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Background: Globally, thousands of patients suffer from postacute COVID-19 syndrome, a condition that already affects our health system. Although there is a growing literature upon the long-term effects of SARS-CoV-2 infection, there are up to date only a few reports on long-term follow-up of pulmonary function after severe COVID-19. Objective: To investigate risk factors for in-hospital COVID-19 fatalities and to assess the lung function and health status at one year after hospital discharge. Methods: Patients who were admitted to the hospital with confirmed COVID-19 and required supplementary oxygen delivery were included in this observational cohort study. Baseline and demographic data and information about hospital stay were obtained by medical charts. Patients were divided in 3 groups: group 1 (intensive care unit (ICU)-invasive mechanical ventilation (IMV), group 2 (high-flow nasal cannula (HFNC) and/or none-invasive ventilation (NIV) and group 3 (regular oxygen delivery treatment). All patients were required to answer health questionnaires at one year after acute infection, while patients in groups 1 and 2 performed dynamic spirometry. Results: The study population consisted of 130 patients. Forty five (35%) patients died at the hospital. Risk factors for in-hospital mortality were age, hypertension, ischemic heart disease, and renal disease. We did not find any significant difference in health scales between the 3 groups. Mean values of both FEV1% and FVC% in groups 1 and 2 were detected within the lower normal limits with no difference between the two groups. Conclusion: The main result of the study is lung function in the lower limit of normal evaluated at one-year follow-up. There were no significant differences related to initial disease severity in lung function and long-term health status, which suggests that more generous lung function testing even in less severely affected patients, could be indicated.

Background: Globally, thousands of patients suffer from long-term COVID-19 symptoms, often referred to as post-acute COVID-19 syndrome, a condition that already affects our health systems. Although there is a growing literature upon the long-term effects of SARS-CoV-2 infection, there are up to date only a few reports on long-term follow up of pulmonary function after severe COVID-19.Methods: The study is an observational cohort of patients who were admitted to hospital care with confirmed COVID-19 during the first pandemic wave and those who required some form of supplementary oxygen delivery. Baseline characteristics, demographic data and information about hospital stay including mortality were obtained by medical charts. Patients were divided in to 3 groups: group 1 (intensive care unit (ICU)-invasive mechanical ventilation (IMV), group 2 (high-flow nasal-cannula (HFNC) and/or none-invasive ventilation (NIV) and group 3 (regular oxygen delivery treatment). All patients were required to answer questionnaires (mMRC scale, Post-COVID-19 Functional Status Scale (PCFS), Hospital anxiety and depression scale (HAD) and 36-Item Short Form Health Survey (SF-36)) at one year after acute infection, while patients in groups 1 and 2 performed also dynamic spirometry. The R-language of statistics was used for all calculations and visualisations.Results: The study population consisted of 130 patients. Forty five (35%) patients died at the hospital. Risk factors for in-hospital mortality were age, hypertension, ischemic heart disease and renal disease. Patients who survived had, on average, a longer period from symptoms onset to hospital admission. No significant difference in all health scales between the 3 patient groups were found. Mean values of both FEV1% and FVC% in the groups 1 and 2 were detected within the lower normal limits while no difference was found between the two groups who were examined with spirometry. Conclusions: The main result of the study is lung function values in the lower limit of normal evaluated with dynamic spirometry at one-year follow-up. There were no significant differences related to initial disease severity in lung function and long-term health status at 12 months, which suggests that more generous lung function testing even in less severe individuals with lingering symptoms could be indicated.

Digital technology has been used in the field of diagnostic breast pathology and immunohistochemistry (IHC) for decades. Examples include automated tissue processing and staining, digital data processing, storing and management, voice recognition systems, and digital technology-based production of antibodies and other IHC reagents. However, the recent application of whole slide imaging technology and artificial intelligence (AI)-based tools has attracted a lot of attention. The use of AI tools in breast pathology is discussed briefly as it is covered in other reviews. Here, we present the main application of digital technology in IHC. This includes automation of IHC staining, using image analysis systems and computer vision technology to interpret IHC staining, and the use of AI-based tools to predict marker expression from haematoxylin and eosin-stained digitalized images.

Summary: A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and assessed their performance on multiple clinical cohorts. We demonstrate that deep neural networks outperform the current state in machine learning frameworks. We provide a proof of concept for the use of deep neural network-based frameworks to aid precision oncology strategies. : Sakellaropoulos et al. designed a machine learning workflow to predict drug response and survival of cancer patients. All pipelines are trained on a large panel of cancer cell lines and tested in clinical cohorts. DNN outperforms other machine learning algorithms by capturing pathways that link gene expression with drug response. Keywords: drug response prediction, precision medicine, machine learning, deep neural networks, DNN

The ubiquitin-proteasome pathway (UPP) is central to proteostasis network (PN) functionality and proteome quality control. Yet, the functional implication of the UPP in tissue homeodynamics at the whole organism level and its potential cross-talk with other proteostatic or mitostatic modules are not well understood. We show here that knock down (KD) of proteasome subunits in Drosophila flies, induced, for most subunits, developmental lethality. Ubiquitous or tissue specific proteasome dysfunction triggered systemic proteome instability and activation of PN modules, including macroautophagy/autophagy, molecular chaperones and the antioxidant cncC (the fly ortholog of NFE2L2/Nrf2) pathway. Also, proteasome KD increased genomic instability, altered metabolic pathways and severely disrupted mitochondrial functionality, triggering a cncC-dependent upregulation of mitostatic genes and enhanced rates of mitophagy. Whereas, overexpression of key regulators of antioxidant responses (e.g., cncC or foxo) could not suppress the deleterious effects of proteasome dysfunction; these were alleviated in both larvae and adult flies by modulating mitochondrial dynamics towards increased fusion or by enhancing autophagy. Our findings reveal the extensive functional wiring of genomic, proteostatic and mitostatic modules in higher metazoans. Also, they support the notion that age-related increase of proteotoxic stress due to decreased UPP activity deregulates all aspects of cellular functionality being thus a driving force for most age-related diseases. Abbreviations: ALP: autophagy-lysosome pathway; ARE: antioxidant response element; Atg8a: autophagy-related 8a; ATPsynβ: ATP synthase, β subunit; C-L: caspase-like proteasomal activity; cncC: cap-n-collar isoform-C; CT-L: chymotrypsin-like proteasomal activity; Drp1: dynamin related protein 1; ER: endoplasmic reticulum; foxo: forkhead box, sub-group O; GLU: glucose; GFP: green fluorescent protein; GLY: glycogen; Hsf: heat shock factor; Hsp: Heat shock protein; Keap1: kelch-like ECH-associated protein 1; Marf: mitochondrial assembly regulatory factor; NFE2L2/Nrf2: nuclear factor, erythroid 2 like 2; Opa1: optic atrophy 1; PN: proteostasis network; RNAi: RNA interference; ROS: reactive oxygen species; ref(2)P: refractory to sigma P; SQSTM1: sequestosome 1; SdhA: succinate dehydrogenase, subunit A; T-L: trypsin-like proteasomal activity; TREH: trehalose; UAS: upstream activation sequence; Ub: ubiquitin; UPR: unfolded protein response; UPP: ubiquitin-proteasome pathway.

Breast cancer (BC) is a highly heterogeneous disease encompassing multiple subtypes with different molecular and histopathological features, disease prognosis, and therapeutic responses. Among these, the Triple Negative BC form (TNBC) is an aggressive subtype with poor prognosis and therapeutic outcome. With respect to HER2 overexpressing BC, although advanced targeted therapies have improved the survival of patients, disease relapse and metastasis remains a challenge for therapeutic efficacy. In this study the aim was to identify key membrane-associated proteins which are overexpressed in these aggressive BC subtypes and can serve as potential biomarkers or drug targets. We leveraged on the development of a membrane enrichment protocol in combination with the global profiling GeLC-MS/MS technique, and compared the proteomic profiles of a HER2 overexpressing (HCC-1954) and a TNBC (MDA-MB-231) cell line with that of a benign control breast cell line (MCF-10A). An average of 2300 proteins were identified from each cell line, of which approximately 600 were membrane-associated proteins. Our global proteomic methodology in tandem with invigoration by Western blot and Immunofluorescence analysis, readily detected several previously-established BC receptors like HER2 and EPHA2, but importantly STEAP4 and CD97 emerged as novel potential candidate markers. This is the first time that the mitochondrial iron reductase STEAP4 protein up-regulation is linked to BC (HER2+ subtype), while for CD97, its role in BC has been previously described, but never before by a global proteomic technology in TNBC. STEAP4 was selected for further detailed evaluation by the employment of Immunohistochemical analysis of BC xenografts and clinical tissue microarray studies. Results showed that STEAP4 expression was evident only in malignant breast tissues whereas all the benign breast cases had no detectable levels. A functional role of STEAP4 intervention was established in HER2 overexpressing BC by pharmacological studies, where blockage of the STEAP4 pathway with an iron chelator (Deferiprone) in combination with the HER2 inhibitor Lapatinib led to a significant reduction in cell growth in vitro. Furthermore, siRNA mediated knockdown of STEAP4 also suppressed cell proliferation and enhanced the inhibition of Lapatinib in HER2 overexpressing BC, confirming its potential oncogenic role in BC. In conclusion, STEAP4 may represent a novel BC related biomarker and a potential pharmacological target for the treatment of HER2 overexpressing BC.

Saliva is identified as functional equivalent to serum, reflecting the physiological state of the body, as well as hormonal, emotional, nutritional and metabolic alterations. The application of mass spectrometry based approaches has allowed a thorough characterization of the saliva proteome and led to the discovery of putative biomarkers. Several salivary biomarkers have been recently explored as potentially useful screening tools in patients diagnosed with metabolic disorders. In this review, we provide an overview of saliva proteomics studies, with a focus on diabetes, and we explore the evidence for the utility of well identified markers for the diagnosis and monitoring of the disease. Emerging approaches in salivary diagnostics that may significantly advance the field of diabetes research are also highlighted.

Cancer cells acquire unique secretome compositions that contribute to tumor development and metastasis. The aim of our study was to elucidate the biological processes involved in cervical cancer, by performing a proteomic analysis of the secretome from the following informative cervical cell lines: SiHa (HPV16+), HeLa (HPV18+), C33A (HPV−), and HCK1T (normal). Proteins were analyzed by 2D gel electrophoresis coupled to MALDI-TOF-MS. Enrichment of secreted proteins with characteristic profiles for each cell line was followed by the identification of differentially expressed proteins. Particularly, transforming growth factor-beta-induced protein ig-h3 (Beta ig-h3) and peroxiredoxin-2 (PRDX2) overexpression in the secretome of cancer cell lines was detected and confirmed by Western blot. Bioinformatics analysis identified the transcription factor NRF2 as a regulator of differentially expressed proteins in the cervical cancer secretome. NRF2 levels were measured by both Western blot and Multiple Reaction Monitoring (MRM) in the total cell extract of the four cell lines. NRF2 was upregulated in SiHa and C33A compared to HCK1T. In conclusion, the secreted proteins identified in cervical cancer cell lines indicate that aberrant NRF2-mediated oxidative stress response (OSR) is a prominent feature of cervical carcinogenesis.

Cervical cancer remains the fourth most common and most lethal type of cancer in women, despite the applied regular screening and prevention strategies, while the available treatment schemes still pose a threat to fertility. Substantial understanding of the underlying mechanisms and development of novel diagnostic, prognostic and therapeutic approaches are critical steps for improving cervical cancer management. Towards this goal, a comparative proteomic analysis was conducted between three cervical cancer cell lines (HeLa: HPV18+, SiHa: HPV16+, C33A: HPV‑) and normal cervical keratinocytes (HCK1T). The total cell extract of each cell line was analyzed by liquid chromatography coupled to tandem mass spectrometry (LC‑MS/MS). Differential expression analysis revealed 919, 826 and 1,370 deregulated proteins in the comparisons of HeLa, SiHa and C33A with HCK1T cell lines, respectively. Pathway enrichment analysis of the differentially expressed proteins highlighted common cancer characteristics such as high metabolic demands and increased cell turnover, confirming the validity of the proteomic results. Extensive literature mining of the consistently differentially expressed proteins that resulted from the three comparisons was performed leading to a shortlist of 21 proteins that are potentially involved in cervical malignancy. The criteria for this shortlisting were the association of the proteins with various types of cancer, while there is no study as yet associating their expression to cervical cancer. Moreover, the expression trend of two of the shortlisted proteins was validated using western blot analysis. The proteomic datasets generated in this study can be utilized to enrich the current knowledge on cervical cancer pathology and unveil key molecular mechanisms of carcinogenesis. In conclusion, the shortlist of consistently deregulated proteins between cervical cancer cell lines and normal cervical keratinocytes can be used for validation in clinical samples and in functional investigation experiments that could ultimately lead to the discovery of novel disease biomarkers and drug targets.

A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big "omic" data into effective drug-response models. Machine learning is both an expanding and an evolving computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into these frameworks and 4) pitfalls and challenges to maximize model performance. In this context we also describe a novel in silico screening process, based on Association Rule Mining, for identifying genes as candidate drivers of drug response and compare it with relevant data mining frameworks, for which we generated a web application freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large sample-spaces, while is able to detect low frequency events and evaluate statistical significance even in the multidimensional space, presenting the results in the form of easily interpretable rules. We conclude with future prospects and challenges of applying machine learning based drug response prediction in precision medicine.

Metazoans viability depends on their ability to regulate metabolic processes and also to respond to harmful challenges by mounting anti‐stress responses; these adaptations were fundamental forces during evolution. Central to anti‐stress responses are a number of short‐lived transcription factors that by functioning as stress sensors mobilize genomic responses aiming to eliminate stressors. We show here that increased expression of nuclear factor erythroid 2‐related factor (Nrf2) in Drosophila activated cytoprotective modules and enhanced stress tolerance. However, while mild Nrf2 activation extended lifespan, high Nrf2 expression levels resulted in developmental lethality or, after inducible activation in adult flies, in altered mitochondrial bioenergetics, the appearance of Diabetes Type 1 hallmarks and aging acceleration. Genetic or dietary suppression of Insulin/IGF‐like signaling (IIS) titrated Nrf2 activity to lower levels, largely normalized metabolic pathways signaling, and extended flies’ lifespan. Thus, prolonged stress signaling by otherwise cytoprotective short‐lived stress sensors perturbs IIS resulting in re‐allocation of resources from growth and longevity to somatic preservation and stress tolerance. These findings provide a reasonable explanation of why most (if not all) cytoprotective stress sensors are short‐lived proteins, and it also explains the build‐in negative feedback loops (shown here for Nrf2); the low basal levels of these proteins, and why their suppressors were favored by evolution.

International audience; Cellular senescence is a cell state implicated in various physiological processes and a wide spectrum of age-related diseases. Recently, interest in therapeutically targeting senescence to improve healthy aging and age-related disease, otherwise known as senotherapy, has been growing rapidly. Thus, the accurate detection of senescent cells, especially in vivo, is essential. Here, we present a consensus from the International Cell Senescence Association (ICSA), defining and discussing key cellular and molecular features of senescence and offering recommendations on how to use them as biomarkers. We also present a resource tool to facilitate the identification of genes linked with senescence, SeneQuest (available at http://Senequest.net). Lastly, we propose an algorithm to accurately assess and quantify senescence, both in cultured cells and in vivo.

Predicting response to therapy is a major challenge in medicine. Machine learning algorithms are promising tools for assisting this aim. Amongst them, Deep Neural Networks are emerging as the most capable of interrogating across multiple data types. Their further development will lead to sophisticated knowledge extraction, shaping the medicine of tomorrow.

The composition of the salivary proteome is affected by pathological conditions. We analyzed by high resolution mass spectrometry approaches saliva samples collected from children and adolescents with type 1 diabetes and healthy controls. The list of more than 2000 high confidence protein identifications constitutes a comprehensive characterization of the salivary proteome. Patients with good glycemic regulation and healthy individuals have comparable proteomic profiles. In contrast, a significant number of differentially expressed proteins were identified in the saliva of patients with poor glycemic regulation compared to patients with good glycemic control and healthy children. These proteins are involved in biological processes relevant to diabetic pathology such as endothelial damage and inflammation. Moreover, a putative preventive therapeutic approach was identified based on bioinformatic analysis of the deregulated salivary proteins. Thus, thorough characterization of saliva proteins in diabetic pediatric patients established a connection between molecular changes and disease pathology. This proteomic and bioinformatic approach highlights the potential of salivary diagnostics in diabetes pathology and opens the way for preventive treatment of the disease.

The characteristic feature of healthy living organisms is the preservation of homeostasis. Compelling evidence highlight that the DNA damage response and repair (DDR/R) and immune response (ImmR) signaling networks work together favoring the harmonized function of (multi)cellular organisms. DNA and RNA viruses activate the DDR/R machinery in the host cells both directly and indirectly. Activation of DDR/R in turn favors the immunogenicity of the incipient cell. Hence, stimulation of DDR/R by exogenous or endogenous insults triggers innate and adaptive ImmR. The immunogenic properties of ionizing radiation, a prototypic DDR/R inducer, serve as suitable examples of how DDR/R stimulation alerts host immunity. Thus, critical cellular danger signals stimulate defense at the systemic level and vice versa. Disruption of DDR/R-ImmR cross talk compromises (multi)cellular integrity, leading to cell-cycle-related and immune defects. The emerging DDR/R-ImmR concept opens up a new avenue of therapeutic options, recalling the Hippocrates quote "everything in excess is opposed by nature."

Cardiovascular diseases (CVDs) have become a predominant cause of death worldwide. Although CVDs encompass a variety of pathological conditions that affect the heart and blood vasculature, they can be classified into two major groups according to their basic pathophysiologic mechanisms, namely atherosclerosis and aneurysm formation. Increasing evidence implicates micro-RNAs (miRNAs or miRs) as vital factors both in the initiation and progression processes of CVDs. Some miRs have a promoting role in CVD development, while others have a protective role. Today, miRs have been shown to be potential biomarkers for several types of CVDs, while strategies targeting miRs are under consideration as potential tools for exploitation in therapeutic approaches for CVD treatments. In this review, we present the available data on the involvement of miRs in CVDs, focusing on their role as regulators of the inflammatory process as an initiating and driving component of CVDs.

Background Both HPV-positive and -negative cervical cancers are primarily associated with features of cell cycle and cytoskeletal disruption; however, the actual biological processes affected remain elusive. To this end, we systematically characterized the intracellular proteomic profiles of four distinct and informative cervical cell lines. Materials and methods Cell extracts from a normal cervical (HCK1T) and three cervical cancer cell lines, one HPV-negative (C33A), and two HPV-positive, SiHa (HPV16+) and HeLa (HPV18+), were analyzed by 2-dimensional electrophoresis and differentially expressed proteins were identified by MALDI-TOF mass spectrometry, while differential expression was confirmed by western blot analysis. Results In total, 113 proteins were found differentially expressed between the normal and the cervical cancer lines. Bioinformatics analysis revealed the actin cytoskeleton signaling pathway to be significantly affected, while up-regulation of cofilin-1, an actin depolymerizing factor, was documented and further validated by western blotting. Furthermore, two-way comparisons among the four cell lines, revealed a set of 18 informative differentially expressed proteins. Conclusion These novel identified proteins provide the impetus for further functional studies to dissect the mechanisms operating in the two distinct pathways of cervical carcinogenesis.

To examine the clinical usefulness of heart rate recovery (HRR) post 6-minute walking test (6MWT) as a simple marker of cardiovascular risk in obstructive sleep apnea (OSA) patients in comparison to HRR post cycle ergometry, the validated and more sophisticated protocol. Seventy-four participants underwent full overnight polysomnography, cycle ergometry and 6MWT. The HRR at 1, 2 and 3 min (HRR-1, HRR-2 and HRR-3) 6MWT was compared to HRR at 1, 2, and 3 min post cycle ergometry in normal subjects and in moderate and severe OSA patients before and after 6-month CPAP treatment. The HRR-1, HRR-2 and HRR-3 in 6MWT were significantly different between normal, moderate and severe OSA patients with higher rates achieved in normal. The higher the severity of OSA the lower the HRR was. There were also no differences found between work rate and distance walked during cycle ergometry or 6MWT, respectively, concerning normal, moderate and severe OSA patients. Heart rate recovery was further associated with minimum saturation of oxygen during sleep independently of the duration of apnea episodes of BMI and ESS. The treatment with CPAP had a beneficial effect on HRR both post-6MWT and post cycle ergometry. Autonomic nervous system dysfunction in OSA can be found even with submaximal exertion. Heart rate recovery post-6MWT, such as HRR post cycle ergometry, was significantly impaired in OSA patients in comparison to normals and was favorably influenced from CPAP treatment. Furthermore, it was found to be more sensitive compared with distance walked in 6MWT in discriminating severity of OSA. The HRR post-6MWT was found to be an easily measured and reliable marker of OSA severity both before and after CPAP treatment.

The DNA damage response (DDR) pathway and ARF function as barriers to cancer development. Although commonly regarded as operating independently of each other, some studies proposed that ARF is positively regulated by the DDR. Contrary to either scenario, we found that in human oncogene-transformed and cancer cells ATM suppressed, in a transcription-independent manner, ARF protein levels and activity. Mechanistically, ATM activated protein phosphatase 1, which antagonized Nek2-dependent phosphorylation of nucleophosmin (NPM), thereby liberating ARF from NPM and rendering it susceptible to degradation by the ULF E3-ubiquitin ligase. In human clinical samples, loss of ATM expression correlated with increased ARF levels and in xenograft and tissue culture models, inhibition of ATM stimulated the tumour-suppressive effects of ARF. These results provide insights into the functional interplay between the DDR and ARF anti-cancer barriers, with implications for tumorigenesis and treatment of advanced tumours.

Breast cancer cells are suggested to be organized in a hierarchical manner with a subpopulation of stem cells, termed as breast cancer stem cells (BCSCs), which contribute significantly to tumorigenesis, cancer recurrence and metastasis. BCSCs haye been demonstrated to exhibit significant resistance to conventional chemo- and radiotherapy. Recent evidence suggests that treatment of breast cancers with radiation or chemotherapy agents induces stem cell-like properties in non-stem cells. Herein, we provide an overview of the key determinants of resistance to chemotherapy and radiation in BCSCs. To this end and by the use of bioinformatics, the molecular pathways, the defining markers, as well as the microenvironmental and genetic factors, which are implicated in the maintenance of sternness, chemo- and radioresistance in BCSCs, are identified and presented. Our findings could provide the foundation for the design of targeted chemo- or radiotherapeutic regimens in order to eliminate or sensitize BCSCs to cytotoxic therapies and prevent tumor relapse and metastasis. (C) 2016 Elsevier Ireland Ltd. All rights reserved.

A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs for each individual patient. For complex diseases such as cancer, characterized by high inter-patient variance, the implementation of precision medicine approaches is dependent upon understanding the pathological processes at the molecular level. While the “omics” era provides unique opportunities to dissect the molecular features of diseases, the ability to utilize it in targeted therapeutic efforts is hindered by both the massive size and diverse nature of the “omics” data. Recent advances with Deep Learning Neural Networks (DLNNs), suggests that DLNN could be trained on large data sets to efficiently predict therapeutic responses in cancer treatment. We present the application of Association Rule Mining combined with DLNNs for the analysis of high-throughput molecular profiles of 1001 cancer cell lines, in order to extract cancer-specific signatures in the form of easily interpretable rules and use these rules as input to predict pharmacological responses to a large number of anti-cancer drugs. The proposed algorithm outperformed Random Forests (RF) and Bayesian Multitask Multiple Kernel Learning (BMMKL) classification which currently represent the state-of-the-art in drug-response prediction. Moreover, thein silicopipeline presented, introduces a novel strategy for identifying potential therapeutic targets, as well as possible drug combinations with high therapeutic potential. For the first time, we demonstrate that DLNNs trained on a large pharmacogenomics data-set can effectively predict the therapeutic response of specific drugs in different cancer types. These findings serve as a proof of concept for the application of DLNNs to predict therapeutic responsiveness, a milestone in precision medicine.

Background Excessive autoantibody production characterizing systemic lupus erythematosus (SLE) occurs irrespective of the disease’s clinical status and is linked to increased lymphocyte apoptosis. Herein, we tested the hypothesis that defective DNA damage repair contributes to increased apoptosis in SLE. Methods We evaluated nucleotide excision repair at the N-ras locus, DNA double-strand breaks repair and apoptosis rates in peripheral blood mononuclear cells from anti-dsDNA autoantibody-positive patients (six with quiescent disease and six with proliferative nephritis) and matched healthy controls following ex vivo treatment with melphalan. Chromatin organization and expression levels of DNA repair- and apoptosis-associated genes were also studied in quiescent SLE. Results Defective nucleotide excision repair and DNA double-strand breaks repair were found in SLE, with lupus nephritis patients showing higher DNA damage levels than those with quiescent disease. Melphalan-induced apoptosis rates were higher in SLE than control cells and correlated inversely with DNA repair efficiency. Chromatin at the N-ras locus was more condensed in SLE than controls, while treatment with the histone deacetylase inhibitor vorinostat resulted in hyperacetylation of histone H4, chromatin decondensation, amelioration of DNA repair efficiency and decreased apoptosis. Accordingly, genes involved in DNA damage repair and signaling pathways, such as DDB1, ERCC2, XPA, XPC, MRE11A, RAD50, PARP1, MLH1, MLH3, and ATM were significantly underexpressed in SLE versus controls, whereas PPP1R15A, BARD1 and BBC3 genes implicated in apoptosis were significantly overexpressed. Conclusions Epigenetically regulated functional abnormalities of DNA repair machinery occur in SLE, regardless of clinical disease activity, and may promote lymphocyte apoptosis. Approaches to correct these abnormalities may be of therapeutic value in SLE. Electronic supplementary material The online version of this article (doi:10.1186/s13075-016-1081-3) contains supplementary material, which is available to authorized users.

The cyclin-dependent kinase inhibitor p21(WAF1/Cip1) is the prototype downstream effector of the tumor suppressor protein p53. Yet, evidence from human cancer and mice models, imply that p21(WAF1/Cip1), under certain conditions, can exercise oncogenic activity. The mechanism behind this behavior is still obscure. Within this context we unexpectedly noticed, predominantly in p53 mutant human cancers, that a subset of highly atypical cancerous cells expressing strongly p21(WAF1/Cip1) demonstrated also signs of proliferation. This finding suggests either tolerance to high p21(WAF1/Cip1) levels or that p21(WAF1/Cip1) per se guided a selective process that led to more aggressive off-springs. To address the latter scenario we employed p21(WAF1/Cip1)-inducible p53-null cellular models and monitored them over a prolonged time period, using high-throughput screening means. After an initial phase characterized by stalled growth, mainly due to senescence, a subpopulation of p21(WAF1/Cip1) cells emerged, demonstrating increased genomic instability, aggressiveness and chemo-resistance. At the mechanistic level unremitted p21(WAF1/Cip1) production “saturates” the CRL4(CDT2) and SCF(Skp2) ubiquitin ligase complexes reducing the turn-over of the replication licensing machinery. Deregulation of replication licensing triggered replication stress fuelling genomic instability. Conceptually, the above notion should be considered when anti-tumor strategies are designed, since p21(WAF1/Cip1) responds also to p53-independent signals, including various chemotherapeutic compounds.

Research in the last decade revealed an additional role for the Replication Licensing Factor Cdc6 in transcriptional regulation. This novel function has been linked to human cancer development. Here, we summarize all the findings arguing over a role of Cdc6 as a transcriptional repressor and shed light toward new research directions for this field.

The objective of this study was to investigate the effects of two sources of electromagnetic fields (EMFs) on the proteome of cerebellum, hippocampus, and frontal lobe in Balb/c mice following long-term whole body irradiation. Three equally divided groups of animals (6 animals/group) were used; the first group was exposed to a typical mobile phone, at a SAR level range of 0.17‐ 0.37W/kg for 3h daily for 8 months, the second group was exposed to a wireless DECT base (Digital Enhanced Cordless Telecommunications/Telephone) at a SAR level range of 0.012‐ 0.028W/kg for 8h/day also for 8 months and the third group comprised the sham-exposed animals. Comparative proteomics analysis revealed that long-term irradiation from both EMF sources altered significantly (p , 0.05) the expression of 143 proteins in total (as low as 0.003 fold downregulation up to 114 fold overexpression). Several neural function related proteins (i.e., Glial Fibrillary Acidic Protein (GFAP), Alpha-synuclein, Glia Maturation Factor beta (GMF), and apolipoprotein E (apoE)), heat shock proteins, and cytoskeletal proteins (i.e., Neurofilaments and tropomodulin) are included in this list as well as proteins of the brain metabolism (i.e., Aspartate aminotransferase, Glutamate dehydrogenase) to nearly all brain regions studied. Western blot analysis on selected proteins confirmed the proteomics data. The observed protein expression changes may be related to brain plasticity alterations, indicative of oxidative stress in the nervous Authors’ contributions: AFF and LHM conceived the concept and design of the experiments, made the literature survey and the final biologically valid interpretation of the EMF impact upon the brain, wrote and finalized the manuscript. AFF carried out all animal handling, welfare, EMF exposure, part of brain dissection and immunoassays. AS performed the brain dissection and brain regions’ separation, contributed to the non-EMF writing of the manuscript and together with MHA, EK and EA carried out a part of the immunoassays and contributed to the data evaluation related to neuroproteomics. AX, AP and KV were involved in 2-DE experiments, Maldi ToF/MS, protein identification and statistical analysis. DJS participated in the conception of the design and contributed to the interpretation and evaluation of the overall data. GThT participated in the experimental design and experimental protocols optimization, coordinated the proteomics study, carried out the overall differential proteomics analysis and data evaluation and contributed to the proteomics writing of the manuscript. All authors read and approved the final manuscript.

Childhood pilocytic astrocytoma is the most frequent brain tumor affecting children. Proteomics analysis is currently considered a powerful tool for global evaluation of protein expression and has been widely applied in the field of cancer research. In the present study, a series of proteomics, genomics, and bioinformatics approaches were employed to identify, classify and characterize the proteome content of low-grade brain tumors as it appears in early childhood. Through bioinformatics database construction, protein profiles generated from pathological tissue samples were compared against profiles of normal brain tissues. Additionally, experiments of comparative genomic hybridization arrays were employed to monitor for genetic aberrations and sustain the interpretation and evaluation of the proteomic data. The current study confirms the dominance of MAPK pathway for the childhood pilocytic astrocytoma occurrence and novel findings regarding the ERK-2 expression are reported.

Metazoans respond to harmful challenges by mounting anti-stress responses. This adaptation along with the evolvement of metabolic networks, were fundamental forces during evolution. Central to anti-stress responses are a number of short-lived transcription factors that by functioning as stress sensors mobilize genomic responses aiming to eliminate stressors and restore tissue homeodynamics. We have found that increased expression of nuclear factor erythroid 2-related factor (Nrf2) in Drosophila flies activated cytoprotective modules and enhanced stress tolerance. However, while mild Nrf2 activation extended lifespan, high Nrf2 expression levels resulted in larvae lethality or, after inducible activation in adult flies, in altered mitochondrial bioenergetics; the appearance of diabetic phenotypes and reduced longevity. Genetic or dietary suppression of Insulin/IGF-like signaling (IIS) alleviated these effects and extended flies’ lifespan. Our observations indicate that prolonged stress signaling by otherwise cytoprotective short-lived stress sensors re-allocates resources from growth and longevity to somatic preservation and stress tolerance. They also suggest a role for potential dietary-restriction-like therapeutic interventions in various diseases of chronic stress including progeroid genome instability syndromes and/or neurodegeneration. Finally, as our in vivo insights indicate that long-term mild Nrf2 activation exerts benign effects we screen marine natural products (derived from the mesophotic zone) libraries for the discovery of (among others) novel bioactive Nrf2-activating small molecules.

Turner syndrome (TS) is the most common sex chromosome abnormality in females, caused by the complete or partial absence of one X chromosome. To identify biomarkers for TS, we compared the protein composition of maternal plasma samples from pregnant women with normal and TS fetuses, using a proteomic approach consisting of 2D-E separation and MS analysis for the identification of the differentially expressed proteins. Samples were routinely obtained in the second trimester of pregnancy, stored, and used after prenatal determination of the fetal karyotype. Nine proteins (C1S, CO3, CLUS, AFAM, HABP2, IGHA1, HPT, SHBG, and CD5L) were significantly increased in the plasma of women carrying TS fetuses, whereas KNG1, IGJ, and TTHY were decreased. Identified proteins were further evaluated by immunoblot analysis while functional network association was carried out to asses significance. The identification of specific biomarkers may facilitate the development of noninvasive prenatal diagnosis and improve our understanding of the pathology of TS. Nevertheless, testing a larger cohort of pregnant women is necessary to evaluate the relevance of the reported findings.

The aim of this study was to investigate the progression of myelodysplastic syndrome (MDS) to acute myeloid leukemia (AML) and to provide additional data regarding the proteomic analysis of AML. The protein profiles obtained were correlated to cytogenetic and molecular analyses. Bone marrow (BM) and peripheral blood (PB) samples were obtained during MDS diagnosis, at MDS transformation to AML, at de novo AML diagnosis and 3 months following treatment. As controls, non-leukemic pediatric patients were studied. Cytogenetic and molecular analyses were carried out by G banding and polymerase chain reaction followed by sequencing, respectively. Differential proteomic analysis was performed by two-dimensional gel electrophoresis and protein identification by matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry. No significant correlations were noted between protein patterns and cytogenetic or molecular analyses. Certain suppressor genes, metabolic enzymes, immunoglobulins and actin-binding proteins were differentially expressed by BM or PB plasma and cell lysates compared to controls. The obtained data showed that vitamin D and gelsolin played contradicting roles in contributing and restraining leukemogenesis, while MOES, EZRI and AIFM1 could be considered as biomarkers for AML.

Klinefelter syndrome is a sex chromosomal abnormality (47, XXY karyotype), occurring approximately in 1 in 1000 male live births. In the present study proteomic analysis was performed in twelve 2nd trimester amniotic fluid samples, eight coming from pregnancies with normal males and four with Klinefelter syndrome foetuses, as shown by routine prenatal cytogenetic analysis. Samples were analysed by 2-DE, coupled with MALDI-TOF-MS analysis. Three proteins (Ceruloplasmin, Alpha-1-antitrypsin and Zinc-alpha-2-glycoprotein) were found to be up-regulated in samples obtained from pregnancies with Klinefelter syndrome foetuses, whereas four proteins (Apolipoprotein A-I, Plasma retinol-binding protein, Gelsolin, and Vitamin D-binding protein) were down regulated when compared to proteins detected in samples from normal foetuses. The differential expression of Ceruloplasmin, Apolipoprotein A-I and Plasma retinol-binding protein was further confirmed by immunoblotting. Since these proteins are likely to cross the placenta barrier and be detected in maternal plasma they could be used as biomarkers for the non-invasive prenatal diagnosis of Klinefelter syndrome.

Osteosarcoma is the most common primary bone cancer. Mutations of the RB gene represent the most frequent molecular defect in this malignancy. A major consequence of this alteration is that the activity of the key cell cycle regulator E2F1 is unleashed from the inhibitory effects of pRb. Studies in animal models and in human cancers have shown that deregulated E2F1 overexpression possesses either "oncogenic" or "oncosuppressor" properties, depending on the cellular context. To address this issue in osteosarcomas, we examined the status of E2F1 relative to cell proliferation and apoptosis in a clinical setting of human primary osteosarcomas and in E2F1-inducible osteosarcoma cell line models that are wild-type and deficient for p53. Collectively, our data demonstrated that high E2F1 levels exerted a growth-suppressing effect that relied on the integrity of the DNA damage response network. Surprisingly, induction of p73, an established E2F1 target, was also DNA damage response-dependent. Furthermore, a global proteome analysis associated with bioinformatics revealed novel E2F1-regulated genes and potential E2F1-driven signaling networks that could provide useful targets in challenging this aggressive neoplasm by innovative therapies.

Intrauterine growth restriction (IUGR) has been associated with increased perinatal morbidity and mortality and increased morbidity and metabolic abnormalities later in life. IUGR is characterized as the failure of a fetus to achieve his or her genetic growth potential in utero. Altered protein expression profiles associated with IUGR may be informative on the pathologic mechanisms of this condition and might reveal potential markers for postnatal complications. The aim of this study was to compare protein profiles of umbilical cord plasma from IUGR and appropriate for gestational age full-term neonates. Blood samples from doubly clamped umbilical cord at delivery from 10 IUGR and 10 appropriate for gestational age full-term neonates were analyzed by two-dimensional electrophoresis and MS. Prominent changes of the α2-HS glycoprotein/fetuin-A were observed in IUGR cases. Specifically we showed that these changes occur primarily at the level of post-translational modifications of the protein. Using a combination of mass spectrometry and classical biochemical assays, single and heavy chain forms of fetuin-A were found to lack the normally present O-linked sialic acids in IUGR neonates. Fetuin A is a glycoprotein that has been associated with promotion of in vitro cell replication, fetal growth and osteogenesis, and protection from Gram-negative bacterial endotoxins. Prominent defects in glycosylation/sialylation of fetuin-A revealed by our study might be responsible for impaired function of fetuin-A, leading to deficient fetal growth, especially osteogenesis, and/or to the development of complications frequently seen later in the lives of IUGR neonates.

Exposure of cells to any form of ionizing radiation (IR) is expected to induce a variety of DNA lesions, including double strand breaks (DSBs), single strand breaks (SSBs) and oxidized bases, as well as loss of bases, i.e., abasic sites. The damaging potential of IR is primarily related to the generation of electrons, which through their interaction with water produce free radicals. In their turn, free radicals attack DNA, proteins and lipids. Damage is induced also through direct deposition of energy. These types of IR interactions with biological materials are collectively called 'targeted effects', since they refer only to the irradiated cells. Earlier and sometimes 'anecdotal' findings were pointing to the possibility of IR actions unrelated to the irradiated cells or area, i.e., a type of systemic response with unknown mechanistic basis. Over the last years, significant experimental evidence has accumulated, showing a variety of radiation effects for 'out-of-field' areas (non-targeted effects-NTE). The NTE involve the release of chemical and biological mediators from the 'in-field' area and thus the communication of the radiation insult via the so called 'danger' signals. The NTE can be separated in two major groups: bystander and distant (systemic). In this review, we have collected a detailed list of proteins implicated in either bystander or systemic effects, including the clinically relevant abscopal phenomenon, using improved text-mining and bioinformatics tools from the literature. We have identified which of these genes belong to the DNA damage response and repair pathway (DDR/R) and made protein-protein interaction (PPi) networks. Our analysis supports that the apoptosis, TLR-like and NOD-like receptor signaling pathways are the main pathways participating in NTE. Based on this analysis, we formulate a biophysical hypothesis for the regulation of NTE, based on DNA damage and apoptosis gradients between the irradiation point and various distances corresponding to bystander (5mm) or distant effects (5cm). Last but not least, in order to provide a more realistic support for our model, we calculate the expected DSB and non-DSB clusters along the central axis of a representative 200.6MeV pencil beam calculated using Monte Carlo DNA damage simulation software (MCDS) based on the actual beam energy-to-depth curves used in therapy.

High resolution proteomics approaches have been successfully utilized for the comprehensive characterization of the cell proteome. However, in the case of quantitative proteomics an open question still remains, which quantification strategy is best suited for identification of biologically relevant changes, especially in clinical specimens. In this study, a thorough comparison of a label-free approach (intensity-based) and 8-plex iTRAQ was conducted as applied to the analysis of tumor tissue samples from non-muscle invasive and muscle-invasive bladder cancer. For the latter, two acquisition strategies were tested including analysis of unfractionated and fractioned iTRAQ-labeled peptides. To reduce variability, aliquots of the same protein extract were used as starting material, whereas to obtain representative results per method further sample processing and MS analysis were conducted according to routinely applied protocols. Considering only multiple-peptide identifications, LC-MS/MS analysis resulted in the identification of 910, 1092 and 332 proteins by label-free, fractionated and unfractionated iTRAQ, respectively. The label-free strategy provided higher protein sequence coverage compared to both iTRAQ experiments. Even though pre-fraction of the iTRAQ labeled peptides allowed for a higher number of identifications, this was not accompanied by a respective increase in the number of differentially expressed changes detected. Validity of the proteomics output related to protein identification and differential expression was determined by comparison to existing data in the field (Protein Atlas and published data on the disease). All methods predicted changes which to a large extent agreed with published data, with label-free providing a higher number of significant changes than iTRAQ. Conclusively, both label-free and iTRAQ (when combined to peptide fractionation) provide high proteome coverage and apparently valid predictions in terms of differential expression, nevertheless label-free provides higher sequence coverage and ultimately detects a higher number of differentially expressed proteins. The risk for receiving false associations still exists, particularly when analyzing highly heterogeneous biological samples, raising the need for the analysis of higher sample numbers and/or application of adjustment for multiple testing.

PLoS one 10(7), e0133773 (2015). doi:10.1371/journal.pone.0133773, Published by PLoS, Lawrence, Kan.

The aim of this study is the detection, identification and quantification of known and new candidate cell membrane receptors that are overexpressed in Breast Cancer (BC) cells. In order to achieve this goal we have developed State-of-the-Art Proteomic approaches. These novel membrane target receptors can be used either as biomarkers or for the design of targeted drugs against aggressive disease subtypes with poor prognosis and therapeutic outcomes, such as Triple Negative BC (TNBC) and HER2 overexpressing BC. For our study, four well characterized BC epithelial cell lines were selected: HCC-1954 and SKBR3 (HER2 overexpressing), MDA-MB-231 (TNBC) and MCF-10A (benign control). We employed a combination of subcellular fractionation and membrane enrichment protocols and combined those with quantitative, tandem Mass-Spectrometry (MS) based Proteomics. The discovery phase of our approach included: the GeLC-MS/MS technique, where protein fractions were first separated by 1D-gel electrophoresis followed by In-Gel enzymatic digestion prior to identification by a high resolution Orbitrap mass analyser. A complementary approach for known proteins associated with BC that cannot be readily detected in the discovery phase, such as GPCR receptors, was based on nano LC-MRM analysis. Proof of Concept experiments, with the use of a Triple Quadrupole (QqQ) instrument, were based on the detection of standard peptides derived from the protein of interest after following an in solution tryptic digestion protocol. Using GeLC-MS/MS approach we identified the well-known BC involved receptors, EGFR and HER2, as well as other potential protein targets in concordance with the literature (e.g. TFR1, EPHA2, GPCR5A). We further confirmed these results by Western blot analysis. Certain GPCRs expected to be present in the cell lines tested were not detected in the studies used on the GeLC-MS/MS analysis, thus we proceeded to the more sensitive and quantitative nanoLC-MRM approach. As a paradigm target we chose the GnRH receptor, an established target for personalized therapy known to be expressed in BC. The quantitative nanoLC-MRM approach revealed GnRHR significant expression in the MDA-MB-231 BC line and in the WPE-NB26-3 Prostate cancer cell line (used as an overexpressing GNRHR positive control). Our results indicate that the strategy of combining and applying various Proteomic approaches into selected cell lines for the identification of new protein targets will add more information to the genetic and histological classification of the tumor in order to achieve our goal, which is a holistic view of a patient’s molecular profiling that can ultimately lead to treatment in the most effective way. Citation Format: Ioanna-Maria Orfanou, Theodoros Karampelas, George Mermelekas, Konstantinos Vougas, Constantin Tamvakopoulos. Proteomic approaches in the discovery of novel drug targets or potential biomarkers in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 230. doi:10.1158/1538-7445.AM2017-230

Summary: The uniqueness of a measured molecular mass or peptide sequence plays a very important role in the fields of protein identification and peptide/protein-biomarker investigation. We present a publicly available web application that offers information concerning the uniqueness of one or more molecular masses and one or more peptide sequences in the human proteome. When a sequence is found to be unique in humans, the application is able to search across all species querying whether this sequence is unique, not only in humans but also in other species found in the Swiss-Prot Database. The application is also able to search for unique protein fragments derived computationally from enzymatic digestion driven by certain enzymes. Furthermore, the application can list all the unique masses and peptides of a given protein. Through this application, researchers are able to find unique tags, either on a molecular mass level or on a sequence level. These unique tags are remarkably important in research related to protein identification or biomarker discovery and measurements. Availability: UniMaP web-application is available at http://bioserver -1.bioacademy.gr/Bioserver/UniMaP/ Contact: gspyrou@bioacademy.gr Supplementary information: Supplementary data are available at Bioinformatics online. The precise identification of peptide sequences and proteins is of great significance in proteomics. Specifically, in mass spectrometry, isolated proteins or protein mixtures are digested by enzymes into peptides. These peptides are then ionized by different techniques (TOF, ESI) and detected producing mass spectra that are computationally analyzed. The peptides identified with statistical significance are used for protein identification (Liebler, 2002; Marcotte, 2007). In proteomics technology of selected reaction monitoring (SRM) or multiple reaction monitoring (MRM), a predefined precursor ion and one of its fragments are selected by a filtering device and monitored over time for precise quantification (Lange et al., 2008). The determination of unique characteristics for a protein is profoundly interesting in the application of the new concepts of