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Genetic variation at insulin-like growth factor 1 (IGF1) has been linked to prostate cancer risk. However, the specific predisposing variants have not been identified. In this study, we fine-mapped the IGF1 locus for prostate cancer risk in African Americans. We conducted targeted Roche GS-Junior 454 resequencing of a 156-kb region of IGF1 in 80 African American aggressive prostate cancer cases. Three hundred and thirty-four IGF1 SNPs were examined for their association with prostate cancer risk in 1,000 African American prostate cancer cases and 991 controls. The top associated SNP in African Americans, rs148371593, was examined in an additional 3,465 prostate cancer cases and 3,425 controls of non-African American ancestry—European Americans, Japanese Americans, Latinos, and Native Hawaiians. The overall association of 334 IGF1 SNPs and prostate cancer risk was assessed using logistic kernel-machine methods. The association between each SNP and prostate cancer risk was evaluated through unconditional logistic regression. A false discovery rate threshold of q < 0.1 was used to determine statistical significance of associations. We identified 8 novel IGF1 SNPs. The cumulative effect of the 334 IGF1 SNPs was not associated with prostate cancer risk (P = 0.13) in African Americans. Twenty SNPs were nominally associated with prostate cancer at P < 0.05. The top associated SNP among African Americans, rs148371593 [minor allele frequency (MAF) = 0.03; P = 0.0014; q > 0.1], did not reach our criterion of statistical significance. This polymorphism was rare in non-African Americans (MAF < 0.003) and was not associated with prostate cancer risk (P = 0.98). Our findings do not support the role of IGF1 variants and prostate cancer risk among African Americans. Cancer Epidemiol Biomarkers Prev; 23(9); 1928–32. ©2014 AACR.

Supplementary methods containing descriptions of PCR primer sequences, Reverse Phase Protein Arrays, RNA-seq, and Rescue Experiments.

Supplementary figures: 1): molecular structure of mibefradil, 2) Mibefradil effects on HIFs, 3) effects of sh-RNA inhibition of Cav3.2 in GSCs, 4) Schematic summary of the findings.

PDF file - 1238K, Supplemental Figure 1. Locus Zoom Plot for SNP rs148371593 in African Americans. Supplemental Table 1. Study subject characteristics. Supplemental Table 2. Association between 334 IGF1 variants and prostate cancer risk among African American. Supplemental Table 3. FASTA sequences of 8 novel IGF1 variants.

Glioblastoma (GBM) stem-like cells (GSC) promote tumor initiation, progression, and therapeutic resistance. Here, we show how GSCs can be targeted by the FDA-approved drug mibefradil, which inhibits the T-type calcium channel Cav3.2. This calcium channel was highly expressed in human GBM specimens and enriched in GSCs. Analyses of the The Cancer Genome Atlas and REMBRANDT databases confirmed upregulation of Cav3.2 in a subset of tumors and showed that overexpression associated with worse prognosis. Mibefradil treatment or RNAi-mediated attenuation of Cav3.2 was sufficient to inhibit the growth, survival, and stemness of GSCs and also sensitized them to temozolomide chemotherapy. Proteomic and transcriptomic analyses revealed that Cav3.2 inhibition altered cancer signaling pathways and gene transcription. Cav3.2 inhibition suppressed GSC growth in part by inhibiting prosurvival AKT/mTOR pathways and stimulating proapoptotic survivin and BAX pathways. Furthermore, Cav3.2 inhibition decreased expression of oncogenes (PDGFA, PDGFB, and TGFB1) and increased expression of tumor suppressor genes (TNFRSF14 and HSD17B14). Oral administration of mibefradil inhibited growth of GSC-derived GBM murine xenografts, prolonged host survival, and sensitized tumors to temozolomide treatment. Our results offer a comprehensive characterization of Cav3.2 in GBM tumors and GSCs and provide a preclinical proof of concept for repurposing mibefradil as a mechanism-based treatment strategy for GBM. Cancer Res; 77(13); 3479–90. ©2017 AACR.

Genome-wide association studies (GWAS) are being conducted at an unprecedented rate in population-based cohorts and have increased our understanding of the pathophysiology of many complex diseases. Regardless of the context, the practical utility of this information ultimately depends upon the quality of the data used for statistical analyses. Quality control (QC) procedures for GWAS are constantly evolving. Here, we enumerate some of the challenges in QC of genotyped GWAS data and describe the approaches involving genotype imputation of a sample dataset along with post-imputation quality assurance, thereby minimizing potential bias and error in GWAS results. We discuss common issues associated with QC of the GWAS data (genotyped and imputed), including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We provide detailed guidelines along with a sample dataset to suggest current best practices and discuss areas of ongoing and future research. © 2022 Wiley Periodicals LLC.

The ForenSeq Kintelligence kit developed by Verogen is a targeted Illumina sequencing assay that genotypes 10,230 single nucleotide polymorphisms designed for forensic genetic genealogy, forensic DNA phenotyping, and ancestry inference. We developedk2v, a containerized workflow for creating standard specification-compliant variant call format (VCF) files from the custom output data produced by the Kintelligence Universal Analysis Software. VCF files produced withk2venable the use of many pre-existing, widely used, community-developed tools for manipulating and analyzing genetic data in the standard VCF format. Here we describe thek2vimplementation, demonstrate its usage, and use the VCF produced byk2vto demonstrate downstream analyses that can easily be performed with pre-existing tools using VCF data as input: concordance analysis, ancestry inference, and relationship estimation.k2vis distributed as a Docker container available on Docker Hub. Documentation and source code fork2vis freely available under the GNU Public License (GPL-3.0) athttps://github.com/signaturescience/k2v.

Forensic Genetic Genealogy (FGG) requires high density single nucleotide polymorphism (SNP) profiles to infer distant relationships. The ForenSeq Kintelligence kit is a recently developed method targeting approximately 10,000 SNPs that were selected to be compatible with genetic genealogy databases, while avoiding medically relevant SNPs. The targeted PCR method is ideal for low input and degraded samples, which are particularly challenging for microarray analysis. We made modifications to library preparation and sample pooling to further enhance the performance of low input samples. To align this FGG method with forensic laboratory standards, we performed an internal validation study in accordance with the Scientific Working Group on DNA Analysis Methods Validation Guidelines for DNA Analysis Methods. The sensitivity and precision and accuracy studies produced accurate and reliable profiles down to 0.05 ng of DNA. The mixture and contamination studies demonstrated that the method could detect the presence of a minor contributor down to 0.02 ng, while system wide contamination was negligible. Nonprobative bone, fired shell casing, and adhesive tape samples performed well with inputs ranging from 0.05 to 1.0 ng of DNA. A bone with a high degradation index and tested at 0.1 ng DNA input resulted in a call rate of 92.9% and a heterozygosity rate of 0.387. We demonstrated the importance of both call rate and heterozygosity rate to assess profile performance. Validating FGG methods is critical to ensuring the reliability and utility of SNP profiles that will be uploaded to genetic genealogy databases for the purpose of generating investigative leads.

Near-term probabilistic forecasts for infectious diseases such as COVID-19 and influenza play an important role in public health communication and policymaking. From 2013-2019, the FluSight challenge run by the Centers for Disease Control and Prevention invited researchers to develop and submit forecasts using influenza-like illness (ILI) as a measure of influenza burden. Here we examine how several statistical models and an autoregressive neural network model perform for forecasting ILI during the COVID-19 pandemic, where historical patterns of ILI were highly disrupted. We find that the autoregressive neural network model which forecasted ILI well pre-COVID still performs well for some locations and forecast horizons, but its performance is highly variable, and performs poorly in many cases. We found that a simple exponential smoothing statistical model is in the top half of ranked models we evaluated nearly 75% of the time. Our results suggest that even simple statistical models may perform as well as or better than more complex machine learning models for forecasting ILI during the COVID-19 pandemic. We also created an ensemble model from the limited set of time series forecast models we created here. The limited ensemble model was rarely the best or the worst performing model compared to the rest of the models assessed, confirming previous observations from other infectious disease forecasting efforts on the less variable and generally favorable performance of ensemble forecasts. Our results support previous findings that no single modeling approach outperforms all other models across all locations, time points, and forecast horizons, and that ensemble forecasting consortia such as the COVID-19 Forecast Hub and FluSight continue to serve valuable roles in collecting, aggregating, and ensembling forecasts using fundamentally disparate modeling strategies.

Microarray processing, which interrogates hundreds of thousands of single nucleotide polymorphisms (SNPs) across the human genome, has recently gained traction in forensics due to its use in forensic genetic genealogy, which is based on analysis using SNPs to compare distant relatives in publicly curated databases for the purposes of developing investigative leads or identifying human remains. To date, there has been no published developmental validation of microarray processing using the Scientific Working Group on DNA Analysis Methods (SWGDAM) Validation Guidelines for DNA Analysis Methods and Federal Bureau of Investigation Quality Assurance Standards. Validation of these methods are warranted to identify samples suitable for microarray analysis and to assess the quality of the data obtained prior to upload to genealogical databases. In this study, we validated the Global Screening Array (GSA) for use in forensic investigations according to SWGDAM guidelines, including the following studies: precision and accuracy, sensitivity, contamination, degradation, species specificity, mock case-type samples, mixtures, repeatability and reproducibility, and stability. Results indicated accurate genotype calls with SNP call rates >95% at DNA input as low as 0.20 ng. In addition to SNP call rate, we developed interpretation thresholds for signal intensity and heterozygosity to allow for sample quality assessment and identification of highly degraded and/or non-human DNA. This study demonstrates that high quality data can be generated from multiple sample types, including mock forensic evidence that simulated the challenges that are often encountered in forensic cold cases.

The strength and durability of systemic anti-tumor immune responses induced by cancer vaccines depends on adjuvants to support an immunogenic vaccine site microenvironment (VSME). Adjuvants include water-in-oil emulsions with incomplete Freund's adjuvant (IFA) and combinations of toll-like receptor (TLR) agonists, including a preparation containing TLR4 and TLR9 agonists with QS-21 (AS15). IFA-containing vaccines can promote immune cell accumulation at the VSME, whereas effects of AS15 are largely unexplored. Therefore, we assessed innate and adaptive immune cell accumulation and gene expression at the VSME after vaccination with AS15 and compared to effects with IFA. We hypothesized that AS15 would promote less accumulation of innate and adaptive immune cells at the VSME than IFA vaccines. In two clinical trials, patients with resected high-risk melanoma received either a multipeptide vaccine with IFA or a recombinant MAGE-A3 protein vaccine with AS15. Vaccine site biopsies were obtained after one or multiple vaccines. T cells accumulated early after vaccines with AS15, but this was not durable or of the same magnitude as vaccination in IFA. Vaccines with AS15 increased durable expression of DC- and T cell-related genes, as well as PD-L1 and IDO1, suggesting complex activation and regulation of innate and adaptive immune function with AS15. These changes were generally greater with vaccines containing IFA, but IFA induced reduction in myeloid suppressor cells markers. Evidence of tertiary lymphoid structure (TLS) formation was observed with both adjuvants. Our findings highlight adjuvant-dependent changes in immune features at the VSME that may impact systemic immune responses.

Context Crosstalk through receptor ligand interactions at the maternal-fetal interface is impacted by fetal sex. This affects placentation in the first trimester and differences in outcomes. Sexually dimorphic signaling at early stages of placentation are not defined. Objective Investigate the impact of fetal sex on maternal-fetal crosstalk. Design Receptors/ligands at the maternal-fetal surface were identified from sexually dimorphic genes between fetal sexes in the first trimester placenta and defined in each cell type using single-cell RNA-Sequencing (scRNA-Seq). Setting Academic institution. Samples Late first trimester (~10-13 weeks) placenta (fetal) and decidua (maternal) from uncomplicated ongoing pregnancies. Main outcome measures Transcriptomic profiling at tissue and single-cell level; immunohistochemistry of select proteins. Results We identified 91 sexually dimorphic receptor-ligand pairs across the maternal-fetal interface. We examined fetal sex differences in 5 major cell types (trophoblasts, stromal cells, Hofbauer cells, antigen-presenting cells, and endothelial cells). Ligands from the CC family chemokine ligand (CCL) family were most highly representative in females, with their receptors present on the maternal surface. Sexually dimorphic trophoblast transcripts, Mucin-15 (MUC15) and notum, palmitoleoyl-protein carboxylesterase (NOTUM) were also most highly expressed in syncytiotrophoblasts and extra-villous trophoblasts respectively. Gene Ontology (GO) analysis using sexually dimorphic genes in individual cell types identified cytokine mediated signaling pathways to be most representative in female trophoblasts. Upstream analysis demonstrated TGFB1 and estradiol to affect all cell types, but dihydrotestosterone, produced by the male fetus, was an upstream regulator most significant for the trophoblast population. Conclusions Maternal-fetal crosstalk exhibits sexual dimorphism during placentation early in gestation.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

MotivationGenotyping error can impact downstream SNP-based analyses. Simulating various modes and and levels of error can help investigators better understand potential biases caused by miscalled genotypes.ResultsWe have developed and validated vcferr, a tool to probabilistically simulate genotyping error and missigness in VCF files. We demonstrate how vcferr could be used to address a research question by introducing varying levels of error of different type into a sample in a simulated pedigree, and assessed how kinship analysis degrades as a function of kind and type of error.Software Availabilityvcferr is available for installation via PyPi (https://pypi.org/project/vcferr/) or conda (https://anaconda.org/bioconda/vcferr). The software is released under the MIT license with source code available on GitHub (https://github.com/signaturescience/vcferr).

Harmful Algal Blooms (HABs) pose a significant and increasing risk, both to human health and to the Blue Economy. Genomics approaches to early detection promise to help mitigate these risks. We have developed and prototyped HABSSED (HAB Surveillance by Sequencing of Environmental DNA), a portable, reliable, rapid, low-cost pipeline for detecting HABs in the field using 3rd generation sequencing with the Oxford Nanopore MinION device. We demonstrated the efficacy of our approach by sequencing existing samples collected from a National Oceanic and Atmospheric Administration (NOAA) Great Lakes Environmental Research Laboratory (GLERL) on Lake Erie. We sequenced environmental DNA (eDNA) from samples drawn before, during, and after a Microcystis bloom, and estimate the abundanced of HAB-associated taxa. While sequencing results showed some evidence of human and E. coli contamination, we find that the abundance of Mycrocystis and other HAB-associated orgnisms significantly differs between pre- and post-bloom environments. Here we describe the publicly available sequencing data that was generated as part of this research, which is available in the Sequence Read Archive (SRA) under accession number PRJNA812770.

The transition metal nickel is used in a wide variety of alloys and medical devices. Nickel can cause a range of toxicities from allergy in humans to tumors when implanted in animals. Several microarray studies have examined nickel toxicity, but so far none have comprehensively profiled expression over an extended period. In this work, male mice were implanted with a single nickel pellet in the muscle of the right leg with the left leg used as a control. At 3 week intervals up to 12 months, nickel concentrations in bioflulids and microarrays of surrounding tissue were used to track gene expression patterns. Pellet biocorrosion resulted in varying levels of systemic nickel over time, with peaks of 600 μg L−1 in serum, while global gene expression was cyclical in nature with immune related genes topping the list of overexpressed genes. IPA and KEGG pathway analyses was used to attribute overall biological function to changes in gene expression levels, supported by GO enrichment analysis. IPA pathways identified sirtuin, mitochondria, and oxidative phosphorylation as top pathways, based predominantly on downregulated genes, whereas immune processes were associated with upregulated genes. Top KEGG pathways identified were lysosome, osteoclast differentiation, and phasgosome. Both pathway approaches identified common immune responses, as well as hypoxia, toll like receptor, and matrix metalloproteinases. Overall, pathway analysis identified a negative impact on energy metabolism, and a positive impact on immune function, in particular the acute phase response. Inside the cell the impacts were on mitochondria and lysosome. New pathways and genes responsive to nickel were identified from the large dataset in this study which represents the first long-term analysis of the effects of chronic nickel exposure on global gene expression.

Infectious disease forecasting has been a useful tool for public health planning and messaging during the COVID-19 pandemic. In partnership with the CDC, the organizers of the COVID-19 Forecast Hub have created a mechanism for forecasters from academia, industry, and government organizations to submit weekly near-term predictions of COVID-19 targets in the United States. Here we describe our efforts to participate in the COVID-19 Forecast Hub through theForecastingCOVID-19 in theUnitedStates (FOCUS) project. The effort led to more than three months of weekly submissions and development of an automated pipeline to generate forecasts. The models used in FOCUS yielded forecasts that ranked relatively well in terms of precision and accuracy.

Environmental teratogens such as smoking are known risk factors for developmental disorders such as cleft palate. While smoking rates have declined, a new type of smoking, called vaping is on the rise. Vaping is the use of e-cigarettes to vaporize and inhale an e-liquid containing nicotine and food-like flavors. There is the potential that, like smoking, vaping could also pose a danger to the developing human. Rather than waiting for epidemiological and mammalian studies, we have turned to an aquatic developmental model, Xenopus laevis, to more quickly assess whether e-liquids contain teratogens that could lead to craniofacial malformations. Xenopus, like zebrafish, has the benefit of being a well-established developmental model and has also been effective in predicting whether a chemical could be a teratogen. We have determined that embryonic exposure to dessert flavored e-liquids can cause craniofacial abnormalities, including an orofacial cleft in Xenopus. To better understand the underlying mechanisms contributing to these defects, transcriptomic analysis of the facial tissues of embryos exposed to a representative dessert flavored e-liquid vapor extract was performed. Analysis of differentially expressed genes in these embryos revealed several genes associated with retinoic acid metabolism or the signaling pathway. Consistently, retinoic acid receptor inhibition phenocopied the craniofacial defects as those embryos exposed to the vapor extract of the e-liquid. Such malformations also correlated with a group of common differentially expressed genes, two of which are associated with midface birth defects in humans. Further, e-liquid exposure sensitized embryos to forming craniofacial malformations when they already had depressed retinoic acid signaling. Moreover, 13-cis-retinoic acid treatment could significantly reduce the e-liquid induced malformation in the midface. Such results suggest the possibility of an interaction between retinoic acid signaling and e-liquid exposure. One of the most popular and concentrated flavoring chemicals in dessert flavored e-liquids is vanillin. Xenopus embryos exposed to this chemical closely resembled embryos exposed to dessert-like e-liquids and a retinoic acid receptor antagonist. In summary, we determined that e-liquid chemicals, in particular vanillin, can cause craniofacial defects potentially by dysregulating retinoic acid signaling. This work warrants the evaluation of vanillin and other such flavoring additives in e-liquids on mammalian development.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

In tumors, extravascular fibrin forms provisional scaffolds for endothelial cell (EC) growth and motility during angiogenesis. We report that fibrin-mediated angiogenesis was inhibited and tumor growth delayed following postnatal deletion of Tgfbr2 in the endothelium of Cdh5-Cre(ERT2) Tgfbr2(fl/fl) mice (Tgfbr2(iECKO) mice). ECs from Tgfbr2(iECKO) mice failed to upregulate the fibrinolysis inhibitor plasminogen activator inhibitor 1 (Serpine1, also known as PAI-1), due in part to uncoupled TGF-β–mediated suppression of miR-30c. Bypassing TGF-β signaling with vascular tropic nanoparticles that deliver miR-30c antagomiRs promoted PAI-1–dependent tumor growth and increased fibrin abundance, whereas miR-30c mimics inhibited tumor growth and promoted vascular-directed fibrinolysis in vivo. Using single-cell RNA-Seq and a NanoString miRNA array, we also found that subtypes of ECs in tumors showed spectrums of Serpine1 and miR-30c expression levels, suggesting functional diversity in ECs at the level of individual cells; indeed, fresh EC isolates from lung and mammary tumor models had differential abilities to degrade fibrin and launch new vessel sprouts, a finding that was linked to their inverse expression patterns of miR-30c and Serpine1 (i.e., miR-30c(hi) Serpine1(lo) ECs were poorly angiogenic and miR-30c(lo) Serpine1(hi) ECs were highly angiogenic). Thus, by balancing Serpine1 expression in ECs downstream of TGF-β, miR-30c functions as a tumor suppressor in the tumor microenvironment through its ability to promote fibrin degradation and inhibit blood vessel formation.

Objective To identify differences in the transcriptomic profiles during placentation from pregnancies conceived spontaneously vs. those with infertility using non-in vitro fertilization (IVF) fertility treatment (NIFT) or IVF. Design Cohort study. Setting Academic medical center. Patient(s) Women undergoing chorionic villus sampling at gestational age 11–13 weeks (n = 141), with pregnancies that were conceived spontaneously (n = 74), with NIFT (n = 33), or with IVF (n = 34), resulting in the delivery of viable offspring. Intervention(s) Collection of chorionic villus samples from women who conceived spontaneously, with NIFT, or with IVF for gene expression analysis using RNA sequencing. Main Outcome Measure(s) Baseline maternal, paternal, and fetal demographics, maternal medical conditions, pregnancy complications, and outcomes. Differential gene expression of first-trimester placenta. Result(s) There were few differences in the transcriptome of first-trimester placenta from NIFT, IVF, and spontaneous pregnancies. There was one protein-coding differentially expressed gene (DEG) between the spontaneous and infertility groups, CACNA1I, one protein-coding DEG between the spontaneous and IVF groups, CACNA1I, and five protein-coding DEGs between the NIFT and IVF groups, SLC18A2, CCL21, FXYD2, PAEP, and DNER. Conclusion(s) This is the first and largest study looking at transcriptomic profiles of first-trimester placenta demonstrating similar transcriptomic profiles in pregnancies conceived using NIFT or IVF and spontaneous conceptions. Gene expression differences found to be highest in the NIFT group suggest that the underlying infertility, in addition to treatment-related factors, may contribute to the observed gene expression profiles.

The placenta serves as a regulator of fetal growth throughout pregnancy. Signaling at the maternal-fetal interface is critical during placentation and lays the groundwork for placenta function, affecting pregnancy outcomes. Fetal growth is impacted by fetal sex, with males larger than females, and maternal gestational diabetes and obesity independently increase the risk of macrosomia in male fetuses only. We previously demonstrated differentially expressed genes (DEGs) among sexes involves ancient canonical pathways and metabolic functions in placenta tissue. As these are likely impacted by signaling at the maternal-fetal interface, our aim here was to identify sex differences in signaling at the maternal-fetal interface and among individual cell types within the placenta to explain these differences. RNA-sequencing of first trimester placenta and maternal decidua as well as single cell RNA-sequencing in first trimester placenta was performed in ongoing pregnancies. We identified 91 sexually dimorphic receptor-ligand pairs across the maternal-fetal interface. From these, 35 of 115 receptors and/or ligand genes were also found to be upstream regulators of pathways critical in sexually dimorphic placentation which may define regulation. Single cell analysis identified five major cell types (trophoblasts, stromal cells, hofbauer cells, antigen presenting cells, and endothelial cells), and all had sexually dimorphic genes. Among individual cell types, ligands from the CC-family of cytokines were most highly representative in females, with their corresponding receptors present on the maternal surface. Furthermore, upstream regulator analysis of sexually dimorphic genes demonstrated TGFβ1 and estradiol to significantly affect all cell types. Dihydrotestosterone, which is produced by the male fetus, was an upstream regulator that was most significant for the trophoblast population. In addition, gene ontology enrichment analysis identified distinctive enriched functions between male and female trophoblasts, with cytokine mediated signaling pathways most representative. MUC15 and NOTUM were the most highly expressed sexually dimorphic autosomal genes found in distinct cell types of the trophoblast population, cell types critical for placentation and nutrient exchange. Thus, differences in hormone and immune signaling pathways may account for differential gene expression and differences in trophoblast function during placentation, which may in turn explain developmental differences, including fetal size, well-being, and overall outcomes.