Biomarkers during pregnancy point to early autism diagnosis

NIEHS-funded researchers identified patterns in maternal autoantibodies, immune proteins that attack a person’s own proteins or tissues, that were highly associated with the diagnosis and severity of autism spectrum disorder (ASD). This is the first study to use machine learning to identify with 100% accuracy patterns specific to maternal autoantibody-related ASD (MAR ASD), which may serve as potential biomarkers of ASD risk. MAR ASD accounts for around 20% of all autism cases.

The team previously identified maternal autoantibodies against eight key proteins related to ASD. In the current study, the researchers sought to develop an accurate test for reactivity patterns against those proteins that could predict ASD. They analyzed plasma samples from 450 mothers of children with autism and 342 mothers of typically developing children from the Childhood Autism Risks from Genetics and Environment (CHARGE) study.

To train a machine learning algorithm, they identified autoantibody reactivity patterns associated with ASD. Next, they tested the accuracy of the patterns and built a high-precision model to predict ASD outcome. The machine learning program analyzed nearly 10,000 patterns and identified three top patterns associated with ASD.

Autoantibody reactivity to a single protein did not correlate with ASD diagnosis. Instead, reactivity to a combination of two or more proteins was needed for an association with ASD. For example, mothers with autoantibodies to CRIMP1 and GDA, two proteins involved in brain cell development, had 31 times greater odds of having a child with autism than the general population. In fact, autoantibodies to CRIMP1 combined with any of the top proteins increased the likelihood of a higher ASD severity score. According to the authors, this approach may aid early ASD detection, which can help facilitate early interventions.

Citation: Ramirez-Celis A, Becker M, Nuno M, Schauer J, Aghaeepour N, Van de Water J. 2021. Risk assessment analysis for maternal autoantibody-related autism (MAR-ASD): a subtype of autism. Mol Psychiatry; doi: 10.1038/s41380-020-00998-8. [Online 22 Jan 2021].

New graphene nanochannel filters hold promise for contaminant clean-up

A new strategy to design nanomaterials to better filter contaminants from water overcomes previous limitations, according to a new NIEHS-funded study.

Graphene oxide has already been used as a highly selective membrane. However, nanosheets typically assemble in horizontal layers, creating long narrow channels that liquid must pass through. To improve the usefulness of graphene oxide nanosheets for filtering contaminants from liquid, the researchers modified how the sheets assemble to create shorter vertical nanochannels. This approach reduces the distance water must pass through while optimizing the amount of contact it has with the membrane.

The researchers found that by stacking nanosheets on a stretched elastic substrate and releasing the tension, the graphene sheets then wrinkled into hundreds of sharp peaks and valleys. Because the sheets and the substrate both carried a negative electrical charge, the researchers determined adding zirconium as a positive ion helped optimize the membrane. Then the researchers used epoxy to encase and stabilize the wrinkled structure and trimmed away the top and bottom of the zig-zag film to create open channels. Their technique increased the active area of the membrane 300-fold compared to simply tilting a flat nanosheet by 90 degrees.

In proof-of-concept tests, the team demonstrated that water vapor could easily pass through the vertically aligned zirconium-graphene membranes, while the organic molecules hexane and 2-propanol could not. They also showed that their strategy successfully retained molecular selectivity while remaining stable at high temperatures and resistant to swelling, all important factors for scaling up the approach.

Citation: Liu M, Weston PJ, Hurt RH. 2021. Controlling nanochannel orientation and dimensions in graphene-based nanofluidic membranes. Nat Commun 12(1):507.

New method predicts prenatal air pollution exposure

In a proof-of-concept study, NIEHS grantees developed an approach that uses DNA methylation signatures to predict prenatal exposure to environmental contaminants that may harm health.

Using air pollution as a case-study, the researchers sought to identify a biomarker that would distinguish newborns with higher risk from a toxic prenatal exposure to nitrogen dioxide (NO2), polycyclic aromatic hydrocarbons (PAHs), and particulate matter (PM2.5). Specifically, they looked for DNA methylation signatures in umbilical cord blood from two long-term New York City cohorts and analyzed those signatures in relation to modeled exposure to air pollutants during pregnancy.

In a screening step, the researchers identified prenatal exposures that could potentially be predicted by measured changes in DNA methylation and specific DNA regions that could be used to make predictions. Their analysis revealed that alterations to 500 top DNA sites, called CpGs, reliably identified pollutant exposures. The researchers used these sites to build predictive models.

The team ranked and selected the most informative CpG sites. Then they tested how well the top sites predicted exposure to each pollutant and how many CpG sites were needed to make the best prediction. Cord blood DNA methylation reliably predicted high versus low exposure to PM2.5 averaged across the entire pregnancy. The method also predicted exposure to NO2 averaged across the third trimester and the entire pregnancy. It did not work as well for predicting exposure to PAHs.

According to the authors, the simple approach is generalizable to other exposures, which may help identify children at risk of exposure-related developmental disorders to facilitate earlier interventions.

Citation: Wang Y, Perera F, Guo J, Riley KW, Durham T, Ross Z, Ananth CV, Baccarelli A, Wang S, Herbstman JB. 2021. A methodological pipeline to generate an epigenetic marker of prenatal exposure to air pollution indicators. Epigenetics; doi: 10.1080/15592294.2021.1872926. [Online 19 Jan 2021].

PFAS exposure increases cardiometabolic risk in children

Early life exposure to certain per- and polyfluoroalkyl substances (PFAS) was associated with higher risk for cardiometabolic disease in children, according to new NIEHS-funded research.

The researchers measured levels of four PFAS chemicals — PFOA, PFHxS, PFOS, PFNO — in serum from 221 mother–child pairs collected during pregnancy; at birth; and at ages 3, 8, and 12 years. Then they assessed cardiometabolic risk factors at 12 years using physical examinations and fasting serum biomarkers. They calculated two continuous cardiometabolic risk scores. The first was based on traditional cardiometabolic risk factors, including glucose, insulin, and waist circumference. The second was based on novel cardiometabolic risk factors, including the homeostatic model assessment for insulin resistance (HOMA-IR), adiponectin to leptin hormones ratio, and a cross-sectional area of fat inside the abdominal cavity.

Prenatal PFOA and PFHxS concentrations and levels at birth were positively associated with higher cardiometabolic risk scores at age 12 years. These positive associations were based on traditional risk factors, as well as the HOMA-IR index and adiponectin to leptin ratio. PFOA levels measured across childhood were also associated with higher high-density lipoprotein levels. Childhood PFHxS was associated with the cross-sectional area of fat in the abdomen. Early life PFOS and PFNA levels were generally not associated with either traditional or novel cardiometabolic risk scores at age 12 years.

According to the authors, these findings suggest future studies should consider using continuous cardiometabolic risk summary scores and evaluate the effect of PFAS mixtures on cardiometabolic risk.

Citation: Li N, Liu Y, Papandonatos GD, Calafat AM, Eaton CB, Kelsey KT, Cecil KM, Kalkwarf HJ, Yolton K, Lanphear BP, Chen A, Braun JM. 2021. Gestational and childhood exposure to per- and polyfluoroalkyl substances and cardiometabolic risk at age 12 years. Environ Int 147:106344.