Circulating Adipocytokines and Insulin Like-Growth Factors and Their Modulation in Obesity-Associated Endometrial Cancer.

The rising global incidence of uterine cancer is linked to the escalating prevalence of obesity. Obesity results in alterations in adipocytokines and IGFs, driving cancer progression via inflammation, increased cell proliferation, and apoptosis inhibition, although the precise mechanisms are still unclear. This study examined a set of six markers, namely, adiponectin, leptin, IL6, TNFα, IGF1, and IGF2 and compared them between fifty age-matched endometrial cancer patients (study group) and non-cancer patients with benign gynaecological conditions (control group). We also assessed the relationship of these markers with obesity and explored the correlation between these markers and various tumour characteristics. In the cancer population, these markers were also assessed 24 h and 6 months post-surgery. Remarkably, low adiponectin levels were associated with a 35.8% increase in endometrial cancer risk. Interestingly, compared to control subjects where IGF levels decreased after menopause, post-menopausal women in the study group showed elevated IGF1 and IGF2 levels, suggesting a potential influence of endometrial cancer on the IGF system, particularly after menopause. Lastly, it is noteworthy that a discernible inverse relationship trend was observed in the levels of adipocytokines and IGFs 6 months post-surgery. This indicates that treatment for endometrial cancer may have a differential impact on adipocytokines and IGFs, potentially holding clinical significance that merits further investigation.

Mistimed sleep and waking activity in humans disrupts glucocorticoid signalling transcripts and SP1, but not plasma cortisol rhythms.

Cortisol is a robust circadian signal that synchronises peripheral circadian clocks with the central clock in the suprachiasmatic nucleus via glucocorticoid receptors that regulate peripheral gene expression. Misalignment of the cortisol rhythm with the sleep-wake cycle, as occurs in shift work, is associated with negative health outcomes, but underlying molecular mechanisms remain largely unknown. We experimentally induced misalignment between the sleep-wake cycle and melatonin and cortisol rhythms in humans and measured time series blood transcriptomics while participants slept in-phase and out-of-phase with the central clock. The cortisol rhythm remained unchanged, but many glucocorticoid signalling transcripts were disrupted by mistimed sleep. To investigate which factors drive this dissociation between cortisol and its signalling pathways, we conducted bioinformatic and temporal coherence analyses. We found that glucocorticoid signalling transcripts affected by mistimed sleep were enriched for binding sites for the transcription factor SP1. Furthermore, changes in the timing of the rhythms of SP1 transcripts, a major regulator of transcription, and changes in the timing of rhythms in transcripts of the glucocorticoid signalling pathways were closely associated. Associations between the rhythmic changes in factors that affect SP1 expression and its activity, such as STAT3, EP300, HSP90AA1, and MAPK1, were also observed. We conclude that plasma cortisol rhythms incompletely reflect the impact of mistimed sleep on glucocorticoid signalling pathways and that sleep-wake driven changes in SP1 may mediate disruption of these pathways. These results aid understanding of mechanisms by which mistimed sleep affects health.

PBX4 functions as a potential novel oncopromoter in colorectal cancer: a comprehensive analysis of the PBX gene family.

Pre-B-cell leukaemia (PBX) is a transcription factor family (PBX1, PBX2, PBX3 and PBX4) that regulates important cellular functions and has been identified to be involved in human cancers. This study aimed to explore the expression of PBX genes and their clinical significance in colorectal cancer (CRC). We analysed the differential expression of PBX genes in CRC vs. normal tissue, using the Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) and ONCOMINE platform (https://www.oncomine.org/). The UALCAN (http://ualcan.path.uab.edu/) interactive OMICS web-server was used to evaluate the epigenetic regulation of PBX genes via their promoter methylation status. We found that only PBX4 was upregulated whereas PBX1 and PBX3 were downregulated (644 tumour vs. 51 normal samples) (P<0.001). The methylation status of PBX4 promoter appeared to be decreased (P=1.4e-07) whereas the methylation status of PBX1 and PBX3 promoters was increased (P=3.8e-04 and P=3.2e-07, respectively) in cancer vs. normal samples. To determine the prognostic value of PBXs, we conducted a Kaplan-Meier survival analysis and multivariable COX regression. We observed that high PBX4 expression was associated with increased risk for a worse overall survival (OS) in the TCGA CRC patient cohort (n=639), (HR 1.46, 95% CI 1.14-1.88, P=0.003) adjusted for age, gender, tumour location and metastases. We conducted in vitro gene expression modulation experiments to investigate the impact of PBX4 overexpression in CRC cell (HCT116) growth. Additionally, we evaluated the RNA expression of epithelial-mesenchymal transition (EMT) and angiogenesis markers. In vitro studies showed that PBX4 overexpression increased CRC cell proliferation (P<0.001) and upregulated the expression of EMT markers VIM, CDH1, CDH2, ZEB1, SNAI1 (P<0.05) and angiomarker VEGFA (P<0.0001). Lastly, through the Cistrome data browser (http://dbtoolkit.cistrome.org/) we investigated putative transcriptional regulators and we performed gene set enrichment analysis in Enrichr server (https://maayanlab.cloud/Enrichr/) to identify related biological processes. Nineteen factors were identified to be putative regulators of PBX4 and gene set enrichment analysis showed that biological processes related to cell cycle and cell proliferation were enriched (GO:0051726: CDK8, JUN, JUND, and IRF1, P=0.001). In conclusion, our study identified PBX4 as a potential novel oncopromoter in CRC and its overexpression was found to be associated with increased risk for worse survival rate.

Diurnal and circadian rhythmicity of the human blood transcriptome overlaps with organ- and tissue-specific expression of a non-human primate.

BACKGROUND: Twenty-four-hour rhythmicity in mammalian tissues and organs is driven by local circadian oscillators, systemic factors, the central circadian pacemaker and light-dark cycles. At the physiological level, the neural and endocrine systems synchronise gene expression in peripheral tissues and organs to the 24-h-day cycle, and disruption of such regulation has been shown to lead to pathological conditions. Thus, monitoring rhythmicity in tissues/organs holds promise for circadian medicine; however, most tissues and organs are not easily accessible in humans and alternative approaches to quantify circadian rhythmicity are needed. We investigated the overlap between rhythmic transcripts in human blood and transcripts shown to be rhythmic in 64 tissues/organs of the baboon, how these rhythms are aligned with light-dark cycles and each other, and whether timing of tissue-specific rhythmicity can be predicted from a blood sample. RESULTS: We compared rhythmicity in transcriptomic time series collected from humans and baboons using set logic, circular cross-correlation, circular clustering, functional enrichment analyses, and least squares regression. Of the 759 orthologous genes that were rhythmic in human blood, 652 (86%) were also rhythmic in at least one baboon tissue and most of these genes were associated with basic processes such as transcription and protein homeostasis. In total, 109 (17%) of the 652 overlapping rhythmic genes were reported as rhythmic in only one baboon tissue or organ and several of these genes have tissue/organ-specific functions. The timing of human and baboon rhythmic transcripts displayed prominent 'night' and 'day' clusters, with genes in the dark cluster associated with translation. Alignment between baboon rhythmic transcriptomes and the overlapping human blood transcriptome was significantly closer when light onset, rather than midpoint of light, or end of light period, was used as phase reference point. The timing of overlapping human and baboon rhythmic transcriptomes was significantly correlated in 25 tissue/organs with an average earlier timing of 3.21 h (SD 2.47 h) in human blood. CONCLUSIONS: The human blood transcriptome contains sets of rhythmic genes that overlap with rhythmic genes of tissues/organs in baboon. The rhythmic sets vary across tissues/organs, but the timing of most rhythmic genes is similar in human blood and baboon tissues/organs. These results have implications for development of blood transcriptome-based biomarkers for circadian rhythmicity in tissues and organs.

Genome-wide analysis of the role of the antibiotic biosynthesis regulator AbsA2 in Streptomyces coelicolor A3(2).

The AbsA1-AbsA2 two component signalling system of Streptomyces coelicolor has long been known to exert a powerful negative influence on the production of the antibiotics actinorhodin, undecylprodiginine and the Calcium-Dependent Antibiotic (CDA). Here we report the analysis of a ΔabsA2 deletion strain, which exhibits the classic precocious antibiotic hyper-production phenotype, and its complementation by an N-terminal triple-FLAG-tagged version of AbsA2. The complemented and non-complemented ΔabsA2 mutant strains were used in large-scale microarray-based time-course experiments to investigate the effect of deleting absA2 on gene expression and to identify the in vivo AbsA2 DNA-binding target sites using ChIP-on chip. We show that in addition to binding to the promoter regions of redZ and actII-orfIV AbsA2 binds to several previously unidentified sites within the cda biosynthetic gene cluster within and/or upstream of SCO3215-SCO3216, SCO3217, SCO3229-SCO3230, and SCO3226, and we relate the pattern of AbsA2 binding to the results of the transcriptomic study and antibiotic phenotypic assays. Interestingly, dual 'biphasic' ChIP peaks were observed with AbsA2 binding across the regulatory genes actII-orfIV and redZ and the absA2 gene itself, while more conventional single promoter-proximal peaks were seen at the CDA biosynthetic genes suggesting a different mechanism of regulation of the former loci. Taken together the results shed light on the complex mechanism of regulation of antibiotic biosynthesis in Streptomyces coelicolor and the important role of AbsA2 in controlling the expression of three antibiotic biosynthetic gene clusters.

Circadian regulation in human white adipose tissue revealed by transcriptome and metabolic network analysis.

Studying circadian rhythms in most human tissues is hampered by difficulty in collecting serial samples. Here we reveal circadian rhythms in the transcriptome and metabolic pathways of human white adipose tissue. Subcutaneous adipose tissue was taken from seven healthy males under highly controlled 'constant routine' conditions. Five biopsies per participant were taken at six-hourly intervals for microarray analysis and in silico integrative metabolic modelling. We identified 837 transcripts exhibiting circadian expression profiles (2% of 41619 transcript targeting probes on the array), with clear separation of transcripts peaking in the morning (258 probes) and evening (579 probes). There was only partial overlap of our rhythmic transcripts with published animal adipose and human blood transcriptome data. Morning-peaking transcripts associated with regulation of gene expression, nitrogen compound metabolism, and nucleic acid biology; evening-peaking transcripts associated with organic acid metabolism, cofactor metabolism and redox activity. In silico pathway analysis further indicated circadian regulation of lipid and nucleic acid metabolism; it also predicted circadian variation in key metabolic pathways such as the citric acid cycle and branched chain amino acid degradation. In summary, in vivo circadian rhythms exist in multiple adipose metabolic pathways, including those involved in lipid metabolism, and core aspects of cellular biochemistry.

REM sleep's unique associations with corticosterone regulation, apoptotic pathways, and behavior in chronic stress in mice.

One of sleep's putative functions is mediation of adaptation to waking experiences. Chronic stress is a common waking experience; however, which specific aspect of sleep is most responsive, and how sleep changes relate to behavioral disturbances and molecular correlates remain unknown. We quantified sleep, physical, endocrine, and behavioral variables, as well as the brain and blood transcriptome in mice exposed to 9 weeks of unpredictable chronic mild stress (UCMS). Comparing 46 phenotypic variables revealed that rapid-eye-movement sleep (REMS), corticosterone regulation, and coat state were most responsive to UCMS. REMS theta oscillations were enhanced, whereas delta oscillations in non-REMS were unaffected. Transcripts affected by UCMS in the prefrontal cortex, hippocampus, hypothalamus, and blood were associated with inflammatory and immune responses. A machine-learning approach controlling for unspecific UCMS effects identified transcriptomic predictor sets for REMS parameters that were enriched in 193 pathways, including some involved in stem cells, immune response, and apoptosis and survival. Only three pathways were enriched in predictor sets for non-REMS. Transcriptomic predictor sets for variation in REMS continuity and theta activity shared many pathways with corticosterone regulation, in particular pathways implicated in apoptosis and survival, including mitochondrial apoptotic machinery. Predictor sets for REMS and anhedonia shared pathways involved in oxidative stress, cell proliferation, and apoptosis. These data identify REMS as a core and early element of the response to chronic stress, and identify apoptosis and survival pathways as a putative mechanism by which REMS may mediate the response to stressful waking experiences.

Identifying and validating blood mRNA biomarkers for acute and chronic insufficient sleep in humans: a machine learning approach.

Acute and chronic insufficient sleep are associated with adverse health outcomes and risk of accidents. There is therefore a need for biomarkers to monitor sleep debt status. None are currently available. We applied elastic net and ridge regression to transcriptome samples collected in 36 healthy young adults during acute total sleep deprivation and following 1 week of either chronic insufficient (<6 hr) or sufficient sleep (~8.6 hr) to identify panels of mRNA biomarkers of sleep debt status. The size of identified panels ranged from 9 to 74 biomarkers. Panel performance, assessed by leave-one-subject-out cross-validation and independent validation, varied between sleep debt conditions. Using between-subject assessments based on one blood sample, the accuracy of classifying "acute sleep loss" was 92%, but only 57% for classifying "chronic sleep insufficiency." A reasonable accuracy for classifying "chronic sleep insufficiency" could only be achieved by a within-subject comparison of blood samples. Biomarkers for sleep debt status showed little overlap with previously identified biomarkers for circadian phase. Biomarkers for acute and chronic sleep loss also showed little overlap but were associated with common functions related to the cellular stress response, such as heat shock protein activity, the unfolded protein response, protein ubiquitination and endoplasmic reticulum-associated protein degradation, and apoptosis. This characteristic response of whole blood to sleep loss can further aid our understanding of how sleep insufficiencies negatively affect health. Further development of these novel biomarkers for research and clinical practice requires validation in other protocols and age groups.

Blood transcriptome based biomarkers for human circadian phase.

Diagnosis and treatment of circadian rhythm sleep-wake disorders both require assessment of circadian phase of the brain's circadian pacemaker. The gold-standard univariate method is based on collection of a 24-hr time series of plasma melatonin, a suprachiasmatic nucleus-driven pineal hormone. We developed and validated a multivariate whole-blood mRNA-based predictor of melatonin phase which requires few samples. Transcriptome data were collected under normal, sleep-deprivation and abnormal sleep-timing conditions to assess robustness of the predictor. Partial least square regression (PLSR), applied to the transcriptome, identified a set of 100 biomarkers primarily related to glucocorticoid signaling and immune function. Validation showed that PLSR-based predictors outperform published blood-derived circadian phase predictors. When given one sample as input, the R2 of predicted vs observed phase was 0.74, whereas for two samples taken 12 hr apart, R2 was 0.90. This blood transcriptome-based model enables assessment of circadian phase from a few samples.

Exploiting human and mouse transcriptomic data: Identification of circadian genes and pathways influencing health.

The power of the application of bioinformatics across multiple publicly available transcriptomic data sets was explored. Using 19 human and mouse circadian transcriptomic data sets, we found that NR1D1 and NR1D2 which encode heme-responsive nuclear receptors are the most rhythmic transcripts across sleep conditions and tissues suggesting that they are at the core of circadian rhythm generation. Analyzes of human transcriptomic data show that a core set of transcripts related to processes including immune function, glucocorticoid signalling, and lipid metabolism is rhythmically expressed independently of the sleep-wake cycle. We also identify key transcripts associated with transcription and translation that are disrupted by sleep manipulations, and through network analysis identify putative mechanisms underlying the adverse health outcomes associated with sleep disruption, such as diabetes and cancer. Comparative bioinformatics applied to existing and future data sets will be a powerful tool for the identification of core circadian- and sleep-dependent molecules.

Mistimed sleep disrupts circadian regulation of the human transcriptome.

Circadian organization of the mammalian transcriptome is achieved by rhythmic recruitment of key modifiers of chromatin structure and transcriptional and translational processes. These rhythmic processes, together with posttranslational modification, constitute circadian oscillators in the brain and peripheral tissues, which drive rhythms in physiology and behavior, including the sleep-wake cycle. In humans, sleep is normally timed to occur during the biological night, when body temperature is low and melatonin is synthesized. Desynchrony of sleep-wake timing and other circadian rhythms, such as occurs in shift work and jet lag, is associated with disruption of rhythmicity in physiology and endocrinology. However, to what extent mistimed sleep affects the molecular regulators of circadian rhythmicity remains to be established. Here, we show that mistimed sleep leads to a reduction of rhythmic transcripts in the human blood transcriptome from 6.4% at baseline to 1.0% during forced desynchrony of sleep and centrally driven circadian rhythms. Transcripts affected are key regulators of gene expression, including those associated with chromatin modification (methylases and acetylases), transcription (RNA polymerase II), translation (ribosomal proteins, initiation, and elongation factors), temperature-regulated transcription (cold inducible RNA-binding proteins), and core clock genes including CLOCK and ARNTL (BMAL1). We also estimated the separate contribution of sleep and circadian rhythmicity and found that the sleep-wake cycle coordinates the timing of transcription and translation in particular. The data show that mistimed sleep affects molecular processes at the core of circadian rhythm generation and imply that appropriate timing of sleep contributes significantly to the overall temporal organization of the human transcriptome.

Effects of insufficient sleep on circadian rhythmicity and expression amplitude of the human blood transcriptome.

Insufficient sleep and circadian rhythm disruption are associated with negative health outcomes, including obesity, cardiovascular disease, and cognitive impairment, but the mechanisms involved remain largely unexplored. Twenty-six participants were exposed to 1 wk of insufficient sleep (sleep-restriction condition 5.70 h, SEM = 0.03 sleep per 24 h) and 1 wk of sufficient sleep (control condition 8.50 h sleep, SEM = 0.11). Immediately following each condition, 10 whole-blood RNA samples were collected from each participant, while controlling for the effects of light, activity, and food, during a period of total sleep deprivation. Transcriptome analysis revealed that 711 genes were up- or down-regulated by insufficient sleep. Insufficient sleep also reduced the number of genes with a circadian expression profile from 1,855 to 1,481, reduced the circadian amplitude of these genes, and led to an increase in the number of genes that responded to subsequent total sleep deprivation from 122 to 856. Genes affected by insufficient sleep were associated with circadian rhythms (PER1, PER2, PER3, CRY2, CLOCK, NR1D1, NR1D2, RORA, DEC1, CSNK1E), sleep homeostasis (IL6, STAT3, KCNV2, CAMK2D), oxidative stress (PRDX2, PRDX5), and metabolism (SLC2A3, SLC2A5, GHRL, ABCA1). Biological processes affected included chromatin modification, gene-expression regulation, macromolecular metabolism, and inflammatory, immune and stress responses. Thus, insufficient sleep affects the human blood transcriptome, disrupts its circadian regulation, and intensifies the effects of acute total sleep deprivation. The identified biological processes may be involved with the negative effects of sleep loss on health, and highlight the interrelatedness of sleep homeostasis, circadian rhythmicity, and metabolism.

Exon array analysis of head and neck cancers identifies a hypoxia related splice variant of LAMA3 associated with a poor prognosis.

The identification of alternatively spliced transcript variants specific to particular biological processes in tumours should increase our understanding of cancer. Hypoxia is an important factor in cancer biology, and associated splice variants may present new markers to help with planning treatment. A method was developed to analyse alternative splicing in exon array data, using probeset multiplicity to identify genes with changes in expression across their loci, and a combination of the splicing index and a new metric based on the variation of reliability weighted fold changes to detect changes in the splicing patterns. The approach was validated on a cancer/normal sample dataset in which alternative splicing events had been confirmed using RT-PCR. We then analysed ten head and neck squamous cell carcinomas using exon arrays and identified differentially expressed splice variants in five samples with high versus five with low levels of hypoxia-associated genes. The analysis identified a splice variant of LAMA3 (Laminin alpha 3), LAMA3-A, known to be involved in tumour cell invasion and progression. The full-length transcript of the gene (LAMA3-B) did not appear to be hypoxia-associated. The results were confirmed using qualitative RT-PCR. In a series of 59 prospectively collected head and neck tumours, expression of LAMA3-A had prognostic significance whereas LAMA3-B did not. This work illustrates the potential for alternatively spliced transcripts to act as biomarkers of disease prognosis with improved specificity for particular tissues or conditions over assays which do not discriminate between splice variants.

Exploiting sample variability to enhance multivariate analysis of microarray data.

MOTIVATION: Biological and technical variability is intrinsic in any microarray experiment. While most approaches aim to account for this variability, they do not actively exploit it. Here, we consider a novel approach that uses the variability between arrays to provide an extra source of information that can enhance gene expression analyses. RESULTS: We develop a method that uses sample similarity to incorporate sample variability into the analysis of gene expression profiles. This allows each pairwise correlation calculation to borrow information from all the data in the experiment. Results on synthetic and human cancer microarray datasets show that the inclusion of this information leads to a significant increase in the ability to identify previously characterized relationships and a reduction in false discovery rate, when compared to a standard analysis using Pearson correlation. The information carried by the variability between arrays can be exploited to significantly improve the analysis of gene expression data. AVAILABILITY: Matlab script files are available from the author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modeling and analysis of gene expression time-series based on co-expression.

In this paper a novel approach is introduced for modeling and clustering gene expression time-series. The radial basis function neural networks have been used to produce a generalized and smooth characterization of the expression time-series. A co-expression coefficient is defined to evaluate the similarities of the models based on their temporal shapes and the distribution of the time points. The profiles are grouped using a fuzzy clustering algorithm incorporated with the proposed co-expression coefficient metric. The results on artificial and real data are presented to illustrate the advantages of the metric and method in grouping temporal profiles. The proposed metric has also been compared with the commonly used correlation coefficient under the same procedures and the results show that the proposed method produces better biologically relevant clusters.

Microarray data clustering based on temporal variation: FCV with TSD preclustering.

The aim of this paper is to present a new clustering algorithm for short time-series gene expression data that is able to characterise temporal relations in the clustering environment (ie data-space), which is not achieved by other conventional clustering algorithms such as k -means or hierarchical clustering. The algorithm called fuzzy c -varieties clustering with transitional state discrimination preclustering (FCV-TSD) is a two-step approach which identifies groups of points ordered in a line configuration in particular locations and orientations of the data-space that correspond to similar expressions in the time domain. We present the validation of the algorithm with both artificial and real experimental datasets, where k -means and random clustering are used for comparison. The performance was evaluated with a measure for internal cluster correlation and the geometrical properties of the clusters, showing that the FCV-TSD algorithm had better performance than the k -means algorithm on both datasets.