Enhancing Health Benefits of Tomato by Increasing its Antioxidant Contents through Different Techniques: A Review

Tomato is known to be a great dietary source of antioxidant lycopene which is found to be linked with reduced risk of life-threatening diseases like heart attack and cancers. Antioxidants delay the aging process by mopping up reactive free radicals from cells, those if present may damage our DNA and other vital cellular organelles. Antioxidant metabolites are a group of vitamins, carotenoids, phenolic compounds, and phenolic acids that can provide effective protection against Reactive Oxygen Species (ROS) by neutralizing free radicals, which are unstable molecules linked to the development of many degenerative diseases and medical conditions. There are pre and postharvest techniques available in the literature and these when adopted by the researchers showed significant progress in enhancing antioxidant contents of tomato fruit. In addition, there are various biochemical and genetic modification approaches to improve the expression of several antioxidant enhancing phytonutrients, enzymes and genes in tomato fruit. Trichoderma enriched bio-fertilizer application in tomato enhanced ascorbic acid under the treatment of 100% bio-fertilizer and beta-carotene was increased under 75% Bio-Fertilizer+25% N whereas elevated lycopene contents were observed in case of recommended dose of NPK. Various omics approaches like genomics, transcriptomics, miRNAomics, proteomics, and metabolomics have emerged as extremely helpful tools for the plant scientists in improving the beta-carotene, lycopene and antioxidant levels resulting in highly desirable new tomato cultivars.  Thus, in light of immense advantages of these techniques, the present study was undertaken to collect all the necessary information about different techniques employed by numerous researchers to increase the antioxidant contents in tomato and to document here the optimized experimental conditions that can be beneficial for future studies in this field. However, still in-depth genome wide studies are needed for better understanding and further enhancement of traits like flavor, quality and antioxidant contents in context to rapidly changing and uncertain climate.Keywords: Antioxidants; tomato; lycopene; β-carotene; reactive oxygen species (ROS)

An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs

We combined clinical, cytokine, genomic, methylation and dietary data from 43 young adult monozygotic twin pairs (aged 22-36 years, 53% female), where 25 of the twin pairs were substantially weight discordant (delta body mass index > 3 kg m(-2)). These measurements were originally taken as part of the TwinFat study, a substudy of The Finnish Twin Cohort study. These five large multivariate datasets (comprising 42, 71, 1587, 1605 and 63 variables, respectively) were jointly analysed using an integrative machine learning method called group factor analysis (GFA) to offer new hypotheses into the multi-molecular-level interactions associated with the development of obesity. New potential links between cytokines and weight gain are identified, as well as associations between dietary, inflammatory and epigenetic factors. This encouraging case study aims to enthuse the research community to boldly attempt new machine learning approaches which have the potential to yield novel and unintuitive hypotheses. The source code of the GFA method is publically available as the R package GFA.Peer reviewe

Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets

Peer reviewe

Outcomes with chimeric antigen receptor t-cell therapy in relapsed or refractory acute myeloid leukemia: a systematic review and meta-analysis

BackgroundWe conducted a systematic review and meta-analysis to evaluate outcomes following chimeric antigen receptor T cell (CAR-T) therapy in relapsed/refractory acute myeloid leukemia (RR-AML).MethodsWe performed a literature search on PubMed, Cochrane Library, and Clinicaltrials.gov. After screening 677 manuscripts, 13 studies were included. Data was extracted following PRISMA guidelines. Pooled analysis was done using the meta-package by Schwarzer et al. Proportions with 95% confidence intervals (CI) were computed.ResultsWe analyzed 57 patients from 10 clinical trials and 3 case reports. The pooled complete and overall response rates were 49.5% (95% CI 0.18-0.81, I2 =65%) and 65.2% (95% CI 0.36-0.91, I2 =57%). The pooled incidence of cytokine release syndrome, immune-effector cell associated neurotoxicity syndrome, and graft-versus-host disease was estimated as 54.4% (95% CI 0.17-0.90, I2 =77%), 3.9% (95% CI 0.00-0.19, I2 =22%), and 1.6% (95%CI 0.00-0.21, I2 =33%), respectively.ConclusionCAR-T therapy has demonstrated modest efficacy in RR-AML. Major challenges include heterogeneous disease biology, lack of a unique targetable antigen, and immune exhaustion

Bayesian multi-source regression and monocyte-associated gene expression predict BCL-2 inhibitor resistance in acute myeloid leukemia

The FDA recently approved eight targeted therapies for acute myeloid leukemia (AML), including the BCL-2 inhibitor venetoclax. Maximizing efficacy of these treatments requires refining patient selection. To this end, we analyzed two recent AML studies profiling the gene expression and ex vivo drug response of primary patient samples. We find that ex vivo samples often exhibit a general sensitivity to (any) drug exposure, independent of drug target. We observe that this "general response across drugs" (GRD) is associated with FLT3-ITD mutations, clinical response to standard induction chemotherapy, and overall survival. Further, incorporating GRD into expression-based regression models trained on one of the studies improved their performance in predicting ex vivo response in the second study, thus signifying its relevance to precision oncology efforts. We find that venetoclax response is independent of GRD but instead show that it is linked to expression of monocyte-associated genes by developing and applying a multi-source Bayesian regression approach. The method shares information across studies to robustly identify biomarkers of drug response and is broadly applicable in integrative analyses

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Correction: vol 7, 13205, 2016, doi:10.1038/ncomms13205Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in Bone-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2) = 0.18, P value = 0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.Peer reviewe

Machine learning methods for improving drug response prediction in cancer

Personalizing medicine, by choosing therapies that maximize effectiveness and minimize side effects for individual patients, is one of the prime challenges in cancer treatment. At the core of personalized medicine is a machine learning problem: Given a set of patients whose response to some drugs has been observed, predict the response of a new patient or to a new drug. Computationally predicted responses can then be used to generate hypotheses for selecting therapies tailored to individual patients. However, the prediction task is exceedingly challenging, raising the need for the development of new machine learning methods.  This thesis undertakes a unique multi-disciplinary approach to predict drug responses by utilizing multiple data sources in cancer, while simultaneously advancing the computational methods to improve accuracy. Specifically, the thesis presents a new Bayesian multi-view multi-task method that outperformed existing computational models in an international crowdsourcing challenge to predict drug responses. The method is further extended to solve the more challenging task of predicting drug responses in multiple cancer types. Notably, the thesis extends the kernelized Bayesian matrix factorization method with component-wise multiple kernel learning for effectively inferring associations between a large number of biologically motivated data sources and the latent factors. The results demonstrate that the new formulation of the method, supplemented with prior biological knowledge, is helpful for discovering interpretable associations as well as for predicting the drug responses of new cancer cells.  The original contribution of this thesis is two-fold: First, the thesis proposes novel multi-view and multi-task methods to predict drug responses in cancer cells with increased accuracy. Second, new ways of incorporating prior biological knowledge are explored to further improve drug response predictions. Open source implementations of the new methods have been released to facilitate further research

GFA

The R package GFA provides a full pipeline for factor analysis of multiple data sources that are represented as matrices with co-occurring samples. It allows learning dependencies between subsets of the data sources, decomposed into latent factors. The package also implements sparse priors for the factorization, providing interpretable biclusters of the multi-source data.Peer reviewe