scholarly journals PD-L1+ neutrophils contribute to injury-induced infection susceptibility

2021 ◽  
Vol 7 (10) ◽  
pp. eabd9436
Author(s):  
Ajitha Thanabalasuriar ◽  
Abby J. Chiang ◽  
Christopher Morehouse ◽  
Margarita Camara ◽  
Shonda Hawkins ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Neutrophil Accumulation ◽  
Susceptibility To Infection ◽  
Infection Susceptibility ◽  
Epidermal Growth ◽  
Lung Endothelial Cells ◽  
Altered Gene ◽  
Underlying Mechanisms ◽  
Skin Healing

The underlying mechanisms contributing to injury-induced infection susceptibility remain poorly understood. Here, we describe a rapid increase in neutrophil cell numbers in the lungs following induction of thermal injury. These neutrophils expressed elevated levels of programmed death ligand 1 (PD-L1) and exhibited altered gene expression profiles indicative of a reparative population. Upon injury, neutrophils migrate from the bone marrow to the skin but transiently arrest in the lung vasculature. Arrested neutrophils interact with programmed cell death protein 1 (PD-1) on lung endothelial cells. A period of susceptibility to infection is linked to PD-L1+ neutrophil accumulation in the lung. Systemic treatment of injured animals with an anti–PD-L1 antibody prevented neutrophil accumulation in the lung and reduced susceptibility to infection but augmented skin healing, resulting in increased epidermal growth. This work provides evidence that injury promotes changes to neutrophils that are important for wound healing but contribute to infection susceptibility.

scholarly journals Chamber-enriched gene expression profiles in failing human hearts with reduced ejection fraction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Luo ◽  
Jun Yin ◽  
Denise Dwyer ◽  
Tracy Yamawaki ◽  
Hong Zhou ◽  
...  
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Human Genetics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Left Heart ◽  
Reduced Ejection Fraction ◽  
Cell Type Composition ◽  
Type Composition ◽  
Underlying Mechanisms

AbstractHeart failure with reduced ejection fraction (HFrEF) constitutes 50% of HF hospitalizations and is characterized by high rates of mortality. To explore the underlying mechanisms of HFrEF etiology and progression, we studied the molecular and cellular differences in four chambers of non-failing (NF, n = 10) and HFrEF (n = 12) human hearts. We identified 333 genes enriched within NF heart subregions and often associated with cardiovascular disease GWAS variants. Expression analysis of HFrEF tissues revealed extensive disease-associated transcriptional and signaling alterations in left atrium (LA) and left ventricle (LV). Common left heart HFrEF pathologies included mitochondrial dysfunction, cardiac hypertrophy and fibrosis. Oxidative stress and cardiac necrosis pathways were prominent within LV, whereas TGF-beta signaling was evident within LA. Cell type composition was estimated by deconvolution and revealed that HFrEF samples had smaller percentage of cardiomyocytes within the left heart, higher representation of fibroblasts within LA and perivascular cells within the left heart relative to NF samples. We identified essential modules associated with HFrEF pathology and linked transcriptome discoveries with human genetics findings. This study contributes to a growing body of knowledge describing chamber-specific transcriptomics and revealed genes and pathways that are associated with heart failure pathophysiology, which may aid in therapeutic target discovery.

scholarly journals Integrative Genomics with Mediation Analysis in a Survival Context

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Szilárd Nemes ◽  
Toshima Z. Parris ◽  
Anna Danielsson ◽  
Zakaria Einbeigi ◽  
Gunnar Steineck ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Genomic Analysis ◽  
Mrna Levels ◽  
Integrative Genomics ◽  
Asymptotic Results ◽  
Data Set ◽  
Cancer Data ◽  
Dna And Rna ◽  
Altered Gene

DNA copy number aberrations (DCNA) and subsequent altered gene expression profiles may have a major impact on tumor initiation, on development, and eventually on recurrence and cancer-specific mortality. However, most methods employed in integrative genomic analysis of the two biological levels, DNA and RNA, do not consider survival time. In the present note, we propose the adoption of a survival analysis-based framework for the integrative analysis of DCNA and mRNA levels to reveal their implication on patient clinical outcome with the prerequisite that the effect of DCNA on survival is mediated by mRNA levels. The specific aim of the paper is to offer a feasible framework to test the DCNA-mRNA-survival pathway. We provide statistical inference algorithms for mediation based on asymptotic results. Furthermore, we illustrate the applicability of the method in an integrative genomic analysis setting by using a breast cancer data set consisting of 141 invasive breast tumors. In addition, we provide implementation in R.

High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning

2021 ◽  
Author(s):  
Xiangtao Li ◽  
Shaochuan Li ◽  
Lei Huang ◽  
Shixiong Zhang ◽  
Ka-chun Wong
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Single Cell ◽  
Deep Neural Networks ◽  
Expression Profiles ◽  
Marker Gene ◽  
Gene Expression Profiles ◽  
Underlying Mechanisms ◽  
Cell Data ◽  
Gene Expression Levels

Abstract Single-cell RNA sequencing (scRNA-seq) technologies have been heavily developed to probe gene expression profiles at single-cell resolution. Deep imputation methods have been proposed to address the related computational challenges (e.g. the gene sparsity in single-cell data). In particular, the neural architectures of those deep imputation models have been proven to be critical for performance. However, deep imputation architectures are difficult to design and tune for those without rich knowledge of deep neural networks and scRNA-seq. Therefore, Surrogate-assisted Evolutionary Deep Imputation Model (SEDIM) is proposed to automatically design the architectures of deep neural networks for imputing gene expression levels in scRNA-seq data without any manual tuning. Moreover, the proposed SEDIM constructs an offline surrogate model, which can accelerate the computational efficiency of the architectural search. Comprehensive studies show that SEDIM significantly improves the imputation and clustering performance compared with other benchmark methods. In addition, we also extensively explore the performance of SEDIM in other contexts and platforms including mass cytometry and metabolic profiling in a comprehensive manner. Marker gene detection, gene ontology enrichment and pathological analysis are conducted to provide novel insights into cell-type identification and the underlying mechanisms. The source code is available at https://github.com/li-shaochuan/SEDIM.

Altered Gene Expression Profiles in the Frontal Cortex of Cirrhotic Alcoholics

2007 ◽  
Vol 31 (9) ◽  
pp. 1460-1466 ◽  
Author(s):  
Jianwen Liu ◽  
Joanne M. Lewohl ◽  
R. Adron Harris ◽  
Peter R. Dodd ◽  
R. Dayne Mayfield
Keyword(s):  
Gene Expression ◽  
Frontal Cortex ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Altered Gene Expression ◽  
Altered Gene

Altered gene expression profiles and higher frequency of spontaneous DNA strand breaks in APEX2-null thymus

DNA Repair ◽  
2008 ◽  
Vol 7 (9) ◽  
pp. 1437-1454 ◽  
Author(s):  
Yukihiko Dan ◽  
Yutaka Ohta ◽  
Daisuke Tsuchimoto ◽  
Mizuki Ohno ◽  
Yasuhito Ide ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Dna Strand Breaks ◽  
Strand Breaks ◽  
Altered Gene Expression ◽  
Altered Gene ◽  
Dna Strand

scholarly journals Behavioral appraisal by implementing a short sequence of stress resolves adaptively changed stress gains

2020 ◽  
Author(s):  
Eun-Hwa Lee ◽  
Jin-Young Park ◽  
Hye-jin Kwon ◽  
Pyung-Lim Han
Keyword(s):  
Chronic Stress ◽  
Basolateral Amygdala ◽  
Low Dose ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Repeated Treatment ◽  
Glucocorticoid Treatment ◽  
Bed Nucleus ◽  
Antidepressive Effects ◽  
Altered Gene

Abstract Chronic stress produces adaptive changes in the brain via the cumulative action of glucocorticoids, which causes psychiatric illnesses such as depression. Here we show that a behavioral method implementing weak stress does not strengthen but resolves existing stress gains. Chronic stress produces persistent depressive behaviors in mice, and repeated daily treatment with 5-min restraint produces antidepressive effects. Repeated treatment with low-dose glucocorticoids mimics the anti-depressive effects of weak stress. Repeated weak stress or low-dose glucocorticoid treatment distinctively activates the prelimbic cortex (PL), and reverses the stress-induced altered gene expression profiles. Chemogenetic inhibition of PL outputs projecting to the nucleus accumbens, basolateral amygdala, or bed nucleus of the stria terminalis (BNST) dissipates antidepressive effects of weak stress, but only the PL-to-BNST circuit produces changes in dysregulated glucocorticoid release. Our results suggest that behavioral appraisal by implementing weak stress can resolve adaptively altered stress gains and rectify stress-induced depressive behaviors.

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