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Accurate cancer subtype prediction is crucial for personalized medicine. Integrating multi-omics data represents a viable approach to comprehending the intricate pathophysiology of complex diseases like cancer. Conventional machine learning techniques are not ideal for analyzing the complex interrelationships among different categories of omics data. Numerous models have been suggested using graph-based learning to uncover veiled representations and network formations unique to distinct types of omics data to heighten predictions regarding cancers and characterize patients' profiles, amongst other applications aimed at improving disease management in medical research. The existing graph-based state-of-the-art multi-omics integration approaches for cancer subtype prediction, MOGONET, and SUPREME, use a graph convolutional network (GCN), which fails to consider the level of importance of neighboring nodes on a particular node. To address this gap, we hypothesize that paying attention to each neighbor or providing appropriate weights to neighbors based on their importance might improve the cancer subtype prediction. The natural choice to determine the importance of each neighbor of a node in a graph is to explore the graph attention network (GAT). Here, we propose MOGAT, a novel multi-omics integration approach, leveraging GAT models that incorporate graph-based learning with an attention mechanism. MOGAT utilizes a multi-head attention mechanism to extract appropriate information for a specific sample by assigning unique attention coefficients to neighboring samples. Based on our knowledge, our group is the first to explore GAT in multi-omics integration for cancer subtype prediction. To evaluate the performance of MOGAT in predicting cancer subtypes, we explored two sets of breast cancer data from TCGA and METABRIC. Our proposed approach, MOGAT, outperforms MOGONET by 32% to 46% and SUPREME by 2% to 16% in cancer subtype prediction in different scenarios, supporting our hypothesis. Our results also showed that GAT embeddings provide a better prognosis in differentiating the high-risk group from the low-risk group than raw features.

Conditionally reprogrammed cell (CRC) technique is a promising model for biomedical and toxicological research. In the present study, our data first demonstrated an increased level of PARP-1 in conditionally reprogrammed human foreskin keratinocytes (CR-HFKs). We then found that PARP inhibitor ABT-888 (ABT), reactive oxygen species (ROS) scavenger N-acetyl-l-cysteine (NAC), or combination (ABT + NAC) were able to inhibit cell proliferation, ROS, PARP-1, and ROS related protein, NRF2, and NOX1. Interestingly, knockdown of endogenous PARP-1 significantly inhibited cell proliferation, indicating that the increased PARP-1 expression was critical for CR. Importantly, we found that a moderate level of ROS contributed the cell proliferation and increased PARP-1 since knockdown of PARP-1 also inhibited the ROS. The similar inhibition of cell proliferation, ROS, and expression of PARP-1 and NRF2 proteins was observed when CR-HFKs were treated with hydroquinone (HQ), a key component from skin-lightening products. Moreover, the treatment of HQ plus treatment of ABT, NAC, or combination can further inhibit cell proliferation, ROS, expression of PARP-1, and NRF2 proteins. PARP-1 knockdown inhibited the population doubling (PDL) and treatment of HQ inhibited the PDL further, as well as the change of ROS. Finally, we discovered that pathways including cyclin D1, NRF2, Rb and pRb, CHK2, and p53, were involved in cell proliferation inhibition with HQ. Taken together, our findings demonstrated that crosstalk between ROS and PARP-1 involves in the cell proliferation in CR-HFKs, and that inhibition of CR-HFK proliferation with HQ is through modulating G1 cell cycle arrest., (© 2022 The Authors. Journal of Biochemical and Molecular Toxicology published by Wiley Periodicals LLC.)

Molecular mimicry between viral antigens and host proteins can produce cross-reacting antibodies leading to autoimmunity. The coronavirus SARS-CoV-2 causes COVID-19, a disease curiously resulting in varied symptoms and outcomes, ranging from asymptomatic to fatal. Autoimmunity due to cross-reacting antibodies resulting from molecular mimicry between viral antigens and host proteins may provide an explanation. Thus, we computationally investigated molecular mimicry between SARS-CoV-2 Spike and known epitopes. We discovered molecular mimicry hotspots in Spike and highlight two examples with tentative high autoimmune potential and implications for understanding COVID-19 complications. We show that a TQLPP motif in Spike and thrombopoietin shares similar antibody binding properties. Antibodies cross-reacting with thrombopoietin may induce thrombocytopenia, a condition observed in COVID-19 patients. Another motif, ELDKY, is shared in multiple human proteins, such as PRKG1 involved in platelet activation and calcium regulation, and tropomyosin, which is linked to cardiac disease. Antibodies cross-reacting with PRKG1 and tropomyosin may cause known COVID-19 complications such as blood-clotting disorders and cardiac disease, respectively. Our findings illuminate COVID-19 pathogenesis and highlight the importance of considering autoimmune potential when developing therapeutic interventions to reduce adverse reactions.

Background: Long non-coding RNA plays a vital role in changing the expression profiles of various target genes that lead to cancer development. Thus, identifying prognostic lncRNAs related to different cancers might help in developing cancer therapy., Method: To discover the critical lncRNAs that can identify the origin of different cancers, we propose the use of the state-of-the-art deep learning algorithm concrete autoencoder (CAE) in an unsupervised setting, which efficiently identifies a subset of the most informative features. However, CAE does not identify reproducible features in different runs due to its stochastic nature. We thus propose a multi-run CAE (mrCAE) to identify a stable set of features to address this issue. The assumption is that a feature appearing in multiple runs carries more meaningful information about the data under consideration. The genome-wide lncRNA expression profiles of 12 different types of cancers, with a total of 4768 samples available in The Cancer Genome Atlas (TCGA), were analyzed to discover the key lncRNAs. The lncRNAs identified by multiple runs of CAE were added to a final list of key lncRNAs that are capable of identifying 12 different cancers., Results: Our results showed that mrCAE performs better in feature selection than single-run CAE, standard autoencoder (AE), and other state-of-the-art feature selection techniques. This study revealed a set of top-ranking 128 lncRNAs that could identify the origin of 12 different cancers with an accuracy of 95%. Survival analysis showed that 76 of 128 lncRNAs have the prognostic capability to differentiate high- and low-risk groups of patients with different cancers., Conclusion: The proposed mrCAE, which selects actual features, outperformed the AE even though it selects the latent or pseudo-features. By selecting actual features instead of pseudo-features, mrCAE can be valuable for precision medicine. The identified prognostic lncRNAs can be further studied to develop therapies for different cancers.

Background: Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment., Results: The Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes - one from non-treatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups., Conclusion: A computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies - non-treatment and treatment - are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.

Conventional cancer and transformed cell lines are widely used in cancer biology and other fields within biology. These cells usually have abnormalities from the original tumor itself, but may also develop abnormalities due to genetic manipulation, or genetic and epigenetic changes during long-term passages. Primary cultures may maintain lineage functions as the original tissue types, yet they have a very limited life span or population doubling time because of the nature of cellular senescence. Primary cultures usually have very low yields, and the high variability from any original tissue specimens, largely limiting their applications in research. Animal models are often used for studies of virus infections, disease modeling, development of antiviral drugs, and vaccines. Human viruses often need a series of passages in vivo to adapt to the host environment because of variable receptors on the cell surface and may have intracellular restrictions from the cell types or host species. Here, we describe a long-term cell culture system, conditionally reprogrammed cells (CRCs), and its applications in modeling human viral diseases and drug discovery. Using feeder layer coculture in presence of Y-27632 (conditional reprogramming, CR), CRCs can be obtained and rapidly propagated from surgical specimens, core or needle biopsies, and other minimally invasive or noninvasive specimens, for example, nasal cavity brushing. CRCs preserve their lineage functions and provide biologically relevant and physiological conditions, which are suitable for studies of viral entry and replication, innate immune responses of host cells, and discovery of antiviral drugs. In this review, we summarize the applications of CR technology in modeling host-virus interactions and human viral diseases including severe acute respiratory syndrome coronavirus-2 and coronavirus disease-2019, and antiviral discovery., (© 2020 The Authors. Journal of Medical Virology published by Wiley Periodicals LLC.)

Monte Carlo based method by using either SMILES based or combination of SMILES and Graph-based descriptors is an important strategy to build the QSAR/QSTR model for prediction of different biological endpoints. In this study, Monte Carlo based QSTR approach was applied to the dataset of 90 nitroaromatic compounds related to their in vivo toxicity, represented by 50% lethal dose concentration for rats (LD 50 ). Both classification and regression-based QSTR models were developed to get an idea about different fingerprints for promoters and hinderers of nitroaromatics toxicity. The best classification model was obtained by using SMILES and graph-based (GAO) descriptor with 1 EC K connectivity (sensitivity = 0.7143, specificity = 1.0000, accuracy = 0.8889, and MCC = 0.7774). The best regression model calculated by using SMILES and hydrogen-suppressed graph descriptors with 0 EC k connectivity ( R 2 = 0.7386, Q 2 = 0.6315, S   =   0.467, and MAE = 0.340). Finally, a consensus QSTR model was generated to predict efficiently the toxicity of new compounds. The study highlighted that the comparative QSTR models by using the Monte Carlo method can also be generated and will be a useful tool for structural fingerprint analysis in case of nitroaromatics for preliminary evaluation of its toxicity to mammals.

Patient-derived xenografts (PDXs) are widely recognised as a more physiologically relevant preclinical model than standard cell lines, but are expensive and low throughput, have low engraftment rate and take a long time to develop. Our newly developed conditional reprogramming (CR) technology addresses many PDX drawbacks, but lacks many in vivo factors. Here we determined whether PDXs and CRCs of the same cancer origin maintain the biological fidelity and complement each for translational research and drug development. Four CRC lines were generated from bladder cancer PDXs. Short tandem repeat (STR) analyses revealed that CRCs and their corresponding parental PDXs shared the same STRs, suggesting common cancer origins. CRCs and their corresponding parental PDXs contained the same genetic alterations. Importantly, CRCs retained the same drug sensitivity with the corresponding downstream signalling activity as their corresponding parental PDXs. This suggests that CRCs and PDXs can complement each other, and that CRCs can be used for in vitro fast, high throughput and low cost screening while PDXs can be used for in vivo validation and study of the in vivo factors during translational research and drug development., (Copyright © 2019 Elsevier Inc. All rights reserved.)

We previously developed the technique of conditional reprogramming (CR), which allows primary epithelial cells from fresh or cryopreserved specimens to be propagated long-term in vitro, while maintaining their genetic stability and differentiation potential. This method requires a combination of irradiated fibroblast feeder cells and a Rho-associated kinase (ROCK) inhibitor. In the present study, we demonstrate increased levels of full-length p53 and its natural isoform, Δ133p53α, in conditionally reprogrammed epithelial cells from primary prostate, foreskin, ectocervical, and mammary tissues. Increased Δ133p53α expression is critical for CR since cell proliferation is rapidly inhibited following siRNA knockdown of endogenous Δ133p53α. Importantly, overexpression of Δ133p53α consistently delays the onset of cellular senescence of primary cells when cultured under non-CR conditions in normal keratinocyte growth medium (KGM). More significantly, the combination of Δ133p53α overexpression and ROCK inhibitor, without feeder cells, enables primary epithelial cells to be propagated long-term in vitro. We also show that Δ133p53α overexpression induces hTERT expression and telomerase activity and that siRNA knockdown of hTERT causes rapid inhibition of cell proliferation, indicating a critical role of hTERT for mediating the effects of Δ133p53α. Altogether, these data demonstrate a functional and regulatory link between p53 pathways and hTERT expression during the conditional reprogramming of primary epithelial cells.

An aspect of mycotoxin biosynthesis that remains unclear is its relationship with the cellular management of reactive oxygen species (ROS). Here we conduct a comparative study of the total ROS production in the wild-type strain (SU-1) of the plant pathogen and aflatoxin producer, Aspergillus parasiticus , and its mutant strain, AFS10, in which the aflatoxin biosynthesis pathway is blocked by disruption of its pathway regulator, aflR . We show that SU-1 demonstrates a significantly faster decrease in total ROS than AFS10 between 24 h to 48 h, a time window within which aflatoxin synthesis is activated and reaches peak levels in SU-1. The impact of aflatoxin synthesis in alleviation of ROS correlated well with the transcriptional activation of five superoxide dismutases (SOD), a group of enzymes that protect cells from elevated levels of a class of ROS, the superoxide radicals (O₂ - ). Finally, we show that aflatoxin supplementation to AFS10 growth medium results in a significant reduction of total ROS only in 24 h cultures, without resulting in significant changes in SOD gene expression. Our findings show that the activation of aflatoxin biosynthesis in A. parasiticus alleviates ROS generation, which in turn, can be both aflR dependent and aflatoxin dependent., Competing Interests: The authors declare no conflict of interest.

p53 functions to induce cellular senescence, which is incompatible with self-renewal of pluripotent stem cells such as induced pluripotent stem cells (iPSC) and embryonic stem cells (ESC). However, p53 also has essential roles in these cells through DNA damage repair for maintaining genomic integrity and high sensitivity to apoptosis for eliminating severely damaged cells. We hypothesized that Δ133p53, a physiological inhibitory p53 isoform, is involved in the balanced regulation of self-renewing capacity, DNA damage repair and apoptosis. We examined 12 lines of human iPSC and their original fibroblasts, as well as three ESC lines, for endogenous protein levels of Δ133p53 and full-length p53 (FL-p53), and mRNA levels of various p53 target genes. While FL-p53 levels in iPSC and ESC widely ranged from below to above those in the fibroblasts, all iPSC and ESC lines expressed elevated levels of Δ133p53. The p53-inducible genes that mediate cellular senescence (p21 WAF1 , miR-34a, PAI-1 and IGFBP7), but not those for apoptosis (BAX and PUMA) and DNA damage repair (p53R2), were downregulated in iPSC and ESC. Consistent with these endogenous expression profiles, overexpression of Δ133p53 in human fibroblasts preferentially repressed the p53-inducible senescence mediators and significantly enhanced their reprogramming to iPSC. The iPSC lines derived from Δ133p53-overexpressing fibroblasts formed well-differentiated, benign teratomas in immunodeficient mice and had fewer numbers of somatic mutations than an iPSC derived from p53-knocked-down fibroblasts, suggesting that Δ133p53 overexpression is non- or less oncogenic and mutagenic than total inhibition of p53 activities. Overexpressed Δ133p53 prevented FL-p53 from binding to the regulatory regions of p21 WAF1 and miR-34a promoters, providing a mechanistic basis for its dominant-negative inhibition of a subset of p53 target genes. This study supports the hypothesis that upregulation of Δ133p53 is an endogenous mechanism that facilitates human somatic cells to become self-renewing pluripotent stem cells with maintained apoptotic and DNA repair activities.

Δ133p53α, a p53 isoform that can inhibit full-length p53, is downregulated at replicative senescence in a manner independent of mRNA regulation and proteasome-mediated degradation. Here we demonstrate that, unlike full-length p53, Δ133p53α is degraded by autophagy during replicative senescence. Pharmacological inhibition of autophagy restores Δ133p53α expression levels in replicatively senescent fibroblasts, without affecting full-length p53. The siRNA-mediated knockdown of pro-autophagic proteins (ATG5, ATG7 and Beclin-1) also restores Δ133p53α expression. The chaperone-associated E3 ubiquitin ligase STUB1, which is known to regulate autophagy, interacts with Δ133p53α and is downregulated at replicative senescence. The siRNA knockdown of STUB1 in proliferating, early-passage fibroblasts induces the autophagic degradation of Δ133p53α and thereby induces senescence. Upon replicative senescence or STUB1 knockdown, Δ133p53α is recruited to autophagosomes, consistent with its autophagic degradation. This study reveals that STUB1 is an endogenous regulator of Δ133p53α degradation and senescence, and identifies a p53 isoform-specific protein turnover mechanism that orchestrates p53-mediated senescence.

Cellular senescence contributes to aging and decline in tissue function. p53 isoform switching regulates replicative senescence in cultured fibroblasts and is associated with tumor progression. Here, we found that the endogenous p53 isoforms Δ133p53 and p53β are physiological regulators of proliferation and senescence in human T lymphocytes in vivo. Peripheral blood CD8+ T lymphocytes collected from healthy donors displayed an age-dependent accumulation of senescent cells (CD28-CD57+) with decreased Δ133p53 and increased p53β expression. Human lung tumor-associated CD8+ T lymphocytes also harbored senescent cells. Cultured CD8+ blood T lymphocytes underwent replicative senescence that was associated with loss of CD28 and Δ133p53 protein. In poorly proliferative, Δ133p53-low CD8+CD28- cells, reconstituted expression of either Δ133p53 or CD28 upregulated endogenous expression of each other, which restored cell proliferation, extended replicative lifespan and rescued senescence phenotypes. Conversely, Δ133p53 knockdown or p53β overexpression in CD8+CD28+ cells inhibited cell proliferation and induced senescence. This study establishes a role for Δ133p53 and p53β in regulation of cellular proliferation and senescence in vivo. Furthermore, Δ133p53-induced restoration of cellular replicative potential may lead to a new therapeutic paradigm for treating immunosenescence disorders, including those associated with aging, cancer, autoimmune diseases, and HIV infection.

Most human pre-mRNA transcripts are alternatively spliced, but the significance and fine-tuning of alternative splicing in different biological processes is only starting to be understood. SRSF3 (SRp20) is a member of a highly conserved family of splicing factors that have critical roles in key biological processes, including tumor progression. Here, we show that SRSF3 regulates cellular senescence, a p53-mediated process to suppress tumorigenesis, through TP53 alternative splicing. Downregulation of SRSF3 was observed in normal human fibroblasts undergoing replicative senescence, and was associated with the upregulation of p53β, an alternatively spliced isoform of p53 that promotes p53-mediated senescence. Knockdown of SRSF3 by short interfering RNA (siRNA) in early-passage fibroblasts induced senescence, which was associated with elevated expression of p53β at mRNA and protein levels. Knockdown of p53 partially rescued SRSF3-knockdown-induced senescence, suggesting that SRSF3 acts on p53-mediated cellular senescence. RNA pulldown assays demonstrated that SRSF3 binds to an alternatively spliced exon uniquely included in p53β mRNA through the consensus SRSF3-binding sequences. RNA crosslinking and immunoprecipitation assays (CLIP) also showed that SRSF3 in vivo binds to endogenous p53 pre-mRNA at the region containing the p53β-unique exon. Splicing assays using a transfected TP53 minigene in combination with siRNA knockdown of SRSF3 showed that SRSF3 functions to inhibit the inclusion of the p53β-unique exon in splicing of p53 pre-mRNA. These data suggest that downregulation of SRSF3 represents an endogenous mechanism for cellular senescence that directly regulates the TP53 alternative splicing to generate p53β. This study uncovers the role for general splicing machinery in tumorigenesis, and suggests that SRSF3 is a direct regulator of p53.

Background: Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms., Results: This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors., Conclusions: We proposed a method for rational design of minimalist ensemble algorithms using feature selection and classifiers. The proposed minimalist ensemble algorithm based on logistic regression can achieve equal or better prediction performance while using only half or one-third of individual predictors compared to other ensemble algorithms. The results also suggested that meta-predictors that take advantage of a variety of features by combining individual predictors tend to achieve the best performance. The LR ensemble server and related benchmark datasets are available at http://mleg.cse.sc.edu/LRensemble/cgi-bin/predict.cgi.

The telomere-capping complex shelterin protects functional telomeres and prevents the initiation of unwanted DNA-damage-response pathways. At the end of cellular replicative lifespan, uncapped telomeres lose this protective mechanism and DNA-damage signalling pathways are triggered that activate p53 and thereby induce replicative senescence. Here, we identify a signalling pathway involving p53, Siah1 (a p53-inducible E3 ubiquitin ligase) and TRF2 (telomere repeat binding factor 2; a component of the shelterin complex). Endogenous Siah1 and TRF2 were upregulated and downregulated, respectively, during replicative senescence with activated p53. Experimental manipulation of p53 expression demonstrated that p53 induces Siah1 and represses TRF2 protein levels. The p53-dependent ubiquitylation and proteasomal degradation of TRF2 are attributed to the E3 ligase activity of Siah1. Knockdown of Siah1 stabilized TRF2 and delayed the onset of cellular replicative senescence, suggesting a role for Siah1 and TRF2 in p53-regulated senescence. This study reveals that p53, a downstream effector of telomere-initiated damage signalling, also functions upstream of the shelterin complex.

As a consequence of the adulteration of infant formulas and milk powders with melamine (MEL) in China in 2008, much attention has been devoted to the analysis of MEL [and cyanuric acid (CA)] in dairy products. Several methods based on high-performance liquid chromatography (HPLC), liquid chromatography-tandem mass spectrometry (LC-MS/MS), nuclear magnetic resonance (NMR), or Raman spectroscopy have been described in the literature. However, no method is available for the simultaneous determination of MEL and CA in other raw materials, which are considered as high-risk materials for economically motivated adulteration. The present paper reports the results of an interlaboratory-based performance evaluation conducted with seven laboratories worldwide. The purpose was to demonstrate the ability of a cleanup-free LC-MS/MS method, originally developed for cow's milk and milk-powdered infant formula, to quantify MEL and CA in egg powder and soy protein. Limit of detection (LOD) and limit of quantification (LOQ) were 0.02 and 0.05 mg/kg for MEL in egg powder and soy protein, respectively. For CA, LOD and LOQ were 0.05 and 0.10 mg/kg in egg powder and 1.0 and 1.50 mg/kg in soy protein, respectively. Recoveries ranged within a 97-113% range for both MEL and CA in egg powder and soy protein. Reproducibility values (RSD(R)) from seven laboratories were within a 5.4-11.7% range for both analytes in the considered matrices. Horwitz ratio (HorRat) values between 0.4 and 0.7 indicate acceptable among-laboratory precision for the method described.

The finite proliferative potential of normal human cells leads to replicative cellular senescence, which is a critical barrier to tumour progression in vivo. We show that the human p53 isoforms Delta133p53 and p53beta function in an endogenous regulatory mechanism for p53-mediated replicative senescence. Induced p53beta and diminished Delta133p53 were associated with replicative senescence, but not oncogene-induced senescence, in normal human fibroblasts. The replicatively senescent fibroblasts also expressed increased levels of miR-34a, a p53-induced microRNA, the antisense inhibition of which delayed the onset of replicative senescence. The siRNA (short interfering RNA)-mediated knockdown of endogenous Delta133p53 induced cellular senescence, which was attributed to the regulation of p21(WAF1) and other p53 transcriptional target genes. In overexpression experiments, whereas p53beta cooperated with full-length p53 to accelerate cellular senescence, Delta133p53 repressed miR-34a expression and extended the cellular replicative lifespan, providing a functional connection of this microRNA to the p53 isoform-mediated regulation of senescence. The senescence-associated signature of p53 isoform expression (that is, elevated p53beta and reduced Delta133p53) was observed in vivo in colon adenomas with senescent phenotypes. The increased Delta133p53 and decreased p53beta isoform expression found in colon carcinoma may signal an escape from the senescence barrier during the progression from adenoma to carcinoma.

Background and Objectives: Vitamin D and its metabolites act through vitamin D receptor (VDR). We hypothesized that subjects with low serum 25(OH)D levels but normal PTH might have increased VDR expression., Design and Methods: VDRmRNA expression was assessed by real time PCR in duodenal mucosa and PBMC (peripheral blood mononuclear cells) in 45 subjects with normal duodenoscopy and in PBMC alone in 48 healthy volunteers with hypovitaminosis D. 25(OH)D, PTH and VDRmRNA expression in PBMC was reassessed after 8 weeks of oral cholecalciferol (60 000 IU per week) in a subset (n=23) of healthy volunteers., Results and Conclusions: The VDRmRNA expressions in the duodenum and PBMC were significantly correlated (r=0.42), but the expression was 13 times higher in the former than the latter. The mean VDRmRNA expression was similar in 25(OH)D-deficient subjects with or without PTH elevation, both in the duodenum and PBMC. The PBMC VDRmRNA expression showed no significant change after cholecalciferol supplementation. A weak correlation coefficient between duodenal mucosa and PBMC VDRmRNA suggests that caution needs to be exercised while using the latter as a surrogate for other sites.

DDT (bis[4-chlorophenyl]-1,1,1-trichloroethane) is responsible for many immuno-dysregulatory functions in exposed animals, but data particularly on complement system and macrophages are limited. In this study we have shown that DDT activates the complement system through the alternative pathway in the absence of any pathogen. A significant (p<0.05) increase in C3b, C3d and C3a generation, and decline in complement hemolytic activity was observed in insecticide exposed sera. The uncontrolled complement consumption reduces the lytic activity of the complement, which enhances the susceptibility to pyogenic infection if the exposure to DDT remains unabated. Further, DDT induced the significant (p<0.05) production of tumor necrosis factor-alpha (TNF-alpha) and nitric oxide (NO) in macrophages and thus contributes inflammatory reactions, cytokine imbalance and immune-dysregulation. These molecular changes in macrophages lead to structural aberrations like heterochromatin condensation, loss of pseudopodia, cytoplasmic vacuolization, DNA fragmentation and hypodiploid nuclei as seen in our study, suggesting apoptosis. However, in presence lipopolysaccharide, DDT induced significant (p<0.05) suppression of TNF-alpha and NO generation, suggestive of impairment of macrophage microbiocidal effects. This study concludes that the functional and structural derangements of macrophages in association with uncontrolled and excessive complement consumption by DDT are perhaps one of the major mechanisms contributing to the immunosuppressive effects of insecticide.

Reduced expression of Erythrocyte Complement Receptor 1 (E-CR1) is envisaged to contribute significantly to the pathophysiology of systemic lupus erythematosus (SLE). We determined the levels of CR1 transcript in the neutrophils from 25 untreated patients with active SLE and 25 normal healthy individuals and, studied the effect of interferon-gamma (IFN-gamma), interleukin-4 (IL-4) and immune complexes (IC) on the same. The study revealed a marked decline in the levels of neutrophil CR1 (N-CR1) transcript in the patients with SLE, and differential pattern of IFN-gamma and IL-4 expression in the neutrophils from normals and patients. Opsonized immune complexes down regulated CR1 transcript in patients and IFN-gamma up regulated the same both in normals and patients. Immune complexes suppressed this effect of IFN-gamma. IL-4 also suppressed the effect of IFN-gamma but effect confined only to the normals. This is the first real-time RT-PCR data comparing the neutrophil CR1 expression in normals and patients with SLE and its modulation by IFN-gamma, IL-4 and immune complexes. IFN-gamma and immune complexes, respectively, emerged as the positive and negative modulators of neutrophil CR1 transcript in SLE.

The wild-type p53 gene has been widely implicated in the regulation of hypermethylated in cancer-1 (HIC-1) transcription, a master growth regulatory gene with multiple promoters and, consequently, multiple alternatively spliced transcripts. We investigated the role of p53 (wild type and mutant, both endogenous and exogenous) in modulating the various HIC-1 transcripts. We discovered a novel unspliced HIC-1 transcript, identified as "f" in leukocytes and in the human cell lines U87MG (wild-type p53), U373MG (mutant p53), MCF-7 (wild-type p53), HeLa (p53 degraded by HPV18-E6 oncoprotein), and Saos-2 (p53 null). This transcript is initiated from a new transcription start site and has an intervening stop codon that would result in a possibly truncated 22-amino-acid polypeptide. When U87MG (wild-type p53) and MCF-7 cells (wild-type p53) were exposed to adverse growth conditions of serum starvation or treated with the chemotherapeutic agent cisplatin, cells underwent apoptosis and cell cycle arrest accompanied by increase in p53 and HIC-1 transcript levels. Although the increase of the HIC-1-spliced transcripts followed the increase of p53, increase in f transcript coincided with declining p53 and HIC-1 transcript and protein levels. Moreover, the levels of HIC-1 f transcript were not induced by exogenously transfected wild-type p53 in p53-mutated U373MG and p53-null Saos-2 cells, unlike the spliced transcripts that code for full-length HIC-1 protein. These findings suggest a working model wherein the status of f transcript, which is not under direct transcriptional control of wild-type p53, may influence the level of HIC-1 protein in cancer cells.