Antiviral strategies: What can we learn from natural reservoirs?

Viruses cause many severe diseases in both plants and animals, urging us to explore new antiviral strategies. In their natural reservoirs, viruses live and replicate while causing mild or no symptoms. Some animals, such as bats, are the predicted natural reservoir of multiple viruses, indicating that they possess broad-spectrum antiviral capabilities. Mechanisms of host defenses against viruses are generally studied independently in plants and animals. In this article, we speculate that some antiviral strategies of natural reservoirs are conserved between kingdoms. To verify this hypothesis, we created null mutants of 10-formyltetrahydrofolate synthetase (AtTHFS), an Arabidopsis thaliana homologue of methylenetetrahydrofolate dehydrogenase, cyclohydrolase and formyltetrahydrofolate synthetase 1 (MTHFD1), which encodes a positive regulator of viral replication in bats. We found that disruption of AtTHFS enhanced plant resistance to three different types of plant viruses, including the tomato spotted wilt virus (TSWV), the cucumber mosaic virus (CMV) and the beet severe curly top virus (BSCTV). These results demonstrate a novel antiviral strategy for plant breeding. We further discuss the approaches used to identify and study natural reservoirs of plant viruses, especially those hosting many viruses, and highlight the possibility of discovering new antiviral strategies from them for plant molecular breeding and antiviral therapy.

Machine learning in prediction of genetic risk of nonsyndromic oral clefts in the Brazilian population

OBJECTIVES

Genetic variants in multiple genes and loci have been associated with the risk of nonsyndromic cleft lip with or without cleft palate (NSCL ± P). However, the estimation of risk remains challenge, because most of these variants are population-specific rendering the identification of the underlying genetic risk difficult. Herein we examined the use of machine learning network in previously reported single nucleotide polymorphisms (SNPs) to predict risk of NSCL ± P in the Brazilian population.

MATERIALS AND METHODS

Random forest and neural network methods were applied in 72 SNPs in a case-control sample composed by 722 NSCL ± P and 866 controls for discrimination of NSCL ± P risk. SNP-SNP interactions and functional annotation biological processes associated with the identified NSCL ± P risk genes were verified.

RESULTS

Supervised random forest decision trees revealed high scores of importance for the SNPs rs11717284 and rs1875735 in FGF12, rs41268753 in GRHL3, rs2236225 in MTHFD1, rs2274976 in MTHFR, rs2235371 and rs642961 in IRF6, rs17085106 in RHPN2, rs28372960 in TCOF1, rs7078160 in VAX1, rs10762573 and rs2131960 in VCL, and rs227731 in 17q22, with an accuracy of 99% and an error rate of approximately 3% to predict the risk of NSCL ± P. Those same 13 SNPs were considered the most important for the neural network to effectively predict NSCL ± P risk, with an overall accuracy of 94%. Multivariate regression model revealed significant interactions among all SNPs, with an exception of those in FGF12 and MTHFD1. The most significantly biological processes for selected genes were those involved in tissue and epithelium development; neural tube closure; and metabolism of methionine, folate, and homocysteine.

CONCLUSIONS

Our results provide novel clues for genetic mechanism studies of NSCL ± P and point out for a machine learning model composed by 13 SNPs that is capable of predicting NSCL ± P risk.

CLINICAL RELEVANCE

Although validation is necessary, this genetic panel can be useful in the near future to assist in NSCL ± P genetic counseling.

Genetic and Physiological Factors Affecting Human Milk Production and Composition

Human milk is considered the optimal nutrition for infants as it provides additional attributes other than nutritional support for the infant and contributes to the mother's health as well. Although breastfeeding is the most natural modality to feed infants, nowadays, many mothers complain about breastfeeding difficulties. In addition to environmental factors that may influence lactation outcomes including maternal nutrition status, partner's support, stress, and latching ability of the infant, intrinsic factors such as maternal genetics may also affect the quantitative production and qualitative content of human milk. These genetic factors, which may largely affect the infant's growth and development, as well as the mother's breastfeeding experience, are the subject of the present review. We specifically describe genetic variations that were shown to affect quantitative human milk supply and/or its qualitative content. We further discuss possible implications and methods for diagnosis as well as treatment modalities. Although cases of nutrient-deficient human milk are considered rare, in some ethnic groups, genetic variations that affect human milk content are more abundant, and they should receive greater attention for diagnosis and treatment when necessary. From a future perspective, early genetic diagnosis should be directed to target and treat breastfeeding difficulties in real time.