The role of umbilical cord blood (CB)-derived stem cell therapy in neonatal lung injury remains undetermined. We investigated the capacity of human CB-derived CD34(+) hematopoietic progenitor cells to regenerate injured alveolar epithelium in newborn mice. Double-transgenic mice with doxycycline (Dox)-dependent lung-specific Fas ligand (FasL) overexpression, treated with Dox between embryonal day 15 and postnatal day 3, served as a model of neonatal lung injury. Single-transgenic non-Dox-responsive littermates were controls. CD34(+) cells (1 × 10(5) to 5 × 10(5)) were administered at postnatal day 5 by intranasal inoculation. Engraftment, respiratory epithelial differentiation, proliferation, and cell fusion were studied at 8 weeks after inoculation. Engrafted cells were readily detected in all recipients and showed a higher incidence of surfactant immunoreactivity and proliferative activity in FasL-overexpressing animals compared with non-FasL-injured littermates. Cord blood-derived cells surrounding surfactant-immunoreactive type II-like cells frequently showed a transitional phenotype between type II and type I cells and/or type I cell-specific podoplanin immunoreactivity. Lack of nuclear colocalization of human and murine genomic material suggested the absence of fusion. In conclusion, human CB-derived CD34(+) cells are capable of long-term pulmonary engraftment, replication, clonal expansion, and reconstitution of injured respiratory epithelium by fusion-independent mechanisms. Cord blood-derived surfactant-positive epithelial cells appear to act as progenitors of the distal respiratory unit, analogous to resident type II cells. Graft proliferation and alveolar epithelial differentiation are promoted by lung injury.

Bidirectional gene promoters affect the transcription of two genes, leading to the hypothesis that they should exhibit protection against genetic or epigenetic changes in cancer. Therefore, they provide an excellent opportunity to learn about promoter susceptibility to somatic alteration in tumors. We tested this hypothesis using data from genome-scale DNA methylation (14 cancer types), simple somatic mutation (10 cancer types), and copy number variation profiling (14 cancer types). For DNA methylation, the difference in rank differential methylation between tumor and tumor-adjacent normal matched samples based on promoter type was tested by the Wilcoxon rank sum test. Logistic regression was used to compare differences in simple somatic mutations. For copy number alteration, a mixed effects logistic regression model was used. The change in methylation between non-diseased tissues and their tumor counterparts was significantly greater in single compared to bidirectional promoters across all 14 cancer types examined. Similarly, the extent of copy number alteration was greater in single gene compared to bidirectional promoters for all 14 cancer types. Furthermore, among 10 cancer types with available simple somatic mutation data, bidirectional promoters were slightly more susceptible. These results suggest that selective pressures related with specific functional impacts during carcinogenesis drive the susceptibility of promoter regions to somatic alteration.

Selective overexpression of Human epididymal secretory protein E4 (HE4) points to a role in ovarian cancer tumorigenesis but little is known about the role the HE4 gene or the gene product plays. Here we show that elevated HE4 serum levels correlate with chemoresistance and decreased survival rates in EOC patients. HE4 overexpression promoted xenograft tumor growth and chemoresistance against cisplatin in an animal model resulting in reduced survival rates. HE4 displayed responses to tumor microenvironment constituents and presented increased expression as well as nuclear translocation upon EGF, VEGF and Insulin treatment and nucleolar localization with Insulin treatment. HE4 interacts with EGFR, IGF1R, and transcription factor HIF1α. Constructs of antisense phosphorothio-oligonucleotides targeting HE4 arrested tumor growth in nude mice. Collectively these findings implicate increased HE4 expression as a molecular factor in ovarian cancer tumorigenesis. Selective targeting directed towards the HE4 protein demonstrates therapeutic benefits for the treatment of ovarian cancer.

The underlying biological mechanisms through which epidemiologically defined breast cancer risk factors contribute to disease risk remain poorly understood. Identification of the molecular changes associated with cancer risk factors in normal tissues may aid in determining the earliest events of carcinogenesis and informing cancer prevention strategies.

Fetal programming describes the theory linking environmental conditions during embryonic and fetal development with risk of diseases later in life. Environmental insults in utero may lead to changes in epigenetic mechanisms potentially affecting fetal development.

Human embryonal carcinoma (EC) cells are the stem cells of nonseminoma testicular germ cells tumors (TGCTs) and share remarkable similarities to human embryonic stem (ES) cells. In prior work we found that EC cells are hypersensitive to low nanomolar doses of 5-aza deoxycytidine (5-aza) and that this hypersensitivity partially depended on unusually high levels of the DNA methyltransferase, DNMT3B. We show here that low-dose 5-aza treatment results in DNA damage and induction of p53 in NT2/D1 cells. In addition, low-dose 5-aza results in global and gene specific promoter DNA hypomethylation. Low-dose 5-aza induces a p53 transcriptional signature distinct from that induced with cisplatin in NT2/D1 cells and also uniquely downregulates genes associated with pluripotency including NANOG, SOX2, GDF3 and Myc target genes. Changes in the p53 and pluripotency signatures with 5-aza were to a large extent dependent on high levels of DNMT3B. In contrast to the majority of p53 target genes upregulated by 5-aza that did not show DNA hypomethylation, several other genes induced with 5-aza had corresponding decreases in promoter methylation. These genes include RIN1, SOX15, GPER, and TLR4 and are novel candidate tumors suppressors in TGCTs. Our studies suggest that the hypersensitivity of NT2/D1 cells to low-dose 5-aza is multifactorial and involves the combined activation of p53 targets, repression of pluripotency genes, and activation of genes repressed by DNA methylation. Low-dose 5-aza therapy may be a general strategy to treat those tumors that are sustained by cells with embryonic stem-like properties.GEO NUMBER FOR THE MICROARRAY DATA: GSE42647.

Despite recent evidence that 5-hydroxymethylcytosine (5hmC) possesses roles in gene regulation distinct from 5-methylcytosine (5mC), relatively little is known regarding the functions of 5hmC in mammalian tissues. To address this issue, we utilized an approach combining both paired bisulfite (BS) and oxidative bisulfite (oxBS) DNA treatment, to resolve genome-wide patterns of 5hmC and 5mC in normal breast tissue from disease-free women. Although less abundant than 5mC, 5hmC was differentially distributed, and consistently enriched among breast-specific enhancers and transcriptionally active chromatin. In contrast, regulatory regions associated with transcriptional inactivity, such as heterochromatin and repressed Polycomb regions, were relatively depleted of 5hmC. Gene regions containing abundant 5hmC were significantly associated with lactate oxidation, immune cell function, and prolactin signaling pathways. Furthermore, genes containing abundant 5hmC were enriched among those actively transcribed in normal breast tissue. Finally, in independent data sets, normal breast tissue 5hmC was significantly enriched among CpG loci demonstrated to have altered methylation in pre-invasive breast cancer and invasive breast tumors. Primarily, our findings identify genomic loci containing abundant 5hmC in breast tissues and provide a genome-wide map of nucleotide-level 5hmC in normal breast tissue. Additionally, these data suggest 5hmC may participate in gene regulatory programs that are dysregulated during breast-related carcinogenesis.

Prior candidate gene studies have shown tumor suppressor DNA methylation in breast milk related with history of breast biopsy, an established risk factor for breast cancer. To further establish the utility of breast milk as a tissue-specific biospecimen for investigations of breast carcinogenesis, we measured genome-wide DNA methylation in breast milk from women with and without a diagnosis of breast cancer in two independent cohorts. DNA methylation was assessed using Illumina HumanMethylation450k in 87 breast milk samples. Through an epigenome-wide association study we explored CpG sites associated with a breast cancer diagnosis in the prospectively collected milk samples from the breast that would develop cancer compared with women without a diagnosis of breast cancer using linear mixed effects models adjusted for history of breast biopsy, age, RefFreeCellMix cell estimates, time of delivery, array chip and subject as random effect. We identified 58 differentially methylated CpG sites associated with a subsequent breast cancer diagnosis (q-value <0.05). Nearly all CpG sites associated with a breast cancer diagnosis were hypomethylated in cases compared with controls and were enriched for CpG islands. In addition, inferred repeat element methylation was lower in breast milk DNA from cases compared to controls, and cases exhibited increased estimated epigenetic mitotic tick rate as well as DNA methylation age compared with controls. Breast milk has utility as a biospecimen for prospective assessment of disease risk, for understanding the underlying molecular basis of breast cancer risk factors and improving primary and secondary prevention of breast cancer.

The diagnosis of disease often requires analysis of a biopsy. Many diagnoses depend not only on the presence of certain features but on their location within the tissue. Recently, a number of deep learning diagnostic aids have been developed to classify digitized biopsy slides. Clinical workflows often involve processing of more than 500 slides per day. But, clinical use of deep learning diagnostic aids would require a preprocessing workflow that is cost-effective, flexible, scalable, rapid, interpretable, and transparent. Here, we present such a workflow, optimized using Dask and mixed precision training via APEX, capable of handling any patch-level or slide level classification and prediction problem. The workflow uses a flexible and fast preprocessing and deep learning analytics pipeline, incorporates model interpretation and has a highly storage-efficient audit trail. We demonstrate the utility of this package on the analysis of a prototypical anatomic pathology specimen, liver biopsies for evaluation of hepatitis from a prospective cohort. The preliminary data indicate that PathFlowAI may become a cost-effective and time-efficient tool for clinical use of Artificial Intelligence (AI) algorithms.

Triple-negative breast cancer (TNBC) is an aggressive disease subtype with limited treatment options. Eribulin is a chemotherapeutic approved for the treatment of advanced breast cancer that has been shown to elicit epigenetic changes. We investigated the effect of eribulin treatment on genome-scale DNA methylation patterns in TNBC cells. Following repeated treatment, The results showed that eribulin-induced changes in DNA methylation patterns evident in persister cells. Eribulin also affected the binding of transcription factors to genomic ZEB1 binding sites and regulated several cellular pathways, including ERBB and VEGF signaling and cell adhesion. Eribulin also altered the expression of epigenetic modifiers including DNMT1, TET1, and DNMT3A/B in persister cells. Data from primary human TNBC tumors supported these findings: DNMT1 and DNMT3A levels were altered by eribulin treatment in human primary TNBC tumors. Our results suggest that eribulin modulates DNA methylation patterns in TNBC cells by altering the expression of epigenetic modifiers. These findings have clinical implications for using eribulin as a therapeutic agent.

Immune profiles have been associated with bladder cancer outcomes and may have clinical applications for prognosis. However, associations of detailed immune cell subtypes with patient outcomes remain underexplored and may contribute crucial prognostic information for better managing bladder cancer recurrence and survival.

The advent of spatial transcriptomics technologies has heralded a renaissance in research to advance our understanding of the spatial cellular and transcriptional heterogeneity within tissues. Spatial transcriptomics allows investigation of the interplay between cells, molecular pathways, and the surrounding tissue architecture and can help elucidate developmental trajectories, disease pathogenesis, and various niches in the tumor microenvironment. Photoaging is the histological and molecular skin damage resulting from chronic/acute sun exposure and is a major risk factor for skin cancer. Spatial transcriptomics technologies hold promise for improving the reliability of evaluating photoaging and developing new therapeutics. Challenges to current methods include limited focus on dermal elastosis variations and reliance on self-reported measures, which can introduce subjectivity and inconsistency. Spatial transcriptomics offers an opportunity to assess photoaging objectively and reproducibly in studies of carcinogenesis and discern the effectiveness of therapies that intervene in photoaging and preventing cancer. Evaluation of distinct histological architectures using highly-multiplexed spatial technologies can identify specific cell lineages that have been understudied due to their location beyond the depth of UV penetration. However, the cost and interpatient variability using state-of-the-art assays such as the 10x Genomics Spatial Transcriptomics assays limits the scope and scale of large-scale molecular epidemiologic studies. Here, we investigate the inference of spatial transcriptomics information from routine hematoxylin and eosin-stained (H&E) tissue slides. We employed the Visium CytAssist spatial transcriptomics assay to analyze over 18,000 genes at a 50-micron resolution for four patients from a cohort of 261 skin specimens collected adjacent to surgical resection sites for basal cell and squamous cell keratinocyte tumors. The spatial transcriptomics data was co-registered with 40x resolution whole slide imaging (WSI) information. We developed machine learning models that achieved a macro-averaged median AUC and F1 score of 0.80 and 0.61 and Spearman coefficient of 0.60 in inferring transcriptomic profiles across the slides, and accurately captured biological pathways across various tissue architectures.

Glioblastomas (GBM) are lethal central nervous system cancers associated with tumor and systemic immunosuppression. Heterogeneous monocyte myeloid-derived suppressor cells (M-MDSC) are implicated in the altered immune response in GBM, but M-MDSC ontogeny and definitive phenotypic markers are unknown. Using single-cell transcriptomics, we revealed heterogeneity in blood M-MDSC from GBM subjects and an enrichment in a transcriptional state reminiscent of neutrophil-like monocytes (NeuMo), a newly described pathway of monopoiesis in mice. Human NeuMo gene expression and Neu-like deconvolution fraction algorithms were created to quantitate the enrichment of this transcriptional state in GBM subjects. NeuMo populations were also observed in M-MDSCs from lung and head and neck cancer subjects. Dexamethasone (DEX) and prednisone exposures increased the usage of Neu-like states, which were inversely associated with tumor purity and survival in isocitrate dehydrogenase wildtype (IDH WT) gliomas. Anti-inflammatory ZC3HA12/Regnase-1 transcripts were highly correlated with NeuMo expression in tumors and in blood M-MDSC from GBM, lung, and head and neck cancer subjects. Additional novel transcripts of immune-modulating proteins were identified. Collectively, these findings provide a framework for understanding the heterogeneity of M-MDSCs in GBM as cells with different clonal histories and may reshape approaches to study and therapeutically target these cells.

To evaluate whether placental transfusion influences brain myelination at 4 months of age.

The process of breastfeeding exposes infants to bioactive substances including a diversity of bacteria from breast milk as well as maternal skin. Knowledge of the character of and variation in these microbial communities, as well as the factors that influence them, is limited. We aimed to identify profiles of breastfeeding-associated microbial communities and their association with maternal and infant factors. Bilateral milk samples were collected from women in the New Hampshire Birth Cohort Study at approximately 6 weeks postpartum without sterilization of the skin in order to capture the infant-relevant exposure. We sequenced the V4-V5 hypervariable region of the bacterial 16S rRNA gene in 155 human milk samples. We used unsupervised clustering (partitioning around medoids) to identify microbial profiles in milk samples, and multinomial logistic regression to test their relation with maternal and infant variables. Associations between alpha diversity and maternal and infant factors were tested with linear models. Four breastfeeding microbiome types (BMTs) were identified, which differed in alpha diversity and in Streptococcus, Staphylococcus, Acinetobacter, and Pseudomonas abundances. Higher maternal pre-pregnancy BMI was associated with increased odds of belonging to BMT1 [OR (95% CI) = 1.13 (1.02, 1.24)] or BMT3 [OR (95% CI) = 1.12 (1.01, 1.25)] compared to BMT2. Independently, increased gestational weight gain was related to reduced odds of membership in BMT1 [OR (95% CI) = 0.66 (0.44, 1.00) per 10 pounds]. Alpha diversity was positively associated with gestational weight gain and negatively associated with postpartum sample collection week. There were no statistically significant associations of breastfeeding microbiota with delivery mode. Our results indicate that the breastfeeding microbiome partitions into four profiles and that its composition and diversity is associated with measures of maternal weight.

Differentiation therapy utilizes our understanding of the hierarchy of cellular systems to pharmacologically induce a shift toward terminal commitment. While this approach has been a paradigm in treating certain hematological malignancies, efforts to translate this success to solid tumors have met with limited success. Mammary-specific activation of PKA in mouse models leads to aberrant differentiation and diminished self-renewing potential of the basal compartment, which harbors mammary repopulating cells. PKA activation results in tumors that are more benign, exhibiting reduced metastatic propensity, loss of tumor-initiating potential, and increased sensitivity to chemotherapy. Analysis of tumor histopathology revealed features of overt differentiation with papillary characteristics. Longitudinal single-cell profiling at the hyperplasia and tumor stages uncovered an altered path of tumor evolution whereby PKA curtails the emergence of aggressive subpopulations. Acting through the repression of SOX4, PKA activation promotes tumor differentiation and represents a possible adjuvant to chemotherapy for certain breast cancers.

The epithelial-to-mesenchymal transition (EMT) is frequently co-opted by cancer cells to enhance migratory and invasive cell traits. It is a key contributor to heterogeneity, chemoresistance, and metastasis in many carcinoma types, where the intermediate EMT state plays a critical tumor-initiating role. We isolate multiple distinct single-cell clones from the SUM149PT human breast cell line spanning the EMT spectrum having diverse migratory, tumor-initiating, and metastatic qualities, including three unique intermediates. Using a multiomics approach, we identify CBFβ as a key regulator of metastatic ability in the intermediate state. To quantify epithelial-mesenchymal heterogeneity within tumors, we develop an advanced multiplexed immunostaining approach using SUM149-derived orthotopic tumors and find that the EMT state and epithelial-mesenchymal heterogeneity are predictive of overall survival in a cohort of stage III breast cancer. Our model reveals previously unidentified insights into the complex EMT spectrum and its regulatory networks, as well as the contributions of epithelial-mesenchymal plasticity (EMP) in tumor heterogeneity in breast cancer.

Various pathogens use differing strategies to evade host immune response including modulating the host's epigenome. Here, we investigate if EVs secreted from P. aeruginosa alter DNA methylation in human lung macrophages, thereby potentially contributing to a dysfunctional innate immune response. Using a genome-wide DNA methylation approach, we demonstrate that P. aeruginosa EVs alter certain host cell DNA methylation patterns. We identified 1,185 differentially methylated CpGs (FDR < 0.05), which were significantly enriched for distal DNA regulatory elements including enhancer regions and DNase hypersensitive sites. Notably, all but one of the 1,185 differentially methylated CpGs were hypomethylated in association with EV exposure. Significantly hypomethylated CpGs tracked to genes including AXL, CFB and CCL23. Gene expression analysis identified 310 genes exhibiting significantly altered expression 48 hours post P. aeruginosa EV treatment, with 75 different genes upregulated and 235 genes downregulated. Some CpGs associated with cytokines such as CSF3 displayed strong negative correlations between DNA methylation and gene expression. Our infection model illustrates how secreted products (EVs) from bacteria can alter DNA methylation of the host epigenome. Changes in DNA methylation in distal DNA regulatory regions in turn can modulate cellular gene expression and potential downstream cellular processes.

Multiple studies have related psychiatric disorders and immune alterations. Panic disorder (PD) has been linked with changes in leukocytes distributions in several small studies using different methods for immune characterization. Additionally, alterations in the methylation of repetitive DNA elements, such as LINE-1, have been associated with mental disorders. Here, we use peripheral blood DNA methylation data from two studies and an updated DNA methylation deconvolution library to investigate the relation of leukocyte proportions and methylation status of repetitive elements in 133 patients with panic disorder compared with 118 controls.

What is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks? Background: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care, and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking. Objective: We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images. Methods: Satellite images of neighborhoods surrounding schools were extracted with the Google Static Maps application programming interface for 430 counties representing ~68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors. Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r = 0.72). Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g., sidewalks, driveways, and hiking trails) associated with lower mortality. Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race, and age. Conclusions: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions.