ISG15 fast-tracks DNA replication

In this issue, Raso et al. (2020. J. Cell Biol.https://doi.org/10.1083/jcb.202002175) uncover a novel replication fork speed regulatory network controlled by the ubiquitin-like modifier interferon-stimulated gene 15 (ISG15), which plays a central role in the innate immune response and regulates tumorigenesis as well as chemotherapy response

DNA combing versus DNA spreading and the separation of sister chromatids

DNA combing and DNA spreading are two central approaches for studying DNA replication fork dynamics genome-wide at single-molecule resolution by distributing labeled genomic DNA on coverslips or slides for immunodetection. Perturbations in DNA replication fork dynamics can differentially affect either leading or lagging strand synthesis, for example, in instances where replication is blocked by a lesion or obstacle on only one of the two strands. Thus, we sought to investigate whether the DNA combing and/or spreading approaches are suitable for resolving adjacent sister chromatids during DNA replication, thereby enabling the detection of DNA replication dynamics within individual nascent strands. To this end, we developed a thymidine labeling scheme that discriminates between these two possibilities. Our data suggests that DNA combing resolves sister chromatids, allowing the detection of strand-specific alterations, whereas DNA spreading typically does not. These findings have important implications when interpreting DNA replication dynamics from data obtained by these two commonly used techniques

NEDDylated Cullin 3 mediates the adaptive response to topoisomerase 1 inhibitors

DNA topoisomerase 1 (TOP11) inhibitors are mainstays of anticancer therapy. These drugs trap TOP1 on DNA, stabilizing the TOP1-cleavage complex (TOP1-cc). The accumulation of TOP1-ccs perturbs DNA replication fork progression, leading to DNA breaks and cell death. By analyzing the genomic occupancy and activity of TOP1, we show that cells adapt to treatment with multiple doses of TOP1 inhibitor by promoting the degradation of TOP1-ccs, allowing cells to better tolerate subsequent doses of TOP1 inhibitor. The E3-RING Cullin 3 ligase in complex with the BTBD1 and BTBD2 adaptor proteins promotes TOP1-cc ubiquitination and subsequent proteasomal degradation. NEDDylation of Cullin 3 activates this pathway, and inhibition of protein NEDDylation or depletion of Cullin 3 sensitizes cancer cells to TOP1 inhibitors. Collectively, our data uncover a previously unidentified NEDD8-Cullin 3 pathway involved in the adaptive response to TOP1 inhibitors, which can be targeted to improve the efficacy of TOP1 drugs in cancer therapy

Rapid profiling of DNA replication dynamics using mass spectrometry-based analysis of nascent DNA

The primary method for probing DNA replication dynamics is DNA fiber analysis, which utilizes thymidine analog incorporation into nascent DNA, followed by immunofluorescent microscopy of DNA fibers. Besides being time-consuming and prone to experimenter bias, it is not suitable for studying DNA replication dynamics in mitochondria or bacteria, nor is it adaptable for higher-throughput analysis. Here, we present mass spectrometry-based analysis of nascent DNA (MS-BAND) as a rapid, unbiased, quantitative alternative to DNA fiber analysis. In this method, incorporation of thymidine analogs is quantified from DNA using triple quadrupole tandem mass spectrometry. MS-BAND accurately detects DNA replication alterations in both the nucleus and mitochondria of human cells, as well as bacteria. The high-throughput capability of MS-BAND captured replication alterations in an E. coli DNA damage-inducing gene library. Therefore, MS-BAND may serve as an alternative to the DNA fiber technique, with potential for high-throughput analysis of replication dynamics in diverse model systems

Measurement of χ c1 and χ c2 production with s√ = 7 TeV pp collisions at ATLAS

The prompt and non-prompt production cross-sections for the χ c1 and χ c2 charmonium states are measured in pp collisions at s√ = 7 TeV with the ATLAS detector at the LHC using 4.5 fb−1 of integrated luminosity. The χ c states are reconstructed through the radiative decay χ c → J/ψγ (with J/ψ → μ + μ −) where photons are reconstructed from γ → e + e − conversions. The production rate of the χ c2 state relative to the χ c1 state is measured for prompt and non-prompt χ c as a function of J/ψ transverse momentum. The prompt χ c cross-sections are combined with existing measurements of prompt J/ψ production to derive the fraction of prompt J/ψ produced in feed-down from χ c decays. The fractions of χ c1 and χ c2 produced in b-hadron decays are also measured

Intelligenza artificiale e sicurezza: opportunità, rischi e raccomandazioni

L'IA (o intelligenza artificiale) è una disciplina in forte espansione negli ultimi anni e lo sarà sempre più nel prossimo futuro: tuttavia è dal 1956 che l’IA studia l’emulazione dell’intelligenza da parte delle macchine, intese come software e in certi casi hardware. L’IA è nata dall’idea di costruire macchine che - ispirandosi ai processi legati all’intelligenza umana - siano in grado di risolvere problemi complessi, per i quali solitamente si ritiene che sia necessario un qualche tipo di ragionamento intelligente. La principale area di ricerca e applicazione attuale dell’IA è il machine learning (algoritmi che imparano e si adattano in base ai dati che ricevono), che negli ultimi anni ha trovato ampie applicazioni grazie alle reti neurali (modelli matematici composti da neuroni artificiali) che a loro volta hanno consentito la nascita del deep learning (reti neurali di maggiore complessità). Appartengono al mondo dell’IA anche i sistemi esperti, la visione artificiale, il riconoscimento vocale, l’elaborazione del linguaggio naturale, la robotica avanzata e alcune soluzioni di cybersecurity. Quando si parla di IA c'è chi ne è entusiasta pensando alle opportunità, altri sono preoccupati poiché temono tecnologie futuristiche di un mondo in cui i robot sostituiranno l'uomo, gli toglieranno il lavoro e decideranno al suo posto. In realtà l'IA è ampiamente utilizzata già oggi in molti campi, ad esempio nei cellulari, negli oggetti smart (IoT), nelle industry 4.0, per le smart city, nei sistemi di sicurezza informatica, nei sistemi di guida autonoma (drive o parking assistant), nei chat bot di vari siti web; questi sono solo alcuni esempi basati tutti su algoritmi tipici dell’intelligenza artificiale. Grazie all'IA le aziende possono avere svariati vantaggi nel fornire servizi avanzati, personalizzati, prevedere trend, anticipare le scelte degli utenti, ecc. Ma non è tutto oro quel che luccica: ci sono talvolta problemi tecnici, interrogativi etici, rischi di sicurezza, norme e legislazioni non del tutto chiare. Le organizzazioni che già adottano soluzioni basate sull’IA, o quelle che intendono farlo, potrebbero beneficiare di questa pubblicazione per approfondirne le opportunità, i rischi e le relative contromisure. La Community for Security del Clusit si augura che questa pubblicazione possa fornire ai lettori un utile quadro d’insieme di una realtà, come l’intelligenza artificiale, che ci accompagnerà sempre più nella vita personale, sociale e lavorativa.AI (or artificial intelligence) is a booming discipline in recent years and will be increasingly so in the near future.However, it is since 1956 that AI has been studying the emulation of intelligence by machines, understood as software and in some cases hardware. AI arose from the idea of building machines that-inspired by processes related to human intelligence-are able to solve complex problems, for which it is usually believed that some kind of intelligent reasoning is required. The main current area of AI research and application is machine learning (algorithms that learn and adapt based on the data they receive), which has found wide applications in recent years thanks to neural networks (mathematical models composed of artificial neurons), which in turn have enabled the emergence of deep learning (neural networks of greater complexity). Also belonging to the AI world are expert systems, computer vision, speech recognition, natural language processing, advanced robotics and some cybersecurity solutions. When it comes to AI there are those who are enthusiastic about it thinking of the opportunities, others are concerned as they fear futuristic technologies of a world where robots will replace humans, take away their jobs and make decisions for them. In reality, AI is already widely used in many fields, for example, in cell phones, smart objects (IoT), industries 4.0, for smart cities, cybersecurity systems, autonomous driving systems (drive or parking assistant), chat bots on various websites; these are just a few examples all based on typical artificial intelligence algorithms. Thanks to AI, companies can have a variety of advantages in providing advanced, personalized services, predicting trends, anticipating user choices, etc. But not all that glitters is gold: there are sometimes technical problems, ethical questions, security risks, and standards and legislation that are not entirely clear. Organizations already adopting AI-based solutions, or those planning to do so, could benefit from this publication to learn more about the opportunities, risks, and related countermeasures. Clusit's Community for Security hopes that this publication will provide readers with a useful overview of a reality, such as artificial intelligence, that will increasingly accompany us in our personal, social and working lives

Measurement of the inclusive jet cross-section in proton-proton collisions at √s=7 TeV using 4.5 fb−1 of data with the ATLAS detector

The inclusive jet cross-section is measured in proton-proton collisions at a centre-of-mass energy of 7 TeV using a data set corresponding to an integrated luminosity of 4.5 fb−1 collected with the ATLAS detector at the Large Hadron Collider in 2011. Jets are identified using the anti-kt algorithm with radius parameter values of 0.4 and 0.6. The double-differential cross-sections are presented as a function of the jet transverse momentum and the jet rapidity, covering jet transverse momenta from 100 GeV to 2 TeV. Next-to-leading-order QCD calculations corrected for non-perturbative effects and electroweak effects, as well as Monte Carlo simulations with next-to-leading-order matrix elements interfaced to parton showering, are compared to the measured cross-sections. A quantitative comparison of the measured cross-sections to the QCD calculations using several sets of parton distribution functions is performed

The Laboratory Definition of the Thermal Resistance of Growing Media for Green Roofs: New Experimental Setups

Green roofs are one of the most extensively investigated roofing technologies. Most of the bibliographical studies show results of researches focused on the analysis of different configurations of green roofs, but only few researches deal with the calculation of the growing media thermal resistance using laboratory tests. From 2009 to 2013, ITC-CNR, the Construction Technologies Institute of the National Research Council of Italy, carried out a first laboratory experimental campaign focused on the definition of thermal performances curves of growing media for green roofs as a function of both density and percentage of internal moisture. During this campaign, the experimental results underlined some existing gaps, such as the absence of specific standards concerning the sample laboratory preparation, the absence of shared references concerning the compaction level reached by samples in real working conditions and the evaluation of the internal moisture content of growing media exposed to atmospheric agents. For this reason, the ITC-CNR has set up a second experimental campaign focused on the solution of the gaps underlined by the first phase concerning the preparation of samples for the laboratory calculation of the thermal resistance of growing media for green roofs. This paper proposes and presents methodological approaches, methods and new test devices implemented to solve these gaps, and the results obtained

Nutrition and Genetics in NAFLD: The Perfect Binomium

Nonalcoholic fatty liver disease (NAFLD) represents a global healthcare burden since it is epidemiologically related to obesity, type 2 diabetes (T2D) and Metabolic Syndrome (MetS). It embraces a wide spectrum of hepatic injuries, which include simple steatosis, nonalcoholic steatohepatitis (NASH), fibrosis, cirrhosis and hepatocellular carcinoma (HCC). The susceptibility to develop NAFLD is highly variable and it is influenced by several cues including environmental (i.e., dietary habits and physical activity) and inherited (i.e., genetic/epigenetic) risk factors. Nonetheless, even intestinal microbiota and its by-products play a crucial role in NAFLD pathophysiology. The interaction of dietary exposure with the genome is referred to as ‘nutritional genomics,’ which encompasses both ‘nutrigenetics’ and ‘nutriepigenomics.’ It is focused on revealing the biological mechanisms that entail both the acute and persistent genome-nutrient interactions that influence health and it may represent a promising field of study to improve both clinical and health nutrition practices. Thus, the premise of this review is to discuss the relevance of personalized nutritional advices as a novel therapeutic approach in NAFLD tailored management

Is a Higher Protein-Lower Glycemic Index Diet More Nutritious Than a Conventional Diet? A PREVIEW Sub-study

High protein diets and low glycemic index (GI) diets have been associated with improved diet quality. We compared the changes in nutrient intakes of individuals at high risk of developing type-2 diabetes over 3 y who followed either a higher protein-lower GI diet (HPLG) or a conventional moderate protein-moderate GI diet (MPMG). This post hoc analysis included 161 participants with overweight and pre-diabetes from the Australian cohort of the PREVIEW study (clinical trial registered in ) who were randomly assigned to a HPLG diet (25% energy from protein, dietary GI = 56, n = 76). Food records were collected at 0-mo (baseline) and at 6-, 12-, 24-, and 36-mo (dietary intervention period). Linear mixed models were used to compare the differences in total energy, macro- and micronutrients, dietary GI, glycemic load (GL) and body weight between the two diet groups at the 4 dietary intervention time points. At 3 y, 74% participants from the HPLG diet and 74% participants from the MPMG diet completed the trial. The HPLG group showed significantly higher protein intake and lower dietary GI and GL than the MPMG group (group fixed effect P <0.001 for all three parameters). By 6-, 12-, 24-, and 36-mo there was a 3.0, 2.7, 2.2, and 1.4% point difference in protein intake and 6.2, 4.1, 4.8, and 3.9 GI unit difference between the groups. The intake of energy and saturated fat decreased (mostly in the first 6-mo), while the intake of dietary fiber increased (from mo-0 to mo-12 only) in both diets, with no significant differences between the diets. The dietary intakes of zinc (group fixed effect P = 0.05), selenium (P = 0.01), niacin (P = 0.01), vitamin B12 (P = 0.01) and dietary cholesterol (group by time fixed effect P = 0.001) were higher in the HPLG group than in the MPMG group. Despite both diets being designed to be nutritionally complete, a HPLG diet was found to be more nutritious in relation to some micronutrients, but not cholesterol, than a MPMG diet.Peer reviewe