Abstract Herpes stromal keratitis (HSK), caused by herpes simplex virus 1 (HSV-1), is the leading cause of infectious blindness in developed countries. Innate-acting γδ T17 cells are critical for protection against ocular HSV-1 infection. It is well established these cells can facilitate neutrophil influx into the cornea, but whether γδ T17 cells regulate other innate immune cells following ocular HSV-1 infection remains unclear. Natural killer (NK) cells also participate in the innate immune response generated against HSV-1 by directly killing infected cells through secretion of granzymes and promoting the antiviral response by production of IFN-γ. Our data strongly suggest that γδ T17 cell secretion of IL-17A promotes NK cell accumulation in the infected cornea. This is demonstrated in several ways: 1) in mice that lack γδ T cells (TCRδ-/-) there are fewer antiviral NK cells following corneal HSV-1 infection compared to wild-type (WT) mice; 2) administering IL-17A to TCRδ-/- mice restored the NK cell population; and 3) neutralization of IL-17A or 4) inhibiting γδ T17 cell influx to corneas in WT mice diminished NK cell accumulation leading to increased viral titers. In contrast, IL-17A production is enhanced in the absence of IFN-γ. In sum, this study identifies a novel mechanism by which IL-17A production by γδ T17 cells promotes antiviral NK cell responses that, in turn limit further IL-17A production via INF-γ secretion by NK cells in the HSV-1 infected cornea.

Purpose:To determine whether γδ T cells regulate natural killer (NK) cells in the herpes simplex virus 1 (HSV-1)-infected cornea. Methods:CD57Bl/6 (wild-type [WT]), TCRδ-/-, and IFN-γ-/- mice were infected intracorneally with HSV-1. TCR-/- mice were treated with IL-17A at 24 hours post-infection (PI), and the WT mice received treatments of fingolimod (FTY720) and anti-IL-17A. At 48 hours PI, corneas were excised, and intracellular staining flow cytometry was performed, as well as multiplex analysis. Additionally, single-cell RNA sequencing (scRNAseq) was done to analyze the transcriptome of NK cells from WT and TCRδ-/- mice. Results:In mice lacking γδ T cells, there were significantly fewer NK cells following ocular HSV-1 infection. This reduction of NK cells corresponded with lower levels of cytokines and chemokines associated with the antiviral response. Furthermore, NK cells from WT mice had enriched IL-17A signaling compared to those from TCRδ-/- mice. The NK cell response was partially rescued in TCRδ-/- mice by administration of IL-17A. Correspondingly, the NK cell response could be blunted in WT mice by administration of anti-IL-17A. Finally, IFN-γ-/- mice had significantly less IL-17A production compared to WT mice. Conclusions:γδ T17 cells promote NK cell accumulation in HSV-1-infected corneas. In turn, NK cells secrete IFN-γ, which negatively regulates further IL-17A production by γδ T cells.

Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. FC in particular usually contains tens of thousands of features per subject, and can only be summarized and efficiently explored using visualizations. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and single nucleotide polymorphism (SNP) data of healthy adolescents. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer to visualize trends in achievement scores between races, we find a clear potential for confounding effects if race can be predicted using FC. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10-15% of WRAT score variation, this predictive ability disappears when controlling for race. We also use ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP) dataset. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.

It can be difficult to identify trends and perform quality control in large, high-dimensional fMRI or omics datasets. To remedy this, we develop ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level features. The tool allows visualization of phenotype correlation with functional connectivity (FC), partial connectivity (PC), dictionary components (PCA and our own method), and genomic data (single-nucleotide polymorphisms, SNPs). In addition, it allows visualization of weights from arbitrary ML models. ImageNomer is built with a Python backend and a Vue frontend. We validate ImageNomer using the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and SNP data of healthy adolescents. Using correlation, greedy selection, or model weights, we find that a set of 10 FC features can explain 15% of variation in age, compared to 35% for the full 34,716 feature model. The four most significant FCs are either between bilateral default mode network (DMN) regions or spatially proximal subcortical areas. Additionally, we show that whereas both FC (fMRI) and SNPs (genomic) features can account for 10-15% of intelligence variation, this predictive ability disappears when controlling for race. We find that FC features can be used to predict race with 85% accuracy, compared to 78% accuracy for sex prediction. Using ImageNomer, this work casts doubt on the possibility of finding unbiased intelligence-related features in fMRI and SNPs of healthy adolescents.

We use the Philadelphia Neurodevelopmental Cohort (PNC) dataset to identify that intelligence prediction using fMRI data is almost entirely dependent on racial confounds. Race prediction using fMRI connectivity data (85%) is more effective that sex prediction (78%), while intelligence prediction using within-race groups reveals no advantage over the null model. This is surprising because race is not a feature that has traditionally been predicted using connectivity data. The PNC dataset is available to research groups on request from the database of genotypes of phenotypes under ascession ID phs000607.v3.p2 Neurodevelopmental Genomics: Trajectories of Complex Phenotypes. Linear models (Ridge or Logistic Regression) were used throughout on correlation-based connectivity data and SNPs. All required aprovals were obtained.

Epithelial cells of the conducting airways are a pivotal first line of defense against airborne pathogens and allergens that orchestrate inflammatory responses and mucociliary clearance. Nonetheless, the molecular mechanisms responsible for epithelial hyperreactivity associated with allergic asthma are not completely understood. Transcriptomic analysis of human airway epithelial cells (HAECs), differentiated in-vitro at air-liquid interface (ALI), showed 725 differentially expressed immediate-early transcripts, including putative long noncoding RNAs (lncRNAs). A novel lncRNA on the antisense strand of ICAM-1 or LASI was identified, which was induced in LPS-primed HAECs along with mucin MUC5AC and its transcriptional regulator SPDEF. LPS-primed expression of LASI, MUC5AC, and SPDEF transcripts were higher in ex-vivo cultured asthmatic HAECs that were further augmented by LPS treatment. Airway sections from asthmatics with increased mucus load showed higher LASI expression in MUC5AC+ goblet cells following multi-fluorescent in-situ hybridization and immunostaining. LPS- or IL-13-induced LASI transcripts were mostly enriched in the nuclear/perinuclear region and were associated with increased ICAM-1, IL-6, and CXCL-8 expression. Blocking LASI expression reduced the LPS or IL-13-induced epithelial inflammatory factors and MUC5AC expression, suggesting that the novel lncRNA LASI could play a key role in LPS-primed trained airway epithelial responses that are dysregulated in allergic asthma.

Hypoxia, a common attribute of many lung diseases, causes an adaptive response characterized by glycolytic reprogramming, which is initiated in part by the master transcriptional regulator, Hif‐1, accumulating in response to reactive oxygen species (ROS) generated as second messengers in hypoxic signaling. ROS produced in hypoxia also appear to engage a DNA damage and repair pathway in promoter regulatory elements that impacts core events in transcription (Pastukh, et al., 2015). Because the genome‐wide disposition of base damage in hypoxia could provide clues about mechanisms of persistent metabolic reprogramming in the lung, we executed a proof‐of‐concept study to define the landscape of the common base oxidation product 8‐oxoguanine (8‐oxoG) in the genomes of normoxic and acutely hypoxic human umbilical vein endothelial cells (HUVECs), focusing on localization of base damage within genes comprising the hypoxic transcriptome. Using ChIP‐seq analysis to delineate sequences harboring 8‐oxoG, we found that base damage was enriched in multiple regulatory regions, including those defined by transcription factor binding motifs and epigenetic marks. Of particular interest, base damage in these regions occurred in distinctly different genes in normoxic and hypoxic cells. SIM super‐resolution immunofluorescence microscopy revealed prominent base damage in facultative heterochromatin residing in close proximity to chromatin‐free channels that appeared to be in direct communication with the nuclear membrane. A comparison of sequences demarcated by 8‐oxoG ChIP‐seq signals to the RNA‐seq‐defined hypoxic transcriptome revealed that base damage was prominent in genes occupying regulatory nodes in pathways controlling the metabolic response to hypoxia as well as other key adaptive pathways. Finally, after noting that sequences harboring 8‐oxoG displayed elevated variant densities, we discovered that a high proportion of these variants were likely mutational artifacts since they were corrected by enzymatic excision of damaged bases in template DNA prior to PCR‐based sequencing library preparation. Collectively, these findings suggest that hypoxia fundamentally alters the genomic landscape of 8‐oxoG in HUVECs, causing accumulation or dissipation of the base oxidation product in non‐coding regulatory regions in differentially‐transcribed genes. The presence of base damage‐associated mutational artifacts supports the concept that the DNA damage and repair pathway engaged during hypoxic transcriptional signaling could impose a mutagenic threat leading to formation of permanent and currently uncharacterized somatic variants with the potential to drive persistent metabolic dysregulation in hypoxia‐related lung diseases.Support or Funding InformationNIH

Action rules are rules that describe how to transition a decision attribute from an undesired state to a desired state, with the understanding that some attributes are stable and others are flexible. Stable attributes, such as "age", may not be changed, whereas flexible attributes, such as "interest rate", may be changed. Action rules have great potential in data mining, as they output easily interpretable rules which can immediately be useful to a decision maker. However, at present, the methods to generate all valid action rules are computationally expensive. To address this, methods have been proposed that prune swaths of the search space as rules are generated; this results in computational efficiency, at the expense of potentially not discovering many useful rules. In this work, a method, called Multi-Objective Evolutionary Action Rule (MOEAR) mining, is introduced. MOEAR optimizes the discovery of action rules using standard evolutionary algorithm principles. Experimental results show that MOEAR is able to generate a large number of potentially interesting action rules, including those rules that could be categorized as "rare", while achieving good computational performance.