Artemisia annua is known to be the source of artemisinin worldwide which is an antimalarial compound but is synthesised in very limited amount in the plant. Most research laid emphasis on the methods of enhancing artemisinin but our study has been planned in a way that it may simultaneously address two problems encountered by the plant. Firstly, to know the effect on the artemisinin content in the era of climate change because the secondary metabolites tend to increase under stress. Secondly, to identify some of the stress responsive genes that could help in stress tolerance of the plant under abiotic stress. Hence, the A. annua plants were subjected to four abiotic stresses  and it was observed that the artemisinin content increased in all the stress conditions except drought. Next, in order to identify the stress responsive genes, the transcriptome sequencing of the plants under stress was carried out resulting in 89,362 transcripts for control and 81,328, 76,337, 90,470 and 96,493 transcripts for salt, cold, drought, and water logging stresses. This investigation provides new insights for functional studies of genes involved in multiple abiotic stresses and potential candidate genes for multiple stress tolerance in A. annua. Plants undergo a series of physiological, morphological, molecular and biochemical changes on encountering stress. Plants have a remarkable ability to cope with the changing environmental conditions and try to adapt accordingly, depending upon the extent of stress1. Numerous genes and biological pathways are known to be involved in abiotic stress tolerance in many plants, and several others are thought to be involved3. Many genes and pathways are specific for a single type of stress and several others show co-regulation in different stresses5. It has also been reported that when a plant is made tolerant to one type of stress, it shows tolerance to some other stress also, indicating involvement of some common molecular mechanisms in different types of stresses8. Transcriptome sequencing and analysis is a reliable and effort -saving method to understand the global molecular response of the stressed plants9 which has been reported in many plants for a single type of stress11, however only few reports are available for combination and/or multiple stresses. In addition, there are limited studies on the impact of abiotic stresses on the medicinal plants. Hence, the present investigation reports about the response of Artemisia annua plant against four abiotic stresses .The stresses such as drought, salinity, chilling, freezing, water-logging, variable light conditions, nutrient starvation and heat adversely affect the plant growth and productivityA. annua L. belongs to Asteraceae family and is well known for its medicinal value to combat malaria13. Artemisinin is the anti-malarial compound found in the plant which also possesses other activities like anti-parasitic and anti-viral. The natural abundance of artemisinin in A. annua is very low (0.01–0.8%). Hence, several strategies have been used to fulfil the demand of high supply at a reduced rate15. Despite this, a limited success has been achieved for obtaining artemisinin in vitro and we are still dependent on A. annua plants. The artemisinin content of the plant depends on many parameters including the conditions like salinity stress, water stress, chilling stress etc.18 indicating that the stress related pathways and mechanisms somehow regulate the artemisinin biosynthesis pathway also affecting the yield of artemisinin. As a result, it becomes desirable to get a comprehension about the regulatory mechanisms involved in controlling artemisinin biosynthesis as well as strategies and mechanisms for increasing the overall plant yield, oil content and trichome density in severe and prolonged stress conditions. In non-model plants like- A. annua, identification of genes and pathways that are co-regulated in multiple stress conditions and help the plant to withstand the adverse conditions still need to be worked out and next generation transcriptome sequencing may prove a promising tool for such study. The hardy nature of this plant in tolerating various abiotic stress conditions makes it a strong candidate for carrying out next generation sequencing in order to explore the regulatory mechanism(s) of the artemisinin biosynthetic pathway as well as stress responsive pathways. Due to the limited transcriptomic data comparing multiple stress conditions in non-model plants, the present investigation describes the comparative analysis of A. annua leaf transcriptomes under four different stress conditions: cold, drought, salt and water-logging. In future, the data collected in the present study will prove to be a valuable asset for genomic studies of abiotic stresses in Artemisia sp.A. annua control sample and four abiotic stress samples - i.e. salt, cold, drought and water-logging stresses. Leaves from the whole plant were taken to carry out molecular and biochemical analysis. The sequencing of the prepared libraries was carried out on the Illumina NextSeq 500 platform with a sequencing depth range of 100× to 120×. The paired-end sequencing-by-synthesis generated a raw data of 32.9, 45.8, 64.8, 65.4 and 73.3 million reads from control, salt, cold, drought and water-logging stress sample libraries, respectively. Maximum read length was 151-bp for all the samples. After quality checking and processing of the raw reads data, 30.25, 43.17, 60.14, 61.45 and 69.45 million reads of control, salt, cold, drought and water-logging stress samples, respectively, were retained for further assembly  libraries constructed using leaves of Transcripts from all stress samples were clustered with the transcripts from control sample using CD-HIT at 95% identity resulting in master control transcript data comprised of unigenes. A total of 1,01,995 transcripts were obtained for control and salt stress; 99,283 transcripts for control and cold stress; 1,05,165 for control and drought stress while 1,08,903 transcripts for control and water-logging stress. The distribution pattern of these transcripts is presented in Fig. O. sanctum and O. basilicum transcriptomes also represented these three functional groups with maximum percentages of genes19. Percentages of genes in ‘unknown molecular functions’, ‘unknown cellular components’ and ‘unknown biological processes’ ranged between 42.35–61.03%, 72.65–78.59% and 67.13–75.78%, respectively, in the 5 sample libraries. The distribution pattern of the unigenes for all the five libraries under different GO terms exhibited similarity.To assign putative functions to the assembled sequences, Blast x search was carried out for control and all 4 stress samples against viridiplantae protein sequences available in Uniprot database. In the case of control sample 41,618 transcripts (46.57%) while in case of salt stress 38,047 transcripts (46.78%), in cold stress 39,863 transcripts (52.21%), in drought stress 43,947 transcripts (48.57%) and water-logging stress sample, 45,091 transcripts (46.72%) were annotated  and Solanum lycopersicum (tomato) as reference organisms, was performed. 14,372 (control), 10,585 , 10,691 (cold), 11,012 (drought) and 11,112 (water-logging) transcripts were functionally assigned to 125 KEGG pathways.  enriched GO terms for DEGs showing up-regulation were: 25 (cold), 49 , 41 (drought) and 39 (water-logging). In contrast, 22 (cold), 36 , 19 (drought) and 33 (water-logging) GO terms were significantly (FDR ≤ 0.05) enriched for DEGs showing down-regulation (Supplementary file The two most enriched significant (FDR ≤ 0.05) KEGG pathways for up-regulated DEGs were ‘metabolic pathways’ and ‘photosynthesis’ in cold; ‘oxidative phosphorylation’ and ‘metabolic pathways’ in salt stress; ‘biosynthesis of secondary metabolites’ and ‘fatty acid metabolism’ in drought stress and ‘metabolic pathways’ and ‘biosynthesis of secondary metabolites’ in water-logging stress. For down-regulated transcripts, most enriched pathway was ‘N-Glycan biosynthesis’ in cold and water-logging stress; ‘protein processing in endoplasmic reticulum’ in salt stress and monoterpenoid biosynthesis in drought stress , various H+-ATPases, serine/threonine protein kinases, glutathione-S-transferase, glyoxalase I etc were highly up-regulated as reported in earlier studies8. Drought stress induced the expression of many genes that are indicative of water stress like Delta 1-pyrroline-5-carboxylate synthetase, aquaporins, glyceraldehyde-3-phosphate dehydrogenase, LEA proteins, dehydrin, heat shock protein, glyoxalase I, glutathione-S-transferase, PR proteins, calcium-dependent protein-kinases, genes involved in ethylene and oxylipin biosynthesis etc20. Plants exposed to water-logging stress exhibited enhanced expression of genes involved in ethylene biosynthesis, alcohol dehydrogenases, catalase etc, as demonstrated by the earlier reports on flood stress21. Based on known stress genes showing up-regulation or exclusive expression during stress condition, Fig. Various stress responsive genes were differentially regulated during different abiotic stresses conferring that the plants were in stress. Large numbers of kinases, peroxidases, genes involved in ABA biosynthesis etc were up-regulated in all stresses. LEA (Late embryogenesis abundant protein) and LEA-like proteins, various desaturases, glyoxalase I family protein, genes involved in oxylipin and polyamine biosynthesis, delta 1-pyrroline-5-carboxylate synthetase, dehydrin etc., which are well known cold stress responsive genes, exhibited up-regulation during cold stressA. annua leaf transcriptome libraries . Transcripts belonging to 29 TF families exhibited significant up-regulation in one or more stresses . PTH activity is responsible for releasing tRNA from the premature translation termination product peptidyl-tRNA, thus rendering the tRNA and peptide reusable for the protein synthesis process. There is limited information available for PTH in eukaryotic system though it has been extensively studied in bacterial system28. Stressful conditions may lead to increase in premature translation termination and accumulation of peptidyl-tRNA in the cytosol, which may interrupt the normal cellular processes and probably the plant cells synthesized this unique transcript to overcome this situation by breaking the ester bond between tRNA and peptide and setting free the tRNA molecules28. But, since the query cover for the blast result was too low, therefore, this transcript may be involved in overcoming stress through this proposed mechanism or by some other mechanism. Other transcripts showing expression in any 3 stresses included proteins with ribonuclease III activity, DNA methyltransferase activity, spermidine synthase, putative helicases etc. that are known to be stress responsive genes31. Since the 41 transcripts showing over-lapping in three or more stresses, as discussed above, could not be detected in the control sample, therefore, either the expression of these transcripts was completely absent or their expression was extremely low or negligible in favourable environmental conditions and hence they can be considered as ‘stress-specific genes’. This study suggests that further characterization and functional analysis of these transcripts may explore some novel ‘stress marker’ genes as well as genes having potential to alter stress-tolerance in the plants.5,963 , 7,135 (cold), 7,565 (drought) and 7,893 (water-logging) transcripts got exclusively expressed during stress and were not detected in the control sample. These exclusive transcripts were investigated for common ones among the four stresses. 41 transcripts were identified to be expressing in three or more stress samples. Interestingly, only 1 unigene (master control_78545) was found that was getting expressed in all the four stress samples , uncoupling proteins etc, help the mitochondria in regulating ROS levels. When energy dissipating alternative oxidase pathway becomes active, which dampens the generation of ROS in mitochondria by preventing over reduction of electron transport chain components, electrons flow from ubiquinone to AOX and two sites of proton pumping (at complexes III and IV) are bypassed32. Since the functional role of complex III (cytochrome bc1 complex) and complex IV (cytochrome c oxidase) is not of primary importance in such conditions. Also, when alternative pathways become active in the mitochondria, energy conservation in the form of ATP is hindered because of the absence of some energy conservation site in this pathway and hence, the role of ATP synthase (complex V) also becomes limited. Thus, oxidative phosphorylation and electron transport chain seems to be affected in all 4 abiotic stress conditions. Zsigmond et al.33, demonstrated that there exists a link between regulation of oxidative respiration and environmental adaptation in Arabidopsis33. The transcripts identified in the present study could provide putative genes that can be targeted for metabolic engineering to strengthen the plant during stressful conditions or to sustain the normal growth and development of the plant even in unfavourable conditions.When transcriptome of each stress sample was compared with the control sample, set of transcripts showing expression only in the control sample and absent during stress was identified for each stress. 5,649, 8,500, 3,639 and 3,488 transcripts exclusively belonged to the control sample when the control transcriptome was compared to the transcriptome of salt, cold, drought and water-logging sample, respectively. The four sets of transcripts identified in this way were further analysed to get the common transcripts between these four sets and a set of 855 transcripts was identified. The transcripts thus identified were subjected to KEGG pathway enrichment analysis using KOBAS , arginine (one of the precursors of putrescine) and GABA. There are several reports showing that high levels of GABA, proline and putrescine accumulate in plant tissues when exposed to various stresses and play an important role in stress-regulation and stress-tolerance38. An important biochemical mechanism for regulating signalling pathways leading to stress-specific response or stress tolerance is reversible protein phosphorylation. Serine-threonine protein kinases are known to be involved in regulation of signalling cascades and some of these when over-expressed enhanced stress-tolerance of the plant39.A total of 1,561 transcripts were commonly regulated in all 4 stresses. 304 transcripts were commonly up-regulated, amongst these 4 transcripts annotated as uncharacterized proteins showed significant up-regulation in all the four stress samples  in all stresses suggest that probably A. annua plants become susceptible to some specific pathogen when exposed to prolonged abiotic stress. Many differentially regulated NBS-LRR proteins have been reported in Arabidopsis plants exposed to a combination of drought and heat stress40.1,257 transcripts were commonly down-regulated in different stresses, out of which only 202 transcripts could be assigned a putative function and only 42 transcripts exhibited significant downregulation in all 4 stresses  was 0.916 (r2 = 0.840)  enriched for co-up-regulated genes and 21 for significantly (FDR ≤ 0.05) co-down-regulated genes  and biosynthetic pathway genes are generally increased in prolonged abiotic stresses.It has been reported that when A. annua under four abiotic stresses . Comparative transcriptome analysis revealed many genes commonly or specifically regulated by different abiotic stresses. Some new genes were also identified which might be of interest in exploring the stress tolerance of the plant against climatic changes. Hence, this data represents a fully characterized A. annua transcriptome, providing new insights into plant responses to unfavourable environmental conditions, new leads for functional studies of genes involved in multiple abiotic stresses and potential candidate genes for metabolic engineering of multiple stress tolerance in A. annua with unaltered artemisinin content.In the present study, high-throughput sequencing was used to generate a comprehensive transcriptome resource for Artemisia annua var. ‘CIM-Arogya’44 were obtained from National Gene Bank for Medicinal and Aromatic Plants (NGBMAP) maintained at the Central Institute of Medicinal and Aromatic Plants in India and sown in four square feet large nursery bed. Two months old seedlings were singled out, transferred to pots and allowed to acclimatize for a period of 15 days before stress treatment. Ten plants were subjected to each stress treatment along with natural control. For salt treatment, plants were irrigated with 100 ml of 100 mM NaCl solution continuously for ten days and then on every 3rd day during the entire stress treatment period of 2 months. For 66% drought treatment, plants were irrigated in such a manner so as to maintain a moisture level of 34% of water holding capacity of the soil during the entire stress treatment period (2 months). For cold treatment, plants were kept in the cold room at 4 °C for 2 months with appropriate lightening. Samples were collected after completion of the treatment period. Plants were given water-logging stress after 2 months of transplantation since the morphology of the plants look severely affected by prolonged treatment. Plants were kept immersed in water such that the water level was 2–2.5 inches above the soil level throughout the stress period and sampled after 15 days. Plants maintained in natural conditions with normal watering  served as control. Samples were collected and pooled for sequencing in triplicates. Samples were stored in RNAlater (Sigma Aldrich) at −80 °C.Mature seeds of Total RNA isolation from leaf samples was performed using TRI reagent (Sigma Aldrich). Nanodrop Spectrophotometer and Qubit Fluorometer were used for estimating RNA concentration and purity while RNA integrity was analysed on Bioanalyzer chip (Agilent). RNA with optimal purity, yield and integrity (RNA integrity number > 6.5) was used for library preparation. Illumina TruSeq RNA library protocol (as mentioned in “TruSeq RNA Sample Preparation Guide”) was used for transcriptome library construction for sequencing . Purification of poly-A enriched RNA was carried out from 1 µg of total RNA and it was fragmented for 2 minutes at 94 °C in the presence of divalent cations followed by reverse transcription using Superscript III Reverse transcriptase by priming with random hexamers. DNA Polymerase I and RnaseH were used for second strand cDNA synthesis and Agencourt Ampure XP SPRI beads (Beckman Coulter) for cleaning up the cDNA. End reparation and 3′ end adenylation was performed for the cDNA molecules followed by ligation of illumina adapters. After ligation, SPRI cleanup was carried out followed by library amplification by 8 cycles of PCR to enrich the adapter ligated fragments. Quantification and qualitative validation for the prepared library were performed using Nanodrop and High Sensitivity Bioanalyzer Chip (Agilent), respectively.http://www.bioinformatiSample1.babraham.ac.uk/projects/fastqc/). Raw reads obtained after sequencing were filtered to remove adaptor and low quality bases to obtain processed reads. Processed reads were used for de novo assembly with Trinity, a short read assembling program45, for default k-mers i.e. 25. In brief, firstly contigs were generated by combining reads with certain length of overlap. The reads used were then mapped back to contigs. Finally, the contigs were connected to generate unigenes. In this way, Trinity generated unique transcripts. Trinity assembly is based on the de Bruijn graph46. The Trinity assembled transcripts with sequence lengths > = 300 bp were considered for downstream analysis. Clustering of these transcripts with 95% identity was carried out using CD-HIT. The transcripts were annotated against all viridiplantae kingdom protein sequences (from Uniprot Protein Database) using NCBI BLAST 2.2.2947. Those Transcripts with more than 30% identity as cut off were taken for further analysis. GO annotations for the transcripts were retrieved from Uniprot database. More than one GO term may be assigned to each annotated sequence that may fall in either same or different GO category . Pathway Analysis was performed by using KAAS Server48. Arabidopsis thaliana  and Solanum lycopersicum (tomato) were used as reference organisms from the available strains in the database. Annotations were retrieved from KEGG.The library was sequenced using Illumina Nextseq 500 platform producing 32.9, 45.8, 64.8, 65.4, 73.3 Mbp of 151-bp paired-end reads for control, salt, cold, drought and water-logging stress samples, respectively. Quality of raw reads was checked by FastQC program (http://www-huber.embl.de/users/anders/DESeq/)49 comparing each stress sample with the control sample. For each comparison between control and a stress sample, three expression profiles were generated; transcripts expressed in both control and stress sample, transcripts expressed only in stress and transcripts expressed only in control. Those transcripts that were expressed in both samples involved in comparison were further classified according to their expression pattern . The negative binomial distribution method was applied for calculating p values and Benjamini-Hochberg method was chosen to adjust for multiple tests. DEGs with p value < = 0.05 were considered significant.Transcripts of all the samples with length > = 300 bp were combined and clustering was performed at 95% identity using CD-HIT, a master control transcript data containing unigenes was thus generated, then alignment of reads of control and treated samples was carried out using Bowtie2 tool and a read count profile was generated. Differential gene expression (DGE) was obtained using DeSeq software (http://bioinfo.cau.edu.cn/agriGO/) supplemented by REVIGO (http://revigo.irb.hr/) visualization toolbox50. Enriched GO terms were statistically analyzed by Fisher’s exact test with Yekutieli multi-testing adjustment and significance level of 0.05. The significantly enriched pathways for DEGs were determined by the KEGG Orthology-Based Annotation System (KOBAS) (http://kobas.cbi.pku.edu.cn/home.do)51. Hyper geometric distribution was used for p-value calculation and Benjamini and Hochberg method for FDR correction. Arabidopsis thaliana, being a model organism was used as background species.Gene ontology term enrichment analysis was performed using AgriGO  of data was carried out. For PCR amplification of stress specific transcripts, primers were designed from the transcriptome sequences and cDNAs synthesized above were used as template. Amplification conditions were as: 95 °C for 1 min followed by 35 cycles of 95 °C for 30 sec, annealing temperature ranging from 48 °C–55 °C for 30 sec, 72 °C for 1 min 30 sec, followed by a final extension of 5 mins at 72 °C. 1.2% agarose gel was prepared to electrophorese and visulalize the amplified product.Total RNA isolation was carried out from all 5 samples, one control and 4 stress samples using TRIzol method and cDNA synthesis Kit  was used for synthesis of template cDNAs from 2 µg of total RNAs. qRT-PCR was performed following the SYBR Green chemistry  and Fast Real Time PCR system  for validation of the Illumina sequencing data following the protocol described by Rastogi et al. while Thin layer chromatography technique was used for its estimation52. Leaves from all the plants belonging to same treatment group were collected, pooled and used for artemisinin estimation in triplicates. Statistical significance was determined by one-way ANOVA with Student’s t-test at a significance level of 0.05 in Excel software.Extraction of artemisinin was carried out following the protocol mentioned by Misra http://www.ncbi.nlm.nih.gov/bioproject/352660).Sequence data generated for the present study has been deposited to NCBI Short Read Archive. The bioproject ID assigned is: PRJNA352660 (supplementary file 1Dataset 1Dataset 2Dataset 3Dataset 4Dataset 5Dataset 6Dataset 7Dataset 8Dataset 9Dataset 10


Aspergillus flavus is a pathogenic fungus that produces toxic and carcinogenic aflatoxins and is the causative agent of aflatoxicosis. A growing body of evidence indicates that reversible phosphorylation plays important roles in regulating diverse functions in this pathogen. However, only a few phosphoproteins of this fungus have been identified, which hampers our understanding of the roles of phosphorylation in A. flavus. So we performed a global and site-specific phosphoproteomic analysis of A. flavus. A total of 598 high-confidence phosphorylation sites were identified in 283 phosphoproteins. The identified phosphoproteins were involved in various biological processes, including signal transduction and aflatoxins biosynthesis. Five identified phosphoproteins associated with MAPK signal transduction and aflatoxins biosynthesis were validated by immunoblotting using phospho-specific antibodies. Further functional studies revealed that phosphorylation of the MAP kinase kinase kinase Ste11 affected aflatoxins biosynthesis in A. flavus. Our data represent the results of the first global survey of protein phosphorylation in A. flavus and reveal previously unappreciated roles for phosphorylation in the regulation of aflatoxins production. The generated dataset can serve as an important resource for the functional analysis of protein phosphorylation in A. flavus and facilitate the elucidation of phosphorylated signaling networks in this pathogen. Aspergillus flavus is associated with disease in plants, animals and humans and is the common causal agent of aflatoxins contamination. Aflatoxins are polyketide-derived secondary metabolites that are produced by both A. flavus and A. parasiticus, which are highly toxic, mutagenic, and carcinogenic to animals and humansSaccharomyces cerevisiae, Synechococcus spp., Drosophila melanogaster, mouse, and human891011A. nidulans and Candida albicans13Reversible protein phosphorylation at serine (S), threonine (T), and tyrosine (Y) residues is the most widespread type of post-translational modification (PTM) in cells, regulating crucial cellular functions, including signal transduction, cell proliferation, and development4A. parasiticus NRRL 2999estA) is regulated by aflR1A. flavus NRRL 3357, despite the fact that, according to genome analysis, this pathogenic fungus contains at least 119 kinases and 54 phosphatases. Global analysis of phosphorylated proteins in this pathogen would facilitate understanding of the role of phosphorylation in aflatoxins biosynthesis and signal transduction pathways, and this analysis could provide a firm basis for future research. However, to our knowledge, only a few A. flavus phosphoproteins have been identified to date, and little progress has been made in ascertaining precisely which A. flavus proteins are phosphorylated, which kinases/phosphatases are involved in this process, and which cellular functions are targeted by this important PTM.In recent years, major advances have been made in elucidating the mechanism underlying aflatoxins production. For example, dephosphorylation of calcineurin-mediated enzymes ensures production of aflatoxins in the pathogenic fungus 2 enrichment and LC-MS/MS analyses. In total, we identified 598 phosphorylation sites on a total of 283 phosphoproteins from A. flavus. Consistent with previous reports, unbiased bioinformatics analyses indicated that a large proportion of these phosphorylation sites are present on proteins involved in signal transduction pathways. Furthermore, we also identified phosphorylated proteins involved in metabolic pathways, related to aflatoxins biosynthesis. Immunoblotting analysis indicated that phosphorylation could play a key role in responses to environmental stimuli and aflatoxins generation. Further functional studies revealed that phosphorylation of Ste11 affected colonial morphology, conidial generation, sclerotial formation and aflatoxins biosynthesis in A. flavus. These results provide novel insights into the range of functions regulated by phosphorylation in A. flavus. The findings of this study may help improve the general understanding of the mechanism underlying aflatoxins biosynthesis and may lead to the development of innovative strategies to control aflatoxins production.To fill this gap, we performed a global phosphoproteomic analysis of this pathogen, using a combination of TiOA. flavus, western blotting analysis was performed using a polyclonal anti-phosphotyrosine antibody. As shown in A. flavus was cultivated for only 1 d, whereas high levels of aflatoxins were generated after cultivation for 6 d , followed by pT (16.4%), and pY (2.5%)  . We reasA. flavus, we performed the Gene Ontology (GO) functional annotation analysis of all identified phosphoproteins according to the biological process, molecular function and cellular compartment. Our results showed that the phosphoproteins were distributed throughout a variety of biological processes, including metabolic processes, cellular processes, and biological regulation , nucleocytoplasmic transport (p = 7.85E-03) and so on, suggesting that protein phosphorylation may play a role in the regulation of protein transport, as previously describedp = 7.94E-03, p = 1.49E-03) and phosphotransferase activity (p = 1.32E-02) within the enzymatic activity cluster, it was easy to associate that phosphoproteins may play a role in signal transduction. Furthermore, phosphoproteins were enriched in the nuclear pore (p = 1.36E-02), pore complex (p = 1.36E-02) and nuclear envelope (p = 1.59E-02) in the cellular component category. Taken together, these results implied that the identified phosphoproteins had a wide distribution of cellular localizations and functions, and phosphorylation may play an important role in the regulation of diverse cellular processes in A. flavus.To gain further insight into the potential functional implications of phosphorylation, we carried out GO term enrichment using DAVID tool . The majA. flavus proteome  with the entire set extracted from the proteome . Interesproteome , such asA. flavus phosphoproteome allowed us to infer kinase–substrate relationships in this fungus and to explore the extent of their phosphorylation pathways. Possible kinase groups targeting each of the 598 phosphorylation sites were predicted by NetworKIN (version 2)  is the most intensively studied PTM in eukaryotic cells and ePKs belong to one of the largest protein familiesrsion 2) . After aA. flavus phosphoproteins using NetSurfPp = 5.161E-06) and less frequently in ordered helical regions  than all S/T/Y, which is quite different from those of lysine PTM eventsp = 9.073E-04).We next predicted the local secondary structures and solvent accessibilities of pS/pT/pY and all S/T/Y of via oxidative decarboxylation of pyruvate and β-oxidation of fatty acidsWe next sought to establish protein interaction networks to identify the physical and functional interactions among the identified phosphoproteins, using protein interaction information from the String database. In total, 89 phosphoproteins and 236 edges were mapped to the protein interaction database . Then, fin vivo, the site-specific anti-phosphopeptide antibodies and anti-non-phosphopeptide antibodies were used to validate the phosphorylation status of five phosphoproteins, viz., acetyl-CoA carboxylase ACC (S1138), pyruvate decarboxylase PdcA (S355), MAP kinase kinase kinase Ste11 (S187), phosphotransmitter protein Ypd1 (T14) and serine/threonine protein kinase (S153). As expected, the relative abundance of phosphoproteins differed depending on the different physiological/developmental stage of A. flavus (2O2) and during aflatoxins formation (6 d culture), as shown in 31To validate our phosphoproteomics findings and to determine whether environmental stimuli affect phosphorylation . flavus . The oriA. flavus. Therefore, we assigned all identified phosphoproteins to KEGG pathways to evaluate whether the phosphoproteins played a role in any biological/signaling pathways. As shown in A. flavus as previously describedvia oxidative decarboxylation of pyruvate, β-oxidation of fatty acids or from ketogenic amino acids, in primary metabolism13437The large group of phosphoproteins observed in this study could yield new insights into metabolic pathways of the model pathogenic fungus Aspergillus nidulans, deletion of the MAPKK gene, steC, a homologue of A. flavus ste11, results in a slower growth rate, the formation of more branched hyphae, altered conidiophore morphology, an inhibition of heterokaryon formation and a block of cleistothecium developmentste11 gene mutagenesis (Δste11), complemental strain (COM) and site-directed mutagenesis (S187A and S187D) of phosphorylation site of Ste11. The identified phosphorylation site 187th S was converted to a non-phosphorylatable alanine (A) and to aspartic acid (D) to mimic a constitutively phosphorylation state. All mutations were verified by PCR or DNA sequencing  Ste11, it is conceivable that phosphorylation plays a vital role in the signal transductionquencing  and then for 4 d . AccordiES media . It was for 10 d . Consequ1 level would increase when strain was grown into the stationary phase that led to nutritional deficiency. Recent study has also revealed the stress response linkage to the production of natural products, including mycotoxinsAspergillus fumigatusA. flavus by regulating the MAP kinase cascade system. To test this hypothesis, we performed the TLC analysis of the WT, Δste11, COM, S187A and S187D. Compared with the WT strain, the production of aflatoxin B1 in Δste11 strain and S187A mutant were decreased memorably, whereas COM strain and S187D mutant had similar yield of aflatoxin B1  in A. nidulans∆ste11. Without phosphorylation, we speculated that Ste11 could not transmit phosphate signal. Hence, downstream Fus3 and VeA could be not activated. Finaly, aflatoxins biosynthesis was suppressed. It is convincible that phosphorylation may play an important role in regulating the aflatoxins biosynthesis in A. flavus.To our knowledge, the MAPK cascade module was well conserved in all eukaryotes, playing a fundamental role in the regulation of various environmental responses Taken together, it is safe to say that phosphorylation is involved in aflatoxins biosynthesis.A. flavus. Phosphorylated proteins in A. flavus were involved in a variety of biological processes. Further functional studies demonstrated that phosphorylation may be a mechanism involved in aflatoxin biosynthesis in A. flavus. Therefore, our results provided a foundation for functional analyses of the phosphorylated proteins and opened an avenue toward better understanding of signaling pathways in this pathogen.In conclusion, we presented a comprehensive phosphoproteome analysis of the model pathogenic fungus A. flavus NRRL 3357, A. fumigatus Af293 and A. flavus CA14Δku70ΔpyrGΔniaD were obtained from Prof. Zhumei He , Dr. Yang Liu  and Dr. Ana M. Calvo , respectively. Conidia of A. flavus NRRL 3357 were cultivated in 150 mL liquid YES media6 conidia/mL liquid media)A. fumigatus Af293 was cultivated with YES agar statically in the dark at 28 °C to extract DNA and amplify pyrG. A. flavus CA14Δku70ΔpyrGΔniaD was cultivated with YGTUU agarvia centrifugation and separated using a separatory funnel. All samples were dried and then resuspended in chloroform. Then, 5 μL of each extract was loaded onto a silica gel TLC plate , and the extracts were separated using acetone: chloroform . Photographs were captured following exposure to UV radiation at 312 nm wavelength. The intensity of the samples was analyzed using a JD-801 Computer-aided Image Analysis System (JEDA Co.) by visual comparison with standard aflatoxin B1.Cultures were added with equal volume of chloroform and agitated for 30 min. Aflatoxins dissolved in the chloroform were collected A. flavus NRRL 3357 were inoculated into liquid YES media and cultivated for 1 d or 6 d. Subsequently, a phosphatase inhibitor solution g for 20 min at 4 °C, and the supernatant was pipetted into Millipore Amicon Ultra-15 Centrifugal Filters  to remove pigment and other small molecules with centrifugation at 4,000 × g for 30 min at 4 °C; this purification process was repeated twice until the lower liquid was colorless. Finally, the entire retentate fraction was transferred into a new centrifuge tube for further processing. Protein concentrations were determined with a BCA Protein Assay Kit (Tiangen).Conidia of 18 material . The columns were washed three times with 15 mL of 100% acetonitrile (ACN) and then equilibrated three times with 15 mL of 20 mM ammonium formate. After the digested solution was loaded sequentially onto the self-packed C18 columns three times, the columns were washed with 5 mL of 20 mM ammonium formate, for sample desalting, and eluted with a series of elution buffers (2 mL) composed of 20 mM ammonium formate and different concentrations of ACN . Fractions were collected in 2 mL centrifuge tubes, concentrated to less than 50 μL, and stored at −80 °C for further use.Protein extracts (20 mg) were subjected to disulfide reduction, alkylation and digestion as described previously122 Kit  with slight modifications of the manufacturer’s protocol. Specifically, the fractions were redissolved in 200 μL of TiO2 phosphobind buffer containing 50 g/L 2,5-dihydroxybenzoic acid. Then, 50 μL of TiO2 phosphobind resin was added to the mixtures, and then incubated on an oscillator. After 1 h incubation, mixtures were centrifuged, and the supernatant was discarded. Subsequently, the TiO2 phosphobind resin was washed three times with wash buffers I and II. Thereafter, phosphopeptides bound to the resin were eluted twice with 0.5% ammonium solution (pH 10.5) in 50% ACN. The eluates were combined and concentrated. Then, the combined eluates were concentrated to dryness and redissolved in 25 μL of an equal volume of 0.1% TFA and 5% ACN for future processing.Phosphopeptides were enriched using a Phosphopeptide Enrichment TiO® binary pump system with tray cooling and analyzed online using a Triple TOF™ 5600 Mass Spectrometer (AB SCIEX). The samples were delivered to a Nano cHiPLC Trap Column  and then trapped on a Nano cHiPLC Column . Peptides were eluted with a linear solvent gradient (described in Phosphopeptides were separated on an Eksigent NanoLC-UltraTM 3000 nano-LC System (Thermo Scientific™ Dionex™) in tandem with an electro-spray ion-trap mass spectrometer HCT Ultra . The methods used for sample loaded, peptide elution from the column and the settings for the mass spectrometer were as described by Yang et al.Enriched phosphopeptides were also separated on an Ultimatehttp://abi.inf.uni-tuebingen.de/Services/YLoc/webloc.cgi)http://david.abcc. ncifcrf.gov/tools.jsp) was used to perform interpro and GO term enrichment analyseshttp://motif-x.med.harvard.edu/motif-x.html)http://www.cbs.dtu.dk/services/NetSurfP/)p-values of the identified phosphorylated residues were calculated as described by Wagner et al.http://networkin.info/version_2_0/newPrediction.php)http://string-db.org/)The identified phosphoproteins were group into biological process, molecular function and cellular compartment based on gene ontology (GO) term by Blast2GO softwareA. flavus NRRL 3357 were denatured in protein-loading buffer and separated by 12% SDS-PAGE. After electrophoresis, gels were stained with Coomassie Brilliant Blue or transferred to PVDF membranes . Immunoblotting was performed with the generated anti-phospho-site-specific antibodies (1:1000 dilution) or anti-non-phosphopeptide antibodies (1:1000 dilution), followed by incubation of the blots with a peroxidase-conjugated secondary antibody . As a loading control, monoclonal mouse anti-actin antibodies were used. Membranes were washed three times with TBST, and proteins were detected by chemiluminescence using SuperSignal® West Pico Chemiluminescent Substrate (Thermo Fisher Scientific). The results of western blotting were recorded with ImageQuant TL .Proteins from A. flavus ste11 gene orthologs and A. fumigatus Af293 pyrG gene ortholog were identified, searched and downloaded from the Aspergillus Comparative Database (http://www.broadinstitute.org/annotation/genome/aspergillus_group/MultiHome.html). The full-length replacement mutation of ste11 gene was introduced by a previous descriptionste11 gene (including upstream promoter element and downstream 3′-non-translated region) was amplified and digested  and attached to digested pPTR I with ClonExpress II One Step Cloning Kit . Then complementation vector was transformed into protoplasts  to construct complementation strain (COM), filtered by pyrithiamine resistance gene. The complementation vector was also used as the template of point mutant vectors (S187A and S187D). DpnI was used to eliminate template (complementation vector), and then point mutant vectors were transformed into protoplasts (∆ste11), filtered by pyrithiamine resistance gene. A. fumigatus Af293 pyrG gene was amplified and transformed into protoplasts (A. flavus CA14Δku70ΔpyrGΔniaD), used as wild type (WT).The 4 spores of WT, ∆ste11, COM, S187A and S187D were point inoculated onto YES agar, cultivated for 4 d. Colonial phenotypes were recorded. Conidia were collected with 7% DMSO and 0.5% Tween-20 and counted using a hemocytometer. Conidial formation was observed as described previously4 conidia of each strain were spread on Wickerham media plates and statically incubated at 37 °C for 10 d in the dark. Plates were sprayed with 70% ethanol to kill and wash away conidia to aid in enumeration of sclerotia. Each experiment was performed with four replicates for three times.10How to cite this article: Ren, S. et al. Global Phosphoproteomic Analysis Reveals the Involvement of Phosphorylation in Aflatoxins Biosynthesis in the Pathogenic Fungus Aspergillus flavus. Sci. Rep.6, 34078; doi: 10.1038/srep34078 (2016).


Advances in cochlear implant (CI) technology allow for acoustic and electric hearing to be combined within the same ear  and/or across ears . Integration efficiency  can be used to estimate how well acoustic and electric hearing are combined. The goal of this study was to evaluate factors that affect IE in EAS and bimodal listening. Vowel recognition was measured in normal-hearing subjects listening to simulations of unimodal, EAS, and bimodal listening. The input/output frequency range for acoustic hearing was 0.1–0.6 kHz. For CI simulations, the output frequency range was 1.2–8.0 kHz to simulate a shallow insertion depth and the input frequency range was varied to provide increasing amounts of speech information and tonotopic mismatch. Performance was best when acoustic and electric hearing was combined in the same ear. IE was significantly better for EAS than for bimodal listening; IE was sensitive to tonotopic mismatch for EAS, but not for bimodal listening. These simulation results suggest acoustic and electric hearing may be more effectively and efficiently combined within rather than across ears, and that tonotopic mismatch should be minimized to maximize the benefit of acoustic-electric hearing, especially for EAS. Adding detailed low-frequency information via acoustic hearing (aided or unaided) to electric hearing can benefit CI users’ speech and music perception21. However, some CI users do not experience significant benefits with acoustic-electric hearing24, and others experience interference between acoustic and electric hearing26. This suggests differences among CI users’ abilities to combine acoustic and electric stimulation patterns28.The coarse spectral resolution provided by cochlear implants (CIs) greatly limits performance in challenging listening conditions such as perception of speech in noise, speech prosody, vocal emotion, tonal language, music, etc.31 and in CI simulations34. Passive learning and/or explicit training can often offset some of the deficits associated with tonotopic mismatch43. However, adaptation may not be complete, and gradual adaptation to a tonotopic mismatch may be less difficult for CI users45. Interestingly, work by Reiss and colleagues has shown that acoustic-electric pitch matches may change as bimodal CI users gain experience with their device, suggesting perceptual adaptation driven by the CI frequency allocation47. Other research has shown that acoustic-electric pitch matching can be difficult and highly variable49.It is unclear whether acoustic and electric stimulation patterns are best combined within the same ear or across ears. With electric-acoustic stimulation (EAS), electric hearing via CI is combined with acoustic hearing in the ipsilateral ear; several CI manufacturers combine a hearing aid with the CI processor. With bimodal hearing, electric hearing via CI is combined with acoustic hearing in the contralateral ear. For EAS patients, the current spread associated with electric stimulation may interfere with acoustic hearing at the periphery. For bimodal listeners, there is no peripheral interaction between acoustic and electric hearing, which may be advantageous. For bimodal patients, clinical fitting of the CI is often performed without regard to the extent of residual acoustic hearing. For EAS patients, the low input frequency to the CI is often adjacent to the extent of acoustic hearing. Especially for bimodal listeners, the acoustic-to-electric frequency allocation typically maximizes the amount of acoustic information within the CI. Depending on the insertion depth and extent of the electrode array, the input acoustic frequency may be lower than the characteristic frequency associated with the electrode position, resulting in tonotopic mismatch. Large tonotopic mismatches have been shown to negatively affect speech performance with the CI onlyet al.50 found no significant difference among 3 EAS patients between a wide input frequency range  and a clinical range in which the low CI input frequency was adjacent to the extent of acoustic hearing. Fowler et al.51 found better speech performance in bimodal listeners with good residual acoustic hearing when the low CI input frequency was increased, presumably reducing tonotopic mismatch. Peters et al.49 found no significant correlation in CI patients with single-sided deafness between the degree of tonotopic mismatch (as inferred from imaging and acoustic-electric pitch-matching) and speech performance. Gifford et al.52 reported that a broad frequency allocation did not always produce the best performance in bimodal or bimodal-EAS listening, possibly due to low-frequency tonotopic mismatch. Thus, data with real bimodal and EAS presents a somewhat confusing picture. There is likely a tradeoff between preserving speech information within the CI and reducing tonotopic mismatch between acoustic and electric hearing.The effect of tonotopic mismatch in electric hearing is less understood in the context of acoustic-electric hearing. Some studies have compared different CI frequency allocations for bimodal and EAS patients. Reiss 53, other frequency components important for speech, such as vowel first formant  and second formant  may also be sensitive to tonotopic mismatch. This may be especially true when one component is delivered to the correct place  and another is delivered to a shifted place , resulting in interference between F1 cues and/or distortion to the ratio between F1 and F2 frequencies. It is unclear whether such distortions to speech features produce more interference with peripheral (EAS) or central processing .While stimulation at the correct tonotopic place is necessary for complex pitch perception57. We also hypothesized that there would be a tradeoff between the amount of speech information in the CI simulation and the degree of tonotopic mismatch.In this study, vowel recognition was measured in normal-hearing (NH) subjects listening to simulations of residual acoustic hearing and electric hearing. NH listeners and simulations were used to explicitly control the extent of stimulation within the cochlea and to directly compare perception of combined acoustic and electric hearing within and across ears. Such comparisons cannot be easily made in real EAS and bimodal CI listeners, as the extent/quality of residual acoustic hearing and the electrode-neural interface  is likely to vary across ears and/or patients. Vowel recognition was used to measure speech performance to explore the effects of preservation of speech information, spectral resolution (acoustic versus electric hearing) and tonotopic mismatch; sensitivity to speech features such as F1 and F2 might be reduced for sentence recognition where context cues are available. We hypothesized that vowel recognition would be similar between the EAS and bimodal simulations, as previous studies have shown that listeners are able to integrate independent information across earsth order Butterworth filter; 240 dB/octave). This range was selected to represent residual hearing available to some EAS and bimodal CI listeners, and to convey F1 information for most English vowels. The output frequency range of the CI simulations was 1.2–8.0 kHz. The lowest output frequency (1.2 kHz) corresponds to a cochlear location of a 20-mm insertion of an electrode array according to Greenwood58 and is slightly higher than the median upper edge of residual acoustic hearing (approximately 1.1 kHz) for hybrid CI patients reported by Karsten et al.59. The highest output frequency (8.0 kHz) is similar to the highest input frequency commonly used in commercial CI speech processors. Note that the output range of the CI simulations was not intended to necessarily simulate commercial CI devices, which vary in terms of array length, the number of electrodes, electrode spacing, etc. Rather, the output frequency range was fixed, and the input frequency range was varied to preserve different amounts of acoustic information while introducing different amounts of tonotopic mismatch. The CI input high-cutoff frequency was always 8.0 kHz. The CI input low-cutoff frequency was 0.2, 0.5, 0.8, or 1.2 kHz. When the CI input low-cutoff frequency = 0.2 kHz, there was maximal information within the CI, but with 9.8 mm of frequency mismatch at the apical end of the simulated electrode array; the CI input frequency range also greatly overlapped the AH input frequency range  were 90.4 (SE = 0.8), 92.4 (SE = 2.1), 78.3 (SE = 1.9) and 82.1 (SE = 3.5) percent correct for overall vowel recognition, F1, F2, and duration, respectively. Figure Vowel recognition was measured for the AH and CI simulations alone , as well for simulated EAS (AH + CI in the same ear) and bimodal listening (AH + CI in opposite ears). Performance was analyzed in terms of overall percent correct, as well as production-based categories of F1, F2, and durationThe pattern of results was similar for overall vowel recognition and F1 perception. For unimodal listening, performance was significantly poorer only when the CI input low-cutoff frequency was 0.2 kHz; performance was similar but generally poor among the remaining simulations. Bimodal and EAS performance was significantly better than with AH or with the CI simulations when the input low-cutoff frequencies ≥0.5 kHz. Bimodal and EAS performance significantly worsened for CI input low-cutoff frequencies ≤0.5 kHz.For perception of F2 cues, CI-only performance was best when the input low-cutoff frequency was 0.8 kHz; performance was very poor with AH (due to information loss above 0.6 kHz) and with the CI simulation when the input low-cutoff frequency was 0.2 kHz. EAS performance was significantly better than CI-only when the CI input low-cutoff frequency was 1.2 kHz; there was no significant difference in performance between EAS and CI-only when the CI input low-cutoff frequency was 0.5 or 0.8 kHz. There was no significant difference in EAS performance between the 0.8 and 1.2 kHz CI input low-cutoff frequencies. Strikingly, bimodal performance was significantly poorer than CI-only for CI input low-cutoff frequencies ≥0.5 kHz.For perception of duration cues, unimodal performance was significantly better with AH than with the CI simulations when the input low-cutoff frequency was 0.2, 0.5, or 1.2 kHz. Bimodal and EAS performance was significantly better than CI-only performance at all input low-cutoff frequencies. EAS performance was significantly better than AH for CI input low-cutoff frequencies ≥0.5 kHz; bimodal performance was significantly better than AH only when the CI input low-cutoff frequencies was 0.8 kHz. EAS performance was significantly better than bimodal only when the CI input low-cutoff frequencies was 1.2 kHz.27. Figure Integration efficiency (IE) for acoustic-electric hearing was calculated for the present bimodal and EAS data as the ratio between the observed and the predicted acoustic-electric hearing performance Contrary to our hypothesis, vowel recognition was much better with simulated EAS than with simulated bimodal listening. With no tonotopic mismatch (CI input low-cutoff frequency = 1.2 kHz), acoustic and electric hearing was more effectively combined in the same ear than across ears. The benefit with EAS (relative to CI-only performance) was 35.1, 26.7, 19.3, and 34.9 percentage points for overall vowel recognition, F1, F2, and duration cues, respectively. The benefit with bimodal (relative to CI-only performance) listening was considerably less: 15.6, 10.1, −13.8, and 26.1 percentage points for overall vowel recognition, F1, F2, and duration cues, respectively. Even with slight tonotopic mismatch (CI input low-cutoff frequency = 0.8 kHz), benefits were much larger for EAS than for bimodal listening.53; in those studies, the models made no distinction between EAS and bimodal listening. Differences in mechanisms - combining information across ears versus combining energy within the same ear - may explain differences in IE. With bimodal listening, subjects had to combine independent information from separate ears and separate frequency regions with differing spectral resolution. With EAS, AH and CI information was not truly independent, but rather fused into a stimulation pattern that included detailed spectral envelope information ≤0.6 kHz and coarse envelope information ≥1.2 kHz.While acoustic and electric hearing was effectively combined with either EAS or bimodal listening for CI input low-cutoff frequencies ≥0.5 kHz, IE was significantly better with EAS than with bimodal hearing. Previous probabilistic models of combined acoustic-electric hearing assumed that AH and CI information were independent53. With the CI alone or bimodal listening, there was very little difference in vowel recognition for CI input low-cutoff frequencies between 0.5 and 1.2 kHz, which explicitly shows the tradeoff between the amount of speech information and the degree of tonotopic mismatch. With EAS, no such tradeoff was observed, as performance was best with either the 0.8 or 1.2 kHz CI input low-cutoff frequencies, and dropped sharply for CI input low-cutoff frequencies ≤0.5 kHz. This suggests that bimodal listeners are able to more efficiently integrate increased amounts of speech information (and the associated increased tonotopic mismatch) from electric hearing with residual acoustic hearing. Conversely, EAS listeners are able to more efficiently integrate acoustic and electric stimulation patterns with small amounts of tonotopic mismatch. EAS performance was much better when the CI input low cut-off frequency was ≥0.8 kHz, despite the information loss. This finding is different from that of Karsten et al.59, who reported better consonant recognition in real EAS patients when the CI input low cut-off frequency was adjacent to the extent of acoustic hearing. Vowel recognition has been shown to be more sensitive to tonotopic mismatch than consonant or sentence recognition30. Again, depending on the position of the intra-cochlear electrodes and the frequency allocation, there may be tonotopic mismatch between the acoustic input and place of stimulation. The present EAS simulation data suggest that tonotopic mismatch had a more deleterious effect on vowel recognition than did the information loss between 0.6 and 1.2 kHz. It is possible that long-term adaptation and/or training might help to accommodate larger mismatches, in which case EAS listeners can make better use of the additional speech information within electric hearing45.Somewhat consistent with our hypothesis, the degree of tonotopic mismatch significantly affected IE for EAS, but not for bimodal listening. The effect of tonotopic mismatch within electric hearing was not captured in previous studies examining integration of acoustic and electric hearing44. No training was provided in this study; as such, the data represent somewhat acute measures. When the low CI input frequency was 0.2 kHz, mean vowel recognition was 9.4, 30.1, and 24.6% correct for CI-only, bimodal and EAS, respectively. Previous training studies show a 10–15 percentage point improvement with training39. With a 15-point improvement with training, mean scores might have improved to 24.4, 45.1, and 39.6% correct for CI-only, bimodal and EAS, respectively. For EAS, such performance would have remained well below the 65.7% correct observed when the low CI input frequency was 1.2 kHz . Interestingly, Zhang et al. observed that mean speech performance in EAS listeners improved by approximately 10 percentage points after training with EAS listening for 20 hours61. The present data suggest that for EAS patients, the best vowel recognition may be observed when the tonotopic mismatch in the CI is minimized, even when training benefits are considered.Auditory training may only offset some of the deficits associated with tonotopic mismatch66. The present results suggest that minimizing tonotopic mismatch within electric hearing may increase the benefit of acoustic-electric hearing, especially for EAS. Radiological imaging may help to identify the position of the electrodes within the cochlea and identify the extent of acoustic hearing68. In the clinic, the input low-cutoff frequency for the CI is generally set to be adjacent to the high edge of residual acoustic hearing, as frequency gaps between acoustic and electric hearing have been shown to be detrimental69. The present data suggest that tonotopic mismatch may greatly affect EAS performance as well acoustic-electric integration efficiency. Depending on the position of the intra-cochlear electrodes, adjusting the CI input low-cutoff frequency to be adjacent to the extent of acoustic hearing may introduce detrimental tonotopic mismatch. Again, experience with the CI and/or training may help to accommodate this mismatch47, but minimizing tonotopic mismatch may be preferable for initial CI fitting, possibly followed by progressive widening of the CI input frequency range45. Clinical fitting of bimodal CI patients is generally performed without regard to the extent of contralateral acoustic hearing; as such, speech information is maximized within the CI, often with some degree of tonotopic mismatch. The present bimodal results showed a tradeoff between speech information and tonotopic mismatch, and bimodal integration efficiency was not affected by tonotopic mismatch. Still, the best bimodal performance was observed with an optimal tradeoff between speech information and tonotopic mismatch (CI input low-cutoff frequency = 0.8 kHz).As indications for cochlear implantation continue to expand, and as surgical techniques and electrode designs continue to improve, combining acoustic and electric hearing in the same ear, opposite ears, and even both ears will become more commonplace. Depending on the stability of residual acoustic hearing and/or the insertion depth, complete or partial acoustic hearing preservation may be possible in the implanted earSimulations of residual acoustic hearing and electric hearing were better combined in the same ear (EAS) than across ears . Relative to CI-only, benefits of acoustic-electric hearing were nearly twice as large for EAS than for bimodal listening.Acoustic-electric integration efficiency was generally better for EAS than for bimodal listening. EAS integration efficiency was significantly affected by tonotopic mismatch, while bimodal integration efficiency was not.For CI-only and bimodal listening, there appeared to be an optimal tradeoff between the amount of speech information and tonotopic mismatch. Compared to bimodal listening, EAS was less affected by small amounts of tonotopic mismatch, but more sensitive to larger mismatches.To maximize the benefits of acoustic-electric hearing, CI fitting should minimize tonotopic mismatch, especially for EAS.The present study examined vowel recognition in NH subjects listening to simulations of residual acoustic hearing, CI signal processing, bimodal, and EAS. Key findings include:This study was approved by the Institutional Review Board of University of California, Los Angeles (UCLA). Prior to participation, written informed consent was obtained from all participants, in accordance with a protocol approved by the Institutional Review Board at University of California, Los Angeles.Ten NH subjects  participated in this study. The mean age at testing was 40.1 years (range: 18–62 years). All subjects had thresholds <20 dB HL for audiometric frequencies 250, 500, 1000, 2000, 4000, and 8000 Hz.et al.70. Two male and 2 female talkers each produced 16 vowels in a/h-V-d/context , for a total of 48 stimuli in the set. Stimuli were delivered via circumaural headphones (Sennheiser HDA-200) connected to separate channels of a mixer (Mackie 402 VLZ3), which was connected to an audio interface (Edirol UA-EX). Before signal processing, all stimuli were normalized to have the same long-term root-mean-square (RMS) energy (65 dB).Multi-talker vowel stimuli were digitized recordings drawn from Hillenbrand During testing, a stimulus was randomly selected from the set (without replacement) and presented to the subject, who responded by clicking on one of the 12 response boxed labelled in a/h-V-d/context. No trial-by-trial feedback was provided. Performance was first measured with unprocessed stimuli to familiarize subjects with the task and to ensure high levels of performance before testing with the EAS and bimodal simulations. The remaining test conditions were randomized within and across subjects. Scores were averaged across 2–4 test runs.th order Butterworth filters; 240 dB/octave). CI simulations were 8-channel noise vocoders, similar to Shannon et al.71. The input frequency range was divided into 8 channels , distributed according to Greenwood’s58 frequency-to-place formula. The temporal envelope was extracted from each analysis band by half-wave rectification and low-pass filtering (4th order Butterworth filter with 160 Hz envelope cutoff). The temporal envelope from each channel was used to modulate corresponding noise bands; the filter slope for the noise band carriers was same as that of the analysis filters. The modulated noise-bands were summed and the output was adjusted to have the same long-term root-mean-square (RMS) energy as the input speech signal.Residual acoustic hearing was simulated by bandpass filtering the speech signal between 0.1 and 0.6 kHz  were measured with one ear only. For the EAS simulation, AH and CI simulations were delivered to one ear of the headphones. For the bimodal simulation, AH and CI simulations were delivered to opposite ears of the headphones. Unprocessed signals, AH and CI simulations were presented at 60 dBA.The data used for the current study are provided as supplementary material.Supplementary Information


P > 0.05) between anuric and nonanuric pts. There was a positive but not significant correlation between IDWG and both pre-HD SBPI  and pre-HD DBPI . Pre-HD SBPI (1.01 ± 0.12 versus 1.13 ± 0.18) and DBPI (0.92 ± 0.16 versus 1.01 ± 0.24) were higher in nonanuric patents . Pre-HD HTN may not be solely related to IDWG and therapies beyond fluid removal may be needed. Individualized approach to HTN management is necessary in pediatric dialysis population.Overhydration is reported to be the main cause of hypertension (HTN) as well as to have no association with HTN in hemodialysis (HD) population. This is the first report of the relationship between interdialytic weight gain (IDWG) and pre-HD blood pressure (BP) in pediatric patients in relation to residual urine output (RUO). We studied 170 HD sessions and interdialytic periods performed during a 12-week period in 5 patients . BP is presented as systolic BP index (SBPI) and diastolic BP index (DBPI), calculated as systolic or diastolic BP/95th percentile for age, height, and gender. IDWG did not differ ( Role of interdialytic weight gain (IDWG) in hypertension (HTN) is poorly defined. While some report overhydration as the main cause of HTN, others find that it is frequently not associated with HTN, in both adults and children –4. FluidThe study was approved by Yale University Human Investigation Committee and DaVita Clinical Research® . Hemodialysis was performed at the outpatient DaVita Dialysis Unit , affiliated with Yale New Haven Children's Hospital. This is a retrospective, longitudinal study, with repeated observations obtained from the same pt. Informed consent was not required. To be included in the study, patients needed to be stable  and treated with the same and constant dialysate sodium (dNa) concentration (138 mEq/L) during a standardized period of 12 weeks, allowing steady state in BP and fluid balance. Patients received three-times-weekly standard HD using Fresenius 2008K machines .We analyzed data collected as part of routine clinical care. Data collected and/or calculated included the following: HD characteristics, BP before and after HD, weight (Wt) before and after HD, estimated dry weight (EDW), ultrafiltration, IDWG , and monBP was measured by pediatric dialysis nurse using the measuring device that is part of the dialysis machine with a size-appropriate cuff in sitting position  and was EDW was continuously assessed and adjusted at least monthly, with routine use of intradialytic blood volume monitoring in correlation with clinical outcomes  , 6. EchoP values. Comparisons were made using Student's t-test.Data are presented using descriptive statistics and box plots. Correlation analysis was performed using Pearson correlation coefficients and corresponding n = 102 HD sessions in anuric group; n = 68 HD sessions in nonanuric group) in five pts that fulfilled inclusion criteria. Equal number of HD sessions per patient (34/pt) were studied. Three pts were anuric  and 2 were nonanuric (pts 4 and 5). All subjects had normal cardiac function with left ventricular mass (LVM) index (gr/m2.7) percentiles 50 to 99 [Total of 170 HD sessions with corresponding interdialytic periods were studied in relation to RUO . IDWG was higher in nonanuric pts (2.78 ± 2.38%) in comparison to anuric pts (2.30 ± 1.79%), but this difference was not statistically significant (P = 0.06)  and IDWG and DBPI . Individual data analysis showed different relationships between IDWG and pre-HD BP (P < 0.01) than in nonanuric (P < 0.05) pts. Both SBPI and DBPI remained higher (P < 0.001) after HD and UF in nonanuric pts in comparison to anuric pts.Results are presented in relation to RUO in Tables  = 0.06) . Using mre-HD BP ; 3/5 ptsWe found a positive association between IDWG and pre-HD BP. This is in agreement with prior studies where fluid retention was reported to be the main determinant of HTN but contrary to those that found it was not related to HTN , 4. PaglEfforts to decrease IDWG include decreasing dNa and dietary salt intake. Patients studied here were dialyzed using dNa of 138 mEq/L shown to result in lower IDWG than dNa 140 mEq/L . To poteRelationship between RUO, IDWG, and pre-HD BP has not been studied in pediatric HD. Since fluid retention is thought to be the main cause of HTN in HD pts, RUO is assumed to be protective from high IDWG and pre-HD HTN. However, results from our nonanuric pts suggest the opposite. In comparison to anuric pts, our pts with RUO had significantly higher pre-HD BP without significant difference in IDWG. In addition, although post-HD SBP decreased with fluid removal in both anuric and nonanuric pts, the response to UF was better in anuric pts. Post-HD BP remained higher in nonanuric pts. Paglialonga et al. reported that, in their study of 16 oligoanuric children on HD, the 9 that had RUO showed no difference in IDWG in comparison to 7 anuric subjects. The authors did not study the effect of RUO on BP . The relMechanisms responsible for HTN in dialysis pts include sodium retention with increase in extracellular volume, sympathetic activation, renin-angiotensin-aldosterone system activity, and abnormalities in properties of the vasculature , 12. In Similarly to other studies in pediatric hemodialysis, our study has disadvantages that include small number of subjects, retrospective design, and single center experience , 3, 11. The patients presented here exemplify the different relationships between IDWG and pre-HD HTN that can occur in children and adolescents on HD. Pre-HD HTN may not be solely related to IDWG and therapies beyond fluid removal may be needed. Further studies are needed to investigate the relationship between IDWG and HTN, as well as etiology of HTN in children on maintenance HD with and without RUO. Development of individualized strategies for HTN management is necessary in pediatric HD population.


The risk and importance of transfusion-transmitted hepatitis E virus (TT-HEV) infections by contaminated blood products is currently a controversial discussed topic in transfusion medicine. The infectious dose, in particular, remains an unknown quantity. In the present study, we illuminate and review this aspect seen from the viewpoint of a blood donation service with more than 2 years of experience in routine HEV blood donor screening. We systematically review the actual status of presently known cases of TT-HEV infections and available routine NAT-screening assays. The review of the literature revealed a significant variation regarding the infectious dose causing hepatitis E. We also present the outcome of six cases confronted with HEV-contaminated blood products, identified by routine HEV RNA screening of minipools using the highly sensitive RealStar HEV RT-PCR Kit (95% LOD: 4.7 IU/mL). Finally, the distribution of viral RNA in different blood components  was quantified using the first WHO international standard for HEV RNA for NAT-based assays. None of the six patients receiving an HEV-contaminated blood product from five different donors  developed an acute hepatitis E infection, most likely due to low viral load in donor plasma (<100 IU/mL). Of note, the distribution of viral RNA in blood components depends on the plasma content of the component; nonetheless, HEV RNA could be detected in RBCs even when low viral plasma loads of 100–1,000 IU/mL are present. Comprehensive retrospective studies of TT-HEV infection offered further insights into the infectivity of HEV RNA-positive blood products. Minipool HEV NAT screening (96 samples) of blood donations should be adequate as a routine screening assay to identify high viremic donors and will cover at least a large part of viremic phases. Hepatitis E virus (HEV) is an emerging infectious threat to blood safety. In the recent decade, there have been several reports of transfusion-transmitted hepatitis E virus (TT-HEV) infection , althouvia blood products, most authorities have recommended HEV monitoring of immunosuppressed patients. The implementation of a HEV run control for screening human plasma pools requested by the Ph. Eur. 1646 , as described previously . Total nScreening for the presence of HEV-specific IgM and IgG antibodies was performed using the Anti-HEV ELISA  according to the manufacturer’s instructions. Serum concentrations of glutamate dehydrogenase (GLDH), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and total bilirubin were measured in plasma samples using the respective enzymatic assays on the Architect system . All HEV-infected donors underwent pre-donation medical examination and negated current diseases or any known risk factors for viral infection. The study protocol followed the ethical guidelines of the Ruhr University, Bochum, and was approved by the institutional review board; donors provided informed consent.g and 22°C for 45 min. Automated fractionation was carried out using the CompoMat G5 separator (Fresenius Kabi), and RBCs were stored directly at 4 ± 2°C. The final product volume averaged from 200 to 400 mL. The residual plasma volume was estimated to be 10 mL per RBC. The corresponding plasma products (FFP) contained a total volume of 180–380 mL with 0.75–0.82 mL/mL human plasma.Apheresis-derived single-donor PCs (APCs) were prepared after standard processing with the Haemonetics MCS + . The final product consisted of 2.0–4.0 × 10E+11 platelets/unit (205–295 mL) containing 0.76–0.84 mL/mL human plasma and 0.16–0.24 mL/mL ACD-A stabilizer. For the preparation of RBC, whole blood donations were collected into a multiple bag system with inline filtration for leukoreduction , followed by centrifugation of the filtered whole blood unit at 4,182 × Viral RNA in the different blood components  was extracted from 4.8-mL sample with the Chemagic Viral DNA/RNA reagent kit  combined with the automated Chemagic magnetic separation module MSMI (PerkinElmer Chemagen Technology GmbH) according to the manufacturer’s instructions. For the recovery of RBC supernatant, 50 mL of RBC were transferred to EDTA-containing monovettes followed by centrifugation for 10 min at 5,000 rpm. Therefore, the RBC supernatant contained CPD/PAGGS-M stabilizator and a minimal proportion of residual plasma. For RNA extraction of RBCs, the alternative lysis buffer CMG-825  was used. The 95% lower limit of detection was calculated by Probit analysis to 4.7 IU/mL [confidence interval: 3.6–7.5 IU/mL ] for FFPhttp://www.ncbi.nlm.nih.gov/pubmed/), a public search engine maintained by the United States National Library of Medicine (NLM) at the National Institutes of Health (NIH) that provides access to over 24 million citations in all fields of life sciences, mostly from the MEDLINE , was searched for publications between 2004 and 2017 (publications dates) using specific search strings including “hepatitis E/HEV infection,” “transfusion transmitted hepatitis E/HEV infection,” and “hepatitis E/HEV blood donor screening.”For the systematic review of HEV cases, the PubMed database . Statistical analysis to assess differences between values was performed using the Mann–Whitney A systematic review of individual case reports regarding the transfusion of HEV-contaminated blood products from 2004 to 2017 is summarized in chronological order of occurrence in Table S1 in Supplementary Material. Cases including those patients who died shortly after transfusion for reasons other than HEV infection were excluded. We further describe six new cases of patients transfused with HEV-contaminated blood products, and none of the recipients developed HEV infection.Table All recipients were anti-HEV IgM and anti-HEV IgG negative at the time of transfusion of HEV RNA-positive blood products. The viral load in plasma samples of donors 1, 2, and 4 was determined to be 17, 12, and 20 IU/mL, respectively. These values were below the linear range of quantification (<25 IU/mL), and therefore a maximum infectious dose was calculated assuming a viral load of 25 IU/mL. Recipient 1, an immunocompromised man after heart transplantation, received one RBC. Assuming a residual plasma volume of 10 mL per RBC, the maximum corresponding infectious dose was calculated as 250 IU. This patient did not develop HEV infection within the follow-up period of 134 days, and neither HEV RNA nor anti-HEV antibodies were detectable.Each apheresis platelet donation resulted in two APCs. The two immunocompetent recipients R2 and R3 received APCs from donor 2 with infectious doses of 4.68E+03 IU (APC1) and 4.86E+03 IU (APC2), assuming an average residual plasma volume of 0.8 mL per APC. Neither patients developed an HEV infection within the follow-up period of 35 days (recipient R2) or 50 days (recipient R3); moreover, no HEV RNA or anti-HEV antibodies were detectable. Accordingly, recipient R6 received two apheresis platelets  from donor 4 with a total maximum infectious dose of 7.97E+03 IU , but she died shortly after transfusion for reasons other than HEV infection.Donor 3 donated platelets regularly approximately every 14 days and showed the highest viral load compared with the other donors. The first viremic donation (donation 1) contained 27.8 IU/mL HEV RNA, and the second donation, 18 days later (donation 2), contained 69.4 IU/mL HEV RNA. The double APCs were transfused to two immunocompetent recipients (recipients R4 and R5). Recipient 4 received a total infectious dose of 1.08E+04 IU HEV RNA . This patient did not develop an acute HEV infection within 16 days after transfusion, and neither HEV RNA nor anti-HEV antibodies were detectable. However, the observation period was short and the possibility that HEV infection may have occurred later could not be ruled out. This patient was released from hospital and unfortunately no follow-up samples were sent to our laboratory for further follow-up. The second HEV-positive donation of donor 3 had a total infectious dose corresponding to 2.71E+04 IU HEV RNA . The recipient (R5) of these two apheresis platelets died shortly after transfusion for reasons other than HEV infection.The cases of recipients 4–6 were excluded from the subsequent overview due to the short follow-up period.r = 0.9418 (95% CI: 0.9065–0.9641) for FFP and a good correlation of r = 0.9290 (95% CI: 0.8538–0.9663) for RBC. The wider distribution between the measured and calculated HEV titer in RBC is based on the considerably lower plasma amount of only 10 mL per RBC  and the resultant higher method-specific quantification error.In order to determine if a reduction of the viral load occurs during the manufacturing process of blood products, e.g., by centrifugation or by adsorption to components of the blood bag system, including the filter used for leukoreduction, viral loads were quantified in the plasma of HEV RNA-positive donors and additionally quantified in the corresponding blood products, FFP and RBC. Results from the respective blood products were correlated with the expected viral loads calculated with quantified results for plasma of HEV RNA-positive donors, assuming no removal during the production process. Calculation of virus titer assumed a mean plasma content of 0.80 mL/mL human plasma (80%) for FFP. For RBCs, the remaining plasma volume of 10 mL per RBC was assumed for consideration of the total volume of each individual RBC after processing. A total of 73 value pairs were available for correlation analysis of FFPs , indicating that no specific binding to red blood cell surfaces had occurred.In order to determine if HEV RNA or virus particles are bound to the surface of red blood cells, HEV RNA was quantified in 20 different RBCs as well as in the cell-free supernatants of RBCs Figure C. SpearmFigure For RBCs, where the viral load in plasma was quantified >1,000 IU/mL Figure B, a maxiGerman public health authorities have recognized an increasing number of acute HEV infections, which is probably due to higher clinical awareness but more likely due to detection of HEV-infected asymptomatic blood donors identified through NAT screening by blood donation establishments. A high frequency of HEV viremic donors have been reported in recent large screening studies in several European countries –17. The Analysis of 19 Japanese cases of TT-HEV infection by Satake et al. found a comparable rate of infection of 50% . All TT-Unfortunately, some cases only revealed poor data sets, missing important facts for both, the recipients of contaminated blood products or the respective donors. For example, the pretransfusion serostatus in recipients is often only assumable based on the posttransfusion status, or the serostatus is entirely absent. The serological status of the blood donors is also often missing. Additionally, the duration between transfusion, determination of infection, and follow-up of patients including the accompanying therapy and laboratory parameters is often incomplete or untraceable. Most important, the viral HEV load and the resulting infectious dose is not determined. Taken into consideration only the individual cases included in Tables S1 and S2 where the viral load infused is available, 39 patients received blood products containing HEV, of whom 28 patients develop TT-HEV infection.p < 0.0001, Figure p = 0.0002), whereas no differences were observed between HEV-infected (median: 4.80E+05 IU) and non-infected immunocompromised patients . When the immune status of the recipient, which was mentioned to have a major impact on the actual risk of TT-HEV infection, was also taken into account, no differences were observed in the median viral load transfused between immunocompromised and non-immunocompromised patients, independently from the infection outcome . The lowest infectious dose resulting in TT-HEV infection observed in general was 7.05E+03 IU. When the type of blood product was considered, the lowest infectious dose transfused was 7.05E+03 IU for PCs without subdivision of APC and PPCs, 3.16E+04 IU for RBCs, and 3.60E+04 IU for FFPs (Figure Tedder and colleagues performed an estimation of the infectious dose of the individual blood product types involved in the UK study in a subsequent analysis, demonstrating that components causing TT-HEV infection had a considerably higher median infectious dose of 1.44E+06 IU than components not causing TT-HEV infection [median total viral load transfused: 2.40E+04 IU ]. Accords Figure . In our s Figure  and two s Figure , 39, 46,In addition to the cases mentioned in Table S1 in Supplementary Material, Arankalle et al. described two cases of putative, but in our opinion unlikely, posttransfusion hepatitis. HEV infection was assumed due to seroconversion of both patients within weeks after transfusion, but no HEV RNA was detected in patients for comparative sequence analysis. Additionally, donor screening of five of the six involved donations revealed no HEV RNA . In Japain vitro, but HEV cell culture is inefficient and limited, and requires genetic modifications of the HEV isolates (The lack of a small animal model and efficient cell culture system has hampered the study of HEV replication, pathogenesis, and infectious dose determination. HEV isolates of genotypes 3 and 4 have been adapted to grow isolates –51. Expeisolates . Immune-isolates , 54. Forisolates . These mThe plasma proportion of the transfused blood product seemed to affect the risk for TT-HEV at most, but so far no information is available on the partitioning of HEV into the different components from a single blood donation . It is cThe question remains as to which screening strategy is necessary and practicable. Screening may constitute a universal approach to include all blood products or a selective screening can be performed for only the products that would be used in at-risk patients. This issue is primarily influenced by two sides: the hospital-sided clear definition of at-risk patients and the logistic implications for the order of blood products, and supply and availability, managed by the blood establishment. The second question is whether minipool screening of up to 96 samples or ID testing is necessary. We would submit that the required detection limit, which need to be derived from the infectious dose, plays an important role for the second issue. Thus, the decision for ID or pool NAT depends on logistic and costs, which are in part dictated by the required sensitivity. Table JD and TV designed the study, analyzed and interpreted the data, and drafted the manuscript. CK designed the study and revised the manuscript critically. All authors contributed to drafting the text and approved the manuscript.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Mycobacterium tuberculosis without signs and symptoms, or radiographic or bacteriologic evidence of TB disease. CDC and the U.S. Preventive Services Task Force (USPSTF) recommend screening populations at increased risk for LTBI, including persons who have lived in congregate settings at high risk and persons who were born in, or are former residents of countries with TB incidence ≥20 cases per 100,000 population (The majority of tuberculosis (TB) cases in the United States are attributable to reactivation of latent TB infection (LTBI)  to 587 (9.2%), and the number and proportion of TB cases among foreign-born persons who arrived in the United States ≥10 years before diagnosis increased from 1,360 (18.4%) in 1993 to 2,922 (46.0%) in 2015 . During In recent years, more U.S. TB diagnoses among foreign-born persons occurred ≥10 years after arrival in the United States than among foreign-born persons in the United States <10 years. In 2013, for the first time, the number of TB cases diagnosed among foreign-born persons after ≥10 years in the United States was higher than the number diagnosed among persons in the United States for <10 years. Historically, TB prevention measures for foreign-born persons have focused on screening persons before or shortly after arrival in the United States and on finding and treating active TB disease  were among those in whom TB was diagnosed ≥10 years after U.S. arrival; if these persons had been included in this analysis, the number of TB cases diagnosed among foreign-born persons ≥10 years after U.S. arrival would have been even higher.Historically, TB prevention activities in the United States have been implemented primarily by the public health sector (Tuberculosis (TB) screening in the United States of persons from high TB–prevalence countries has historically focused on newly arrived persons. U.S. TB cases typically occur among persons who were infected years before experiencing disease. Persons with latent TB infection have a 5%–10% lifetime risk for developing TB disease in the United States.Beginning in 2013, the number of TB diagnoses among foreign-born persons ≥10 years after U.S. arrival  has exceeded those among persons <10 years after U.S. arrival . In 2015, among 5,763 TB cases diagnosed in foreign-born persons in the United States for whom the date of U.S. entry was known, 2,922 (51%) were diagnosed in persons ≥10 years after U.S. arrival. Foreign-born persons who received a TB diagnosis ≥10 years after U.S. arrival had greater odds of being aged ≥40 years, residing in a long-term care facility at diagnosis, and having non-HIV–related immunocompromising conditions.Promoting testing for TB infection as part of routine primary care among groups at high risk is crucial for advancing TB prevention and elimination initiatives in the United States. Emphasis should be focused on persons who have lived in countries with high TB prevalence, including persons who have resided in the United States for ≥10 years.