Age-dependent assessment of genes involved in cellular senescence, telomere and mitochondrial pathways in human lung tissue of smokers, COPD and IPF: Associations with SARS-CoV-2 COVID-19 ACE2-TMPRSS2-Furin-DPP4 axis

Abstract

Aging is one of the key contributing factors for chronic obstructive pulmonary diseases (COPD) and other chronic inflammatory lung diseases. Cigarette smoke is a major etiological risk factor that has been shown to alter cellular processes involving mitochondrial function, cellular senescence and telomeric length. Here we determined how aging contribute to the alteration in the gene expression of above mentioned cellular processes that play an important role in the progression of COPD and IPF. We hypothesized that aging may differentially alter the expression of mitochondrial, cellular senescence and telomere genes in smokers and patients with COPD and IPF compared to non-smokers. Total RNA from human lung tissues from non-smokers, smokers, and patients with COPD and IPF were processed and analyzed based on their ages (younger: <55 yrs and older: >55 yrs). NanoString nCounter panel was used to analyze the gene expression profiles using a custom designed codeset containing 112 genes including 6 housekeeping controls (mitochondrial biogenesis and function, cellular senescence, telomere replication and maintenance). mRNA counts were normalized, log2 transformed for differential expression analysis using linear models in the limma package (R/Bioconductor). Data from non-smokers, smokers and patients with COPD and IPF were analyzed based on the age groups (pairwise comparisons between younger vs. older groups). Several genes were differentially expressed in younger and older smokers, and patients with COPD and IPF compared to non-smokers which were part of the mitochondrial biogenesis/function (HSPD1, FEN1, COX18, COX10, UCP2 & 3), cellular senescence (PCNA, PTEN, KLOTHO, CDKN1C, TNKS2, NFATC1 & 2, GADD45A) and telomere replication/maintenance (PARP1, SIRT6, NBN, TERT, RAD17, SLX4, HAT1) target genes. Interestingly, NOX4 and TNKS2 were increased in the young IPF as compared to the young COPD patients. Genes in the mitochondrial dynamics and other quality control mechanisms like FIS1 and RHOT2 were decreased in young IPF compared to their age matched COPD subjects. ERCC1 (Excision Repair Cross-Complementation Group 1) and GADD45B were higher in young COPD as compared to IPF. Aging plays an important role in various infectious diseases. Elderly patients with chronic lung disease and smokers were found to have high incidence and mortality rates in the current pandemic of SARS-CoV-2 infection. Immunoblot analysis in the lung homogenates of smokers, COPD and IPF subjects revealed increased protein abundance of important proteases and spike proteins like TMPRSS2, furin and DPP4 in association with a slight increase in SARS-CoV-2 receptor ACE2 levels. This may further strengthen the observation that smokers, COPD and IPF subjects are more prone to COVID-19 infection. Overall, these findings suggest that altered transcription of target genes that regulate mitochondrial function, cellular senescence, and telomere attrition add to the pathobiology of lung aging in COPD and IPF and other smoking-related chronic lung disease in associated with alterations in SARS-CoV-2 ACE2-TMPRSS2-Furin-DPP4 axis for COVID-19 infection.

Figure 1

Boxplot analysis of normalized mRNA transcript analyzed by NanoString. Boxplot shows distribution of normalized gene expression levels from young and old non-smokers, smokers and COPD subjects.

Figure 2

Venn diagram showing the number of altered mRNA transcripts analyzed by NanoString. Venn diagram representing the number of gene changes among Non-smokers, smokers and COPD, which were further divided for pairwise comparisons into A) young vs young, B) old vs old, and C) young vs old aged subjects. Lung RNA was isolated, processed and analyzed by NanoString. Normalized gene expressions were used for all the comparisons.

Figure 3

Volcano plots showing differentially expressed genes related to mitochondrial biogenesis and function, telomere replication and cellular senescence genes among non- smokers, smokers and COPD subjects. Altered genes in comparisons between A) Young (Smokers vs. Non-smokers). B) Young (Smokers vs. COPD). C) Young (COPD vs. Non-smokers). The genes differentially expressed in each comparison are indicated in green (increased) and red (decreased) colors. The green dotted horizontal line indicates the significance threshold of p-values from comparisons at P < 0.05. The Benjamini-Hochberg procedure was further used to adjust the p-values to control the false discovery rate at 5%.

Figure 4

Volcano plots showing differentially expressed genes related to mitochondrial biogenesis and function, telomere replication and cellular senescence genes among non- smokers, smokers and COPD subjects. Altered genes in comparisons between A) Old (Smokers vs. Non-smokers). B) Old (Smokers vs. COPD). C) Old (COPD vs. Non-smokers). The genes differentially expressed in each comparison are indicated in green (increased) and red (decreased) colors. The green dotted horizontal line indicates the significance threshold of p-values from comparisons at P < 0.05. The Benjamini-Hochberg procedure was further used to adjust the p-values to control the false discovery rate at 5%.

Figure 5

Volcano plots showing differentially expressed genes related to mitochondrial biogenesis and function, telomere replication and cellular senescence genes among non-smokers, smokers and COPD subjects. Altered genes in comparisons between A) Non-smokers (Young vs. Old). B) Smokers (Young vs. Old). C) COPD (Young vs. Old). The genes differentially expressed in each comparison are indicated in green (increased) and red (decreased) colors. The green dotted horizontal line indicates the significance threshold of p-values from comparisons at P < 0.05. The Benjamini-Hochberg procedure was further used to adjust the p-values to control the false discovery rate at 5%.

Figure 6

Boxplot analysis of normalized mRNA transcript analyzed by NanoString. Boxplot shows distribution of normalized gene expression levels in combined subjects from non-smokers, smokers and COPD subjects.

Figure 7

Venn diagram showing the number of altered mRNA transcripts analyzed by NanoString. Venn diagram representing the number of gene changes among A) Non-smokers, smokers and COPD. B) Non-smokers and IPF groups. Lung RNA was isolated, processed and analyzed by NanoString. Normalized gene expressions were used for all the comparisons.

Figure 8

Volcano plots showing differentially expressed genes related to mitochondrial biogenesis and function, telomere replication and cellular senescence genes among non-smokers, smokers and COPD subjects. Altered genes in comparisons between A) Smokers vs. Non-smokers. B) Smokers vs. COPD. C) COPD vs. Non-smokers. The genes differentially expressed in each comparison are indicated in green (increased) and red (decreased) colors. The green dotted horizontal line indicates the significance threshold of p-values from comparisons at P < 0.05. The Benjamini-Hochberg procedure was further used to adjust the p-values to control the false discovery rate at 5%.

Figure 9

Quantitative PCR validation of the selected genes, which were found to significantly and differentially altered across various pairwise comparisons. The values were deduced based 2− ΔΔCt method. The genes represented were found to be significant in their pairwise comparisons (p0<05). Student t-test was used to compare the level of significance in pairwise comparisons, while ANOVA was used for multiple comparisons.

Figure 10

Western blot analysis of the crucial targets involved in COVID19 in non-smokers, smokers and COPD subjects. Five samples per groups were used to probe for the TMPRSS2, furin, ACE2 and DPP4. Data were shown as mean ± SEM (n = 5/group). Level of significance were indicated as * P < 0.05 and ** P < 0.01 across the groups.

Figure 11

Western blot analysis of the crucial targets involved in COVID19 in non-smokers and IPF subjects. Five samples per groups were used to probe for the TMPRSS2, furin and ACE2. Data were shown as mean ± SEM (n = 5/group). Level of significance were indicated as * P < 0.05 and ** P < 0.01 across the groups.