Caenorhabditis elegans methionine/S-adenosylmethionine cycle activity is sensed and adjusted by a nuclear hormone receptor

Vitamin B12 is an essential micronutrient that functions in two metabolic pathways: the canonical propionate breakdown pathway and the methionine/S-adenosylmethionine (Met/SAM) cycle. In Caenorhabditis elegans, low vitamin B12, or genetic perturbation of the canonical propionate breakdown pathway results in propionate accumulation and the transcriptional activation of a propionate shunt pathway. This propionate-dependent mechanism requires nhr-10 and is referred to as ‘B12-mechanism-I’. Here, we report that vitamin B12 represses the expression of Met/SAM cycle genes by a propionate-independent mechanism we refer to as ‘B12-mechanism-II’. This mechanism is activated by perturbations in the Met/SAM cycle, genetically or due to low dietary vitamin B12. B12-mechanism-II requires nhr-114 to activate Met/SAM cycle gene expression, the vitamin B12 transporter, pmp-5, and adjust influx and efflux of the cycle by activating msra-1 and repressing cbs-1, respectively. Taken together, Met/SAM cycle activity is sensed and transcriptionally adjusted to be in a tight metabolic regime.


Sample-size estimation
• You should state whether an appropriate sample size was computed when the study was being designed • You should state the statistical method of sample size computation and any required assumptions • If no explicit power analysis was used, you should describe how you decided what sample (replicate) size (number) to use Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission:

Replicates
The RNA-seq dataset in Figure 1A, Figure 3C-D and Figure 6 is described in Bulcha et al., 2019. Briefly, 3000 animals each were used for two biological replicates. Differential gene expression between samples was analyzed using the standard output of BGI bioinformatics pipeline using EBSeq (Leng et al., 2013. EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics 29, 1035-43). Figure 3A-B, Figure 4D-E, Figure 5F-H and Figure 6 had a minimum of 400 animals per sample and were used for two biological replicates. For experiments in Figure 3A-B and Figure 4D-E, raw reads were processed on the DolphinNext RSEM v1.2.28 pipeline revision 7 (Yukselen et al., 2020. DolphinNext: a distributed data processing platform for high throughput genomics. BMC Genomics 21, 310). For experiments in Figure 5F-H and Figure 6, read counts for each gene were used in differential expression analysis by DEseq2 package in R 3.6.3 (Love et al., 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550).

RNA-seq datasets
For the EMS screen, at least 8,000 haploid genomes were screened.
Primary RNAi screens were performed two independent times with about 15-20 animals in each trial, and hits were further tested an additional three times with about 200 animals in each trial unless otherwise stated.
Body size measurements in Figure 4C and Figure S1C include approximately 100 animals per sample.
GC-MS data in Figure 5A and 5E had roughly 3000 animals per sample. • You should report how often each experiment was performed • You should include a definition of biological versus technical replication • The data obtained should be provided and sufficient information should be provided to indicate the number of independent biological and/or technical replicates • If you encountered any outliers, you should describe how these were handled • Criteria for exclusion/inclusion of data should be clearly stated • High-throughput sequence data should be uploaded before submission, with a private link for reviewers provided (these are available from both GEO and ArrayExpress) Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: The RNA-seq dataset in Figure 1A, Figure 3C-D and Figure 6 is described in Bulcha et al., 2019. Briefly, 3000 animals each were used for two biological replicates. The RNA-seq datasets in Figure 3A-B, Figure 4D-E, Figure 5F-H and Figure 6 had two biological replicates and a minimum of 400 animals per sample. Biological replicates are defined as independently growing, counting and collecting animals and obtaining the RNA from these animals all on different days. Body size measurements in Figure 4C and Figure S1C contain two biological replicates, with both replicates shown in the figures. Biological replicates are defined as independently growing animals, harvesting and imaging them, and computationally scoring the animal's body size all on different days.
GC-MS data as seen in Figure 5A and 5E have a minimum of four biological replicates. Biological replicates are defined as independently growing animals, harvesting them, extracting, derivatizing and performing the GC-MS all on different days.

Statistical reporting
• Statistical analysis methods should be described and justified • Raw data should be presented in figures whenever informative to do so (typically when N per group is less than 10) • For each experiment, you should identify the statistical tests used, exact values of N, definitions of center, methods of multiple test correction, and dispersion and precision measures (e.g., mean, median, SD, SEM, confidence intervals; and, for the major substantive results, a measure of effect size (e.g., Pearson's r, Cohen's d) • Report exact p-values wherever possible alongside the summary statistics and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.
Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: (For large datasets, or papers with a very large number of statistical tests, you may upload a single table file with tests, Ns, etc., with reference to sections in the manuscript.)

Group allocation
• Indicate how samples were allocated into experimental groups (in the case of clinical studies, please specify allocation to treatment method); if randomization was used, please also state if restricted randomization was applied • Indicate if masking was used during group allocation, data collection and/or data analysis Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: Additional data files ("source data") • We encourage you to upload relevant additional data files, such as numerical data that are represented as a graph in a figure, or as a summary table • Where provided, these should be in the most useful format, and they can be uploaded as "Source data" files linked to a main figure or  Figure 4D, Figure  5F and 5H, and Figure 6A-D bar graphs represent the mean.
Body size measurement as seen in Figure 4C and Figure S1C shows individual data points for each animal and the bar represents the mean with standard deviation shown in brackets. Statistical significance was determined by the Kruskal-Wallis test with post hoc comparison using Dunn's multiple comparison test.
GC-MS data as seen in Figure 5A and 5E is plotted as standard boxplots showing the median and interquartile range. Statistical significance determined by two-tailed t-test for 5A and one-way ANOVA with post-hoc Dunnett's T3 test for 5E.
There was no group allocation in this project. • Include model definition files including the full list of parameters used • Include code used for data analysis (e.g., R, MatLab) • Avoid stating that data files are "available upon request" Please indicate the figures or tables for which source data files have been provided: For body size analysis as seen in Figure 4C and Figure S1C a MATLAB script named wormFinder.m was used and the source code is provided here https://github.com/shiaway/wormFinder/blob/master/wormFinder.m