Pramitha · Advances in genetics 2021 · narrative review · n=?

Diverse role of phytic acid in plants and approaches to develop low-phytate grains to enhance bioavailability of micronutrients.

Cited 108 times in the scientific literature.

Level 5 - mechanism / opinion, no new human data

Narrative review of plant biology and bioengineering techniques with no primary human data.

PubMed 33641749 · doi:10.1016/bs.adgen.2020.11.003 · record verified 2026-08-27

What was done

This book chapter review synthesized evidence on the structure, biosynthesis, accumulation, and biological functions of phytic acid (PA) in food grains. It evaluated both its antinutritional impact (chelating iron and zinc in non-ruminants) and its beneficial roles in plant growth, stress tolerance, and antioxidant signaling, comparing conventional breeding, transgenic overexpression/RNA interference, and CRISPR-Cas9 multiplex genome editing approaches for developing low-PA crop varieties.

What was found

The abstract reports no quantitative values or statistical effect sizes. It qualitatively describes that conventional breeding to produce low-PA lines often results in adverse pleiotropic effects, including reduced grain yield, embryo abnormalities, and compromised seed quality. In contrast, endosperm-specific phytase overexpression, RNAi-mediated silencing of myo-inositol biosynthesis genes, and multiplex CRISPR-Cas9 gene editing are highlighted as methods capable of reducing phytic acid while preserving plant agronomic performance.

Why it matters

Phytic acid is a primary antinutrient causing dietary iron and zinc deficiencies globally. Identifying bioengineering strategies that reduce phytic acid without ruining crop yield provides a path toward biofortified staple grains.

Limits

The abstract provides no empirical data, sample sizes, or quantitative comparisons of micronutrient absorption changes in humans. As a narrative review, it lacks a systematic literature search methodology and relies primarily on preclinical plant physiology and genetic modeling.

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