Treating the placenta to prevent adverse effects of gestational hypoxia on fetal brain development

Some neuropsychiatric disease, including schizophrenia, may originate during prenatal development, following periods of gestational hypoxia and placental oxidative stress. Here we investigated if gestational hypoxia promotes damaging secretions from the placenta that affect fetal development and whether a mitochondria-targeted antioxidant MitoQ might prevent this. Gestational hypoxia caused low birth-weight and changes in young adult offspring brain, mimicking those in human neuropsychiatric disease. Exposure of cultured neurons to fetal plasma or to secretions from the placenta or from model trophoblast barriers that had been exposed to altered oxygenation caused similar morphological changes. The secretions and plasma contained altered microRNAs whose targets were linked with changes in gene expression in the fetal brain and with human schizophrenia loci. Molecular and morphological changes in vivo and in vitro were prevented by a single dose of MitoQ bound to nanoparticles, which were shown to localise and prevent oxidative stress in the placenta but not in the fetus. We suggest the possibility of developing preventative treatments that target the placenta and not the fetus to reduce risk of psychiatric disease in later life.


number of neurons (Supplementary
or number of astrocytes (Supplementary Fig   S2h). Furthermore, MitoQ-NPs or unloaded NPs had no direct effect on cell viability of fibroblast ( Supplementary Fig S2i). Neither MitoQ-NPs nor Gap26-NPs affected DNA integrity in fibroblasts ( Supplementary Fig S2j). Direct application of Gap26-NPs, but not MitoQ-NPs, reduced the amount of DNA damage in fibroblasts caused by direct exposure to benzoquinone and hydroquinone ( Supplementary Fig S2j), suggesting that some of the DNA damage might be caused by bystander signalling through connexin channels within the fibroblast cultures 42 . Furthermore, the BeWo barriers showed high integrity ( Supplementary   Fig S2k), confirming that NP-bound drugs were unable to pass through gaps between the cells to directly affect fibroblast and cortical cultures. Scientific). In brief, the sample was loaded onto an XBridge BEH C18 Column (130 Å, 3.5 µm, 2.1 mm x 150 mm, Waters, UK) in buffer A and peptides eluted with an increasing gradient of buffer B (20 mM Ammonium Hydroxide in acetonitrile, pH 10) from 0-95% over 60 minutes. The resulting fractions were evaporated to dryness and resuspended in 1% formic acid prior to analysis by nano-LC MS/MS using an Orbitrap Fusion Tribrid mass spectrometer (Thermo Scientific).

Nano-LC Mass Spectrometry
High pH RP fractions were further fractionated using an Ultimate 3000 nanoHPLC system in line with an Orbitrap Fusion Tribrid mass spectrometer (Thermo Scientific). In brief, peptides in 1% (vol/vol) formic acid were injected onto an Acclaim PepMap C18 nano-trap column (Thermo Scientific). After washing with 0.5% (vol/vol) acetonitrile 0.1% (vol/vol) formic acid peptides were resolved on a 250 mm × 75 μm Acclaim PepMap C18 reverse phase analytical column (Thermo Scientific) over a 150 min organic gradient, using 7 gradient segments (1-6% solvent B over 1 min, 6-15% B over 58 min, 15-32% B over 58 min, 32-40% B over 5 min., 40-90% B over 1 min, held at 90% B for 6 min and then reduced to 1% B over 1 min) with a flow rate of 300 nL min −1 . Solvent A was 0.1% formic acid and Solvent B was aqueous 80% acetonitrile in 0.1% formic acid. Peptides were ionized by nano-electrospray ionization at 2.0 kV using a stainless steel emitter with an internal diameter of 30 μm (Thermo Scientific) and a capillary temperature of 275°C.
All spectra were acquired using an Orbitrap Fusion Tribrid mass spectrometer controlled by Xcalibur 2.0 software (Thermo Scientific) and operated in data-dependent acquisition mode using an SPS-MS3 workflow. FTMS1 spectra were collected at a resolution of 120,000, with an automatic gain control (AGC) target of 200,000 and a max injection time of 50 ms. The Top N most intense ions were selected for MS/MS. Precursors were filtered according to charge state (to include charge states 2-7) and with monoisotopic precursor selection.
The MS2 precursors were isolated with a quadrupole mass filter set to a width of 1.2 m/z. ITMS2 spectra were collected with an AGC target of 5,000, max injection time of 120 ms and CID collision energy of 35%.
For FTMS3 analysis, the Orbitrap was operated at 60,000 resolution with an AGC target of 50,000 and a max injection time of 120 ms. Precursors were fragmented by high energy collision dissociation (HCD) at a normalised collision energy of 55% to ensure maximal TMT reporter ion yield. Synchronous Precursor Selection (SPS) was enabled to include up to 5 MS2 fragment ions in the FTMS3 scan.

Data Analysis
The raw data files were processed and quantified using Proteome Discoverer software v1.4 (Thermo Scientific) and searched against the UniProt Rat database using the SEQUEST algorithm. Peptide precursor mass tolerance was set at 10 ppm, and MS/MS tolerance was set at 0.6 Da. Search criteria included oxidation of methionine (+15.9949) as a variable modification and carbamidomethylation of cysteine (+57.0214) and the addition of the TMT mass tag (+229.163) to peptide N-termini and lysine as fixed modifications. Searches were performed with full tryptic digestion and a maximum of 1 missed cleavage was allowed. The reverse database search option was enabled and all peptide data was filtered to satisfy false discovery rate (FDR) of 5%.

Analysis of NanoString data
NanoString nCounter data consist of discrete sequence counts as a measure of miRNA expression within each sample. These counts are similar to counts from other high-throughput methods such as RNA sequencing, where discrete statistical models such as the Poisson or negative binomial distributions may be used to estimate differences between samples 99,100 . Consequently, differential expression prediction methods originally designed for RNA sequencing data may also be used with NanoString data 101 . Following the procedures outlined in 101 , we developed the following pipeline to assess DE-miRNA between samples: First, we converted each raw NanoString output file into a list of counts. Next we merged the counts for the replicates in each sample into a table with one column per replicate. To compare two samples, we merged the corresponding tables into a single table that we then passed into our differential expression analysis pipeline.

Differential expression analysis of NanoString and RNA sequencing data
RUVSeq was used to remove unwanted variation 102 and then edgeR 100 to predict differentially-expressed RNAs (miRNAs in the case of NanoString data, mRNAs in the case of RNA sequencing data). We selected edgeR for our pipeline as it may be more sensitive than DESeq 100,103,104 and it facilitates the use of RUVSeq 102 . We used RUVSeq to eliminate, as far as possible, variation from sources unrelated to the treatment groups, such as differences in blood plasma collection, centrifugation, enrichment, and RNA purification. We expected no differential expression between replicates within a treatment group and few differentially-expressed RNAs between treatment groups, so the relative log-expression should be consistent across all samples. In addition, the largest component of variation in the data should reflect RNAs that are differentially expressed between treatment groups. For each comparison, we first used RUVSeq to adjust the counts to account for unwanted variation, then used the adjusted counts with edgeR's generalized linear model to yield the final predictions.
To mitigate possible false-positives, miRNAs were classed as significant differentially secreted miRNAs if p < 0.05, if count ≥ 10 for at least one of the compared conditions (except for fetal plasma miRNAs were counts were generally very low) and if there was an up or down regulation of at least 25%.

Analysis of miRNA-mRNA correlation
Enrichment analysis of the RNA sequencing data for predicted targets (derived from TargetScanHuman 83 ) of significant miRNAs was performed in R/Bioconductor using the Fisher's exact test and investigating enrichment only. Correlation of abundance changes of significant miRNAs with abundance changes of significant mRNAs was analysed with the miRComb 105 package for R/Bioconductor, using Spearman correlation and Benjamini-Hochberg adjustment for multiple comparisons. no NPs). Extracted placentas were additionally exposed to 21% (top) or 2% oxygen (bottom) ex vivo. e, Levels of total amino acids in medium conditioned by rat placentas exposed to maternal normoxia, M(21%), or hypoxia, M(11%), following maternal administration of saline or MitoQ-NPs. To discover any potentially masked effects of amino acid secretion from the placenta, medium diluted 1:2 with PBS was compared with undiluted medium. All measurements are means ± s.e.m.

Supplementary Figure S5 | Analysis of fetal neurons after indirect exposure to hypoxia
and drug delivery NPs. Cortical cultures analysed following exposure to media conditioned by BeWo barriers (a,c,e,g) or rat placenta (b,d,f,h). BeWo barriers had been exposed to 21%, 8% or 2-12% oxygen. Placentas had been exposed to maternal normoxia, shown, along with effects of conditioned medium from directly exposed BeWo barriers.
Interrupted arrow indicates potential interaction between secreted miRNAs and transcriptome changes in the fetal brain.

Supplementary Tables
Supplementary Table S1 | MitoQ concentration in conditioned media. Concentration of soluble MitoQ was measured in conditioned medium collected from below BeWo barriers that had been exposed to 2-12% oxygen with or without application of 0.5 μM MitoQ-NP to the top of the barrier. MitoQ was also quantified in medium conditioned by placentas collected from dams exposed to gestational hypoxia combined with injection of MitoQ-NP (0.5 μM blood concentration) or saline. MitoQ concentration is displayed in pM and as % of original exposure. All measurements are means ± s.e.m. Significance was calculated using student's t-test.

Supplementary Table S2 | Amino acid concentrations in hypoxia-conditioned media
from rat placenta. Placentas were exposed to maternal hypoxia combined with atmospheric oxygen conditions or 2% oxygen for 24 h ex vivo. Either saline or MitoQ-NP were maternally injected at the start of in vivo exposure. Amino acid concentrations in μmol/L (s.e.m.) were measured in the surrounding conditioned medium. # p < 0.05, ## p < 0.01; significant compared to saline condition of the respective oxygen condition.

Supplementary Table S3 | Amino acid concentrations in hypoxia-conditioned media
from rat placenta. Placenta were exposed to maternal hypoxia combined with 8% oxygen for 24 h ex vivo. Either saline or MitoQ-NP were maternally injected at the start of in vivo exposure. Amino acid concentrations in μmol/L (s.e.m.) were measured in the surrounding conditioned medium. No significant differences were detected.

Supplementary Table S4 | Amino acid concentrations in hypoxia-conditioned media
from rat placenta. Placenta were exposed to maternal hypoxia combined with 21% oxygen for 24 h ex vivo. Either saline or MitoQ-NP were maternally injected at the start of in vivo exposure. Amino acid concentrations in μmol/L (s.e.m.) were measured in the surrounding conditioned medium. Culture medium was diluted with PBS prior to exposure to estimate the contribution of placenta-secreted amino acids compared to those already present in the culture medium. *p < 0.05; significant compared to normoxia saline condition.