Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.08.01.668223

Early-life Exposure to Arsenic Primes the Offspring to Increased Asthma Risk: Transcriptome and Epigenome Analysis

Sun, Y.·Watfa, M.·Sun, Q.·Rajasundaram, D.·Schlegel, B.·Yeung-Luk, B.·Pulczinski, J.·Park, B.·Barchowsky, A.·Mitzner, W.·Biswal, S.·Tang, W. Y.
View original source ↗Export metadata ↓

Abstract

Inorganic arsenic (iAs) in drinking water is a global health concern. This study tests whether maternal exposure to iAs in drinking water at the WHO provisional level (10{micro}g/L) increases offspring asthma risk via epigenetic reprogramming. F1 mice prenatally exposed to iAs were analyzed at 5 months for blood transcriptome and methylome changes and challenged with house allergens before lung function testing. Prenatal iAs exposure led to increased airway hyperresponsiveness (AHR) and altered inflammation gene expression and DNA methylation changes. Notably, miR-101c was epigenetically reprogrammed early in development, with persistent downregulation in both target (fetal and adult lungs) and surrogate (amniotic fluid and blood) tissues. These changes correlated with increased allergic AHR and TGF{beta} pathway dysregulation. Findings suggest that maternal iAs exposure primes offspring for asthma risk through epigenetic alterations and may inform risk assessment and biomarker development in affected communities. KEY FINDINGSO_LIIn utero exposure to 10 part per billion (or 10 {micro}g/L, the current WHO and EPA provisional level in drinking water) inorganic arsenic (iAs) increases offspring asthma risk. These results raise concerns about the current safety thresholds for iAs in drinking water (Fig. 1). C_LIO_LITranscriptomic and methylome analyses of blood leukocytes from 5-month-old F1 mice revealed that maternal iAs exposure induces transcriptional changes in genes related to allergic airway responses. Pathway analysis highlighted the involvement of miR-101c and its connection with TGF{beta} downstream targets in regulating extracellular matrix signaling, embryonic development, and inflammatory (Figs. 2 and 3). C_LIO_LITranscriptional changes in col3a1 and miR-101c in blood were strongly correlated with allergen-induced airway hyperresponsiveness (AHR), with similar alterations detected in plasma samples. These findings provide new insights into respiratory health in affected communities and support the development of biomarkers for iAs risk assessment (Figs. 4 and 5). C_LIO_LISeveral gene-specific epigenetic alterations induced by early-life iAs exposure were consistently observed in both surrogate (blood) and target (lung) tissues across developmental stages, offering new panels of easily accessible markers for early detection and monitoring of lung disease risk in offspring. The persistence of these markers over time makes them valuable for predictive modeling and life course studies. (Fig. 6). C_LIO_LIDownregulation of miR-101c was validated in fetal lung and amniotic fluid of the iAs-exposed group, suggesting that epigenetic reprogramming of miR-101c is initiated early in gestation (Fig. 7). These findings help uncover causal pathways linking environmental exposures to asthma pathogenesis. C_LIO_LIDistinct sex-specific patterns in blood transcriptome and methylome alterations in respond to early-life iAs exposure underscore the importance of considering sex as a biological variable in omics research. C_LI O_FIG O_LINKSMALLFIG WIDTH=149 HEIGHT=200 SRC="FIGDIR/small/668223v1_fig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1f5394eorg.highwire.dtl.DTLVardef@855bf8org.highwire.dtl.DTLVardef@1592328org.highwire.dtl.DTLVardef@1133ec4_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 1.C_FLOATNO Maternal iAs exposure and study design to assess offspring asthma risk. A) Schematic shows the exposure window in which F0 dams received iAs in drinking water from preconception through lactation, followed by collection of F1 progeny for transcriptomic and methylomic analysis. A subset of 5-month-old F1 progenies were assessed for the HDM allergen-induced airway reactivity and airway inflammation. B) Lung resistance was assessed following methacholine (30mg/mL) challenge. C) BAL cells were stained with Diff-Quick, and eosinophil counts are shown. Each datapoint represents a pup from an individual dam; error bars indicate SEM. Five to six dams were used. C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=119 SRC="FIGDIR/small/668223v1_fig2.gif" ALT="Figure 2"> View larger version (23K): org.highwire.dtl.DTLVardef@7dc1b1org.highwire.dtl.DTLVardef@378529org.highwire.dtl.DTLVardef@1076fe3org.highwire.dtl.DTLVardef@142d801_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 2.C_FLOATNO Blood transcriptome profiles and predicted canonical pathways and regulatory networks. A) Numbers of differentially expressed genes (DEGs) identified in male and female progeny are illustrated with a Venn diagram. Enriched canonical pathways ranked by the p value are shown. B) Using the set of DEGs common in both sexes, Ingenuity Pathway Analysis (IPA) highlights TGF as a central upstream regulator linking overlapping DEGs related to extracellular matrix signaling (orange), inflammation (yellow), and embryonic development/cell metabolism (blue). C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/668223v1_fig3.gif" ALT="Figure 3"> View larger version (23K): org.highwire.dtl.DTLVardef@1732b53org.highwire.dtl.DTLVardef@216332org.highwire.dtl.DTLVardef@1f794d1org.highwire.dtl.DTLVardef@14b62df_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 3.C_FLOATNO Blood methylome analysis and associations between miR-101c and TGF-related differentially expressed genes. A) Number of differentially methylated CpGs (DMCs) identified in male and female progeny are illustrated with a Venn diagram. There are four DMCs overlapping DMCs annotated to miR-101c. B) Expression levels of miR-101c are shown. C) IPA-derived network linking this miRNA to TGF-related DEGs. Each datapoint represents a pup from an individual dam; error bars indicate SEM. Four to five dams were used. C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=148 SRC="FIGDIR/small/668223v1_fig4.gif" ALT="Figure 4"> View larger version (38K): org.highwire.dtl.DTLVardef@482e86org.highwire.dtl.DTLVardef@e666bdorg.highwire.dtl.DTLVardef@631538org.highwire.dtl.DTLVardef@10618f6_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 4.C_FLOATNO Relationship between iAs-induced blood transcriptional changes and allergen-induced AHR. This figure presents correlation patterns between baseline blood DEGs expression changes prior to HDM challenge and subsequent changes in AHR. Pearson correlation coefficients are depicted using a color gradient to distinguish positive (blue) and negative (peach) associations. * Positive correlation: r>0.8, p<0.05; ^ negative correlation: r<-0.8, p<0.05 C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/668223v1_fig5.gif" ALT="Figure 5"> View larger version (20K): org.highwire.dtl.DTLVardef@150d27eorg.highwire.dtl.DTLVardef@12b4edeorg.highwire.dtl.DTLVardef@4702a8org.highwire.dtl.DTLVardef@1ff9600_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 5.C_FLOATNO Circulating miR-101c levels in progeny exposed to maternal iAs. Plasma levels of miR-101c and COL3A1 protein are shown at different developmental stages. A) Relative expression of miR-101c across F1 progeny at juvenile (3 weeks of age) and adult (5 months of age) prenatally exposed to iAs. B) Levels of COL3A1 protein are shown at juvenile and adult F1 progeny. Each datapoint represents a pup from an individual dam, error bars indicate SEM. C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=160 SRC="FIGDIR/small/668223v1_fig6.gif" ALT="Figure 6"> View larger version (46K): org.highwire.dtl.DTLVardef@d0ec76org.highwire.dtl.DTLVardef@5468a2org.highwire.dtl.DTLVardef@1a17e85org.highwire.dtl.DTLVardef@17956ce_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 6.C_FLOATNO Comparison of transcriptional patterns across tissues and developmental stages. Expression changes (FC: fold changes vs. unexposed) of DEGs and miR-101c are compared between blood and lung tissues in both A) juvenile (3 weeks of age) and B) adult (5 months of age) progeny. Blue and orange bars represent FC in blood from male and female progeny, respectively. Black bars represent FC in lung tissues. Solid bars indicate statistically significant changes. C_FIG O_FIG O_LINKSMALLFIG WIDTH=154 HEIGHT=200 SRC="FIGDIR/small/668223v1_fig7.gif" ALT="Figure 7"> View larger version (22K): org.highwire.dtl.DTLVardef@131ef23org.highwire.dtl.DTLVardef@14b0652org.highwire.dtl.DTLVardef@8c1b30org.highwire.dtl.DTLVardef@4cf6d9_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 7.C_FLOATNO Fetal lung and amniotic fluid levels of miR-101c. Relative levels of miR-101c are shown in fetal lung tissues and matched amniotic fluid at embryonic day 16. Data illustrate expression patterns in the context of maternal iAs exposure. Each datapoint represents a pup from an individual dam, error bars indicate SEM. C_FIG GRAPHIC ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/668223v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@e25f49org.highwire.dtl.DTLVardef@1dddc22org.highwire.dtl.DTLVardef@9f4244org.highwire.dtl.DTLVardef@1b787e9_HPS_FORMAT_FIGEXP M_FIG C_FIG Description: F1 progeny prenatally exposed to iAs were assessed for blood transcriptomic and DNA methylome analysis at 5 months of age. Correlation analysis between transcriptional changes and allergen-induced airway hyperresponsiveness (AHR) was conducted to examine the epigenetic impact of maternal iAs exposure on offspring asthma risk.

Explore related subjects

genomicsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sun, Y., Watfa, M., Sun, Q., Rajasundaram, D., Schlegel, B., Yeung-Luk, B., Pulczinski, J., Park, B., Barchowsky, A., Mitzner, W., Biswal, S., Tang, W. Y.. 2025-08-03. Early-life Exposure to Arsenic Primes the Offspring to Increased Asthma Risk: Transcriptome and Epigenome Analysis. https://doi.org/10.1101/2025.08.01.668223

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Structural variation in repeat elements is widespread in normal human tissues and in tumorigenesis

Somatic mosaicism contributes to genomic variation, yet postzygotic structural variants remain under-characterized. We performed long- and short-read WGS from multiple individuals (n=47 normal tissues; n=168 samples) and identified mosaic structural variants in all individuals and germ layers, impacting a median 285.2 kb/genome. Nearly half of breakpoints were independently validated, with tissue distributions reflecting both early and late developmental origins. Most mosaic variants were repeat-mediated and 8.3% overlapped functional elements, an enrichment compared to germline variants. To extend these analyses in samples where long-read sequencing is infeasible, we measured repeat alterations from short-read sequencing, recapitulating mosaic tissue-specific differences. We characterized tumor- and tissue- specific variation in repeats across 15 cancer types and found tumor-related repeat variation to be similar in scale to that of normal mosaic variation. Tracking repeat changes in cell-free DNA provided a noninvasive approach for tumor monitoring. Our analyses revealed widespread repeat-driven structural variation in health and disease.

genomics↗
bioRxiv2026

RNA isoform-resolved multiplexed sequencing with bioorthogonal barcoding

RNA isoform dysregulation drives disease pathogenesis and is the target of FDA-approved splice-switching therapeutics. However, multiplexed sequencing methods discard splice junction information because only 3' termini are barcoded and counted. Here, we repurpose acylation and click chemistries to conjugate bioorthogonal barcodes (bobcodes) directly onto multiple internal positions along cellular RNAs. Bobcoded RNAs from multiple samples are pooled for multiplexed cDNA synthesis, during which reverse transcriptase switches from each RNA template onto its tethered bobcode with greater than 99% accuracy in species mixing experiments. Bobcode attachment intervals set cDNA insert sizes without a library fragmentation step, and priming with poly(dT) or random hexamers selects between 3'-end counting and full-length isoform capture. A bioorthogonal barcode-sequencing (BOB-seq v0.1) drug screen identifies transcriptome-wide on- and off-target RNA splicing effects and outperforms existing multiplexing RNA sequencing methods in workflow simplicity, sample-to-sample variability, and barcoding accuracy. Bobcodes add isoform resolution to scalable multiplexed RNA sequencing.

genomics↗
bioRxiv2026

Structural polymorphism and population-variable coding capacity of HERV-K(HML-2) in human pangenomes

Approximately 8% of the human genome is derived from ancient retroviral infections. The most recently integrated of these endogenous retroviruses is the HERV-K(HML-2) clade, whose expression has been associated with cancer, amyotrophic lateral sclerosis, and embryogenesis. Studies of HERV expression, particularly HML-2, have relied predominantly on short-read sequencing. However, the high similarity among HML-2 proviruses prevents many short reads from being assigned uniquely to individual loci. We therefore compared haplotype-resolved long-read genome assemblies from 292 donors to resolve variation in proviral structure and coding capacity. Several loci previously thought to be fixed were structurally polymorphic. Tandem arrays occurred at 13 loci and contained up to six proviral copies in a single array. At 8q11.23, we identified a previously undescribed full-length provirus in one haplotype. All 583 other haplotypes carried a solo-LTR. We found that standard reference genomes failed to represent the coding capacity retained in many individuals, whose proviruses contained intact open reading frames despite disruptive mutations in the reference sequences. Short-read genotypes left 32.5% of the tested donor-variant pairs unresolved at sites associated with viral reading frames. These findings show why HML-2 expression must be interpreted in the context of the structural and coding alleles each individual carries.

genomics↗