soil. The eight Mchare cultivars, as well as the Matooke and NARITA hybrids, were planted in plastic pots containing 2 kg potting soil mixed with 20 g of the inoculum. The Muraru cultivars and Mchare hybrids, however, were replanted into 1 kg potting soil inoculated with 25 g of Foc-colonized millet seed. Before the greenhouse trials were conducted, a pilot study showed that the two inoculum concentrations gave the same result. After inoculation, the experiments were set up in a completely randomized block design (CRBD) with three plants per block, replicated three times. Thus a total of nine plants were used for each accession tested. In Arusha, Tanzania, Grand Naine  and Nakitengwa (EAHB) were used as resistant controls for the evaluation of Mchare cultivars, and Sukari Ndizi as the susceptible control . For theFor the screen house experiments plants were considered ready for rating when 50% of the susceptible control plants displayed leaf symptoms. This corresponded to a period ranging from 10–12 weeks after inoculation. During evaluation the rhizomes were cut open and the discolouration of inner rhizomes scored on a rating scale ranging from 1–6 .Field evaluation of Mchare banana cultivars for Foc race 1 resistance was conducted in Arusha, Tanzania. The land used was naturally infested by the fungus, and additional inoculum was added before the trial began by cutting up banana plants with Fusarium wilt symptoms, and spreading the diseased plant tissue across the trial site. Eight Mchare cultivars were then planted in a CRBD in April 2017, with 18 plants per cultivar in each block, replicated three times. . NakitenDisease in the field was monitored for two production cycles, known as the plant crop and the first ratoon. Disease progression in the field were scored using a 1–5 rating scale based on leaf discoloration . The plaAn analysis of variance (ANOVA) was performed to compare Fusarium wilt resistance of the banana genotypes evaluated in the screen house and field trials, based on their RDI. XLSTAT software  was used to compute ANOVA, and multiple comparisons were performed between variables according to Fisher’s test at the 0.05 level of least significance difference (LSD). Pearson correlation test was performed between screen house and field screenings of Mchare cultivars in Arusha. The genotypes were ranked as ‘resistant’ when their RDI was less than ‘2’, as ‘partially resistant’ when it was between 2 and 3, and as ‘susceptible’ when the RDI was more than ‘3. To compare the progression of leaf yellowing in the field trial in Arusha, Tanzania, the AUDPC was measured  and an Aix is the severity of the disease observed at time it, ix+1 the severity of the disease at the time of the subsequent evaluation i + 1, it the time (months) at the time of observation i; it+1 the time (months) at the time of the subsequent evaluation i + 1, and n the total number of evaluations. The AUDPC was calculated as follows: AUDPC =