DOI: 10.5061/dryad.rn8pk0pq2
title: Co-development of gut microbial metabolism and visual neural circuitry over human infancy

description: Infancy is a time of elevated neuroplasticity supporting rapid brain and
 sensory development. The gut microbiome, also undergoing extensive
 developmental changes in early life, may influence brain development
 through metabolism of neuroactive compounds. Here, we leverage
 longitudinal data from 194 infants across the first 18 months of life to
 show that microbial genes encoding enzymes that metabolize molecules
 playing a key role in modulating early neuroplasticity are associated with
 visual cortical neurodevelopment, measured by the Visual-Evoked Potential
 (VEP). Neuroactive compounds included neurotransmitters GABA and
 glutamate, the amino acid tryptophan, and short-chain fatty acids involved
 in myelination, including acetate and butyrate. Microbial gene sets around
 4 months of age were strongly associated with the VEP from around 9 to 14
 months of age and showed more associations than concurrently measured gene
 sets, suggesting microbial metabolism in early life may affect subsequent
 neural plasticity and development.
description: Stool Samples We collected stool and the
 visual evoked potential (VEP) in a longitudinal cohort of 194 children in
 South Africa during the first 18 months of life (visit 1, N = 119, age 3.7
 ± 0.9 months, visit 2, N = 144, age 8.6 ± 1.5 months, and visit 3, N =
 130, age 14.1 ± 1.0 months). Stool samples (n = 315)
 were collected in the clinic by the research assistant
 directly<br>from the diaper, transferred to Zymo DNA/RNA ShieldTM
 Fecal collection Tubes (#R1101, Zymo Research Corp., Irvine, USA), and
 immediately frozen at −80 ˚C. Stool samples were not collected if the
 participant had taken antibiotics within the 2 weeks prior to
 sampling. DNA extraction was performed at Medical
 Microbiology, University of Cape Town, South Africa, from stool samples
 collected in DNA/RNA Shield Fecal collection tube using the Zymo Research
 Fecal DNA MiniPrep kit (# D4300, Zymo Research Corp., Irvine, USA)
 following the manufacturer’s protocol. To assess the extraction process’s
 quality, ZymoBIOMICS Microbial Community Standards (#D6300 and #D6310,
 Zymo Research Corp., Irvine, USA) were incorporated and subjected to the
 same process as the stool samples. The DNA yield and purity were
 determined using the NanoDrop ND−1000 (Nanodrop Technologies Inc.
 Wilmington, USA). Shotgun metagenomic sequencing was
 performed on all samples at the Integrated Microbiome Research Resource
 (IMR, Dalhousie University, NS, Canada). A pooled library (max 96 samples
 per run) was prepared using the Illumina Nextera Flex Kit for MiSeq and
 NextSeq from 1 ng of each sample. Samples were then pooled onto a plate
 and sequenced on the Illumina NextSeq 2000 platform using 150 + 150 bp
 paired-end P3 cells, generating 24 M million raw reads and 3.6 Gb of
 sequence per sample (43). VEP
 Electroencephalography (EEG) data were acquired from infants
 while they were seated in their caregiver’s lap in a dimly-lit, quiet room
 using a 128-channel high-density HydroCel Geodesic Sensor Net (EGI,
 Eugene, OR), amplified with a NetAmps 400 high-input amplifier, and
 recorded via an Electrical Geodesics, Inc. (EGI, Eugene, OR) system with a
 1,000 Hz sampling rate. EEG data were online referenced to the vertex
 (channel Cz) using EGI Netstation software. Impedances were kept below 100
 KΩ in accordance with the impedance capabilities of the high-impedance
 amplifiers. Geodesic Sensor Nets with modified tall pedestals designed to
 improve the inclusion of infants with thick/curly/tall hair were used as
 needed across participants (41). Shea Moisture leave-in castor oil
 conditioner was applied to hair across the scalp prior to net placement to
 improve both impedances and participant comfort (41). This leave-in
 conditioner contains insulating ingredients; hence, there is no risk of
 electrical bridging, and it has not been found to disrupt the EEG signal
 during testing (unpublished data). Conditioning hair in this way allows
 for nets to lay closer to the scalp for curly/coily hair types and makes
 for more comfortable net removal at the end of testing.
 The visual-evoked potential (VEP) task was presented using Eprime
 3.0 software (Psychology Software Tools, Pittsburgh, PA) on a Lenovo
 desktop computer with an external monitor 19.5 inches on the diagonal
 facing the infant (with a monitor approxi mately 65 cm away from the
 infant). A standard phase-reversal VEP was induced with a black and white
 checkerboard (1 × 1 cm squares within the board) stimulus that alternated
 presentation (black squares became white, white squares became black)
 every 500 ms for a total of 100 trials. Participants looking were
 monitored by video and by an assistant throughout data collection. If the
 participant looked away during the VEP task, the task was rerun.
 VEP data were exported from native Netstation .mff format to .raw
 format and then pre-processed using the HAPPE + ER pipeline within HAPPE
 v3.3 software, an automated open-source EEG processing software validated
 for infant data (42). A subset of the 128 channels was selected for
 pre-processing that excluded the rim electrodes, as these are typically
 artifact-laden (channels excluded from pre-processing included in Table
 S1). The HAPPE pre-processing pipeline was run with user-selected
 specifications outlined in Table S1. Pre-processed VEP data were
 considered usable and moved forward to VEP extraction if HAPPE
 pre-processing ran successfully, at least 15 trials were retained
 following bad trial rejection, and at least one good channel was kept
 within the visual ROI. Note that channels marked badly during
 pre-processing had their data interpolated as part of standard
 preprocessing pipelines for ERPs (42). Interpolated
 channels were included in analyses here as is typically done in develop
 mental samples, and given the low overall rates of interpolation present
 (e.g., an average of between 4 and 5 of 5 possible good channels in the
 region of interest were retained at each visit time point). Visual-evoked
 potentials (VEPs) VEP waveforms were extracted and quantified using the
 HAPPE + ER v3.3 GenerateERPs script (42). Electrodes in the occipital
 region were selected as a region of interest (i.e., E70, E71, E75, E76,
 and E83). The VEP waveform has three main components to be quantified: a
 negative N1 peak, a positive P1 peak, and a negative N2 peak. The windows
 for selecting the calculated features were based on preliminary
 visualizations of the waveforms at each visit, such that the selected
 windows would capture the most component peaks across all subjects. Due to
 normative maturation of the waveforms as infants age, one set of
 user-specified windows for calculating component features was used for
 visits 1 and 2, and another was used for visit 3. For visits 1 and 2, the
 window for calculating features for the N1 component was 40–100 ms, 75–175
 ms for the P1 component, and 100–325 ms for the N2 component. For visit 3,
 the window for calculating features for the N1 component was 35–80 ms,
 75–130 ms for the P1 component, and 100–275 ms for the N2 component. All
 VEPs were visually inspected to ensure that the automatically extracted
 values were correct and were adjusted if observable peaks occurred outside
 the automated window bounds. These visual checks ensure that peak
 amplitudes and latencies capture individual variability within and across
 visits. Participants were considered to have failed this visual inspection
 and were subsequently removed from the data set if their VEP did not
 produce three discernible peaks. HAPPE + ER parameters used in extracting
 the ERPs are summarized in Table S2. To correct for the potential
 influence of earlier components on later components, corrected amplitudes
 and latencies were calculated and used in all analyses. Specifically, the
 P1 amplitude was corrected for the N1 amplitude (corrected P1 amplitude =
 P1 N1 amplitude), the P1 latency was corrected for the N1 latency
 (corrected P1 latency = P1 N1 latency), the N2 amplitude was corrected for
 the P1 amplitude (corrected N2 amplitude = N2 P1 amplitude), and the N2
 latency was corrected for the P1 latency (corrected N2 latency = N2 P1
 latency). VEP waveforms of the included participants by
 time point are included in Fig. 2A. Ninety-seven infants provided usable
 VEP data at visit 1, 130 infants provided usable VEP data at visit 2, and
 131 infants provided usable VEP data at visit 3. For included
 participants, EEG data quality metrics are summarized in Table S3. t-tests
 for data quality metrics (i.e., number of trials collected, number of
 trials retained, number of channels<br>retained in the ROI, and
 Pearson’s r for data pre- vs. post-wavelet thresholding at 5, 8, 12, and
 20 Hz) were run between each visit combination (i.e., visit 1 vs. visit 2,
 visit 1 vs. visit 3, and visit 2 vs. visit 3). For visits that differed in
 data quality, follow-up post hoc correlations were run for the data
 quality measure with each VEP feature at each visit in the t-test. In no
 case did the data quality metric relate to VEP features at multiple
 visits, making it highly unlikely the data quality difference contributed
 to results.
description: # Co-development of gut microbial metabolism and visual neural circuitry
 over human infancy Dataset DOI:
 [10.5061/dryad.rn8pk0pq2](10.5061/dryad.rn8pk0pq2) ## Description of the
 data and file structure Data collected for *Codevelopment of gut microbial
 metabolism and visual neural circuitry over human infancy,* to be
 published in mBio
 ([https://doi.org/10.1128/mbio.00835-25](https://doi.org/10.1128/mbio.00835-25), preprint link: [https://www.medrxiv.org/content/10.1101/2024.07.24.24310884](https://www.medrxiv.org/content/10.1101/2024.07.24.24310884)). ## Files and variables ### Subject metadata and VEP features The `allmeta.csv` file contains subject- and sample-specific metadata for all subjects and visits included in the study. There is one header row and 487 data rows. Columns names and contents are described below. Cells where data is missing contain no text. The following columns are included: * `subject_id`: type `String` - a unique identifier for the child participating in the study. N= * `visit`: type `String` - Visit number, one of "v1", "v2", or "v3". Visit 1 corresponds to ~3mo of age, visit 2 ~6mo, visit 3 ~12mo * `age_vep_weeks`: type `Float64` - age in weeks when subjects underwent EEG evaluation.For visits in which both stool and VEP were collected, ages for each should be within 2 weeks of one another (months = weeks / 52 * 12) * `stool_age`: type `Float64` - age in months when stool sample was collected. For visits in which both stool and VEP were collected, ages for each should be within 2 weeks of one another (weeks = months 12 * 52) * biospecimen: type `String` - unique ID for stool sample collected at given visit. * seqprep: type `String` - unique ID for preparation sent to sequencing facility. * S_well: Well ID for sequencing preparation (in 96 well plates) * `peak_(amp|latency)_(N1|P1|N2)`: type - `Float64` 6 columns representing each extracted VEP feature (amplitude and latency) for each peak (N1, P1, and N2). Amplitudes are measured in millivolts (mV) and latencies are measures in milliseconds (ms). * `n_trials`: type `Integer` - number of VEP trials retained for a given visit. * `cohort_*`: type `Bool` - filtering columns for each cohort comparison. Values may be `true` or `false`. Cohorts "v1", "v2", and "v3" compare concurrently collected stool and VEP. Cohorts comparing stool from one visit to VEP from a different visit contain 2 visit identifiers, and end with `_stool` or `_vep`. For example, filtering on rows where `cohort_v1v2_stool` is `true` will select rows that correspond to visit 1 stool samples included when visit 1 stool is compared to visit 2 VEP. To get the corresponding rows for visit 2 VEP, you should filter on `cohort_v1v2_vep`. When filtering on any one `cohort_*` column, the `subject` column should contain all unique values. * `peak_(amp|latency)_(P1|N2)_corrected`: type - `Float64` - values offset from the previous peak. `P1_corrected` is `P1` - `N1`, and `N2_corrected` is `N2` - `P1`. ### Microbial genes The `bonham_margolis_mbio_allunirefs.csv.gz` file contains a gzip-compressed CSV file with the abundance of UniRef90 values in each sample in long format. Contains one header row and 46,226,678 data rows. The following columns are included: * `feature`: type `String` - UniRef90 identifier for a given gene function. * `abundance`: type `Float64` - abundance of a uniref in reads per kilobase per million reads (RPKM). * `sample`: corresponds to `seqprep` from the metadata table. ### Taxonomic profiles While not used in the main figures for this manuscript, taxonomic profiles for these samples can be found in the related dataset: [https://doi.org/10.5061/dryad.dbrv15f9z](https://doi.org/10.5061/dryad.dbrv15f9z) ## Code/software All analysis code written in julia and generated figures can be found on github at [https://github.com/Klepac-Ceraj-Lab/EEGMicrobiome/](https://github.com/Klepac-Ceraj-Lab/EEGMicrobiome/) and archived on github at [https://doi.org/10.5281/zenodo.15723691](https://doi.org/10.5281/zenodo.15723691). Open source packages used for the analysis are documented in the `Project.toml` and `Manifest.toml` file found in the repository. ## Human subjects data Study procedures were offered in English or Xhosa depending on the language preference of the mother. This study was approved by the relevant university Health Research Ethics Committees (University of Cape Town study number: 666/2021). Informed consent was collected from mothers on behalf of themselves and their infants. All data is de-identified and does not contain any personally identifiable information.