Thrilled to see this out. What started out as a chat several years back with @drfejzo.bsky.social about leveraging publicly available data on hyperemesis gravidarum GWAS turned into a wonderful collaboration with April Shu, @mvaudel.bsky.social, @xwww.bsky.social and many others!
rdcu.be/fdl9k
Some reports say over 500 schools, 55 libraries, & 25 universities hit.
You can debate the numbers, but hitting Sharif University & Beheshti is like hitting MIT & Stanford. I keep wondering: How would the scientific community respond differently if it was those universities? What’s the difference?
Huge thank you to @plbaldoni.bsky.social & @robp.bsky.social for InfRV tools, and collaborators across Singapore, Canada & USA. Also to coauthor (and talented undergrad at Rice U BioSciences) Aryun Nemani for a lovely Shiny app for our visualizing our assembly seantbresnahan.com/lr-placenta-...
If there's one takeaway, we hope it's this: tissue-specific annotation works through BOTH discovery (adding novel isoforms) AND filtration (removing irrelevant ones). Neither alone is sufficient, and annotation size doesn't explain the improvement — it has to be tissue-matched.
GDM-birth weight mediators include novel CSH1 (placental lactogen) isoforms with intron retention events that differ by ancestry, supporting population-specific placental endocrine regulation.
Fig 3: Overview of multi-cohort placental RNA-seq analysis framework for differential expression and mediation analyses across GENCODEv45, lr-assembly, and GENCODE+; violin plots of total isoform expression (sum log TPM) per sample in Gen3G (n=152) and GUSTO (n=200); box plots of mean and variance in isoform expression across shared, novel, filtered, and absent transcript categories during discovery and filtration phases; raincloud plots of inferential relative variance (InfRV) in isoform quantification per annotation with diamond indicating mode; UpSet plot of differentially expressed genes and transcripts across cohorts and annotations; and forest plot of GDM-birth weight mediation effect estimates (total, indirect, and proportion mediated) via isoform expression principal components in GUSTO, colored by annotation.
Applying this reference to short-read data from two multi-ancestry birth cohorts (GUSTO, n=200; Gen3G, n=152) reduced isoform quantification uncertainty ~30% vs GENCODE v45. And it revealed that placental transcription mediates ~36% of gestational diabetes effects on birth weight.
The placenta is NOT a transcriptomic void, contrary to previous reports. Using lr-RNA-seq (n=72, the largest placental LR dataset to date), we built a reference of 37,661 high-confidence isoforms (~40% novel) across 12,302 genes (~22% novel). Breadth & complexity rival other adult tissues.
Fig 1: Study design and transcript discovery pipeline showing Nanopore cDNA libraries from villous placenta (n=72 term births, 36 controls and 36 GDM-affected); comparison of annotated features between GENCODE v45 and lr-assembly showing 63.5% reduction in isoforms and 73.1% reduction in genes; transcript distribution by structural category (FSM, ISM, NIC, NNC, and other classes) for all and high-confidence isoforms; transcriptional breadth across 15 GTEx tissues and cell lines; isoforms detected at increasing expression thresholds with placenta shown as thick black line; and transcriptional complexity as mean isoforms per gene (±1 SD) with placenta maximum of 108..
🎉 It's published! Our placental long-read transcriptome is now in @natcomms.nature.com! Thank you to @arjunbhattac.bsky.social, @jonhuang.bsky.social, and @mikelove.bsky.social for collaborating on this first project of my postdoc @mdanderson.bsky.social. A recap 🧬🫄🧵 www.nature.com/articles/s41...
Another preprint from our group @mdanderson.bsky.social led by talented postdoc @seantbres.bsky.social! Joint with @jonhuang.bsky.social, exploring the intersection of environmental toxins, maternal/fetal health, and placental txomics. Tweet thread below!
bsky.app/profile/sean...
I’ll be sharing some of this and more @sriwomenshealth.bsky.social on Friday - if you’re here in San Juan, come chat!
www.biorxiv.org/content/10.6...
10/ Critically: this is detectable only at the isoform level. Gene-level aggregation masks these effects. Together, this helps explain heterogeneous PFAS perinatal effects: compounds with greater fetal exposure engage fundamentally different transcriptional architectures in an outcome-specific way.
9/ But the outcomes diverge on centrality and compartmentalization. For birthweight only, mediators shift closer to network hubs AND maternal vs. fetal mediators occupy increasingly distinct network positions as TPTE increases.
8/ As TPTE increases, fetal mediators grow more numerous and more tightly co-expressed for both 9/ outcomes. Direct fetal exposure systematically recruits larger, more synchronized transcriptional programs regardless of which perinatal outcome is affected.
Outcome-specific scaling of transcriptional mediation properties with transplacental transfer efficiency (TPTE). Relationships between TPTE and four transcript-level mediation properties: mediator count, co-expression strength (peak |kME|), network centrality (inverse of the peak distance between mediators and network hubs), and network compartmentalization of maternal versus fetal mediators. Points represent linear regression slopes with 95% confidence intervals (error bars). Filled points indicate statistically significant associations (p < 0.05); open points indicate non-significant trends. Pink and blue distinguish exposure source (maternal versus fetal, respectively). Black is compartmentalization.
7/ So, leveraging TPTE as a natural experiment, we asked: does increasing fetal dose reorganize the transcriptional networks mediating PFAS effects? We examined 4 network properties: mediator count, co-expression strength, network centrality, and maternal-fetal compartmentalization.
Maternal-fetal correlation of transcript, but not gene-level effect estimates decrease with PFAS transplacental transfer efficiency. Pearson correlation coefficient (r) between maternal and fetal differential expression log2 fold changes (mean ± SE across transcripts) plotted against TPTE for each PFAS compound and analysis level.
6/ We found maternal and fetal concentrations are correlated for low-TPTE compounds but uncorrelated at high TPTE. And maternal vs. fetal differential expression effect estimates become increasingly negatively correlated with TPTE. Thus, we see the placenta responding to both exposures differently.
Scatter plots with regression and confidence intervals between maternal and fetal (cord blood) PFAS measures at delivery.
5/ For TPTE to work as a natural experiment, maternal and fetal exposures must reflect distinct biological signals, not just track each other.
TPTE, measured as the cord:maternal blood concentration ratio, increases with decreasing PFAS carbon chain length.
4/ There are numerous PFAS compounds, that elicit different effects on perinatal outcomes. To study this heterogeneity, we leveraged transplacental transfer efficiency (TPTE, likely influenced by chain length, <0.5 for PFOS to >2.0 for PFBS) as a window into how fetal dose shapes placental response.
PFAS effects on birth weight are mediated through co-expression network hubs rather than differentially expressed features. (A) Conceptual framework: PFAS measured in maternal delivery blood and fetal cord blood influence birth weight and gestational age through placental transcriptional responses. Mediation analysis tests whether effects operate through differentially expressed transcripts (DETs) or co-expression network hubs. (B) Average direct effects (ADE) of PFAS on birth weight vary by compound and exposure source. Maternal exposures generally showed larger direct effects than fetal exposures. (C) Density distributions of absolute average causal mediation effects (|ACME|) for significant mediators vary by PFAS compound and exposure source (fetal = blue, maternal = pink). Mediator counts and effect sizes differ substantially across compounds. (D) Hub transcripts that were significant mediators (n = 583) showed markedly higher |ACME| values than DETs (n = 437; p < 0.001), while hub non-mediators (n = 16,313) were statistically similar to DETs. This demonstrates that PFAS effects on birth weight operate primarily through perturbation of central co-expression network nodes rather than transcripts showing the largest fold-changes. This pattern held at the gene level and generalized to gestational age as an outcome.
3/ We performed transcriptome-wide mediation analysis and found PFAS effects on both birthweight and gestational age at delivery operate through co-expression network hubs, not transcripts with the largest fold-changes. Important, because most exposure studies & transcriptomics assays focus on DEGs.
Long-read placental transcriptome assembly improves concordance between experimental and observational PFAS effect estimates. (A) Study design integrating the GUSTO prebirth cohort (n = 124 placental tissue samples; up to 101 with fetal or maternal PFBS measures) with patient-derived placental explants treated with PFBS at 5 µM and 20 µM for 24 hours (n = 18; n = 3 per group), both analyzed against an isoform-resolved placental transcriptome reference. (B) Log odds ratios for overlap enrichment between concordant PFBS-associated differential expression in explants versus tissue. Gene-level concordance was similar regardless of reference annotation, but isoform-level concordance was markedly improved using the long-read assembly (6.7–8.0-fold enrichment) versus GENCODE (4.3–4.4-fold), for both fetal and maternal exposures. (C) Isoform-level analysis detected 2–5 times more differentially expressed features (|log2FC| > 1, FDR < 10%) than gene-level analysis across all eight PFAS compounds, for both fetal and maternal exposures. Differential expression was consistently more extensive for maternal than fetal exposures.
2/ and patient-derived placental explant experiments. Together these improved concordance between experimental and observational PFAS differential expression effect estimates, & detected more PFAS responsive isoforms than genes. Better measurement is a foundation for more reliable causal inference.
1/ Causal mediation with observational omics data is still in development. In the absence of robust negative control methods (an area we are actively working on!), we used two tools: our long-read placental transcriptome reference (in press
@natcomms.nature.com www.biorxiv.org/content/10.1...)
Transplacental transfer efficiency reveals dose-dependent network architectures linking PFAS exposure to birth outcomes. Per- and polyfluoroalkyl substances (PFAS) exhibit varied transplacental transfer efficiencies (TPTE), quantified as the ratio of fetal cord blood to maternal blood concentrations. PFOS shows low TPTE with limited fetal exposure, while PFBS demonstrates high TPTE with substantial transfer to the fetus. PFAS concentrations were measured in maternal blood and fetal cord blood alongside placental transcriptomics, gestational age at delivery, and birth weight. Placental transcripts mediating PFAS effects on birth weight were characterized by four network properties: mediator count (number of significant mediating transcripts), co-expression strength (|kME|, representing correlation with module eigengene and shown as network connectivity), network centrality (hub positioning within the network), and compartmentalization (spatial clustering of maternal versus fetal mediators). Birth weight exhibited coordinated TPTE-dependent responses across these network metrics, with high-TPTE compounds engaging more numerous, strongly co-expressed mediators occupying central network positions with distinct maternal-fetal compartmentalization. In contrast, gestational age showed minimal coordinated network reorganization in response to TPTE variation, indicating distinct molecular architectures underlying PFAS effects on these different outcomes.
🧬Another new preprint with @jonhuang.bsky.social
@uhmanoa.bsky.social
& @arjunbhattac.bsky.social
@mdanderson.bsky.social
! We used variation in how PFAS cross the placenta to dissect the transcriptional architecture of effects on birthweight & gestational age🧵 www.biorxiv.org/content/10.6...
www.biorxiv.org/content/10.6...
A lot of very good food for thought here of where population and quantitative/statistical genetics have room for improvement in thinking about, performing, and communicating their research
See also Taylor’s thread here: bsky.app/profile/tayl...
New preprint on the genetic regulation of isoform expression and breast cancer risk! TL;DR: tissue-specific, long-read transcript annotations shape eQTL mapping, TWAS, and isoforms we prioritize at GWAS loci. @arjunbhattac.bsky.social
www.biorxiv.org/content/10.6...
www.biorxiv.org/content/10.6...
4/ Transcript annotation defines the biological hypothesis space for regulatory inference. Tissue-resolved long-read annotations improve specificity, reduce spurious hits, and uncover mechanisms missed by standard references, with broad implications for TWAS, colocalization, and fine-mapping.
Panel A: “Exon-intron structures of MARK1 transcript isoforms on chromosome 1. Exons are colored by isoform presence in the fibroblast long-read assembly (FSM, purple) or GENCODE only (gray).” Panel B: “Isoform detection and QC in fibroblasts: gray indicates isoforms not detected in the long-read assembly, blue indicates detected isoforms that failed quality control filtering, and green indicates isoforms that passed.” Panel C: “Regulatory signals in the MARK1 locus. Top panel: -log10(GWAS P-value) for variants within 1 Mb of the gene. The lead eQTL variant for the LR-prioritized isoform (rs11118564) is marked by a red triangle. Variant colors reflect LD R² with this lead variant in LR fibroblasts. Middle panel: eQTL signal for the prioritized isoform (ENST00000366917.6) in the long-read annotation. Bottom three panels: gene-level eQTL signals across all three transcript annotations in fibroblasts.” Panel D: “Short-read alignment transitions to MARK1 transcripts in five randomly selected GTEx fibroblast samples. Left: alignments to GENCODE annotation; right: alignments to LR fibroblast annotation. All transcripts except the LR-prioritized isoform are collapsed for ease of visualization.” Abbreviations: FSM, full-splice match; LR, long-read; LD, linkage disequilibrium; GWAS, genome-wide association study; eQTL, expression quantitative trait loci; isoQTL, isoform eQTL; GTF, gene transfer format file; QC, quality control.
3/ In healthy breast, 46% of long-read eIsoforms were unique despite 93.7% being present in GENCODE. The problem is read dilution across irrelevant isoforms, not transcript absence. At MARK1 and NUP107, long-read annotations recovered regulatory signals entirely missed by GENCODE.