Arsenic poisoning is a global health problem. Chronic exposure to arsenic has been associated with the development of a wide range of diseases and health problems in humans. Arsenic exposure induces the generation of intracellular reactive oxygen species (ROS), which mediate multiple changes to cell behavior by altering signaling pathways and epigenetic modifications, or cause direct oxidative damage to molecules. Antioxidants with the potential to reduce ROS levels have been shown to ameliorate arsenic-induced lesions. However, emerging evidence suggests that constructive activation of antioxidative pathways and decreased ROS levels contribute to chronic arsenic toxicity in some cases. This review details the pathways involved in arsenic-induced redox imbalance, as well as current studies on prophylaxis and treatment strategies using antioxidants. Arsenic is the 33rd element in the periodic table of elements. It displays features of both a metal and a non-metal, and thus called metalloid. However, it is referred as a heavy metal from a toxicological point of view . ArsenicArsenic exists in nature as both an inorganic trivalent (arsenite: iAs III) and inorganic pentavalent forms (arsenate: iAs V), and is metabolized via biomethylation involving a two-electron reduction of pentavalent arsenicals followed by oxidative methylation to form organic pentavalent arsenicals ,12. InorOxidative stress mediated by reactive oxygen species (ROS) is a common denominator in arsenic toxicity. Here, we summarized ROS-related pathways in arsenic toxicity, as well as prophylaxis and therapeutic potential of antioxidants to combat arsenic toxicity.2−•), hydrogen peroxide (H2O2), hydroxyl radical (•OH), hydroperoxyl radical (HOO•), singlet oxygen (1O2), and peroxyl radical (ROO•) [1O2, O2−•, H2O2, •OH, and ROO•. Formation of O2−• and H2O2 in response to arsenic exposure in different cell lines are summarized by Shi et al. [2−•. Arsenic shows mitochondrial toxicity by inhibiting succinic dehydrogenase activity and uncoupling oxidative phosphorylation with production of O2−•, which gives rise to other forms of ROS [The toxic mechanisms of arsenic are complex and not fully understood. At a biochemical level, iAs V can replace phosphate in several reactions. Arsenite (iAs III) and trivalent organic (methylated) arsenicals react with thiols (-SH) in proteins and inhibit their activity. Other mechanisms include epigenetic alteration, oxidative stress, inflammation, and autophagic defects ,25,26. Rl (ROO•) . Arsenici et al. . Mechanis of ROS . (ii) Nis of ROS . (iii) Gs of ROS ,32. For s of ROS . (iv) Res of ROS . (v) Unds of ROS . (vi) Ens of ROS . (vii) Is of ROS , glutaths of ROS ,39, whicDespite of ROS overproduction reported in earlier studies applying high-dose arsenic in acute exposure, emerging evidence suggests that ROS do not increase when cells are exposed to an environmentally relevant dose of arsenic ,41, espeSignal transduction pathways transmit extracellular signals from series intracellular signaling molecules into genes . ArsenicNrf2 is a master transcription factor in antioxidant system. Under physiological conditions, the protein level of Nrf2 is low because it binds to its negative regulatory factor kelch-like epichlorohydrin-associated protein 1 (Keap1), which forms the E3 ubiquitin ligase complex and facilitates ubiquitination and subsequent degradation of Nrf2 by the 26S proteasome . When exp62 is a downstream gene of Nrf2, forming a positive feedback loop [Nrf2 and peroxisome proliferator-activated receptor-γ (PPAR-γ) mRNA expression in arsenic-treated Sprague-Dawley (SD) rats compared with control rats [Arsenic is an activator of Nrf2-Keap1 pathway ,60,61,62ack loop . The looack loop . In addiack loop   significantly increased the sensitivity to acute cytotoxicity of inorganic arsenite, whereas KEAP1-KD cells showed a significant resistance to arsenite toxicity [Nrf2-KD cells compared to scramble cells [NQO1); aldo-keto reductase family 1, member C2 (AKR1C2); and aldo-keto reductase family 1, member C3 (AKR1C3) were overexpressed in chronic arsenic-exposed HaCaT cells [NRF2 in HaCaT cells abolished arsenic-induced acquisition of invasion capacity [Nrf2 is considered as a protective factor against arsenic toxicity by reducing oxidative stress. Our previous study found that stable knockdown (KD) of toxicity . When mole cells . Howeverle cells ,96. On ole cells ,97 and cle cells ,100,101,le cells ,102,103.le cells . Furthercapacity . These dcapacity .miR-199a gene by DNA methyltransferase 1 [Epigenetic modifications contribute to toxic effects by arsenic exposure . Alteratferase 1 . miR-214ferase 1 . Not allferase 1 . Lycium barbarum polysaccharides (LBPs) reduced levels of ROS and nitric oxide (NO) induced by H2O2 through down-regulating miR-194 in PC-12 cells [The levels of ROS can be regulated by miRNAs, as found in diseases such as cerebral ischemia, ischemia/reperfusion injury, and spinal cord injury. Overexpression of miRNA-20b increased the levels of adenosine 5’ triphosphate (ATP) and ROS in the cerebral ischemia of SD rats, whereas suppression of miRNA-20b decreased the levels of ROS . Overexp12 cells .Understanding the molecular mechanisms of arsenic-induced toxicity, such as dynamics of ROS generation, miRNA expression, and the relationship between ROS and miRNAs, will certainly shed new light for future strategies against arsenic toxicity. However, so far, there are only a few studies exploring the molecular mechanism of arsenic toxicity in this regard. More studies are preferably needed to understand the potential relationship between ROS alteration and miRNA expression in response to arsenic exposure, of which research on the role of ROS in epigenetic dysregulation would be top priority.The mitochondrion is the major site for ROS production and leakage ,112. ModMitophagy is a type of autophagy physiologically responsible for mitochondrial quality control and mitochondrial ROS balance by removing damaged mitochondria ,118,119.Arsenic inhibits complex I in the mitochondrial electron transport chain, which leads to excessive generation of ROS, giving rise to lipid peroxidation and protein damage and the subsequent formation of mitochondrial permeability transition (MPT) . ArsenitTyrosine phosphorylation is an important posttranslational modification that is known to regulate receptor kinase (RK)-mediated signaling in mammals ,127. Autp38, extracellular signal-regulated kinase (ERK)1, ERK2, and Jun N-terminal kinase (JNK) and the protein levels of phosphorylated-JNK (p-JNK)/JNK were significantly elevated in cardiomyocytes by arsenic [Mdig), and thereby mediated arsenic-induced malignant transformation of BEAS-2B cells [MAPKs regulate proliferation, gene expression, differentiation, mitosis, survival, and apoptosis in the cell, and can be activated by arsenic in a time- and dose-dependent manner when the concentrations range from 0.1 to 500 μM ,137. The arsenic . It has  arsenic ,142,143, arsenic . Arsenic arsenic ,86,88. I arsenic . JNKs we arsenic . On the 2B cells . Nrf2-deficient mouse astrocytes exhibited increased DNA-binding activity and overproduction of pro-inflammatory cytokines [Nrf2 decreased the level of p65 in inflammation reaction in human adenocarcinoma alveolar basal epithelial cells (A549 cell) [NF-κB is a transcription factor regulating cell-to-cell interaction, intracellular communication, cell recruitment, and transmigration. NF-κB activation induced by arsenic is dependent on the duration of exposure, dose of arsenic, and cell types. Arsenic ranging from 1 μM to 10 μM activated NF-κB ,146, whiytokines . The ove49 cell) . p65 has49 cell) ,159. On 49 cell) . In huma49 cell) . NF-κB s49 cell) .c-Jun and c-Fos, whereas chronic exposure attenuated DNA-binding capacity of AP-1 [AP-1 is a complex composed of homodimers or heterodimers of Jun and Fos proteins. It has a vital role in cell growth and apoptosis . All MAP of AP-1 ,166,167. of AP-1 . MDM2 is a downstream gene of Nrf2 and serves a link between Nrf2 and p53 in pancreas cancer [p53 is a well-known tumor suppressor and plays an importance role in DNA repair, cell cycle, and apoptosis. Reports on the role of arsenic exposure on p53 are controversial, varying with arsenic species, exposure time, and cell types. In a human lyphoblastoid cell line, arsenic induced p53 expression by an ataxia telangiectasia mutated (ATM) (a member of PI3-kinase-related protein kinase)-dependent pathway, which phosphorylated p53 at serine 15 ,170. In s cancer . Yet, inThe overproduction of ROS induced by arsenic exposure causes tremendously harmful outcomes to cells, organs, and organisms . To protCaenorhabditis elegans following 48 h arsenite exposure (50 to 500 μM) [Metabolic reprograming is a feature of cancer cells, which usually show a strong dependence on aerobic glycolysis (the Warburg effect), increase in glutaminolysis, enhancement of macromolecule production and mitochondrial biogenesis, activation of the pentose phosphate pathway, and upregulation of amino acid and lipid metabolism . Althoug 500 μM) . When hu 500 μM) . The lat 500 μM) ,185. Rec 500 μM) ,187,188. 500 μM) ,190,191. 500 μM) ,193, and 500 μM) ,195,196.The first line of defense against acute arsenic toxicity is to reduce the amount of arsenic in the body . The ideBecause it is well accepted that excessive generation of ROS plays an important role in the molecular mechanism of arsenic-induced toxicity and related diseases, application of antioxidants, especially extracts from plants, has been widely studied as therapeutics to counteract arsenic-induced toxicity. Some antioxidants involved in the methylation-mediated arsenic detoxification-excretion process, for example, GSH, can mitigate toxicity . Some anHeme oxygenase-1 (HO-1) expression [Interestingly, different natural compounds used as antioxidants exert varied roles in Nrf2 activation in reducing arsenic toxicity. For example, tetramethylpyrazine (TMP) (50 μM or 100 μM) protected against arsenic-induced nephron toxicity by inhibiting Nrf2 activation, and accordingly reducing pression . Pomegrapression . Howeverpression ,104,211.pression . Grape spression . Alleviapression ,214. Co-pression . In contpression , suggestArsenic activates pro-inflammatory signal pathway and induces ROS generation, thereby resulting the activation of NF-κB . TMP andtrans retinoic acid (ATRA) (0.5 mg/kg) reversed arsenic-induced oxidative stress and apoptosis by inhibiting the MAPK signaling pathways and repressing p53-dependent apoptosis in the rat uterus [Rosmarinus officinalis and Salvia officinalis, significantly attenuated arsenic-induced phosphorylation of p38 and JNK [Some antioxidants attenuated arsenic-induced toxicity via MAPK activation. TMP inhibited arsenic-induced activation of MAPK family, and further reduced the expression of arsenic response protein 2 (ARS2), which contributed to its nephron protective effects . As a prt uterus . When he and JNK .TGF-β1 is a potent profibrogenic cytokine and can be initiated by oxidative stress . ArsenicGenerally speaking, accumulating evidence suggests that antioxidants have the potential to alleviate arsenic toxicity through reducing ROS generation, enhancing antioxidant capacity, regulating ROS-related signaling pathways, and keeping the balance of inflammation and immunomodulation. Natural antioxidants extracted from plants are more promising due to their rich sources, diversity, and few side effects. However, many studies on the role of plant extracts have not been systematically conducted. In vitro assessment results do not provide exact therapeutic implications because effectiveness plant extracts may be influenced by several physiopharmacological processes, such as absorption, distribution, metabolism, storage, and excretion, as well as bioavailability, and presence of co-antioxidants and ions . Hence, An effectively preventive and therapeutic strategy for arsenic poisoning is still a challenge worldwide due to the incomplete understanding of underlying mechanisms. ROS alteration is a widely accepted key event in arsenic toxicity. Earlier studies with acute exposure and relatively high dose arsenic suggest that excessive levels of ROS disturb cellular signaling pathways, as well as damage macromolecules. The effects of antioxidants on arsenic toxicity are also assessed in a scenario of relatively high dose of arsenic. However, the mechanism and function of ROS may be different in the realistic environment, in which a relatively low-dose and long-term exposure to arsenic affects millions of people. In addition, in vitro assay concentrations of arsenic may misrepresent potential in vivo toxic effects and do not provide dose-response data that can be used for a risk assessment ,244; hen


Hedychium coronarium (Zingiberaceae) was reported in this study. The H. coronarium chloroplast genome was 163,949 bp in length and comprised a pair of inverted repeat (IR) regions of 29,780 bp each, a large single-copy (LSC) region of 88,581 bp and a small single-copy (SSC) region of 15,808 bp. It encoded 141 genes, including 87 protein-coding genes (79 PCG species), 46 tRNA genes (28 tRNA species), and eight rRNA genes (four rRNA species). The nucleotide composition was asymmetric  with an overall AT content of 63.92%. Phylogenetic analysis showed that H. coronarium was classified into a monophyletic group within the genus Hedychium in family Zingiberaceae.The first complete chloroplast genome of  Hedychium coronarium is one species of the genus Hedychium (Zingiberaceae), which distributes predominantly in southeast, south to southwest of China, south to southeast Asia and Australia , Guangdong academy of agricultural sciences, Guangzhou, China. Total chloroplast DNA was extracted from about 100 g of fresh leaves of H. coronarium using the sucrose gradient centrifugation method .H. coronarium was 163,949 bp in length and comprised a pair of inverted repeat (IR) regions of 29,780 bp each, a large single-copy (LSC) region of 88,581 bp, and a small single-copy (SSC) region of 15,808 bp. It was predicted to contain a total of 141 genes, including 87 protein-coding genes (79 PCG species), 46 tRNA (28 tRNA species), and eight rRNA (four rRNA species). Twenty gene species occurred in double copies, including eight PCG species , eight tRNA species , and all four rRNA species . All these 20 gene species were located in the IR regions. The ycf1 gene crossed the boundary of SSC-IRb region, whereas the rps12 gene was located its first exon in the LSC region and other two exons in the IRs regions. In addition, 10 PCG genes  and six tRNA genes  had a single intron, whereas two other genes (ycf3 and clpP) possessed two introns. The nucleotide composition was asymmetric  with an overall AT content of 63.92%. The AT contents of the LSC, SSC, and IR regions were 66.15%, 70.48%, and 58.85%, respectively.The complete chloroplast genome of Costus viridis, Costus pulverulentus, and Canna indica as outgroup taxa. The SNP arrays were obtained as previously described method  within the genus Hedychium in family Zingiberaceae with available SNPs.To obtain its phylogenetic position within family Zingiberaceae, a phylogenetic tree was constructed using single nucleotide polymorphisms (SNPs) arrays from available 16 species chloroplast genomes using


Candida auris is an emerging opportunistic yeast species causing nosocomial outbreaks at a global scale. A few studies have focused on the C. auris genotypic structure. Here, we compared five epidemiological typing tools using a set of 96 C. auris isolates from 14 geographical areas. Isolates were analyzed by microsatellite typing, ITS sequencing, amplified fragment length polymorphism (AFLP) fingerprint analysis, matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS), and Fourier-transform infrared (FTIR) spectroscopy methods. Microsatellite typing grouped the isolates into four main clusters, corresponding to the four known clades in concordance with whole genome sequencing studies. The other investigated typing tools showed poor performance compared with microsatellite typing. A comparison between the five methods showed the highest agreement between microsatellite typing and ITS sequencing with 45% similarity, followed by microsatellite typing and the FTIR method with 33% similarity. The lowest agreement was observed between FTIR spectroscopy, MALDI-TOF MS, and ITS sequencing. This study indicates that microsatellite typing is the tool of choice for C. auris outbreak investigations. Additionally, FTIR spectroscopy requires further optimization and evaluation before it can be used as an epidemiological typing method, comparable with microsatellite typing, as a rapid method for tracing nosocomial fungal outbreaks. Candida auris was described in 2009 in Japan and since then it has caused infections at a global scale with a serious nosocomial health risk [C. auris was isolated on all six human-inhabited continents [C. auris outbreaks were first reported from South Korea [C. auris has been studied using whole genome sequencing (WGS) and internal transcribed spacer (ITS) sequencing. These studies suggested that the C. auris isolates belong to four separate geographic clades, namely Eastern Asia (Korea and Japan), Southern Asia (India and Pakistan), South Africa, and South America (Venezuela). Importantly, a fifth lineage was detected recently from a patient in Iran that never traveled outside the country. This Iranian isolate possessed >200,000 single-nucleotide polymorphisms (SNPs) relative to isolates from the other four clades and, thus, may represent a fifth clade [C. auris isolates. A few studies have investigated the geographical distribution of C. auris by other genotyping techniques, such as amplified fragment length polymorphism (AFLP) fingerprinting [Candida species [Candida isolates, but also for typing purposes [C. auris [C. auris strain typing [lth risk ,2,3. In ntinents ,5, and intinents ,6,7. Nosth Korea , followeth Korea , South Ath Korea , Kuwait th Korea , Venezueth Korea , USA [14th clade ,16. Isolth clade . Howeverth clade ,18. Therprinting ,18 and mprinting . The lat species ,20,21,22purposes ,18,23,24C. auris ,28,29,30n typing ,31. The n typing . The IR Saccharomyces cerevisiae and was able to differentiate between different Saccharomyces species [Klebsiella isolates [FTIR spectroscopy had been applied to type isolates of  species , but als species ,35. The isolates ,36 and Gisolates . A few sisolates .C. auris isolates to further contribute to their epidemiology and dispersal routes in order to improve hospital hygiene and patient management.In this study, we compared different typing techniques, namely microsatellite typing, AFLP fingerprinting, ITS sequencing, MALDI-TOF MS, and IR Biotyper FTIR spectroscopy, to evaluate their application in typing C. auris isolates was obtained from the Westerdijk Fungal Biodiversity Institute, Utrecht, The Netherlands (n = 37), the Centers for Disease Control and Prevention , and individual researchers (n = 49). The majority of isolates came from India (n = 38) [n = 1) [n = 2) [n = 3) [n = 9) [n = 3) [n = 3) [n = 13) [n = 2), Saudi Arabia (n = 2) [n = 13) [n = 4), Switzerland (n = 1) [n = 2) [Isolates used in this study were collected from different geographical regions, and belonged to the previously defined four major clades based on molecular studies ,19,26,39 [n = 1) , Belgium [n = 2) , Japan ( [n = 3) , Israel  [n = 9) ,44, Koredia n = 3 [n = 1)  [n = 3) , Oman (n[n = 13) ,47, Paki (n = 2) , Spain (hers n = . The maj (n = 1) , and Ven [n = 2) ,51, and C. auris isolates was extracted by the CTAB method as described elsewhere [All 96 strains were subjected to a recently developed 12-loci microsatellite typing scheme . Genomiclsewhere . The tweThe amplification of the internal transcribed spacer (ITS) region was performed using primers ITS1 and ITS4, followed by Sanger sequencing using the same primers, as described elsewhere . Raw seqC. auris isolates was assessed by AFLP fingerprinting as described elsewhere [2O. One microliter of diluted PCR product was added to a mixture of 8.9 µL ddH2O and 0.1 µL Orange 600 internal size marker  followed by 1 min heating at 100 °C, and thereafter directly analyzed on an ABI3730xL Genetic Analyzer (Applied Biosystems). Raw data were imported into BioNumerics v7.6 (Applied Maths) and automatically processed. The assignment of the internal size standard was visually checked and manually corrected if needed. A UPGMA dendrogram was generated with BioloMICS v12 (BioAware).The genetic diversity of the lsewhere . The selC. auris isolates grown on SDA were used for spectra acquisition. Full extraction was performed according to the MALDI Biotyper standard protocol as described elsewhere [g, the supernatant was discarded, and 1 mL of 70% ethanol was added. The suspension was homogenized and then centrifuged at 3 min at 14,000× g. Following this, based on the pellet size, equal amounts of 70% formic acid and 100% acetonitrile were added. After centrifugation for 5 min at 14,000× g, 1 μL of supernatant was spotted eight times onto a polished steel target plate  and overlaid with 1 µL MALDI matrix (10 mg/mL of α-cyano-4-hydroxy-cinnamic acid (α-HCCA) in 50% acetonitrile–2.5% trifluoroacetic acid; Bruker Daltonik) [Overnight cultured lsewhere ,55,56. Baltonik) . MALDI-Taltonik) . Up to 2C. auris were cultivated on SDA medium. The biomass of two times 10 µL inoculation loops was transferred into 70 µL of 70% (v/v) ethanol in 1.5 mL tubes. The tubes were equipped with metal rods to allow for better homogenization . The suspension was homogenized by 5 min vortexing; then, 70 µL of deionized water was added followed by 5 min additional shaking. Fifteen microliters of the suspension was spotted in triplicate on a silicon sample plate  and dried at 37 °C. The experiment was repeated on three different days to cover cultural variance. Spectra were acquired using the IR Biotyper system v2.1  with the following default analysis settings: 32 scans per sample, 10 kHz scan speed, 6 cm−1 resolution, Blackman–Harris 3-term apodization, and zero filling 4. Spectra with an absorption < 0.4 or > 2, a signal/noise ratio < 40 in the carbohydrate range, and/or fringes > 10−4 were not considered for further analysis as these will not be in agreement to already defined factors to provide the default quality criteria, and may have led to either failed or wrong outcomes [−1 were processed as follows: the second derivative was taken over nine data points, cut to 1300–800 cm−1, and vector-normalized. For each of the isolates, the arithmetic mean of qualified spectra was calculated, so that each mean spectrum was composed of 3–12 single spectra. A UPGMA dendrogram was generated with BioloMICS v12 (BioAware).Overnight cultures of Pearson correlation (Mantel test) between the different typing methods was calculated using BioloMICS v12 (BioAware) .Five UPGMA-based dendrograms were obtained from the calculated distance matrices. Cophenetic (Mantel test) clustering  allowed C. auris typing. As indicated in the analysis of the single typing approaches (see below), some yielded similar clustering patterns to the microsatellite analysis, whereas others differed widely. In general, cophenetic correlation values were low indicating poor concordance between the methods investigated. Using cophenetic clustering analysis, microsatellite typing showed the highest agreement with ITS sequencing showing a correlation of 0.45, followed by results from the IR Biotyper with a score of 0.33  is the type-strain and this strain was included in this study with two other IDs, namely CDC 381 and CWZ 10031064. Additionally, a Korean isolate, namely CBS 12372 (KCTC 17809), was present as duplicate CWZ 10031062. These isolates provided us the capability to test the technical reproducibility of the five typing methods used here. According to the created clusters and sub-clusters, the Japanese triplicate and Korean duplicate isolates correctly grouped into the South East cluster by the microsatellite assay and ITS sequencing. The IR Biotyper typing method classified the triplicated Japanese type-strain into one sub-cluster, whereas the Korean isolates were divided into two sub-clusters. AFLP fingerprinting and MALDI-TOF MS analysis grouped the respective isolates into different clusters and sub-clusters. Furthermore, 10 isolates belonged to the South America cluster and were grouped together with all methods, except MALDI-TOF MS. This method separated one isolate from Venezuela (CDC 386) out of the remaining nine isolates and placed it in cluster MALDI II with isolates from South Asia, East Asia, and South Africa.Among the 96 tested n = 8) and Venezuela (n = 2). Cluster MS I contained six sub-clusters with strains showing the same microsatellite patterns  . Sixty-opatterns . Sub-clupatterns .C. auris isolates into three clusters, ITS I-III. Eighty isolates (83.3%) including Austria (from a Turkish patient), Belgium (Kuwaiti patients), India, Israel, Malaysia, Oman, Pakistan, Saudi Arabia, South Africa, Spain, and Switzerland had identical ITS sequences. The cluster, ITS I, contained representatives of clusters MS I and MS III as revealed by the microsatellite typing analysis. The second cluster ITS II was fully concordant with cluster MS II, as revealed by microsatellite typing, and had six isolates (6.25%) from Japan and Korea. The remaining group, cluster ITS IV, had the same 10 isolates (10.42%) from Israel and Venezuela as the microsatellite typing identified cluster MS IV  that were previously recognized. The eight basal isolates belonged to clusters MS I, MS II, and MS III. Given the considerable proteomic variation, we refrained from assigning further sub-clusters . Two dupC. auris isolates were measured. In total, 860 IR spectra of 96 C. auris isolates and compare the outcome with results obtained by MALDI-TOF MS and molecular typing methods. The main goal for the development of the IR Biotyper method was to establish a rapid typing method to investigate the relatedness between isolates involved in nosocomial outbreaks [This study was undertaken to evaluate the applicability of IR Biotyper as a biochemical typing method to type utbreaks ,36. In tAspergillus flavus, Aspergillus fumigatus, and Candida species, such as Candida albicans, Candida glabrata, Candida parapsilosis, and Candida tropicalis, as well as to investigate three decades of Cryptococcus neoformans epidemiology in the Netherlands [C. auris [C. auris isolates , CDC 381 (Japan), and CDC 385 (Venezuela), were correctly classified into ITS I, ITS II, and ITS IV, respectively, while isolate CDC 383 (South Africa) was grouped incorrectly into ITS I.ITS sequencing largely corroborated the clusters found by microsatellite analysis, except for isolates from South Africa and Spain (MS III) that grouped together with isolates from South Asia (MS I) and that clustered differently with microsatellite typing. Thus, ITS sequence-based typing has less resolution than microsatellite analysis. The CDC C. auris isolates according to the respective geographical origin. The isolates from India clustered together, and the isolates from Japan and Korea created another cluster. In their study, AFLP method could successfully classify isolates into two different clusters based on the isolates’ geographical origins [AFLP fingerprinting has remained a method with broad applications in outbreak investigations, microbial clustering studies, and typing ,67,68,69 origins . Next, P origins . Their A origins . In our Candida typing in several studies [Candida albicans and Candida glabrata isolates as outliers might affect the acquired results differently. The lack of an outlier species in this study may explain why the MALDI-TOF MS-based dendrogram differed widely from the clustering obtained by microsatellite analysis. The use of BioloMICS v12 (BioAware) to generate a UPGMA dendrogram might not have been the optimal analysis method for the acquired MSPs generated by MALDI-TOF MS. Other available analytical methods, such as BioNumerics, and a comparison between dendrograms created by these different approaches may improve the analytical outcomes generated as MSP-based dendrograms. Four MALDI-TOF MS clusters, MALDI III-VI, each containing only two isolates, were generated that occurred distantly from the main MALDI-TOF MS clusters (MALDI I and MALDI II). The reference strain CDC 385 (Venezuela) clustered in MALDI I that included isolates from Israel and Venezuela (=cluster MS IV = South American clade); isolate CDC 383 (South Africa) created a small group with another isolate from the same region (CDC 384) that belonged to the South Africa cluster; isolates CDC 381 (Japan) and CDC 388 (Pakistan) were located in the MALDI II cluster that included isolates from MS I, MS II, MS III, and MS IV. Overall, the MALDI Biotyper provides excellent performance for species identification; however, it is not useful for typing different C. auris strains when compared to the microsatellite typing assay.MALDI-TOF MS is a protein-based method that has been used for  studies ,66,73,74 studies ,73,75,76 studies ,75. MoreC. auris isolates originally came from South Africa. Isolate numbers CDC 381 (Japan) and CDC 388 (Pakistan) grouped in the major cluster IR I (sub-cluster IR Ib) with isolates from India, Israel, Oman, and Spain. CDC 383 (South Africa) and CDC 385 (Venezuela) grouped in sub-clusters IR Ia and IR Ic and belonged to cluster MS III (South Africa) and MS IV (South America), respectively. Thus, more effort is required to evaluate and optimize several parameters in measuring and analyzing data obtained by FTIR spectroscopy before this method can be used to investigate intra-species types in hospital outbreaks caused by C. auris and, likely, isolates of other yeast species.The acquisition of IR Biotyper spectra depends highly on the conditions used during culturing of the yeasts ,77. The C. auris Japanese type-strain, and duplicate Korean isolates. The observed result after microsatellite and ITS typing showed the high technical reproducibility of both methods. The IR Biotyper method indicated reliable reproducibility by clustering all Japanese type-strains into one sub-cluster. In contrast, AFLP fingerprinting and MALDI-TOF MS analysis placed the triplicated Japanese and duplicate Korean isolates in non-concordant subgroups that referred to their low power of technical reproducibility.A further analysis was performed to evaluate the reproducibility of the five typing methods investigated in this study using triplicated C. auris infections. In addition, this result may also be due to intrinsic genetic, biochemical, or phenotypic variations among C. auris isolates. Genomic variation can be generated via small local alteration of the nucleotide sequence of the genome, intragenomic restructuring, and the acquisition of genetic material from other Candida species or even other microbes to adapt to different environmental conditions as well as the human body [C. auris clades, whereas genetic diversity was obvious between the four clades [C. auris, further analyses based on a larger and blinded sample size and inclusion of various independently working laboratories, using WGS as a reference, are needed to evaluate other less costly methods as explored here.In summary, none of the typing methods explored in this study showed a concordance above 50%. Comparing the results obtained by the microsatellite typing assay, the other methods created different clusters or sub-clusters, including separating or combining isolates from different countries and continents. This observation is difficult to explain, but lack of information about the patient’s background may impact our understanding of the epidemiology of man body ,79,80. Ar clades . All ana


Mutations in BRAF at the 600th codon have proven sensitive to combination BRAF and MEK inhibition. Mutations outside this codon, however, are approximately as common but do not have approved targeted therapy approaches. Herein, we discuss targeting these non-V600 mutation and fusions in BRAF with MEK inhibitors. BRAF has been a major success story of the precision-cancer movement. Mutations affecting the 600th codon may be targeted by several approaches, including mutant-specific BRAF inhibitors, blockers of downstream signal partners MEK1/2, or the combination of both. In particular, the management of BRAF V600-mutant melanoma, lung cancer, hairy cell leukemia, and thyroid cancer has been transformed by these agents, which produce high response rates, occasionally resulting in durable responses [Developing active, molecularly targeted agents against mutated esponses .BRAF V600, approaches to targeting other mutations in BRAF outside of the 600th codon have been less active. When taken together, these mutations include over 200 BRAF-mutant alleles corresponding to about 30 distinct BRAF mutations. Non-V600 mutations are approximately as common as V600 mutations, and are found in 1–3% of all cancers, including in many common histology types  as well as less common tumor types  [BRAF V600 mutation, signal as monomers in constitutively active fashion. Class 2 mutations function as constitutively active dimers. Class 2 mutations include L597 and K601 mutations, which have been subject to several case reports demonstrating activity for MEK inhibition. Class 3 mutations, which include G466, D594, and A581 mutations, induce preferential binding to wild type RAF, and are kinase dead or have impaired kinase activity. These mutations, however, are still associated with enhanced RAS/MAPK activation, potentially due to other mechanisms such as growth factor signaling or concurrent MAPK pathway mutations  [In contrast to the high-profile success of pharmacologic blockade of mutant nancies) . These mnancies) –11. Clas or NF1) .BRAF mutations, as well as BRAF fusions [BRAF V600 mutant melanoma), and with class 2 mutations  or fusions. Thus, a larger study to test the concept more generally was planned.Several pre-clinical studies and isolated case reports have demonstrated impressive efficacy for MEK inhibitors in patients with non-V600  fusions –16. ThesBRAF outside the V600 codon, as well as BRAF fusions [BRAF alterations  were enrolled, and received trametinib 2 mg daily until disease progression (dose reduction permitted for severe toxicity). Patients were heavily pre-treated (69% with 3 or more lines of therapy), and most often had cancers of the lung (n = 9), colon/rectum (n = 7), and prostate (n = 4). Unfortunately, activity was disappointing. Of these, one patient with breast cancer and a BRAF G469E mutation (class 2) had a partial response (response rate 3%), one patient with lung adenocarcinoma and a BRAF G469A mutation remains on therapy at 20.4 months, and an additional 3 patients had progression-free survival (PFS) of > 6 months. Median PFS and overall survival were 1.8 and 5.7 months, respectively.With that backdrop, we studied trametinib, a second-generation allosteric inhibitor of MEK1/2 in patients with solid tumors and lymphomas harboring mutations in  fusions . This suBRAF mutant allele frequency, and class 3 mutations, whereas concurrent mutations (including RAS mutations) did not impact outcomes. We speculate that histology does still matter, as many patients in the study had tumors that have historically been resistant to MAPK pathway inhibition  [Why the lack of activity? We did attempt to identify explanations within the trial data, although no subgroups had exceptionally good outcomes. Trends toward worse outcomes were noted in patients with colorectal cancer, high  cancer) , 19, and cancer) . Exceedi cancer) , 20, 21.BRAF mutations [BRAF mutations. Second, one approach might be studying dual blockade of MAPK pathway and other parallel signaling networks . These types of approaches, however, have neither promising activity nor safety in early studies [BRAF V600-mutant colon cancer demonstrate, may hold promise as well [BRAF V600, BRAF non-V600, and RAS mutant tumors. This strategy also prevents paradoxical MAPK upregulation in BRAF wild type cells, thus avoiding the cutaneous squamous cell cancers that are promoted by mutant specific BRAF inhibitors [What are the next steps? First, our study does not change available data suggesting that MEK inhibition may be still considered for melanoma patients whose tumors harbor class 2 utations , 23, or utations . More reutations , 25. How studies –28. Howe as well . Third,  as well . The tol as well , 32. Thihibitors . HoweverBRAF mutations. Innovative approaches to block parallel signaling networks or more thorough downstream MAPK pathway activation may hold promise for this challenging population.In conclusion, the activity of MEK inhibition  outside the narrow window of patients with melanoma and class 2 mutations, does not appear robust in cancers harboring non-V600


VviUCC1 gene. The aim of this study was to characterize the VviUCC1 gene in grapevine and to test the association between the natural variation observed for a series of rachis architecture traits and the polymorphisms detected in the VviUCC1 sequence. This gene encodes an uclacyanin plant-specific cell-wall protein involved in fiber formation and/or lignification processes. A high nucleotide diversity in the VviUCC1 gene promoter and coding regions was observed, but no critical effects were predicted in the protein domains, indicating a high level of conservation of its function in the cultivated grapevine. After correcting statistical models for genetic stratification and linkage disequilibrium effects, marker-trait association results revealed a series of single nucleotide polymorphisms (SNPs) significantly associated with cluster compactness and rachis traits variation. Two of them (Y-984 and K-88) affected two common cis-transcriptional regulatory elements, suggesting an effect on phenotype via gene expression regulation. This work reinforces the interest of further studies aiming to reveal the functional effect of the detected VviUCC1 variants on grapevine rachis architecture.Cluster compactness is a trait with high agronomic relevance, affecting crop yield and grape composition. Rachis architecture is a major component of cluster compactness determinism, and is a target trait toward the breeding of grapevine varieties less susceptible to pests and diseases. Although its genetic basis is scarcely understood, a preliminary result indicated a possible involvement of the  Vitis vinifera ssp. sativa) around the world, originally derived from the wild grape subspecies, Vitis vinifera ssp. sylvestris, in a process that started ca. 8,000 years ago somewhere in the Transcaucasian region [Current grapevine diversity has been shaped over millennia. Genetic analyses estimate that there are 6000–10,000 different genotypes of cultivated grapevines , mainly due to the poorly-aerated and humid microclimate generated in the cluster after rain events [Cluster compactness (or cluster density) was another trait selected during the development of wine and table grape cultivars. Table grape cultivars have, in general, less compact clusters than wine grape cultivars ,8. Grapen events . The pren events , and hasn events . Consequn events  and/or tn events , which in events . As a ren events ,15. In an events . It hindn events  and impan events .ca. 10% to ca. 99%) and flower number (from ca. 130 to ca. 2500 flowers per inflorescence) [Vitis vinifera L. clusters with long rachis ramifications present a lesser degree of compactness than those with short ramifications [VvGRF4) on the elongation of the berry pedicel has been investigated in a series of loose clones of Pinot Noir [Cluster compactness is the result of a two-year course in which berry number, berry traits, and rachis traits interact in a complex and dynamic process ,18. Thesescence) . This coescence) ,23. In cescence)  and refeescence) . Finallyescence) ,25,26,27ications . Neverthications  found twications . More reUclacyanin I-like gene (VviUCC1) between clones with compact and loose clusters [VviUCC1 polymorphisms significantly associated with the variation observed for cluster compactness and the length of the first ramification of the rachis in a diverse collection of wine and table grape cultivars [Arabidopsis thaliana, 46 in Solanum lycopersicum, 74 in Populus trichocarpa, and 89 in Glycine max [VviUCC1 in structural organs of the plant, namely roots (from in vitro cultivation), young inflorescences (with single flowers in compact groups), seedlings, and inflorescence rachises [VviUCC1 in growing young inflorescences [VviUCC1 in grapevine inflorescence formation and/or development, which might have an impact on final rachis architecture and therefore on cluster compactness.Aiming to identify additional candidate genes underpinning grapevine cluster compactness and rachis architecture, a series of comparative transcriptome analyses between Garnacha Tinta and Tempranillo Tinto clones differing in cluster compactness have been performed ,31. Amonclusters . Furtherultivars . Uclacyaultivars ,33. Genocine max . In the cine max . Their bcine max ,35,36. Icine max ,37. Sevecine max , during cine max , or in tcine max . In addicine max , reinforrachises . Similarescences . AltogetVviUCC1 to the phenotypic variation obtained for cluster compactness and a series of rachis architecture traits in a grapevine collection of diverse wine, table, and multi-purpose grape cultivars has been explored. Based on next-generation sequencing data, the VviUCC1 gene and promoter nucleotide diversity have been characterized, evaluating the degree of linkage disequilibrium (LD) between the polymorphisms detected. In addition, a reduced number of haplotype blocks with little evidence of historic genetic recombination events have been inferred. Finally, a series of association mapping analyses were also performed between the VviUCC1 gene polymorphisms and the variation observed for cluster compactness and seven rachis architecture traits, considering the phenotypic data of three consecutive seasons and the best linear unbiased prediction (BLUP) values obtained after data modeling.In this study, the likely contribution of Vitis vinifera L. genotypes and three Vitis spp. interspecific crossings grown as hybrid direct producers. Genotypes are maintained as previously detailed [H2) was estimated as described elsewhere [lmer function of the lme4 package [ggplot2 R package, and correlation analyses were performed by means of the cor function of the corrplot R package using Pearson correlation coefficients (p < 0.05).A grapevine collection of 114 varieties  held at the Grapevine Germplasm Collection of the Instituto de Ciencias de la Vid y del Vino  were analyzed  and sevdetailed . In eachlsewhere  for the  package  for R v.VMC1B11, VMC4F31, VVIB01, VVIH54, VVIN16, VVIN73, VVIP31, VVIP60, VVIQ52, VVIV37, VVIV67, VVMD21, VVMD24, VVMD25, VVMD32, VVMD7, ssrVrZAG67, VVMD27, VVMD5, ssrVrZAG29, ssrVrZAG62, ssrVrZAG112, VVS2, ssrVrZAG83, and VVMD28) [Young and fresh leaves were sampled for each grapevine genotype and frozen at −80 °C. DNA was extracted as previously detailed ,44. DNA  VVMD28) ,48,49, wVviUCC1 gene (Vitvi12g00555 in the VCost.v3 gene annotation version [VIT_12s0059g02640 in the previous V1 version) was sequenced including 1000 bp in the promoter region according to grapevine 12X V1 gene predictions. Thus, a region of 1971 bp (chr12: 7355541:7357512) was targeted for next-generation sequencing (NGS), performed as previously detailed [Vitis vinifera PN40024 reference genome with Bowtie 2 [cis-acting regulatory elements, as implemented in PlantCARE [The  version , correspdetailed . BrieflyBowtie 2 ,44, and Bowtie 2 , and namBowtie 2 , and theBowtie 2 . The likVviUCC1 haplotypes were inferred using the Partition-Ligation-Expectation-Maximization (PLEM) algorithm implemented in PHASE v.2.1 using default settings [settings . These wsettings . The sofsettings  was usedr2) were calculated for each pair of polymorphisms by means of Haploview v.4.2 [Linkage disequilibrium (LD) was calculated considering those polymorphisms with a frequency of the minor allele (MAF) higher than 5%. Thus, standardized disequilibrium coefficients (D’) and squared correlations of allele frequencies  and BLUP values available for cluster compactness and rachis architecture traits, using a mixed model (MLM) capable of correcting for population structure (Q) and kinship (K) effects . To thisrelated  and prevrelated . Associarelated  using th−2 , 3.3 × 10−3 (for α = 0.01), and 3.3 × 10−4 (for α = 0.001).Aware of the high level of LD between the polymorphisms used in this work, a Bonferroni-corrected factor based on the number of independent tests achieved in this work was applied to set an adjusted p-value threshold to control for false positives. According to Duggal et al. , this nuVviUCC1 gene sequence and 1000 bp of the promoter region, was sequenced in a collection of 114 wine, table, and multi-purpose grape varieties. A 100% of coverage of the targeted sequence was achieved in all varieties after alignment with the PN40024 reference genome, with an average coverage depth of 94.1 ± 14.3 reads/nucleotide. BAM files are available at the National Center for Biotechnology Information’s Sequence Read Archive (Accession PRJNA625274). Previous RNAseq analyses [VviUCC1 gene structure annotated for the reference PN40024 genome (VCost.v3), consisting of a 5′-UTR (17 bp), two exons (145 and 539 bp) separated by an intron (146 bp), and a 3′-UTR (125 bp)  . Thus, tVitis spp. interspecific hybrid). The other INDELs involved the insertion/deletion of one nucleotide, but for the IND-260, located in the gene promoter and causing the insertion/deletion of 14 nucleotides. The analysis of the SNPs identified in this work revealed the presence of 36 transitions and 35 transversions. Thus, a balanced transition/transversion ratio of 1.03 was found. Among the transitions, A/G and C/T mutations showed an occurrence of 11 and 25 cases, respectively. Regarding the transversions, 8, 11, 4, and 12 cases for A/C, A/T, C/G, and G/T mutations were detected, respectively. Among all the polymorphisms detected, 37 (46.25%) were represented by a rare allele (MAF < 0.05)  . Conside < 0.05) , a highe < 0.05) . SnpEff VviUCC1 gene. According to prediction, this substitution generates a premature stop codon (TGA) and therefore a truncated  protein product. This alternative allele of W738 (T) showed a high frequency in the studied collection  affecting the first nucleotide of an AGA triplet  located in the second exon of the  65.79%, . In this 65.79%, . The het 65.79%, . Neverth 65.79%, . These tVviUCC1 sequence, 46 different haplotypes were inferred including 23 found exclusively in one cultivar (p > 0.05), with values of −0.579 and −1.142, respectively.From the 80 polymorphisms detected in the cultivar . No evidin silico analysis of the VViUCC1 promoter sequence revealed the presence of 69 different cis-acting regulatory elements belonging to 19 different classes including four MYB and four MYC binding sites, and a series of common enhancers and/or repressor elements such as 14 CAAT boxes, six TATA boxes, and one TTGACC box. Some of the polymorphisms detected in the promoter affected these binding sites, as observed for the SNP Y-984, which affects a CAAT-box and the SNP K-88, which affects a TATA-box located nearby the transcription start site , genes coding for membrane transporters (including four ATP-binding cassette (ABC) transporters), genes coding for transcription factors , and genes involved in different hormone signaling pathways (auxin (3), ethylene (2), gibberellins (2) and jasmonate (2) pathways).Gene co-expression analysis indicated a series of genes correlated with partners . The topVviUCC1 gene sequence with a MAF above 0.05. As observed in the density plot shown in r2 = 1) even when separated by a large distance  with a mean intrablock LD of D’ = 0.99 ± 0.02 and r2 = 0.25 ± 0.31. Finally, the third haploblock included 10 SNPs, from R530 to K889 (seven SNPs from the second exon and three from the 3-UTR’). It has a mean intrablock LD of D’ = 0.97 ± 0.11 and r2 = 0.41 ± 0.32.Three haploblocks were predicted from the Haploview-generated LD-plot obtained from the genetic data of 43 F > 0.05 c. No intH2 values ranging from 38.9 to 47.1 for PDULE and RM1LE, respectively) than for COMP (H2 = 32.1). PDILE obtained the lowest H2 value within the traits here evaluated (H2 = 31.6).A large phenotypic variation was observed for cluster compactness and seven architecture traits in the grapevine cultivars included in this work . COMP vap < 0.05) with all the rachis traits , obtaining negative coefficients in all pairwise correlations. In contrast, rachis traits were positively correlated (p < 0.05). A high positive correlation between RM1LE, RM2LE, RM3LE, RM4LE, and RALE was also obtained  associations with seasonal data. Of them, 23 associations were significant at p-value ≤ 3.3 × 10−3, and six associations at p-value ≤ 3.3 × 10−4. These associations involved six SNPs: Y-984, Y-231, and K-88 , Y158, K217, and W551. Although none of them were associated significantly with the same trait for the three seasons evaluated, SNPs Y-984, Y-231/K-88, Y158, and K217 associated during two consecutive seasons (2011 and 2012) with COMP, explaining between 8.9 and 14.1 of trait variance. SNPs Y-984 and Y-231/K-88 were additionally associated with RM1LE in 2011 and 2013, and SNPs Y-984, Y-231/K-88, Y158 and K217 were also associated with RM2LE, RM3LE, and RM4LE in 2013. No significant associations were found for RALE, PDULE, or PDILE. The whole list of significant associations is shown in The association analysis carried out between the 43 polymorphisms and eight traits evaluated for three years (using the MLM model with structure and kinship matrices as correction factors) allowed us to identify 35 significant (r2= 1), W-207, Y271, and S407  with PDULE. SNPs associated with COMP, RM1LE, and RM2LE explained between 12.5 and 13.3, 8.3 and 11.2, and 7.4 and 9.9% of BLUP variance, respectively.Considering the association tests performed with BLUP values, 16 significant associations involving ten SNPs were obtained. Y-984 and Y-231/K-88 were found to be associated with three traits , Y158 and K217 with COMP, W-497 and R10  between Garnacha Tinta clones with loose and compact clusters was observed during the rapid inflorescence growth occurring prior flowering [VviUCC1 as a candidate gene to test its association with the variation observed for cluster compactness and multiple rachis architecture traits in a diverse collection of grapevine cultivars.Cluster compactness is a complex trait that depends on the interaction of multiple factors . Recent lowering . Likewislowering ,71 and slowering ,73 procelowering ,43. AltoThe grapevine collection used in this work showed a high diversity for the phenotypic traits evaluated , supportVviUCC1 gene has a high nucleotide diversity, with one polymorphism every 24.6 nucleotides. This level of diversity is higher than the reported for other grapevine genes [Vitis spp. interspecific hybrid-direct producer genotypes in this work (such as Jacquez or Rubired) might have contributed to increase this diversity value, as previously observed for the grapevine VvNAC26 gene [VviUCC1 polymorphisms were exclusively found in these three hybrid genotypes  were represented by low frequency alleles, suggesting purifying selection processes of deleterious mutations [On the other hand, the ne genes ,76, probne genes , has higC26 gene . In factenotypes , easily utations . Thus, cin silico analyses are useful to predict whether a genetic variant has an impact on the biological function of the coded protein, especially in the big data era, results should be checked considering its biological context to confirm those predicted impacts. Here, in silico analyses indicated the presence of a polymorphism (W738) in the open reading frame of VviUCCl generating a premature TGA stop codon. Nevertheless, the joint analysis of the allelic diversity observed for this SNP with that obtained for a contiguous non-synonymous SNP (S739) revealed that the predicted TGA stop codon is never present in the grapevine collection  associated with cluster compactness and the length of the first rachis ramification was reported. Here, three additional VviUCC1 polymorphisms  were found to be significantly associated with the seasonal variation observed for the length of the ramifications of the rachis and cluster compactness   . It suggd grapes . Of specgulation .VviUCC1 co-expresses with several genes that participate in the lignification process  has been shown to contribute to the regulation of the phenylpropanoid pathway in grapevine, with an impact on lignin biosynthesis [VviUCC1 also co-expresses with multiple genes of the auxin, ethylene, jasmonate, and gibberellin pathways. These hormones play a complex crosstalk to mediate plant defense response against pathogens and abiotic stresses [Uclacyanins are plant-specific cell-wall proteins  involved process . Thus, Vroyl-CoA . VviUCC1rization . Furtherynthesis . Interesstresses , which mstresses .Taken together, these results suggest that VviUCC1 could be involved in the lignification of the inflorescence rachis that takes place during the elongation and thickening processes occurring along its growth, providing the fundamental structural support needed to bear the weight of the fully developed grapes at the end of the grapevine reproductive cycle, which in some cultivars can be extremely high . In VviUCC1 gene sequence in the cultivated grapevine. Association analyses indicate that VviUCC1 may participate in the development of grapevine inflorescence and impact cluster compactness, a trait with major agronomic relevance. The location of the SNPs associated with rachis traits suggests that they might modulate VviUCC1 expression levels, which could correlate with phenotypic diversity. These findings highlight the need for gene expression studies aimed to assess the functional effect of these SNPs on the determination of grapevine rachis architecture. In addition, this work points out the interest of analyzing the association between the detected VviUCC1 variants and additional related traits including rachis diameter, rachis stiffness/flexibility, and rachis composition (rachis lignification).The present study provides an in-depth analysis of the nucleotide diversity of the


Though accounts for 2.5% of all cancers in female, the death rate of ovarian cancer is high, which is the fifth leading cause of cancer death  in female. The 5-year survival rate of ovarian cancer is less than 50%. The oncogenic molecular signaling of ovarian cancer are complicated and remain unclear, and there is a lack of effective targeted therapies for ovarian cancer treatment.In this study, we propose to investigate activated signaling pathways of individual ovarian cancer patients and sub-groups; and identify potential targets and drugs that are able to disrupt the activated signaling pathways. Specifically, we first identify the up-regulated genes of individual cancer patients using Markov chain Monte Carlo (MCMC), and then identify the potential activated transcription factors. After dividing ovarian cancer patients into several sub-groups sharing common transcription factors using K-modes method, we uncover the up-stream signaling pathways of activated transcription factors in each sub-group. Finally, we mapped all FDA approved drugs targeting on the upstream signaling.The 427 ovarian cancer samples were divided into 3 sub-groups  based on the activated TFs . Multiple up-stream signaling pathways, e.g., MYC, WNT, PDGFRA (RTK), PI3K, AKT TP53, and MTOR, are uncovered to activate the discovered TFs. In addition, 66 FDA approved drugs were identified targeting on the uncovered core signaling pathways. Forty-four drugs had been reported in ovarian cancer related reports. The signaling diversity and heterogeneity can be potential therapeutic targets for drug combination discovery.The proposed integrative network analysis could uncover potential core signaling pathways, targets and drugs for ovarian cancer treatment. In United States, ovarian cancer is the fifth leading cause of cancer-related death in female , which aOn the other hand, comprehensive multi-omics data of ovarian cancer patients have been profiled and analyzed , 8. A seIn this study, we aim to systematically investigate potential activated core signaling pathways in ovarian cancer sub-groups by uncovering the up-stream signaling pathways of activated transcription factors (TFs), and identify all available FDA approved drugs targeting on these up-stream signaling and TFs. The combinations of these drugs have the potential to be synergy with standard platinum chemotherapy by disrupting multiple up-stream signaling and their cross-talk. This study will provide a useful reference resource for repositioning effective drugs and drug combinations for ovarian cancer. The rest of the paper is organized as follows. The details of datasets and methods are provided in Section 2. The analysis results are presented in Section 3, followed by a summary in Section 4.We download the gene expression (RNAseq - RSEM expected_count (DESeq2 standardized)) data of 427 ovarian cancer samples (from The Cancer Genome Atlas (TCGA) ), and 88To obtain KEGG signaling pathways, the “Pathview” R package  was emplThis dataset includes screening results of pairwise combinations of 104 FDA-approved anticancer drugs on NCI-60 cancer cell lines (59 cancer cell lines with detailed genomics profiles) . Specifip-value <= 0.05 (using t test) will result in too many up-regulated genes. The Maximum Likelihood Estimate (MLE) method , σ2 : IG.. To get uninformative priors, we set w0 = 0, v0 = +∞, a0 = 0, b0 = 0. Since it is hard to calculate eq. . We take the gene “CENPH” as an example to illustrate this analysis. The PDF generated by the MCMC model is more robust than generated by Maximum Likelihood Estimate (MLE) .We use the conjugate priors for late eq. , we use p-value threshold, 0.05, was used to select the activated TFs.The Fisher’s exact test (using hyper-geometric distribution) was used to identify the activated TFs by comparing the number of up-regulated targets vs. the number of all target genes, with the number of all the up-regulated genes vs. the number of all the genes tested. The p-value to 0–1 using 0.05 as a threshold. For categorical data, we use the k-modes method [We cluster 427 ovarian cancer samples using the identified activated TFs. We transform s method  for the All 282 signaling pathways from KEGG are investigated, and all the signaling cascades from the starting nodes to the activated TFs are extracted using the python package, NetworkX, to extract the up-stream signaling cascades starting from the beginning genes of individual signaling pathways to the given TFs. Then we score each signaling cascades using the average probability of genes . To control the size of up-stream signaling network, the top 3 signaling cascades are kept.gi), is calculated using the impact analysis method [The impact analysis (IA) evaluates both the topology and dynamics of a signaling pathway by considering the gene expression changes, the direction and type of signaling interaction, and the position and role of every gene in a pathway. A perturbation factor for each gene, PF represents the signed normalized measured gene expression change of gene gi. The second term is the sum of perturbation factors of direct upstream genes of target gene gi, normalized by the number of downstream genes of each such gene Nds(gj). The value of βij quantifies the strength of the interaction between genes gj and gi. We use the probability density of gene expression instead of gene expression, which s will be more accurate considering that the standard deviation of different genes is different.The term ΔE based on the activated TFs. For each sub-group, there is a center sample, and we use the center sample to characterize each sub-group. In another word, the activated TFs in the center sample were used as the activated TFs for this sub-group.Group 2 and Group 3 have 26 and 25 TFs respectively. The additional TFs for Group2 are: AR, ETS1, GABPB1, GFI1, HMG, LHX3, NKX6.2, PAX3, PDX1, PITX2, REST(NRSF), S8. The additional TFs for Group 3 are: ARNT_MAX, ETS1, FOXN1, FOXO4, GABPB1, LHX3, NFATC2, NFIL3_ATF2, NKX6.2, PAX3, SREBF1.For visualization purpose, the principal component analysis (PCA) was employed to reduce the 230 TFs to 2 dimensions (see Fig. The up-stream signaling of activated TFs are shown in Figs. see Table To investigate potential drugs that can potentially perturb these up-stream signaling networks, we mapped the FDA approved drugs on the signaling networks (see Figs. Moreover, we investigated the difference of activated core signaling pathways among these 3 sub-groups. The unique TFs appeared in the core signaling pathways in Group 2 and Group 3 are: PDX1, REST and AR; and ROXO4, SREB F1, NFATC2 respectively. In upstream signaling genes, PML, LEF1, MAPK12, FGF22, JUP, AKT3, MAPK10, MAP2K1, TCF7 are the unique genes for Group1. The CLTA, AR, RPS6KB2, AP2S1, AKT1S1, FOXA2, PDPK1, HTT, MAP2K6, TNFRSF1A, TNF, GNB1, MAFA, TRAF2, REST, HHEX, EFNA4, MAP3K5, PDX1 and AP2M1 are unique genes for Group2. The FOXO4, PLCB4, NFATC2, IRS4, KRAS, PRKCI, PTPRF, ICOS, EGLN3, NOTUM, PPP3R1, SREBF1, EPHA2, EGFR, EGF and PRKCZ are the unique genes for Group3. For Group 1, MAPK10, AKT3, FGF22 are in MAPK signaling pathway and RAS signaling pathway, and PML, JUP, LEF1, TCF7 are signaling cascades linking to MYC. For Group2, the signaling cascade from TNF to p38 is an upstream of p53. For Group3, many genes appeared in RAP1 signaling pathway. For the drugs listed in Table Ovarian cancer is the fifth leading cause of cancer-related death among women, and the 5-year survival rate is fewer than one half. Though a set of biomarkers and signaling pathways have been identified to be associated with ovarian cancer, the functional consequence of these biomarkers and signaling pathways remain unclear. Moreover, there is a lack of effective targeted therapies for ovarian cancer, especially for the platinum resistant ovarian cancer. In this study, we analyzed the gene expression data of ovarian cancer samples and ovarian normal tissues via network analysis. We aim to systematically explore the activated signaling pathways of individual ovarian cancer patients and sub-groups, and identify potential targets and drugs that are able to disrupt the core signaling pathways. There are still several limitations of the study. First, in addition to gene expression, mutation, methylation, and copy number variation data should be integrated in the network analysis to uncover the TFs, and up-stream signaling. Second, the signaling cross-talk among these up-streams are not investigated, which might be responsible for drug resistance. In the future, we will also investigate the signaling network and TFs of platinum resistant ovarian cancer samples; and conduct the network-based drug repositioning approaches , 67 to rThe purpose of this study is to systematically uncover potential activated core signaling pathways in ovarian cancer using integrative network analysis. We identified about 37 activated TFs from three sub-groups of ovarian cancer, as well as a set of up-stream signaling pathways linking to these TFs, e.g., WNT, TP53, MYC, AKT, RAS, mTOR, PDGFRA signaling pathways. In addition, 66 FDA approved drugs were identified targeting on the uncovered core signaling pathways. Forty-four drugs had been reported in ovarian cancer related reports. Combinations of these drugs could be potentially synergy to disrupt the cross-talk of multiple activated signaling pathways and TFs for better ovarian therapy. These uncovered signaling networks, TFs and drugs can be used as reference resources to support biomedical studies in ovarian cancer.


Background: Around the globe, moderate cases account for the largest proportion of all coronavirus disease 2019 (COVID-19) patients, and deteriorated moderate patients contribute the most in mortality. However, published articles failed to address the deterioration details of moderate cases, especially on when and how they deteriorated.Methods: All moderate COVID-19 patients hospitalized in Guangdong Province from January 14 to March 16, 2020, were included in this multicenter retrospective cohort study and were divided into deteriorated and non-deteriorated groups according to clinical status. Symptoms and demographic, therapeutic, and laboratory test result characteristics were collected to explore the features of disease deterioration.Results: Of 1,168 moderate patients included, 148 (13%) deteriorated to severe (130 cases) or critical (18 cases) status. Over 20% of the older subgroup (>50 years old) showed deterioration. The median time for deterioration was 11 days after onset [interquartile range (IQR) 9–14 days]. In addition, 12.2% severe cases could further develop to critical status after 3 days (IQR 2–6.5 days) of having a severe condition. Respiratory dysfunction and hypoxia were the major manifestations as disease deterioration, while 76 cases (52.1%) showed respiratory rate >30 breaths/min, 119 cases (80.4%) showed SaO2 <93%, 100 cases (67.5%) had 201 < PaO2/FiO2 < 300, and 27 cases (18.9%) had blood lactic acid >2.0 mmol/L. In view of multiple organ dysfunction, 87.8% of acute respiratory distress syndrome (ARDS), 20.2% of acute kidney injury (AKI), 6.8% of coagulopathy, 4% of acute heart failure (AHF), 3.4% of acute hepatic injury (AHI), and 5.4% of shock occurred in deteriorated patients, while organ injury occurred in the following sequence: ARDS, AKI, AHF, coagulopathy, AHI, and shock.Conclusions: The deteriorated pattern of moderate COVID-19 patients is characterized as the 11th day from onset (IQR 9–14 days) being an important time point of disease deterioration with further exacerbation to critical condition in 3 days (IQR 2–6.5 days), A RDS followed by AKI being the typical modes of sequential organ damage. Up to October 26, 2020, the rapid spread of coronavirus disease 2019 (COVID-19) has posed a great challenge to one's health and epidemic prevention in many countries and regions, and the number of infected people has exceeded 40 million . TherefoIn view of the above, a multicenter observational study for the moderate population was carried out to describe the clinical features of moderate cases and determine time points of disease deterioration and the sequence of organ disorders explaining the following key issues: 1. What are the clinical characteristics of disease deterioration in moderate cases? 2. What is the time window and risk factors for disease deterioration in this population? 3. What is the order and degree of the main organ damage when patients deteriorate? These findings would provide specific clinical evidence for effective prevention and treatment of COVID-19.A multicenter retrospective cohort study was carried out at 32 hospitals in Guangdong Province designated to treat COVID-19 patients. All patients who were diagnosed with COVID-19 were screened for eligibility in our study. We retrospectively analyzed moderate patients from January 14, 2020, to March 16, 2020. The Guangdong COVID-19 Prevention and Control Headquarters was set up in Guangdong Province to direct and coordinate the treatment of COVID-19 patients across the province. An electronic medical information reporting system (E-System) was put together by the Guangdong Health Commission for the entire provincial medical data collection.Medical records for 1,315 COVID-19 patients in Guangdong province who had completed hospitalization by March 16, 2020, were extracted from the E-System. Medical records covered epidemiology, demographics, clinical symptoms, comorbidities, laboratory tests, and illness severity, as well as the main treatments during hospitalization and key dates in disease development. All patient data were reviewed by two researchers, and a third researcher adjudicated differences in interpretation if applicable.Disease deterioration was defined as the development to severe or critical symptoms in moderate cases.Moderate symptoms: fever and mild respiratory symptoms , multiple patchy shadowing and ground-glass opacity in lung CT, and normal range of vital signs.2 ≤93% and/or arterial oxygen tension/inspiratory oxygen fraction (PaO2/FiO2) ≤300 mmHg under resting condition] (1 mmHg = 0.133 kPa) and/or radiology findings showing that the range of pulmonary lesions increased by more than 50% within 24–48 h, but no mechanical ventilation is required, and no organ failure.Severe symptoms: respiratory distress , higher proportion of male , higher proportion of comorbidities as diabetes , hypertension , cardiovascular disease , and cerebrovascular disease , and higher proportion of pre-admission symptoms as fever , cough , and dyspnea  were found in the deteriorated group  deteriorated during their hospital stay and developed severe (130 cases) or critical symptoms (18 cases). Compared with the non-deteriorated group, older median age , male , incubation period <8 days , diabetes , hypertension , fever , cough , and dyspnea  were pre-hospital risk factors of statistical significance based on filtrated items (p < 0.05) analyzed by univariate regression, while cardiovascular, cerebrovascular, and chronic kidney diseases were ruled out as pre-hospital risk factors. Forest plot displayed each pre-hospital factor for deterioration in moderate cases  has progressed to severe condition while 12.2% (n = 18) to critical condition. When deterioration happened, respiratory dysfunction and hypoxia were major threats in our patients, with 76 cases (52.1%) having anRR >30 breaths/min, 119 cases (80.4%) SaO2 <93%, 100 cases (67.5%) 201 ≤ PaO2/FiO2 <300, 27 cases (18.9%) serum lactate (Lac) >2.0 mmol/L, and 25 cases (17.3%) serum creatinine (Scr) >133 μmol/l  <90 mmHg, 33.3% cases ejection fraction (EF) <45%, 11.1% cases total bilirubin >34 μmol/l, 33.3% (n = 6) prothrombin time (PT) <17 s  <17 s .n = 130) of ARDS, 20.2% (n = 30) of AKI, 6.8% (n = 10) of coagulopathy, 4% (n = 6) of AHF, 3.4% (n = 5) of AHI, and 5.4% (n = 8) of shock occurred in 148 deteriorated patients. Among them, the organ injuries in critical condition appeared on the following time sequence (n = 18) occurred on day 10 (IQR 9–12) from onset, AKI (n = 15) occurred on day 12 (IQR 10–15) from onset, AHF (n = 6) occurred on day 14 (IQR 13–15) from onset, coagulopathy (n = 7) occurred on day 15 (IQR 13–16) from onset, AHI (n = 4) occurred on day 15.5 (IQR 15–17) after onset, and shock (n = 8) on day 17 (IQR 16–18) from onset.In view of multiple organ dysfunction , 87.8%  patients with COVID-19 in Guangdong Province. Moreover, the majority of severe and critical cases resulted from the deteriorated moderate cases . The results indicated that moderate cases were the main component of COVID-19 patients. Although several studies have elucidated the risk factors for deterioration in moderate patients, few information was provided on when and how disease deterioration happens  after symptom onset; however, patients in the deterioration group exacerbated on the 11th day (IQR 9–14) from onset. For the 18 patients who further exacerbated into critical status, only 3 days (IQR 2–6.5) were spent for exacerbation from severe condition. Data above were plotted in a schematic diagram to display. This suggested that the 2nd week from onset was the time window for deterioration, and it may further exacerbate to critical condition in 3 days since that. Study suggested that timely oxygen therapy or even humidified high-flow nasal cannula therapy be implemented to prevent deterioration . Hence, Regression results showed that age ≥50 years, dyspnea, fever, cough, diabetes, male gender, hypertension, and an incubation period <8 days were pre-hospital risk factors for deterioration in moderate COVID-19 cases. Several studies , 21, 23 Then, what is the order and degree of the major organs involved during disease deterioration?2 and oxygenation index, as well as increasing of serum lactate level, indicating that acute lung injury was the jumping-off point for disease deterioration in moderate COVID-19 patients. It has been reported that the viral spike glycoprotein of SARS-CoV-2 would specifically combine with angiotensin-converting enzyme-2 on the membrane of pulmonary epithelial cells when the virus invaded the lungs via the respiratory tract  followed by further development of lung injury and tissue hypoxia (further reduced oxygenation index and increased lactate level), as well as renal injury, continuously worsened.Therefore, from the overall clinical process, lung injury and AKI sequential development is an important pattern of COVID-19 progression, suggesting that clinicians should focus on hypoxia improvement and kidney protection at the early stage to prevent disease from developing MODS.Our study has some limitations. First, by excluding patients who remained in the hospital on the cutoff date, we may have underestimated the effect of potential risk factors for predicting deterioration. Second, because a small number of patients cannot recall the date accurately of contacting with the transmission source, their exact incubation period is uncertain. Third, the lack of data in partial severe patients, like left heart EF, coagulatory function, urine volume, as well as deficiency of baseline organ function level in severe and critical patients prevents organ injury degree measuring, which is an inevitable flaw in a retrospective study. Further studies of cohort-based design will be more helpful for clinicians to understand the deterioration process.In conclusion, our study revealed that COVID-19 patients with deterioration had the characteristics of older age, male, and more basic diseases. The deteriorated pattern of moderate COVID-19 patients was characterized as [1] the 11th day from onset (IQR 9–14) was an important time point of disease deterioration, and it may further exacerbate to critical condition in 3 days (IQR 2–6.5); [2] organ damage induced by COVID-19 with a certain sequence, and ARDS followed by AKI was the common mode of deterioration.The data analyzed in this study was obtained from Guangdong Health Commission and is restricted to protect confidentiality. Requests to access these datasets should be directed to the corresponding author, and will only be provided with approval from the Guangdong Health Commission.The studies involving human participants were reviewed and approved by The Institutional Review Board of Guangdong Health Commission and Guangdong Provincial People's Hospital (No. GDREC2020028H). Written informed consent from the participants' legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.MF and XL had the idea for and designed the study. S-lC, HX, H-yF, and S-sH had full access to all data in the study and took responsibility for the integrity of the data and data analysis. S-sH, J-fS, J-lH, W-lS, and R-jW were responsible for data collection. MF, S-lC, H-yF, S-sH, J-fS, and LZ contributed to writing of the report. All authors contributed to data acquisition and data interpretation, reviewed, and approved the final version. 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.


Dimocarpus longan L.) is one of the most important commercial tropical/subtropical evergreen fruit species in Southeast Asia. In this study, a total of 52 longan sugar transporter (DlST) genes were identified and they were divided into eight clades according to phylogenetic analysis. Out of these 52 DlST genes, many plant hormones , abiotic , and biotic stress responsive element exist in their promoter region. Gene structure analysis exhibited that each of the clades have closely associated gene architectural features based on similar number or length of exons. The numbers of DlSTs, which exhibited alternative splicing (AS) events, in flower bud is more than that in other tissues. Expression profile analysis revealed that ten DlST members may regulate longan flowerbud differentiation. In silico expression profiles in nine longan organs indicated that some DlST genes were tissue specificity and further qRT-PCR analysis suggested that the transcript level of seven DlSTs  was consistent with sugar accumulation in fruit, indicating that they might be involved in sugar accumulations during longan fruit development. Our findings will contribute to a better understanding of sugar transporters in woody plant.Carbohydrates are nutrients and important signal molecules in higher plants. Sugar transporters (ST) play important role not only in long-distance transport of sugar, but also in sugar accumulations in sink cells. Longan ( Carbohydrates are the main components that not only provide energy sources and building blocks for cell but also constitute osmotic and act as signaling molecules throughout the physiological process and corresponding both abiotic and biotic stress responses in higher plants . AdditioIn plants, sugars are the main products of photosynthesis, which are synthesized de novo in leaves (source) and translocated into fruits, roots, and stems (sink) to supply the carbon substrate for plant growth and/or storage. Consequently, sugar transportation is important to maintain the balance between source and sink . SucroseIn plants, sugars are mainly stored in vacuoles, which occupies up to 90% of the plant cell volume ,8. The cTo date, various sugar transporters have been identified in fungi, animals, humans and plants ,11,12. IArabidopsis [SUTs and MSTs, which contain the sugar_tr domain (PF00083), belong to the major facilitator superfamily (MFS), which usually contain 12 transmembrane (TM) domains. The SUTs belong to a rather small protein family and are originally group into five subfamilies, including one dicot-specific (SUT1), two monocot-specific (SUT3 and SUT5), and two monocot and dicot (SUT2 and SUT4) . Howeverbidopsis ,20, ricebidopsis ,22, tomabidopsis , woodlanbidopsis , pear [2bidopsis , apple [bidopsis  and Chinbidopsis .Dimocarpus longan L.), which belongs to the family of Sapindaceae, is an important commercial tropical/subtropical evergreen fruit species with a large number of the production in Southeast Asia and Australia [Longan . These results may contribute to understanding the diverse functions of ST genes in longan growth and development.The present study reports on the identification of putative genes encoding STs in longan genome, together with phylogenetic, structural, conserved motifs, cis-elements analysis. Moreover, RNA-seq databases were used to identify alternative splicing (AS) events and expression profiles in different longan varieties and tissues. We also investigated the expression pattern of 23 selected −1 fresh weight (FW). The sucrose content increased approximately 16-fold from 60 DAF to 120 DAF. However, the concentrations of glucose and fructose has a slightly increase from 60 DAF to 90 DAF, then slightly decreased from 90 DAF to 120 DAF. The content of sucrose was higher than glucose and fructose after 60 DAF. On the whole, sucrose is the main soluble sugar accumulated in mature longan fruit.The content of sucrose, glucose and fructose had different trends during longan fruit development . The conArabidopsis  genes were identified and they were renamed according to previous research in bidopsis . Amino aDlST genes were annotated as SFP, SUC, PLT, INT, pGlcT, and VGT clades, respectively , woodland strawberry (Fragaria vesca L.), rice (Oryza sativa L.), grape (Vitis vinifera), chinese jujube (Ziziphus jujuba Mill.), tomato (Solanum lycopersicum L.) and apple   tree was constructed using the amino acid sequences of 52 DlSTs . The phyectively . Furthermestica) . InteresTo further investigate the characteristic regions of DlST proteins, MEME software was used to analyze the motifs of 52 DlST proteins . A totalDlSFP, DlVGT and DlpGlcT gene families have more than 11 exons, however, DlINT, DlPLT, DlTMT and DlSTP gene families have less than seven exons, suggesting that the number of exons may increase or decrease during evolution of DlSTs, leading to a functional diversity of closely related DlST genes. Overall, all DlST genes showed a closely gene structure because gene members have similar exon numbers or exons length within the same subfamily.Gene structures analysis showed that all DlST genes contained one or more exon, varied from one to 23 and different subfamilies contained different exon numbers . For inscis-elements, which distributed in gene promoter, may reflect the potential function of genes. Thus, we analyzed all DlST promoters (2000 bp upstream coding sequence) using PLACE (https://www.dna.affrc.go.jp/PLACE/?action=newplace)  and the CAT-box (meristem expression) were mainly identified. Furthermore, many plant hormone and stress responsive elements were also identified. For instance, TGA-element and GARE-motif are involved in auxin-responsive, TGACG-motif and CGTCA-motif are involved in MeJA responsiveness, GARE-motif and TATC-Box are involved in gibberellin-responsive, DRE is involved in dehydration, low-temp, salt stresses, MBS is involved in drought inducibility. Overall, 130 ABRE and 54 WUN-motif were found in the promoters of DlSTs, indicating DlSTs may play role in respond to ABA and wound-responsiveness in longan plants.The ewplace) . As a reDlSFP1, DlINT1, DlPLT3, DlSTP6, DlSTP13 and DlSTP18) showed 13 specific AS events in ‘Sijimi’. Interestingly, no skipped exon event exists in ’Sijimi’. Taken together, the specific AS events in ‘Sijimi’ might arrange different proteins to drive various functions.Alternative splicing (AS) is commonly found in plant species and have a regulation of gene expression . To studDlSFP6, DlSTP15 and DlSTP17) underwent specific AS event in SX-T3 stage, and no specific AS event was identified in other developmental stages.To investigate the influence of different development stages in AS events of DlST genes, we examined AS events of different floral bud development stage in ‘Shixia’ . A totalDlPLT4, DlSTP15 and DlSTP17). Taken together, AS events may affect flower bud differentiation by forming diverse transcripts.By analyzing the transcriptome of nine tissues in ‘Sijimi’, we found that young fruit (143) and pulp (58) accounted for the largest and smallest number of AS events in DlST genes, respectively. Alternative 5′splice site (143) and skipped exon (58) was the most and least common, respectively. A total of 42 DlST genes underwent AS events in flower buds, with 38 genes in flower and 33 genes in pericarp, stem and young fruit; the AS events in pulp was the lowest (21) . FurtherDlSFP1, DlSFP2, DlSFP7, DlINT3, DlINT4, DlpGlcT1, DlpGlcT2, DlpGlcT3, DlPLT1, DlPLT2, DlPLT5, DlPLT6, DlSTP1, DlSTP2, DlSTP3, DlSTP16, DlSTP19, DlTMT1, DlVGT1, DlVGT2, DlSUT2, DlSUT3 and DlSUT6) exhibited high expression level in at least six longan tissues. On the contrary, DlSTP17 only showed a significantly low transcript abundence in the flowerbud and nine genes  displayed no expression in all nine tissues.To elucidate the potential roles of the DlST genes during longan development, we downloaded expression profile data for different tissues from the NCBI database (GSE84467). Nine organs represented in this expression array, including flower bud, flower, leaf, root, stem, seed, pulp, young fruit and pericarp. The results showed that 80.77% (42 of 52) of DlSTs were expressed in flower bud and 73.08% showed expression level in the flower . A totalDlINT1, DlpGlcT2, DlPLT1, DlPLT3, DlPLT4, DlPLT6, DlSFP1, DlSFP2, DlSTP1, DlSTP12, DlSTP13, DlSTP16, DlSTP17, DlSTP18, DlSTP20, DlSTP5, DlSTP6, DlSUT2, DlSUT3 and DlSUT5) showed higher transcript abundence in ‘Sijimi’ than that in ‘Lidongben’. Furthermore, a total of six genes  belong to the different expression gene (DEG).‘Lidongben’ and ‘Shixia’ are common longan varieties, while ‘Sijimi’ is a special cultivar exhibiting continuous flowering trait. To study whether the DlST genes affected longan bloom, we compared the transcript levels of DlSTs in two longan varities A. The reDlINT2, DlINT3, DlINT4, DlpGlcT2, DlPLT1, DlPLT2, DlPLT4, DlSFP2, DlSTP1, DlSTP13, DlSTP2, DlSTP20, DlSTP5, DlSTP6, DlSUT2, DlSUT6, DlTMT1 and DlVGT2) showed higher transcript levels in T1 and T2 stage. Taken together, ten genes  showed higher expression in both analyses, suggesting that these genes may function in flower bud differentiation.To analyze whether the DlST plays a role in flower bud differentiation, we compared the transcript levels of DlSTs during floral bud development in ‘Shixia’ B. The reDlSFP2, DlSFP4, DlINT4, DlPLT1, DlPLT6, DlSTP2, DlSTP16, DlSTP19, DlTMT1, DlSUT2 and DlSUT6) was higher at 60 DAF than other developmental stages; besides, other 12 genes  showed higher expressional values at 120 DAF than other stages.Sweetness, determined by sugar composition and content, is a key factor to determine the quality of longan fruits. The main sugar components in longan fruit are sucrose, glucose, and fructose. To further study the potential roles of DlSTs in longan fruit sugar accumulation, we selected 23 genes that might be related to sugar accumulation for qRT-PCR analysis during longan fruit development . The resDlSFP subfamily, the expression of DlSFP2 declined slightly from 60 to 90 DAF and then showed a slight increasing from 90 to 120 DAF. The DlSFP7 transcript showed a stable value from 60 to 90 DAF and then increase toward fruit maturity. The DlSFP4 presented high expression level at 60 DAF, then slightly declined from 60 to 90 DAF and increased slightly from 90 to 120 DAF. For the DlINT clade, the relative expression trend of the two DlINT genes (DlIN3 and DlINT4) are opposite. The expression of DlIN3 and DlIN4 showed up regulation and down regulation during fruit development, respectively. For the DlpGlcT subfamily, DlpGlcT1 and DlpGlcT2 transcript showed persistent up regulation from 60 DAF to 120 DAF, the transcript level of DlpGlcT3 was declined from 60 to 90 DAF, but increased afterward as fruit matured. In DlPLT subfamily, the transcript abundance of DlPLT1 and DlPLT5 showed the highest level at 60 DAF, then declined slightly from 60 to 90 DAF and increased slightly from 90 to 120 DAF. DlPLT4 showed up regulation through fruit development. DlPLT3 remained a stable expression level from 60 to 90 DAF and showed up regulation from 90 to 120 DAF. DlPLT6 showed down regulation from 60 to 90 DAF and remained unchanged from 90 to 120 DAF. For the DlSTP subfamily, The DlSTP2, DlSTP16 and DlSTP19 showed a continuous decline expression toward fruit maturity. However, the expression level of DlSTP1 revealed constant increment toward fruit ripening. For the DlTMT subfamily, DlTMT1 down regulated from 60 to 90 DAF then increased. For the DlVGT subfamily, DlVGT1 and DlVGT2 were both up regulated from 60 to 120 DAF. For the DlSUT subfamily, DlSUT2 and DlSUT6 markedly decreased from 60 to 90 DAF then slightly increased. DlSUT3 showed a slight decrease from 60 to 90 DAF but further increased markedly at 120 DAF.For the Dimocarpus longan genome has identified 52 sugar transporters, including six SUTs and 46 MSTs, suggesting that SUT is a very small gene family among sugar transporter families. The similar results were observed in other plants, for instance, six SUTs were identified in pear [Arabidopsis (53) [Arabidopsis, tomato, grape, and rice [The search against  in pear , five in in pear , four in in pear , and thr in pear  and Chin in pear . Furthersis (53) , grapevisis (53) , rice (6sis (53) , strawbesis (53) , pear (6sis (53) , apple (sis (53) , Chinesesis (53)  and tomasis (53) . Phylogeand rice ,22,23, iand rice .Cis-acting elements are essential in many biological processes and stress responses [cis- elements involved in light responsiveness. In addition, DlST genes may play a role in circadian regulation because of the existence of the circadian element.esponses . In thisDlpGlcT3 promoter, and only one in DlSUC2, DlPLT3, DlSTP19, DlPLT5 and DlSTP12, respectively. DlST promoters carry different cis-elements that may explain differential expression. Additional analysis should be carried on to test this hypothesis.However, there are no common cis-elements detected in the DlSTs promoter, which is different from previous findings in grape and strawberry, indicating a different transcriptional regulation mechanism of ST genes in different species. A total of seven unique cis-elements were identified, which is smaller than that in pear and strawberry. Intriguingly, two of the seven cis-elements were only discovered in the Arabidopsis [RNA-Seq show more benefits than gene expression microarrays, such as broad dynamic range, high sensitivity and accuracy, ability to discover novel genes and alternative splicing (AS), which involves the conversion of precursor mRNA into mature mRNA. AS event exists in the great mass of eukaryotic protein-coding genes and considered to be a key regulatory mechanism, which increase the diversity of transcriptome and proteome and adaptation during plant evolution and stress ,37. AS ibidopsis , rice [3bidopsis  and soybbidopsis . To dateDlSUT2, DlSUT3 and DlSUT6) expressed in pulp. Although the expression level of DlSUT2 was lower than DlSUT3 and DlSUT6  were expressed in pulp. Furthermore, the expression of DlVGT1 and DlVGT2 was high in fruit and in accordance with the sucrose increase, suggesting their important roles in sugar accumulation. Monosaccharides, which were transported into fruits, not only play important roles in the increasing of the monosaccharide content, but also in providing substrates for sucrose synthesize [DlpGlcT1, DlpGlcT2 and DlSTP1, which showed up-regulated trends during the fruit development stages , respectively. A total of 60 fruits were picked from one tree at each developmental stage. In addition, 60 fruits divided into three biological replicates in which containing 20 fruits. After the peel and seed coat removed, the pulp was immediately frozen in liquid nitrogen and stored at −80 °C until used.−1 FW).The extraction and HPLC analysis of sucrose, fructose and glucose in pulp was performed as described by according to the method by Fang et al. . The aveActin1 gene (Dlo_028674) [Total RNA was isolated using a standard RNAprep pure Plant kit  according to the recommended protocol. The amount and quality of the total RNA was confirmed using a NanoDrop 2000 spectrophotometer . One microgram of purified RNA was reverse transcribed into cDNA using TransScript One-Step gDNA Removal and cDNA Synthesis SuperMix  following the manufacturer’s protocol. qRT-PCR was performed using SYBR Green I Master Mix  and the expression levels of ST genes were normalized to the longan _028674) . qRT-PCRArabidopsis ST genes against the longan genome [−5. The 62 Arabidopsis ST proteins were gained from the previous study and the Arabidopsis database (http://www.arabi-dopsis.org/) [Longan ST gene family were identified by performing a BLASTP analysis of the 62 n genome  with E-vis.org/) ,60. Finahttps://www.megasoftware.net/) employing the neighbor-joining (NJ) method [ClustalX program was used to make multiple sequence alignments. A phylogenetic analysis was performed using MEGA6 program  method  with a bhttp://meme.nbcr.net/meme/cgi-bin/meme.cgi) to confirm the conserved motifs [http://gsds.cbi.pku.edu.cn) was used to analyze the constituents of the exons/introns of the DlST genes [https://www.dna.affrc.go.jp/PLACE/?action=newplace).Motifs of DlST proteins was analyzed using Multiple EM for Motif Elicitation (MEME)  , one forDlST genes in different longan varieties and tissues, the three transcriptome datasets mentioned above were used. The fragments per kilo base of the exon model per million mapped values (FPKM) were log2-transformed and normal standardization according to row scale, and heat maps were exhibited using the software TBtools [To study the transcriptional accumulation of  TBtools .P-value = 0.05).The data analyses were conducted by SPSS statistics 21.0  and graphed with Origin 9.1 software. ANOVA was chosen to test the difference between the means of different stages (DlSTs were discovered in Dimocarpus longan genome, and they were divided into eight clades. Different species have different transcriptional regulation in the DlST gene family. AS events analyses indicated that several DlST genes may play a role in longan flower bud differentiation. Expression profile analyses suggested ten and seven DlSTs may play key role in longan bloom and sugar accumulation. Our results help to further understanding the complicated functions of sugar transporter genes in longan and other woody plants.In summary, 52