Sleep Deprivation and Pain Intensity

Little or poor quality sleep is often reported in patients suffering from acute or chronic pain. Conversely, sleep loss has been known to elevate pain perception; thus a potential bi-direction relationship exists between sleep deprivation and pain. The effect of sleep deprivation on the thermal pain intensity has yet to be determined, furthermore, sex differences in pain have not been examined following sleep deprivation. There is also a higher prevalence of insomnia in women, and reports indicate that sleep quality is diminished and pain sensitivity may be greater during high hormone phases of the menstrual cycle. In Study 1 we examined the effects of 24-hour total sleep deprivation (TSD) on pain intensity during a 2-minute cold pressor test (CPT). We hypothesized that TSD would augment thermal pain intensity during CPT and women would demonstrate an elevated response compare to men. In Study 2 we investigated the effects of menstrual phase on pain intensity during CPT following TSD. We hypothesized that pain intensity would be augmented during the mid-luteal (ML) phase of the menstrual cycle. In Study 1, pain intensity was recorded during CPT in 14 men and 13 women after normal sleep (NS) and TSD. Pain intensity responses during CPT were elevated in both conditions; however, pain intensity was augmented (~ 1.2 a.u.) following TSD. When analyzed for sex differences, pain intensity was not different between men and women in either condition. In Study 2, pain intensity was recorded during CPT in 10 female subjects during the early follicular (EF) and ML phases of the menstrual cycle after TSD. Estradiol and progesterone levels were elevated during the ML phase, however, pain intensity was not different between the two phases. We conclude that TSD significantly augments pain intensity during CPT, but this response is not sex dependent. We further demonstrate that the collective effect of TSD and elevated gonadal hormone concentrations do not result in a differential pain response during the EF and ML phases of the menstrual cycle. Collectively, sleep loss augments pain intensity ratings in men and women and may contribute to sleep loss in painful conditions.

DISSECTION OF STRESS RESPONSE NETWORKS REGULATING MULTIPLE STRESSES IN RICE

Important food crops like rice are constantly exposed to various stresses that can have devastating effect on their survival and productivity. Being sessile, these highly evolved organisms have developed elaborate molecular machineries to sense a mixture of stress signals and elicit a precise response to minimize the damage. However, recent discoveries revealed that the interplay of these stress regulatory and signaling molecules is highly complex and remains largely unknown. In this work, we conducted large scale analysis of differential gene expression using advanced computational methods to dissect regulation of stress response which is at the heart of all molecular changes leading to the observed phenotypic susceptibility. One of the most important stress conditions in terms of loss of productivity is drought. We performed genomic and proteomic analysis of epigenetic and miRNA mechanisms in regulation of drought responsive genes in rice and found subsets of genes with striking properties. Overexpressed genesets included higher number of epigenetic marks, miRNA targets and transcription factors which regulate drought tolerance. On the other hand, underexpressed genesets were poor in above features but were rich in number of metabolic genes with multiple co-expression partners contributing majorly towards drought resistance. Identification and characterization of the patterns exhibited by differentially expressed genes hold key to uncover the synergistic and antagonistic components of the cross talk between stress response mechanisms. We performed meta-analysis on drought and bacterial stresses in rice and Arabidopsis, and identified hundreds of shared genes. We found high level of conservation of gene expression between these stresses. Weighted co-expression network analysis detected two tight clusters of genes made up of master transcription factors and signaling genes showing strikingly opposite expression status. To comprehensively identify the shared stress responsive genes between multiple abiotic and biotic stresses in rice, we performed meta-analyses of microarray studies from seven different abiotic and six biotic stresses separately and found more than thirteen hundred shared stress responsive genes. Various machine learning techniques utilizing these genes classified the stresses into two major classes' namely abiotic and biotic stresses and multiple classes of individual stresses with high accuracy and identified the top genes showing distinct patterns of expression. Functional enrichment and co-expression network analysis revealed the different roles of plant hormones, transcription factors in conserved and non-conserved genesets in regulation of stress response.