Genomic imbalance modulates transposable element expression in maize

Genomic imbalance refers to the more severe phenotypic consequences of changing part of a chromosome compared with the whole genome set.Previous genome imbalance studies in maize have identified prevalent inverse modulation of genes on the unvaried chromosomes(trans)with both the addition or subtraction of chromosome arms.Transposable elements(TEs)comprise a substantial fraction of the genome,and their reaction to genomic imbalance is therefore of interest.Here,we analyzed TE expression using RNA-seq data of aneuploidy and ploidy series and found that most aneuploidies showed an inverse modulation of TEs,but reductions in monosomy and increases in disomy and trisomy were also common.By contrast,the ploidy series showed little TE modulation.The modulation of TEs and genes in the same experimental group were compared,and TEs showed greater modulation than genes,especially in disomy.Class Ⅰ and Ⅱ TEs were differentially modulated in most aneuploidies,and some superfamilies in each TE class also showed differential modulation.Finally,the significantly upregulated TEs in three disomies(TB-7Lb,TB9Lc,and TB-10L19)did not increase the proportion of adjacent gene expression when compared with non-differentially expressed TEs,indicating that modulations of TEs do not compound the effect on genes.These results suggest that the prevalent inverse TE modulation in aneuploidy results from stoichiometric upset of the regulatory machinery used by TEs,similar to the response of core genes to genomic imbalance.

Magnitude of modulation of gene expression in aneuploid maize depends on the extent of genomic imbalance

Aneuploidy has profound effects on an organism,typically more so than polyploidy,and the basis of this contrast is not fully understood.A dosage series of the maize long arm of chromosome 1(1L)was used to compa re relative global gene expression in diffe rent types and degrees of aneuploidy to gain insights into how the magnitude of genomic imbalance as well as hypoploidy affects global gene expression.While previously available methods require a selective examination of specific genes,RNA sequencing provides a whole-genome view of gene expression in aneuploids.Most studies of global aneuploidy effects have concentrated on individual types of aneuploids because multiple dose aneuploidies of the same genomic region are difficult to produce in most model genetic organisms.The genetic toolkit of maize allows the examination of multiple ploidies and 1-4 doses of chromosome arms.Thus,a detailed examination of expression changes both on the varied chromosome arms and elsewhere in the genome is possible,in both hypoploids and hyperploids,compared with euploid controls.Previous studies observed the inverse trans effect,in which genes not varied in DNA dosage were expressed in a negative relationship to the varied chromosomal region.This response was also the major type of changes found globally in this study.Many genes varied in dosage showed proportional expression changes,though some were seen to be partly or fully dosage compensated.It was also found that the effects of aneuploidy were progressive,with more severe aneuploids producing effects of greater magnitude.

Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride

It is well-known that the atomic-scale and nano-scale configuration of dopants can play a crucial role in determining the electronic properties of materials.However,predicting such effects is challenging due to the large range of atomic configurations that are possible.Here,we present a case study of how deep learning algorithms can enable bandgap prediction in hybridized boron–nitrogen graphene with arbitrary supercell configurations.A material descriptor that enables correlation of structure and bandgap was developed for convolutional neural networks.Bandgaps calculated by ab initio calculations,and corresponding structures,were used as training datasets.The trained networks were then used to predict bandgaps of systems with various configurations.For 4×4 and 5×5 supercells they accurately predict bandgaps,with a R^(2) of >90% and root-mean-square error of~0.1 eV.The transfer learning was performed by leveraging data generated from small supercells to improve the prediction accuracy for 6×6 supercells.This work will pave a route to future investigation of configurationally hybridized graphene and other 2D materials.Moreover,given the ubiquitous existence of configurations in materials,this work may stimulate interest in applying deep learning algorithms for the configurational design of materials across different length scales.