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Edwin N Aroke,1,* Jai Ganesh Nagidi,2 Vinodh Srinivasasainagendra,3 Tammie L Quinn,4 Fiona BAT Agbor,1 Kiari R Kinnie,1 Hemant K Tiwari,3 Burel R Goodin5,* 1Department of Acute, Chronic, and Continuing Care, School of Nursing, University of Alabama at Birmingham, Birmingham, AL, USA; 2Department of Computer Science, College of Arts and Sciences, University of Alabama at Birmingham, Birmingham, AL, USA; 3Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA; 4Department of Psychology, College of Arts and Sciences, University of Alabama at Birmingham, Birmingham, AL, USA; 5Department of Anesthesiology, School of Medicine, Washington University, St Louis, MO, USA*These authors contributed equally to this workCorrespondence: Edwin N Aroke, School of Nursing, University of Alabama at Birmingham, 1701 2nd Avenue S, NB 470G, Birmingham, AL, 35294-1210, USA, Tel +1205-975-75700, Email earoke@uab.eduIntroduction: Having a lower socioeconomic status (SES) is a predictor of age-related chronic conditions, including chronic low back pain (cLBP). We aimed to examine whether the pace of biological aging mediates the relationship between SES and cLBP outcomes – pain intensity, pain interference, and physical performance.Methods: We used the Dunedin Pace of Aging Calculated from the Epigenome (DunedinPACE) software to determine the pace of biological aging in adults ages 18 to 85 years with no cLBP (n = 74), low-impact pain (n = 56), and high-impact pain (n = 77).Results: The mean chronological age of the participants was 40.9 years (SD= 15.1); 107 (51.7%) were female, and 108 (52.2%) were Black. On average, the pace of biological aging was 5% faster [DunedinPACE = 1.05 (SD = 0.14)] in the sample (DunedinPACE value of 1 = normal pace of aging). Individuals with higher levels of education had a significantly slower pace of biological aging than those with lower education levels (F = 5.546, p = 0.001). After adjusting for sex and race, household income level significantly correlated with the pace of biological aging (r = − 0.17, p = 0.02), pain intensity (r = − 0.21, p = 0.003), pain interference (r = − 0.21, p = 0.003), and physical performance (r = 0.20, p = 0.005). In mediation analyses adjusting for sex, race, and body mass index (BMI), the pace of biological aging mediates the relationship between household income (but not education) level and cLBP intensity, interference, as well as physical performance.Discussion: Results indicate that lower SES contributes to faster biological aging, possibly contributing to greater pain intensity and interference, as well as lower physical performance. Future interventions slowing the pace of biological aging may improve cLBP outcomes.Pain language summary: Individuals of lower socioeconomic status (SES) experience worse outcomes from age-related chronic conditions, including chronic low back pain (cLBP). This work examines the relationship between SES, pace of biological aging, and cLBP intensity, interference with daily activities, and physical function. Lower SES correlates with a faster pace of biological aging, as well as cLBP pain intensity, pain interference, and physical performance. The pace of biological aging helps explain the relationship between household income and cLBP intensity, interference, as well as physical performance. Interventions targeting SES disparities may slow biological aging and improve cLBP outcomes.Keywords: socioeconomic status, health disparities, DunedinPACE, pace of biological aging, chronic low back pain, epigenetic clock

Abstract Background Ribosomal proteins (RPs) have about 2000 pseudogenes in the human genome. While anecdotal reports for RP pseudogene transcription exists, it is unclear to what extent these pseudogenes are transcribed. The RP pseudogene transcription is difficult to identify in microarrays due to potential cross-hybridization between transcripts from the parent genes and pseudogenes. Recently, transcriptome sequencing (RNA-seq) provides an opportunity to ascertain the transcription of pseudogenes. A challenge for pseudogene expression discovery in RNA-seq data lies in the difficulty to uniquely identify reads mapped to pseudogene regions, which are typically also similar to the parent genes. Results Here we developed a specialized pipeline for pseudogene transcription discovery. We first construct a “composite genome” that includes the entire human genome sequence as well as mRNA sequences of real ribosomal protein genes. We then map all sequence reads to the composite genome, and only exact matches were retained. Moreover, we restrict our analysis to strictly defined mappable regions and calculate the RPKM values as measurement of pseudogene transcription levels. We report evidences for the transcription of RP pseudogenes in 16 human tissues. By analyzing the Human Body Map 2.0 study RNA-sequencing data using our pipeline, we identified that one ribosomal protein (RP) pseudogene (PGOHUM-249508) is transcribed with RPKM 170 in thyroid. Moreover, three other RP pseudogenes are transcribed with RPKM > 10, a level similar to that of the normal RP genes, in white blood cell, kidney, and testes, respectively. Furthermore, an additional thirteen RP pseudogenes are of RPKM > 5, corresponding to the 20–30 percentile among all genes. Unlike ribosomal protein genes that are constitutively expressed in almost all tissues, RP pseudogenes are differentially expressed, suggesting that they may contribute to tissue-specific biological processes. Conclusions Using a specialized bioinformatics method, we identified the transcription of ribosomal protein pseudogenes in human tissues using RNA-seq data.

Abstract Background Many efforts in microarray data analysis are focused on providing tools and methods for the qualitative analysis of microarray data. HDBStat! (High-Dimensional Biology-Statistics) is a software package designed for analysis of high dimensional biology data such as microarray data. It was initially developed for the analysis of microarray gene expression data, but it can also be used for some applications in proteomics and other aspects of genomics. HDBStat! provides statisticians and biologists a flexible and easy-to-use interface to analyze complex microarray data using a variety of methods for data preprocessing, quality control analysis and hypothesis testing. Results Results generated from data preprocessing methods, quality control analysis and hypothesis testing methods are output in the form of Excel CSV tables, graphs and an Html report summarizing data analysis. Conclusion HDBStat! is a platform-independent software that is freely available to academic institutions and non-profit organizations. It can be downloaded from our website http://www.soph.uab.edu/ssg_content.asp?id=1164.