Back

Folate deficiency disrupts key metabolic transitions within the developing neural ectoderm

Dias, N.; Lewinsohn, D. P.; Colgan, W. N.; Wang, M.; Kijima, Y.; Villagrana, J.; Hou, T.-C. J.; Gowri, G.; Chau, A.; Aktas, T.; Sumigray, K.; Weissman, J. S.; Koblan, L. W.; Wagner, A.; Smith, Z. D.

2026-09-22 genomics
10.64898/2026.09.18.752622 bioRxiv
Show abstract

Despite long-standing epidemiological associations, the mechanism linking folate availability to gestational neural tube defects remains unclear, partly because measuring and interpreting metabolic activity in dynamic biological systems remains challenging. Here, we apply a deep-learning-based graph-guided variational autoencoder (MeRN; Metabolic Representation Network) to infer single-cell metabolic activity and states from scRNA-seq data of mouse embryogenesis. By analyzing folate-deficient embryogenesis from E7.0 to E9.0, we identify a transient state within the nascent neural lineage that is acutely sensitive to folate availability, leading to an interconnected disruption between key bioenergetic pathways and de novo purine biosynthesis. Moreover, metabolically induced growth defects lead to permanent morphological disruptions along the dorsal-ventral axis, which we confirm by generating whole-embryo fate maps using a prime-editing-based lineage recorder (PEtracer). Collectively, our results establish a highly scalable framework for interpreting dynamic changes in embryonic metabolism and elucidating the mechanistic bases underlying environmentally linked congenital disorders.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 7%
21.8%
Similar papers in this journal
2
Nature
○ 645 papers in training set
Top 2%
7.8%
Similar papers in this journal
3
Cell
○ 431 papers in training set
Top 0.6%
7.8%
Similar papers in this journal
4
Nature Genetics
○ 286 papers in training set
Top 1%
6.7%
Similar papers in this journal
5
Science
● 477 papers in training set
Top 1%
6.2%
Similar papers in this journal
50% of probability mass above
6
eLife
● 5828 papers in training set
Top 22%
5.4%
7
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 11%
4.8%
8
Science Advances
● 1243 papers in training set
Top 7%
4.3%
9
Cell Genomics
○ 172 papers in training set
Top 1%
3.4%
10
Nature Structural & Molecular Biology
236 papers in training set
Top 1%
3.2%
11
Molecular Cell
○ 350 papers in training set
Top 2%
2.6%
12
Cell Reports
○ 1498 papers in training set
Top 19%
1.7%
13
Nature Medicine
○ 125 papers in training set
Top 2%
1.4%
14
Nature Biotechnology
○ 172 papers in training set
Top 3%
1.4%
15
Genome Research
● 468 papers in training set
Top 4%
1.3%
16
Nature Cell Biology
○ 118 papers in training set
Top 2%
1.3%
17
Communications Biology
○ 993 papers in training set
Top 22%
1.1%
18
PLOS Biology
● 486 papers in training set
Top 8%
1.1%
19
Neuron
○ 337 papers in training set
Top 4%
1.1%
20
Nature Neuroscience
○ 252 papers in training set
Top 4%
1.1%
21
Genome Biology
○ 637 papers in training set
Top 8%
1.0%
22
Developmental Cell
○ 196 papers in training set
Top 4%
0.8%
23
Scientific Reports
○ 3612 papers in training set
Top 73%
0.8%
24
Nature Machine Intelligence
○ 70 papers in training set
Top 3%
0.8%
25
Stem Cell Reports
○ 130 papers in training set
Top 2%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.