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Over the last decade, single-cell genomics has revealed remarkable heterogeneity and plasticity of cell types in the lungs and airways. The challenge now is to understand how these cell types interact in three-dimensional space to perform lung functions, facilitating airflow and gas exchange while simultaneously providing barrier function to avoid infection. An explosion in novel spatially resolved gene expression technologies, coupled with computational tools that harness machine learning and deep learning, now promise to address this challenge. Here, we review the most commonly used spatial analysis workflows, highlighting their advantages and limitations, and outline recent developments in machine learning and artificial intelligence that will augment how we interpret spatial data. Together these technologies have the potential to transform our understanding of the respiratory system in health and disease, and we showcase studies in lung development, COVID-19, lung cancer, and fibrosis where spatially resolved transcriptomics is already providing novel insights. [ABSTRACT FROM AUTHOR]

Here we propose "molecular connectomics" to link molecular and morphological cell features in three dimensions across scales, using machine learning and artificial intelligence to reveal emergent properties of complex biological systems. This Perspective introduces "molecular connectomics" to link molecular and morphological cell features in three dimensions across scales, using machine learning and artificial intelligence to reveal emergent properties of complex biological systems. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is the leading cause of cancer-related death in the world. In contrast to many other cancers, a direct connection to modifiable lifestyle risk in the form of tobacco smoke has long been established. More than 50% of all smoking-related lung cancers occur in former smokers, 40% of which occur more than 15 years after smoking cessation. Despite extensive research, the molecular processes for persistent lung cancer risk remain unclear. We thus set out to examine whether risk stratification in the clinic and in the general population can be improved upon by the addition of genetic data and to explore the mechanisms of the persisting risk in former smokers. Methods: We analysed transcriptomic data from accessible airway tissues of 487 subjects, including healthy volunteers and clinic patients of different smoking statuses. We developed a computational model to assess smoking-associated gene expression changes and their reversibility after smoking is stopped, comparing healthy subjects to clinic patients with and without lung cancer. Results: We find persistent smoking-associated immune alterations to be a hallmark of the clinic patients. Integrating previous GWAS data using a transcriptional network approach, we demonstrate that the same immune- and interferon-related pathways are strongly enriched for genes linked to known genetic risk factors, demonstrating a causal relationship between immune alteration and lung cancer risk. Finally, we used accessible airway transcriptomic data to derive a non-invasive lung cancer risk classifier. Conclusions: Our results provide initial evidence for germline-mediated personalized smoke injury response and risk in the general population, with potential implications for managing long-term lung cancer incidence and mortality. [ABSTRACT FROM AUTHOR]

Assessment of single-cell gene expression (single-cell RNA sequencing) and adaptive immune receptor (AIR) sequencing (scVDJ-seq) has been invaluable in studying lymphocyte biology. Here we introduce Dandelion, a computational pipeline for scVDJ-seq analysis. It enables the application of standard V(D)J analysis workflows to single-cell datasets, delivering improved V(D)J contig annotation and the identification of nonproductive and partially spliced contigs. We devised a strategy to create an AIR feature space that can be used for both differential V(D)J usage analysis and pseudotime trajectory inference. The application of Dandelion improved the alignment of human thymic development trajectories of double-positive T cells to mature single-positive CD4/CD8 T cells, generating predictions of factors regulating lineage commitment. Dandelion analysis of other cell compartments provided insights into the origins of human B1 cells and ILC/NK cell development, illustrating the power of our approach. Dandelion is available at https://www.github.com/zktuong/dandelion. A computational pipeline enables differential V(D)J usage analysis and pseudotime trajectory inference from single-cell AIR sequencing. [ABSTRACT FROM AUTHOR]

Cell type-specific differential gene expression analyses based on single-cell transcriptome datasets are sensitive to the presence of cell-free mRNA in the droplets containing single cells. This so-called ambient RNA contamination may differ between samples obtained from patients and healthy controls. Current ambient RNA correction methods were not developed specifically for single-cell differential gene expression (sc-DGE) analyses and might therefore not sufficiently correct for ambient RNA-derived signals. Here, we show that ambient RNA levels are highly sample-specific. We found that without ambient RNA correction, sc-DGE analyses erroneously identify transcripts originating from ambient RNA as cell type-specific disease-associated genes. We therefore developed a computationally lean and intuitive correction method, Fast Correction for Ambient RNA (FastCAR), optimized for sc-DGE analysis of scRNA-Seq datasets generated by droplet-based methods including the 10XGenomics Chromium platform. FastCAR uses the profile of transcripts observed in libraries that likely represent empty droplets to determine the level of ambient RNA in each individual sample, and then corrects for these ambient RNA gene expression values. FastCAR can be applied as part of the data pre-processing and QC in sc-DGE workflows comparing scRNA-Seq data in a health versus disease experimental design. We compared FastCAR with two methods previously developed to remove ambient RNA, SoupX and CellBender. All three methods identified additional genes in sc-DGE analyses that were not identified in the absence of ambient RNA correction. However, we show that FastCAR performs better at correcting gene expression values attributed to ambient RNA, resulting in a lower frequency of false-positive observations. Moreover, the use of FastCAR in a sc-DGE workflow increases the cell-type specificity of sc-DGE analyses across disease conditions. [ABSTRACT FROM AUTHOR]

Rationale: Obesity affects 40% of US adults, is associated with a pro-inflammatory state, and presents a significant risk factor for the development of severe COVID-19. To date, there is limited information on how obesity might affect immune cell responses in SARS-CoV-2 infection.Objectives: To determine the impact of obesity on respiratory tract immunity in COVID-19 across human lifespan.Methods: We analysed single cell transcriptomes from bronchiolar lavage in three ventilated adult cohorts with (n=24) or without COVID-19 (n=9), from nasal immune cells in children with (n=14) or without COVID-19 (n=19), and from peripheral blood mononuclear cells in an independent adult COVID-19 cohort (n=42), comparing obese (Ob) and non-obese subjects (N-Ob).Measurements and Main Results: Surprisingly, we found that adult Ob subjects had attenuated lung immune/inflammatory responses in SARS-CoV-2 infection, with decreased expression of interferon (IFN)α, IFNγ and tumour necrosis factor (TNF) alpha response gene signatures in almost all lung epithelial and immune cell subsets, and lower expression of IFNG and TNF in specific lung immune cells. Peripheral blood immune cells in an independent adult cohort showed a similar, but less marked, reduction in type I IFN and IFNγ response genes, as well as decreased serum IFNα in Ob patients with SARS-CoV-2. Nasal immune cells from Ob children with COVID-19 also showed reduced enrichment of IFNα and IFNγ response genes.Conclusions: These findings show blunted tissue immune responses in Ob COVID-19 patients, with implications for treatment stratification, supporting the specific application of inhaled recombinant type I IFNs in this vulnerable subset. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/). [ABSTRACT FROM AUTHOR]

The balance between self‐renewal and differentiation in human foetal lung epithelial progenitors controls the size and function of the adult organ. Moreover, progenitor cell gene regulation networks are employed by both regenerating and malignant lung cells, where modulators of their effects could potentially be of therapeutic value. Details of the molecular networks controlling human lung progenitor self‐renewal remain unknown. We performed the first CRISPRi screen in primary human lung organoids to identify transcription factors controlling progenitor self‐renewal. We show that SOX9 promotes proliferation of lung progenitors and inhibits precocious airway differentiation. Moreover, by identifying direct transcriptional targets using Targeted DamID, we place SOX9 at the centre of a transcriptional network, which amplifies WNT and RTK signalling to stabilise the progenitor cell state. In addition, the proof‐of‐principle CRISPRi screen and Targeted DamID tools establish a new workflow for using primary human organoids to elucidate detailed functional mechanisms underlying normal development and disease. Synopsis: CRISPRi screening tools and Targeted DamID for human lung organoids enable exploration of the transcription factor network controlling lung epithelial progenitor self‐renewal. A pooled CRISPRi screen in human foetal lung organoids identified transcription factors controlling progenitor cell self‐renewal.SOX9 promotes lung tip progenitor cell proliferation and supresses precocious airway differentiation.Targeted DamID (TaDa) identified SOX9 direct binding targets, revealing that SOX9 amplifies WNT and RTK signalling. [ABSTRACT FROM AUTHOR]

During early human pregnancy the uterine mucosa transforms into the decidua, into which the fetal placenta implants and where placental trophoblast cells intermingle and communicate with maternal cells. Trophoblast-decidual interactions underlie common diseases of pregnancy, including pre-eclampsia and stillbirth. Here we profile the transcriptomes of about 70,000 single cells from first-trimester placentas with matched maternal blood and decidual cells. The cellular composition of human decidua reveals subsets of perivascular and stromal cells that are located in distinct decidual layers. There are three major subsets of decidual natural killer cells that have distinctive immunomodulatory and chemokine profiles. We develop a repository of ligand-receptor complexes and a statistical tool to predict the cell-type specificity of cell-cell communication via these molecular interactions. Our data identify many regulatory interactions that prevent harmful innate or adaptive immune responses in this environment. Our single-cell atlas of the maternal-fetal interface reveals the cellular organization of the decidua and placenta, and the interactions that are critical for placentation and reproductive success. Transcriptomes of about 70,000 single cells from first-trimester deciduas and placentas reveal subsets of perivascular, stromal and natural killer cells in the decidua, with distinct immunomodulatory profiles that regulate the environment necessary for successful placentation. [ABSTRACT FROM AUTHOR]

We have recently identified transcription factors (TFs) that are key drivers of breast cancer risk. To better understand the pathways or sub-networks in which these TFs mediate their function we sought to identify upstream modulators of their activity. We applied the MINDy (Modulator Inference by Network Dynamics) algorithm to four TFs (ESR1, FOXA1, GATA3 and SPDEF) that are key drivers of estrogen receptor-positive (ER+) breast cancer risk, as well as cancer progression. Our computational analysis identified over 500 potential modulators. We assayed 189 of these and identified 55 genes with functional characteristics that were consistent with a role as TF modulators. In the future, the identified modulators may be tested as potential therapeutic targets, able to alter the activity of TFs that are critical in the development of breast cancer. [ABSTRACT FROM AUTHOR]

The fibroblast growth factor receptor 2 (FGFR2) locus is consistently the top hit in genome-wide association studies for oestrogen receptor-positive (ER+) breast cancer. Yet, its mode of action continues to be controversial. Here, we employ a systems biology approach to demonstrate that signalling via FGFR2 counteracts cell activation by oestrogen. In the presence of oestrogen, the oestrogen receptor (ESR1) regulon (set of ESR1 target genes) is in an active state. However, signalling by FGFR2 is able to reverse the activity of the ESR1 regulon. This effect is seen in multiple distinct FGFR2 signalling model systems, across multiple cells lines and is dependent on the presence of FGFR2. Increased oestrogen exposure has long been associated with an increased risk of breast cancer. We therefore hypothesized that risk variants should reduce FGFR2 expression and subsequent signalling. Indeed, transient transfection experiments assaying the three independent variants of the FGFR2 risk locus (rs2981578, rs35054928 and rs45631563) in their normal chromosomal context show that these single-nucleotide polymorphisms (SNPs) map to transcriptional silencer elements and that, compared with wild type, the risk alleles augment silencer activity. The presence of risk variants results in lower FGFR2 expression and increased oestrogen responsiveness. We thus propose a molecular mechanism by which FGFR2 can confer increased breast cancer risk that is consistent with oestrogen exposure as a major driver of breast cancer risk. Our findings may have implications for the clinical use of FGFR2 inhibitors. [ABSTRACT FROM AUTHOR]

Motivation Dendrogram is a classical diagram for visualizing binary trees. Although efficient to represent hierarchical relations, it provides limited space for displaying information on the leaf elements, especially for large trees. Results Here, we present TreeAndLeaf , an R/Bioconductor package that implements a hybrid layout strategy to represent tree diagrams with focus on the leaves. The TreeAndLeaf package combines force-directed graph and tree layout algorithms using a single visualization system, allowing projection of multiple layers of information onto a graph-tree diagram. The Supplementary Information provides two case studies that use breast cancer data from epidemiological and experimental studies. Availability and implementation TreeAndLeaf is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/TreeAndLeaf/ (version≥1.4.2). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The 10q26 locus in the second intron of FGFR2 is the locus most strongly associated with estrogen-receptor-positive breast cancer in genome-wide association studies. We conducted fine-scale mapping in case-control studies genotyped with a custom chip (iCOGS), comprising 41 studies (n = 89,050) of European ancestry, 9 Asian ancestry studies (n = 13,983), and 2 African ancestry studies (n = 2,028) from the Breast Cancer Association Consortium. We identified three statistically independent risk signals within the locus. Within risk signals 1 and 3, genetic analysis identified five and two variants, respectively, highly correlated with the most strongly associated SNPs. By using a combination of genetic fine mapping, data on DNase hypersensitivity, and electrophoretic mobility shift assays to study protein-DNA binding, we identified rs35054928, rs2981578, and rs45631563 as putative functional SNPs. Chromatin immunoprecipitation showed that FOXA1 preferentially bound to the risk-associated allele (C) of rs2981578 and was able to recruit ERα to this site in an allele-specific manner, whereas E2F1 preferentially bound the risk variant of rs35054928. The risk alleles were preferentially found in open chromatin and bound by Ser5 phosphorylated RNA polymerase II, suggesting that the risk alleles are associated with changes in transcription. Chromatin conformation capture demonstrated that the risk region was able to interact with the promoter of FGFR2, the likely target gene of this risk region. A role for FOXA1 in mediating breast cancer susceptibility at this locus is consistent with the finding that the FGFR2 risk locus primarily predisposes to estrogen-receptor-positive disease. [ABSTRACT FROM AUTHOR]

Genetic mapping studies have identified multiple cancer susceptibility regions at chromosome 8q24, upstream of the MYC oncogene. MYC has been widely presumed as the regulated target gene, but definitive evidence functionally linking these cancer regions with MYC has been difficult to obtain. Here we examined candidate functional variants of a haplotype block at 8q24 encompassing the two independent risk alleles for prostate and breast cancer, rs620861 and rs13281615. We used the mapping of DNase I hypersensitive sites as a tool to prioritise regions for further functional analysis. This approach identified rs378854, which is in complete linkage disequilibrium (LD) with rs620861, as a novel functional prostate cancer-specific genetic variant. We demonstrate that the risk allele (G) of rs378854 reduces binding of the transcription factor YY1 in vitro. This factor is known to repress global transcription in prostate cancer and is a candidate tumour suppressor. Additional experiments showed that the YY1 binding site is occupied in vivo in prostate cancer, but not breast cancer cells, consistent with the observed cancer-specific effects of this single nucleotide polymorphism (SNP). Using chromatin conformation capture (3C) experiments, we found that the region surrounding rs378854 interacts with the MYC and PVT1 promoters. Moreover, expression of the PVT1 oncogene in normal prostate tissue increased with the presence of the risk allele of rs378854, while expression of MYC was not affected. In conclusion, we identified a new functional prostate cancer risk variant at the 8q24 locus, rs378854 allele G, that reduces binding of the YY1 protein and is associated with increased expression of PVT1 located 0.5 Mb downstream. [ABSTRACT FROM AUTHOR]

Recent whole-genome scans have identified novel risk genes for many common diseases, challenging researchers to determine how these genes contribute to disease. A new study provides molecular insights into a breast cancer risk factor. [ABSTRACT FROM AUTHOR]

Breast cancer exhibits familial aggregation, consistent with variation in genetic susceptibility to the disease. Known susceptibility genes account for less than 25% of the familial risk of breast cancer, and the residual genetic variance is likely to be due to variants conferring more moderate risks. To identify further susceptibility alleles, we conducted a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls, followed by a third stage in which 30 single nucleotide polymorphisms (SNPs) were tested for confirmation in 21,860 cases and 22,578 controls from 22 studies. We used 227,876 SNPs that were estimated to correlate with 77% of known common SNPs in Europeans at r2 > 0.5. SNPs in five novel independent loci exhibited strong and consistent evidence of association with breast cancer (P < 10-7). Four of these contain plausible causative genes (FGFR2, TNRC9, MAP3K1 and LSP1). At the second stage, 1,792 SNPs were significant at the P < 0.05 level compared with an estimated 1,343 that would be expected by chance, indicating that many additional common susceptibility alleles may be identifiable by this approach. [ABSTRACT FROM AUTHOR]

Expression of the rearranged chicken immunoglobulin light chain (IgL) gene is regulated by a V gene promoter, a matrix attachment region (MAR) in the J-C intron and an enhancer downstream of the Ig constant region. Using knockout analysis, we demonstrate that the 3′-enhancer is not only required for gene activation but is also essential for the maintenance of gene expression. Deletion of the MAR on the other hand increases IgL transcription, indicating that the MAR acts as negative regulator. We demonstrate that Id1 and Id3, dominant-negative regulators of basic-region helix-loop-helix (bHLH) transcription factors, are able to reduce chicken IgL 3′-enhancer activity in transient assays and strongly reduce the rate of gene conversion (GC) in DT40 clone 18 cells. Conversely, overexpression of avian E47, a bHLH transcription factor, leads to a dramatic increase in GC rates independent of IgL or activation-induced cytidine deaminase RNA levels. Thus, E47 is the first transcription factor to activate GC without an apparent increase in transcription. [ABSTRACT FROM AUTHOR]

Background: During B lymphocyte development the E2A gene is a critical regulator of cell proliferation and differentiation. With regards to the immunoglobulin genes the E2A proteins contribute to the regulation of gene rearrangement, expression and class switch recombination. We are now using the chicken cell line DT40 as a model system to further analyse the function of E2A. Results: Here we report the cloning and functional analysis of the transcription factor E2A from chicken. Using RACE PCR on the chicken lymphoma cell line DT40 we have isolated full-length clones for the two E2A splice variants E12 and E47. Sequence conservation between the human and chicken proteins is extensive: the basic-helix-loop-helix DNA binding domain of human and chicken E47 and E12 are 93% and 92% identical, respectively. In addition high levels of conservation are seen in activation domain I, the potential NLS and the ubiquitin ligase interaction domain. E2A is expressed in a variety of tissues in chicken, with higher levels of expression in organs rich in immune cells. We demonstrate that chicken E12 and E47 proteins are strong transcriptional activators whose function depends on the presence of activation domain I. As in mammals, the dominant negative proteins Id1 and Id3 can inhibit the function of chicken E47. Conclusions: The potential for homologous recombination in DT40 allows the genetic dissection of biochemical pathways in somatic cells. With the cloning of avian E2A and the recent description of an in vitro somatic hypermutation assay in this cell line, it should now be possible to dissect the potential role of E2A in the regulation of somatic hypermutation and gene conversion. [ABSTRACT FROM AUTHOR]

IgK gene expression is controlled by two enhancers, one located within the major intron (Ei) and the other located downstream of CK (E3'). Whereas loss of E3' has previously been shown to diminish k expression, we show here that a rearranged k transgene lacking Ei is well expressed, even at the pre-B cell stage. This suggests that E3' alone might be sufficient to give properly regulated transcription throughout B cell development. Indeed, we show that a transgene composed of a β-globin reporter linked to E3' is expressed in a B cell-specific manner, becoming activated at the late pro-B to pre-B cell stage but with dramatically enhanced activity on B cell activation. Thus, E3' becomes active as a transcription enhancer at the stage when Vk-Jk rearrangement is being initiated and is sufficient to yield an expression pattern in a linked reporter gene similar to that of fully rearranged k genes. [ABSTRACT FROM AUTHOR]

The activity of the IgH (E) enhancer in the T lymphocyte lineage has been investigated using both transgenic mice and transfection studies. Thymocyte fractionation experiments indicate that a transgene consisting of the bacterial chloramphenicol acetyl transferase (CAT) gene, linked to Eμ and the SV40 early promoter (E–CAT), is expressed only in thymocytes with a mature medullary phenotype and not in immature cells. Transfection of this same construct into two thymoma cell lines representing different stages of thymocyte development mimics the pattern of activity observed . Further transfection experiments suggest that this pattern of expression might be attributed to the differential activity of the E2E3 and octanucleotide motifs of E during development. In contrast, an Ig λ transgene (linked to E and an Ig V promoter) is expressed in the majority of thymocytes. We envisage that the different patterns of expression of the two transgenes reflect interactions between their respective promoters and the factors which are bound to E at different stages of thymocyte development. Although differing in their pattern of expression within the thymus, the two transgenes share the property of extinction in peripheral T lymphocytes. These results indicate that the expression of E-linked transgenes in the thymus cannot simply be explained by activation of the enhancer in a lymphoid progenitor cell prior to B/T lineage divergence. Rather, the enhancer (or components of it) must be independently activated (and inactivated) during T lymphocyte development. Furthermore, this activity is consistent with the developmental timing of Ig D–J rearrangements in these cells. [ABSTRACT FROM PUBLISHER]

Motivation Increasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration. A number of methods have been developed to combine diverse datasets by removing technical batch effects, but most are computationally intensive. To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration algorithm. We illustrate the power of BBKNN on large scale mouse atlasing data, and favourably benchmark its run time against a number of competing methods. Availability and implementation BBKNN is available at https://github.com/Teichlab/bbknn , along with documentation and multiple example notebooks, and can be installed from pip. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Transcription factors (TFs) are key regulators of gene expression, and can activate or repress multiple target genes, forming regulatory units, or regulons. Understanding downstream effects of these regulators includes evaluating how TFs cooperate or compete within regulatory networks. Here we present RTNduals , an R/Bioconductor package that implements a general method for analyzing pairs of regulons. Results RTNduals identifies a dual regulon when the number of targets shared between a pair of regulators is statistically significant. The package extends the RTN (Reconstruction of Transcriptional Networks) package, and uses RTN transcriptional networks to identify significant co-regulatory associations between regulons. The Supplementary Information reports two case studies for TFs using the METABRIC and TCGA breast cancer cohorts. Availability and implementation RTNduals is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/RTNduals/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Transcriptional networks are models that allow the biological state of cells or tumours to be described. Such networks consist of connected regulatory units known as regulons, each comprised of a regulator and its targets. Inferring a transcriptional network can be a helpful initial step in characterizing the different phenotypes within a cohort. While the network itself provides no information on molecular differences between samples, the per-sample state of each regulon, i.e. the regulon activity, can be used for describing subtypes in a cohort. Integrating regulon activities with clinical data and outcomes would extend this characterization of differences between subtypes. Results We describe RTNsurvival , an R/Bioconductor package that calculates regulon activity profiles using transcriptional networks reconstructed by the RTN package, gene expression data, and a two-tailed Gene Set Enrichment Analysis. Given regulon activity profiles across a cohort, RTNsurvival can perform Kaplan-Meier analyses and Cox Proportional Hazards regressions, while also considering confounding variables. The Supplementary Information provides two case studies that use data from breast and liver cancer cohorts and features uni- and multivariate regulon survival analysis. Availability and implementation RTNsurvival is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/RTNsurvival/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background Improving lung cancer risk assessment is required because current early-detection screening criteria miss most cases. We therefore examined the utility for lung cancer risk assessment of a DNA Repair score obtained from OGG1, MPG, and APE1 blood tests. In addition, we examined the relationship between the level of DNA repair and global gene expression. Methods We conducted a blinded case-control study with 150 non–small cell lung cancer case patients and 143 control individuals. DNA Repair activity was measured in peripheral blood mononuclear cells, and the transcriptome of nasal and bronchial cells was determined by RNA sequencing. A combined DNA Repair score was formed using logistic regression, and its correlation with disease was assessed using cross-validation; correlation of expression to DNA Repair was analyzed using Gene Ontology enrichment. Results DNA Repair score was lower in case patients than in control individuals, regardless of the case's disease stage. Individuals at the lowest tertile of DNA Repair score had an increased risk of lung cancer compared to individuals at the highest tertile, with an odds ratio (OR) of 7.2 (95% confidence interval [CI] = 3.0 to 17.5; P  < .001), and independent of smoking. Receiver operating characteristic analysis yielded an area under the curve  of 0.89 (95% CI = 0.82 to 0.93). Remarkably, low DNA Repair score correlated with a broad upregulation of gene expression of immune pathways in patients but not in control individuals. Conclusions The DNA Repair score, previously shown to be a lung cancer risk factor in the Israeli population, was validated in this independent study as a mechanism-based cancer risk biomarker and can substantially improve current lung cancer risk prediction, assisting prevention and early detection by computed tomography scanning. [ABSTRACT FROM AUTHOR]

Adult stem-cells may serve as the cell-of-origin for cancer, yet their unbiased identification in single cell RNA sequencing data is challenging due to the high dropout rate. In the case of breast, the existence of a bipotent stem-like state is also controversial. Here we apply a marker-free algorithm to scRNA-Seq data from the human mammary epithelium, revealing a high-potency cell-state enriched for an independent mammary stem-cell expression module. We validate this stem-like state in independent scRNA-Seq data. Our algorithm further predicts that the stem-like state is bipotent, a prediction we are able to validate using FACS sorted bulk expression data. The bipotent stem-like state correlates with clinical outcome in basal breast cancer and is characterized by overexpression of YBX1 and ENO1, two modulators of basal breast cancer risk. This study illustrates the power of a marker-free computational framework to identify a novel bipotent stem-like state in the mammary epithelium. Chen et al build on a marker-free entropy-based algorithm to assign specific potency states to individual cells using single-cell RNA sequencing data. They identify a putative bipotent stem-cell like state, which associates with basal breast cancer risk and poor clinical outcome. [ABSTRACT FROM AUTHOR]

Background: The IRE1a-XBP1 pathway is a conserved adaptive mediator of the unfolded protein response. The pathway is indispensable for the development of secretory cells by facilitating protein folding and enhancing secretory capacity. In the immune system, it is known to function in dendritic cells, plasma cells, and eosinophil development and differentiation, while its role in T helper cell is unexplored. Here, we investigated the role of the IRE1a-XBP1 pathway in regulating activation and differentiation of type-2 T helper cell (Th2), a major T helper cell type involved in allergy, asthma, helminth infection, pregnancy, and tumor immunosuppression. Methods: We perturbed the IRE1a-XBP1 pathway and interrogated its role in Th2 cell differentiation. We performed genome-wide transcriptomic analysis of differential gene expression to reveal IRE1a-XBP1 pathway-regulated genes and predict their biological role. To identify direct target genes of XBP1 and define XBP1's regulatory network, we performed XBP1 ChIPmentation (ChIP-seq). We validated our predictions by flow cytometry, ELISA, and qPCR. We also used a fluorescent ubiquitin cell cycle indicator mouse to demonstrate the role of XBP1 in the cell cycle. Results: We show that Th2 lymphocytes induce the IRE1a-XBP1 pathway during in vitro and in vivo activation. Genome-wide transcriptomic analysis of differential gene expression by perturbing the IRE1a-XBP1 pathway reveals XBP1-controlled genes and biological pathways. Performing XBP1 ChIPmentation (ChIP-seq) and integrating with transcriptomic data, we identify XBP1-controlled direct target genes and its transcriptional regulatory network. We observed that the IRE1a-XBP1 pathway controls cytokine secretion and the expression of two Th2 signature cytokines, IL13 and IL5. We also discovered that the IRE1a-XBP1 pathway facilitates activation-dependent Th2 cell proliferation by facilitating cell cycle progression through S and G2/M phase. Conclusions: We confirm and detail the critical role of the IRE1a-XBP1 pathway during Th2 lymphocyte activation in regulating cytokine expression, secretion, and cell proliferation. Our high-quality genome-wide XBP1 ChIP and gene expression data provide a rich resource for investigating XBP1-regulated genes. We provide a browsable online database available at http://data.teichlab.org. [ABSTRACT FROM AUTHOR]

Many SNPs associated with human disease are located in non-coding regions of the genome. A new study shows that SNPs associated with breast cancer risk are located in enhancer regions and alter binding affinity for the pioneer factor FOXA1. [ABSTRACT FROM AUTHOR]

Reports that balancing cell death and proliferation plays a key role in the maintenance of homeostasis and the prevention of cancer in all multicellular organisms. Functions of pro-apoptotic and antiapoptotic members; Number of exons that consists the gene; Assessment on the functional importance of B-Myb in the regulation of the bcl-2 gene.

Harmonizing cell types across the single-cell community and assembling them into a common framework is central to building a standardized Human Cell Atlas. Here, we present CellHint, a predictive clustering tree-based tool to resolve cell-type differences in annotation resolution and technical biases across datasets. CellHint accurately quantifies cell-cell transcriptomic similarities and places cell types into a relationship graph that hierarchically defines shared and unique cell subtypes. Application to multiple immune datasets recapitulates expert-curated annotations. CellHint also reveals underexplored relationships between healthy and diseased lung cell states in eight diseases. Furthermore, we present a workflow for fast cross-dataset integration guided by harmonized cell types and cell hierarchy, which uncovers underappreciated cell types in adult human hippocampus. Finally, we apply CellHint to 12 tissues from 38 datasets, providing a deeply curated cross-tissue database with ∼3.7 million cells and various machine learning models for automatic cell annotation across human tissues. [Display omitted] • CellHint harmonizes independently annotated cell types across single-cell datasets • CellHint enables semi-automated multi-organ atlasing by supervised data integration • Underexplored cell types are revealed in normal hippocampus and diseased lungs • A multi-organ resource with harmonized cell types and models for cell annotation A machine learning architecture to harmonize and integrate cell types across single-cell datasets reveals cell-type relationships in health and disease. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWASs) have revealed SNP rs889312 on 5q11.2 to be associated with breast cancer risk in women of European ancestry. In an attempt to identify the biologically relevant variants, we analyzed 909 genetic variants across 5q11.2 in 103,991 breast cancer individuals and control individuals from 52 studies in the Breast Cancer Association Consortium. Multiple logistic regression analyses identified three independent risk signals: the strongest associations were with 15 correlated variants (iCHAV1), where the minor allele of the best candidate, rs62355902, associated with significantly increased risks of both estrogen-receptor-positive (ER + : odds ratio [OR] = 1.24, 95% confidence interval [CI] = 1.21–1.27, p trend = 5.7 × 10 −44 ) and estrogen-receptor-negative (ER − : OR = 1.10, 95% CI = 1.05–1.15, p trend = 3.0 × 10 −4 ) tumors. After adjustment for rs62355902, we found evidence of association of a further 173 variants (iCHAV2) containing three subsets with a range of effects (the strongest was rs113317823 [p cond = 1.61 × 10 −5 ]) and five variants composing iCHAV3 (lead rs11949391; ER + : OR = 0.90, 95% CI = 0.87–0.93, p cond = 1.4 × 10 −4 ). Twenty-six percent of the prioritized candidate variants coincided with four putative regulatory elements that interact with the MAP3K1 promoter through chromatin looping and affect MAP3K1 promoter activity. Functional analysis indicated that the cancer risk alleles of four candidates (rs74345699 and rs62355900 [iCHAV1], rs16886397 [iCHAV2a], and rs17432750 [iCHAV3]) increased MAP3K1 transcriptional activity. Chromatin immunoprecipitation analysis revealed diminished GATA3 binding to the minor (cancer-protective) allele of rs17432750, indicating a mechanism for its action. We propose that the cancer risk alleles act to increase MAP3K1 expression in vivo and might promote breast cancer cell survival. [ABSTRACT FROM AUTHOR]

Analysis of 4,405 variants in 89,050 European subjects from 41 case-control studies identified three independent association signals for estrogen-receptor-positive tumors at 11q13. The strongest signal maps to a transcriptional enhancer element in which the G allele of the best candidate causative variant rs554219 increases risk of breast cancer, reduces both binding of ELK4 transcription factor and luciferase activity in reporter assays, and may be associated with low cyclin D1 protein levels in tumors. Another candidate variant, rs78540526, lies in the same enhancer element. Risk association signal 2, rs75915166, creates a GATA3 binding site within a silencer element. Chromatin conformation studies demonstrate that these enhancer and silencer elements interact with each other and with their likely target gene, CCND1. [ABSTRACT FROM AUTHOR]

Summary T helper type 2 (Th2) cells are important regulators of mammalian adaptive immunity and have relevance for infection, autoimmunity, and tumor immunology. Using a newly developed, genome-wide retroviral CRISPR knockout (KO) library, combined with RNA-seq, ATAC-seq, and ChIP-seq, we have dissected the regulatory circuitry governing activation and differentiation of these cells. Our experiments distinguish cell activation versus differentiation in a quantitative framework. We demonstrate that these two processes are tightly coupled and are jointly controlled by many transcription factors, metabolic genes, and cytokine/receptor pairs. There are only a small number of genes regulating differentiation without any role in activation. By combining biochemical and genetic data, we provide an atlas for Th2 differentiation, validating known regulators and identifying factors, such as Pparg and Bhlhe40 , as part of the core regulatory network governing Th2 helper cell fates. Graphical Abstract Highlights • A retroviral CRISPR sgRNA library enables screening in primary mouse T helper cells • Genes frequently affect both T helper cell activation and differentiation • Genes of all functional categories impact activation and differentiation • The transcription factor PPARG appears particularly important for Th2 gene regulation A CRISPR-based screen reveals gene networks central to modulating multiple aspects of Th2 cell function. [ABSTRACT FROM AUTHOR]