Zinc oxide and ferric oxide nanoparticles combination increase plant growth, yield, and quality of soybean under semiarid region.

Zinc (Zn) and iron (Fe) malnutrition are global health challenges that need immediate attention. Hence, to address these issues, a two-pronged approach involving the development and application of novel Zn and Fe products for crop fertilization may be a potential solution. Therefore, zinc oxide (ZnO) (∼13.2 nm) and ferric oxide (Fe2O3) (∼15 nm) nanoparticles (NPs) were synthesized and characterized. Seven nutrients treatments viz, control, ZnO- NPs (25 mg kg-1), Fe2O3-NPs (25 mg kg-1), ZnO + Fe2O3-NPs (25 mg kg-1each), ZnSO4 (55.8 mg kg-1), FeSO4 (60.4 mg kg-1) and ZnSO4+ FeSO4 (55.8 and 60.4 mg kg-1) were arranged in five-time replicated Completely Randomized Design model to test the effectiveness of ZnO and Fe2O3 NPs in two soybean cultivars over conventional zinc sulfate (ZnSO4) and ferrous sulfate (FeSO4) fertilizers. The results indicated that the photosynthetic rate (Pn) and chlorophyll content increased (33.9-86.2%) significantly at the flowering stage with ZnO and Fe2O3 NPs applications, compared to their conventional counterparts. Likewise, the combined application of ZnO and Fe2O3 NPs reduced H2O2 production by 17-19% and increased the superoxide dismutase (SOD) and catalase (CAT) activities by 15-17% and 9.6-11.4% over the combined use of ZnSO4 and FeSO4, respectively. The normalized difference vegetation index (NDVI) showed an increase of 6.9-44.2% under ZnO and Fe2O3 NPs, as well as ZnSO4 and FeSO4. Furthermore, the combined application of NPs enhanced soybean seed yield by 4.6-18.3% compared to conventional Zn and Fe fertilizers. Concerning seed Zn and Fe density, conjoint application of ZnO and Fe2O3 NPs increases Zn by 1.8-2.2-fold and Fe by 19.22-22.58% over the combined application of Zn SO4 and FeSO4, respectively. While the application of NPs significantly decreased seed phytic acid concentrations by 7.3-59.9% compared to the control. These findings suggest that the combined application of ZnO and Fe2O3 NPs effectively enhances soybean productivity, seed nutrient density, and overall produce quality. Therefore, the combined application of ZnO and Fe2O3 -NPs in soybean can be a potential approach for sustainable soybean production and to reduce/arrest Zn and Fe malnutrition in a growing population.

Expert pathology for GTD: towards an international multidisciplinary team meeting (MDT).

BACKGROUND: Gestational trophoblastic disease, comprising hydatidiform moles and gestational trophoblastic tumours, is extremely rare. Exact diagnosis is crucial to indicate the appropriate treatment and to prevent complications. The scarcity and variability in the number of cases available for reporting, lack of specialized training in GTD and non-existence of refresher courses available implies that the pathologist dealing with these rare and at times extremely challenging cases are not completely confident in their diagnosis. OBJECTIVES: To explores the benefits of implementation of an international multidisciplinary conference (virtual) to aid diagnosis of difficult cases and support clinical management of GTD. METHODS: A short survey was circulated to all 46 members of the EOTTD pathology and genetics working party, and further spread to other colleagues who practice GTD. This showed that the pathologists and geneticists working with GTD patients do not feel adequately supported and equipped with dealing with these rare diseases. OUTCOME: Virtual cross-border MDTs were initiated in April 2022, bringing together participants from 11 European countries on a bi-yearly basis. Mean numbers of 3 patients are discussed during the MDTs followed by 3-4 QA cases. A participant survey was conducted at the end of virtual meeting with an average satisfaction rate of 9.5. The pathologists felt supported and benefited from networking and clinical collaboration. CONCLUSIONS AND OUTLOOK: This international multidisciplinary team meeting (MDT) continues to provide support in managing the uncertainty with difficult and rare cases and enhances the pathologists training and experience. The frequency of meetings and the number of cases discussed per meeting will be increased in 2023 given the positive response. This will empower individuals and organisations to work together and improve diagnosis and the prognosis for these young patients.

Practical guidelines of the EOTTD for pathological and genetic diagnosis of hydatidiform moles.

Hydatidiform moles are rare and thus most pathologists and geneticists have little experience with their diagnosis. It is important to promptly and correctly identify hydatidiform moles given that they are premalignant disorders associated with a risk of persistent gestational trophoblastic disease and gestational trophoblastic neoplasia. Improvement in diagnosis can be achieved with uniformization of diagnostic criteria and establishment of algorithms. To this aim, the Pathology and Genetics Working Party of the European Organisation for Treatment of Trophoblastic Diseases has developed guidelines that describe the pathological criteria and ancillary techniques that can be used in the differential diagnosis of hydatidiform moles. These guidelines are based on the best available evidence in the literature, professional experience and consensus of the experts' group involved in its development.

QUFIND: tool for comparative prediction and mining of G4 quadruplexes overlapping with CpG islands.

G-quadruplexes (G4s) are secondary structures in DNA that have been shown to be involved in gene regulation. They play a vital role in the cellular processes and several pathogens including bacteria, fungi, and viruses have also been shown to possess G4s that help them in their pathogenesis. Additionally, cross-talk among the CpG islands and G4s has been shown to influence biological processes. The virus-encoded G4s are affected by the mutational landscape leading to the formation/deletion of these G4s. Therefore, understanding and predicting these multivariate effects on traditional and non-traditional quadruplexes forms an important area of research, that is, yet to be investigated. We have designed a user-friendly webserver QUFIND (http://soodlab.com/qufinder/) that can predict traditional as well as non-traditional quadruplexes in a given sequence. QUFIND is connected with ENSEMBL and NCBI so that the sequences can be fetched in a real-time manner. The algorithm is designed in such a way that the user is provided with multiple options to customize the base (A, T, G, or C), size of the stem (2-5), loop length (1-30), number of bulges (1-5) as well as the number of mismatches (0-2) enabling the identification of any of the secondary structure as per their interest. QUFIND is designed to predict both CpG islands as well as G4s in a given sequence. Since G4s are very short as compared to the CpG islands, hence, QUFIND can also predict the overlapping G4s within CpG islands. Therefore, the user has the flexibility to identify either overlapping or non-overlapping G4s along with the CpG islands. Additionally, one section of QUFIND is dedicated to comparing the G4s in two viral sequences. The visualization is designed in such a manner that the user is able to see the unique quadruplexes in both the input sequences. The efficiency of QUFIND is calculated on G4s obtained from G4 high throughput sequencing data (n = 1000) or experimentally validated G4s (n = 329). Our results revealed that QUFIND is able to predict G4-quadruplexes obtained from G4-sequencing data with 90.06% prediction accuracy whereas experimentally validated quadruplexes were predicted with 97.26% prediction accuracy.

Fertility outcomes following surgery and multiagent chemotherapy in malignant ovarian germ cell tumor survivors: a survey study.

OBJECTIVE: To assess fertility outcomes in long-term survivors of malignant ovarian germ cell tumors treated with fertility-sparing surgery with or without additional chemotherapy. METHODS: Women diagnosed and treated for malignant ovarian germ cell tumors at Charing Cross Hospital or Mount Vernon Cancer Centre between 1977 and 2015 were included. Questionnaires assessing fertility issues were sent to patients treated with fertility-sparing surgery. Fertility outcomes were evaluated according to the treatment received. The effect of the mean total dose of cyclophosphamide and cisplatin was assessed. RESULTS: A total of 146 patients were sent the questionnaire; 77 (56.5%) patients were included in the analysis. A total of 49 (64%) patients received platinum-based chemotherapy after surgery, 39 (79.6%) of these with cisplatin, vincristine, methotrexate, bleomycin, actinomycin D, cyclophosphamide, and etoposide, while 10 (20.4%) with bleomycin, etoposide, and cisplatin. After any treatment, 39/46 patients (85%) became pregnant: the conception rate was not different between those receiving surgery only and those receiving also chemotherapy (85.7% vs 84.4%, p=1.0). Live birth rate was 80.4% (37/46), with no statistically significant difference between the treatment groups (p=0.42). Median age of women achieving conception was 29 years (IQR 26-33). The probability of live birth at 5 years was 48% and 40% for patients in the surgery only and chemotherapy group, respectively (p=0.55). Infertility and miscarriage rates did not differ significantly between the two treatment groups (p=0.30 and p=0.32). The mean doses of cisplatin and cyclophosphamide received by patients failing and achieving conception were not different (p=0.10, p=0.47). CONCLUSIONS: Our results suggest that fertility may not be hampered in patients with malignant ovarian germ cell tumor treated with fertility-sparing surgery or receiving additional chemotherapy.

Association of a novel begomovirus species with fenugreek yellow vein disease in India.

BACKGROUND: Fenugreek (Trigonella foenum-graecum L.) is an annual medicinal and spice crop belonging to the family Fabaceae. The occurrence of a yellow vein disease was recorded in fenugreek in Jodhpur (India) in 2022. The infection of begomoviruses in legume crops results in significant yield loss and major economic loss. The current study reports an association of a novel begomovirus species associated with yellow vein disease in Fenugreek. METHODS AND RESULTS: In symptomatic fenugreek plants, geminivirus-like particles were visible under a transmission electron microscope. Further, nucleotide sequence analysis of the rolling circle amplified product revealed 2743 nucleotide DNA-A genome with close relatedness to French bean leaf curl virus (88.21%) and Senna leaf curl virus (87.63%). It was proposed as a new begomovirus species, Fenugreek yellow vein Rajasthan virus. The genome organization suggested the presence of a typical nonanucleotide sequence along with 7 ORFs in DNA-A. A possible recombination event took place in the coat protein (V1) region with Pedilanthus leaf curl virus and Chilli leaf curl virus as major and minor parents. The recombinant virus poses possible threats to several other legume crops. To the best of our knowledge, this is the first report of the association of FeYVRaV with fenugreek yellow vein disease from northwestern India. CONCLUSIONS: In conclusion, the presence of a novel begomovirus species associated with yellow vein disease in fenugreek is alarming and needs further studies on its infectivity to prevent its spread to legume crops.

iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers.

BACKGROUND: Inflammatory mediators play havoc in several diseases including the novel Coronavirus disease 2019 (COVID-19) and generally correlate with the severity of the disease. Interleukin-13 (IL-13), is a pleiotropic cytokine that is known to be associated with airway inflammation in asthma and reactive airway diseases, in neoplastic and autoimmune diseases. Interestingly, the recent association of IL-13 with COVID-19 severity has sparked interest in this cytokine. Therefore characterization of new molecules which can regulate IL-13 induction might lead to novel therapeutics. RESULTS: Here, we present an improved prediction of IL-13-inducing peptides. The positive and negative datasets were obtained from a recent study (IL13Pred) and the Pfeature algorithm was used to compute features for the peptides. As compared to the state-of-the-art which used the regularization based feature selection technique (linear support vector classifier with the L1 penalty), we used a multivariate feature selection technique (minimum redundancy maximum relevance) to obtain non-redundant and highly relevant features. In the proposed study (improved IL-13 prediction (iIL13Pred)), the use of the mRMR feature selection method is instrumental in choosing the most discriminatory features of IL-13-inducing peptides with improved performance. We investigated seven common machine learning classifiers including Decision Tree, Gaussian Naïve Bayes, k-Nearest Neighbour, Logistic Regression, Support Vector Machine, Random Forest, and extreme gradient boosting to efficiently classify IL-13-inducing peptides. We report improved AUC, and MCC scores of 0.83 and 0.33 on validation data as compared to the current method. CONCLUSIONS: Extensive benchmarking experiments suggest that the proposed method (iIL13Pred) could provide improved performance metrics in terms of sensitivity, specificity, accuracy, the area under the curve - receiver operating characteristics (AUCROC) and Matthews correlation coefficient (MCC) than the existing state-of-the-art approach (IL13Pred) on the validation dataset and an external dataset comprising of experimentally validated IL-13-inducing peptides. Additionally, the experiments were performed with an increased number of experimentally validated training datasets to obtain a more robust model. A user-friendly web server ( www.soodlab.com/iil13pred ) is also designed to facilitate rapid screening of IL-13-inducing peptides.

Epithelioid trophoblastic tumor with antecedent molar pregnancy in an HIV-positive patient.

Epithelioid trophoblastic tumor (ETT) is a rare malignancy arising from neoplastic proliferation of chorionic-type intermediate trophoblasts. ETT poses significant challenges to clinicians in the diagnosis and treatment and can hence lead to a poor prognosis. We report a unique case of metastatic ETT in a HIV-positive patient.

Minor millets: a review on nutritional composition, starch extraction/modification, product formulation, and health benefits.

Minor millet grains are the abode of healthy constituents of human concern that contribute to healthy longevity. Additionally, they are excellent in nutritional value including macronutrients namely, protein (7-13%), carbohydrates (60-70%), fat (1.5-5%), fiber (2-7%) and for micronutrients as well namely; iron, calcium, phosphorus, and magnesium, etc. All these beneficial traits along with the availability of bioactive constituents (polyphenols and antioxidants) prove them to be therapeutic in action and also uplift the immunity among users. Employed isolation tactics for starch also govern yield characteristics and is usually preferred by way of wet method. Minor millets are abundant in starch (50-70%) thus application broadness is another attribute which could be addressed in vivid food segments. In case, native starches somehow possess least application credentials in food and non-food sectors thus modification is the only alternative to eliminate shortcomings. As in trend, modification using physical, chemical, and enzymatic ways have a wide impact on the properties of millet starch. The present review summarizes the nutritional, bioactive and therapeutic potential of minor millets, along with ways of starch modification and product development through millet involvement. © 2023 Society of Chemical Industry.

Multi-omics assisted breeding for biotic stress resistance in soybean.

Biotic stress is a critical factor limiting soybean growth and development. Soybean responses to biotic stresses such as insects, nematodes, fungal, bacterial, and viral pathogens are governed by complex regulatory and defense mechanisms. Next-generation sequencing has availed research techniques and strategies in genomics and post-genomics. This review summarizes the available information on marker resources, quantitative trait loci, and marker-trait associations involved in regulating biotic stress responses in soybean. We discuss the differential expression of related genes and proteins reported in different transcriptomics and proteomics studies and the role of signaling pathways and metabolites reported in metabolomic studies. Recent advances in omics technologies offer opportunities to reshape and improve biotic stress resistance in soybean by altering gene regulation and/or other regulatory networks. We suggest using 'integrated omics' to precisely understand how soybean responds to different biotic stresses. We also discuss the potential challenges of integrating multi-omics for the functional analysis of genes and their regulatory networks and the development of biotic stress-resistant cultivars. This review will help direct soybean breeding programs to develop resistance against different biotic stresses.

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.

First report of Tobacco rattle virus infecting Brassica oleracea var. botrytis (cauliflower) in India.

The Brassica oleracea var. botrytis (cauliflower) is an important annual vegetable crop in the Brassicaceae family and is extensively grown worldwide (Singh et al. 2018). In the early summer of 2022, the cauliflower plants grown at the Indian Agricultural Research Institute (IARI), New Delhi, India, showed virus-like symptoms. Symptoms comprised chlorosis, stunted growth, mottling, necrosis, and mosaic. Additionally, the infected plants had deformed, curled leaves and reduced growth. The symptomatic plant leaf samples were collected and examined under the transmission electron microscope (TEM), which showed rigid, rod-shaped particles with tubular morphology resembling tobacco rattle virus (TRV, genus Tobravirus) infection (Basavaraj et al. 2020). TRV has a vast host range and is reported to infect many vegetable crops (beans, beets, peppers, potatoes, and spinach) and ornamental plants (lily, marigold, and tulip) (Adams et al. 2012; Katoch et al. 2004; MacFarlane, 1999). The reverse transcription (RT)-PCR also tested infected samples. Total RNA was extracted with Plant RNeasy Mini Kit (Qiagen, Germany). The cDNA was prepared using a RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific, US). A 600-bp-long coat protein gene of TRV was PCR amplified using coat protein gene (CPG)-specific primers (TRVCPF: ATGGGAGATATGTACGATGAATC and TRVCPR: CTAGGGATTAGGACGTATCGGA). The PCR reaction contained 5.0µl of 5× Go-Taq Flexi buffer, 2.5µl of 25mM MgCl2, 1.0µl of 10mM dNTPs, 0.75µl each of 10µm forward and reverse primers of TRVCP, 1.0µl of cDNA, 13.8µl of nuclease-free water, and 0.2µl of Go-Taq polymerase (Promega, US). No template control was run with this PCR. The PCR (Gradient thermocycler, C-1000TM, BIORAD) was carried out under the following conditions: 94°C for 2 min, followed by 35 cycles of 94°C for 1 min, 50°C for 30 sec, and 72°C for 1 min, and final elongation at 72°C for 10 min. TRV was amplified in three cauliflower samples at IARI, New Delhi (Lat 28.08° N and Long 77.12°E). The amplicon of partial CPG was sequenced by Sanger sequencing (AgriGenome Labs Pvt. Ltd., India). The BLASTN analysis of the CPG revealed 100% nucleotide homology with TRV isolates (Accession No. Z36974) (Hernandez et al. 1995). Three isolates were sequenced and submitted to the GenBank database (Accession Nos. ON983976, ON983977, and ON983978). The sap from the TRV-infected cauliflower leaves were used to confirm the infection of TRV in healthy cauliflower plants grown in the greenhouse condition. TRV may be a new threat to cauliflower production and needs further research to elaborate more about the virus-host interactions and disease resistance. As per our knowledge, this is the first report of TRV infecting cauliflower in India.

Dataset for the Reporting of Gestational Trophoblastic Neoplasia: Recommendations From the International Collaboration on Cancer Reporting (ICCR).

Comprehensive pathology reporting of cancers is important for patient management, tumor staging, and prognostication. Standardized cancer datasets are essential in guiding pathology reporting in a consistent and concise manner and this facilitates effective global cancer information exchange and comparison. The International Collaboration on Cancer Reporting (ICCR) is an alliance of several national and international pathology societies in many countries as well as bodies which are involved in tumor classification and staging. One function of the ICCR is to develop evidence-based, standardized reporting datasets for each cancer site. Herein, we report the development of an evidence-based cancer dataset by an ICCR panel of international experts for the reporting of primary uterine gestational trophoblastic neoplasia. We present the core elements that should be included and noncore elements that are recommended for inclusion in pathology reports. Lists of the response values are provided for each element, along with explanatory commentaries. The dataset also discusses controversial issues in the reporting of gestational trophoblastic neoplasia. Such evidence-based and structured pathology datasets developed through an international effort will facilitate consistent and accurate exchange and comparison of epidemiological and pathologic parameters among different populations and countries. This will ultimately improve gestational trophoblastic neoplasia patient care and facilitate future research.

Enhanced depression detection from speech using Quantum Whale Optimization Algorithm for feature selection.

There is an urgent need to detect depression using a non-intrusive approach that is reliable and accurate. In this paper, a simple and efficient unimodal depression detection approach based on speech is proposed, which is non-invasive, cost-effective and computationally inexpensive. A set of spectral, temporal and spectro-temporal features is derived from the speech signal of healthy and depressed subjects. To select a minimal subset of the relevant and non-redundant speech features to detect depression, a two-phase approach based on the nature-inspired wrapper-based feature selection Quantum-based Whale Optimization Algorithm (QWOA) is proposed. Experiments are performed on the publicly available Distress Analysis Interview Corpus Wizard-of-Oz (DAICWOZ) dataset and compared with three established univariate filtering techniques for feature selection and four well-known evolutionary algorithms. The proposed model outperforms all the univariate filter feature selection techniques and the evolutionary algorithms. It has low computational complexity in comparison to traditional wrapper-based evolutionary methods. The performance of the proposed approach is superior in comparison to existing unimodal and multimodal automated depression detection models. The combination of spectral, temporal and spectro-temporal speech features gave the best result with the LDA classifier. The performance achieved with the proposed approach, in terms of F1-score for the depressed class and the non-depressed class and error is 0.846, 0.932 and 0.094 respectively. Statistical tests demonstrate that the acoustic features selected using the proposed approach are non-redundant and discriminatory. Statistical tests also establish that the performance of the proposed approach is significantly better than that of the traditional wrapper-based evolutionary methods.

Time Series Analysis of SARS-CoV-2 Genomes and Correlations among Highly Prevalent Mutations.

The efforts of the scientific community to tame the recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seem to have been diluted by the emergence of new viral strains. Therefore, it is imperative to understand the effect of mutations on viral evolution. We performed a time series analysis on 59,541 SARS-CoV-2 genomic sequences from around the world to gain insights into the kinetics of the mutations arising in the viral genomes. These 59,541 genomes were grouped according to month (January 2020 to March 2021) based on the collection date. Meta-analysis of these data led us to identify significant mutations in viral genomes. Pearson correlation of these mutations led us to the identification of 16 comutations. Among these comutations, some of the individual mutations have been shown to contribute to viral replication and fitness, suggesting a possible role of other unexplored mutations in viral evolution. We observed that the mutations 241C>T in the 5' untranslated region (UTR), 3037C>T in nsp3, 14408C>T in the RNA-dependent RNA polymerase (RdRp), and 23403A>G in spike are correlated with each other and were grouped in a single cluster by hierarchical clustering. These mutations have replaced the wild-type nucleotides in SARS-CoV-2 sequences. Additionally, we employed a suite of computational tools to investigate the effects of T85I (1059C>T), P323L (14408C>T), and Q57H (25563G>T) mutations in nsp2, RdRp, and the ORF3a protein of SARS-CoV-2, respectively. We observed that the mutations T85I and Q57H tend to be deleterious and destabilize the respective wild-type protein, whereas P323L in RdRp tends to be neutral and has a stabilizing effect. IMPORTANCE We performed a meta-analysis on SARS-CoV-2 genomes categorized by collection month and identified several significant mutations. Pearson correlation analysis of these significant mutations identified 16 comutations having absolute correlation coefficients of >0.4 and a frequency of >30% in the genomes used in this study. The correlation results were further validated by another statistical tool called hierarchical clustering, where mutations were grouped in clusters on the basis of their similarity. We identified several positive and negative correlations among comutations in SARS-CoV-2 isolates from around the world which might contribute to viral pathogenesis. The negative correlations among some of the mutations in SARS-CoV-2 identified in this study warrant further investigations. Further analysis of mutations such as T85I in nsp2 and Q57H in ORF3a protein revealed that these mutations tend to destabilize the protein relative to the wild type, whereas P323L in RdRp is neutral and has a stabilizing effect. Thus, we have identified several comutations which can be further characterized to gain insights into SARS-CoV-2 evolution.

Novel insights into host responses to Japanese Encephalitis Virus infection: Reanalysis of public transcriptome and microRNAome datasets.

PURPOSE: Japanese encephalitis (JE), caused by the Japanese encephalitis virus (JEV), is the principal cause of viral encephalitis in South-East Asian and Western Pacific countries; accounting for 68,000 cases, and up to 20,400 fatalities, annually across the world. Despite being a high-risk condition, there is no specific treatment for JE. Given rapid additions in genomics databases and the power of data reanalysis in addressing critical medical questions, the present study was designed to identify novel host factors that might have potential roles in JEV infection. METHODS: We extracted microarray and RNA-Seq data sets from NCBI-GEO and compared mock and JEV-infected samples. Raw data from all the studies were re-analyzed to identify host factors associated with JEV replication. RESULTS: We identified several coding and non-coding host factors that had no prior known role in viral infections. Of these, the coding transcripts: Myosin Heavy Chain 10 (MYH10), Progestin and AdipoQ Receptor Family Member 8 (PAQR8), and the microRNAs: hsa-miR-193b-5p, hsa-miR-3714 and hsa-miR-513a-5p were found to be novel host factors deregulated during JEV infection. MYH10 encodes a conventional non-muscle myosin, and mutations in MYH10 have been shown to cause neurological defects. PAQR8 has been associated with epilepsy, which exhibits symptoms similar to JEV infection. JE is a neuro-degenerative disease, and the known involvement of MYH10 and PAQR8 in neurological disorders strongly indicates potential roles of these host factors in JEV infection. Additionally, we observed that MYH10 and PAQR8 had a significant negative correlation with Activating transcription factor 3 (ATF3), which is a previously validated modulator of JEV infection. ATF3 is a transcription factor that binds to the promotors of genes encoding other transcription factors or interferon-stimulated genes and negatively regulates host antiviral responses during JE. CONCLUSION: Our findings demonstrate the significance of data reanalysis in the identification of novel host factors that may become targets for diagnosis/ therapy against viral diseases of major concern, such as, JE. The deregulated coding and non-coding transcripts identified in this study need further experimental analysis for validation.

Sensitive screening of single nucleotide polymorphisms in cell free DNA for diagnosis of gestational tumours.

Tumours expressing human chorionic gonadotropin (hCG), the majority of which are difficult to biopsy due to their vascularity, have disparate prognoses depending on their origin. As optimal management relies on accurate diagnosis, we aimed to develop a sensitive cell free DNA (cfDNA) assay to non-invasively distinguish between cases of gestational and non-gestational origin. Deep error-corrected Illumina sequencing of 195 common single nucleotide polymorphisms (SNPs) in cfDNA and matched genomic DNA from 36 patients with hCG-secreting tumours (serum hCG 5 to 3,042,881 IU/L) and 7 controls with normal hCG levels (≤4 IU/L) was performed. cfDNA from confirmed gestational tumours with hCG levels ranging from 1497 to 700,855 IU/L had multiple (n ≥ 12) 'non-host' alleles (i.e. alleles of paternal origin). In such cases the non-host fraction of cfDNA ranged from 0.3 to 40.4% and correlated with serum hCG levels. At lower hCG levels the ability to detect non-host cfDNA was variable, with the detection limit dependent on the type of causative pregnancy. Patients with non-gestational tumours were identifiable by the absence of non-host cfDNA, with copy number alterations detectable in the majority of cases. Following validation in a larger cohort, our sensitive assay will enable clinicians to better inform patients, for whom biopsy is inappropriate, of their prognosis and provide optimum management.

The Genomic Landscape of Early-Stage Ovarian High-Grade Serous Carcinoma.

PURPOSE: Ovarian high-grade serous carcinoma (HGSC) is usually diagnosed at late stage. We investigated whether late-stage HGSC has unique genomic characteristics consistent with acquisition of evolutionary advantage compared with early-stage tumors. EXPERIMENTAL DESIGN: We performed targeted next-generation sequencing and shallow whole-genome sequencing (sWGS) on pretreatment samples from 43 patients with FIGO stage I-IIA HGSC to investigate somatic mutations and copy-number (CN) alterations (SCNA). We compared results to pretreatment samples from 52 patients with stage IIIC/IV HGSC from the BriTROC-1 study. RESULTS: Age of diagnosis did not differ between early-stage and late-stage patients (median 61.3 years vs. 62.3 years, respectively). TP53 mutations were near-universal in both cohorts (89% early-stage, 100% late-stage), and there were no significant differences in the rates of other somatic mutations, including BRCA1 and BRCA2. We also did not observe cohort-specific focal SCNA that could explain biological behavior. However, ploidy was higher in late-stage (median, 3.0) than early-stage (median, 1.9) samples. CN signature exposures were significantly different between cohorts, with greater relative signature 3 exposure in early-stage and greater signature 4 in late-stage. Unsupervised clustering based on CN signatures identified three clusters that were prognostic. CONCLUSIONS: Early-stage and late-stage HGSCs have highly similar patterns of mutation and focal SCNA. However, CN signature analysis showed that late-stage disease has distinct signature exposures consistent with whole-genome duplication. Further analyses will be required to ascertain whether these differences reflect genuine biological differences between early-stage and late-stage or simply time-related markers of evolutionary fitness. See related commentary by Yang et al., p. 2730.