DMSO removal reduces stem-cell infusion-related toxicity and allows excellent engraftment of cryopreserved unrelated cord blood and autologous stem cells

UNIFESP, GRAACC, Pediat Oncol Inst, São Paulo, BrazilUniversidade Federal de São Paulo, São Paulo, BrazilUNIFESP, GRAACC, Pediat Oncol Inst, São Paulo, BrazilUniversidade Federal de São Paulo, São Paulo, BrazilWeb of Scienc

Large volume leukapheresis for autologous peripheral blood stem cell collection in children weighting less than 25 kg

Universidade Federal de São Paulo, GRAACC, Pediat Oncol Inst, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Pathol, São Paulo, BrazilUniversidade Federal de São Paulo, GRAACC, Pediat Oncol Inst, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Pathol, São Paulo, BrazilWeb of Scienc

Kinome rewiring reveals AURKA limits PI3K-pathway inhibitor efficacy in breast cancer.

Dysregulation of the PI3K-AKT-mTOR signaling network is a prominent feature of breast cancers. However, clinical responses to drugs targeting this pathway have been modest, possibly because of dynamic changes in cellular signaling that drive resistance and limit drug efficacy. Using a quantitative chemoproteomics approach, we mapped kinome dynamics in response to inhibitors of this pathway and identified signaling changes that correlate with drug sensitivity. Maintenance of AURKA after drug treatment was associated with resistance in breast cancer models. Incomplete inhibition of AURKA was a common source of therapy failure, and combinations of PI3K, AKT or mTOR inhibitors with the AURKA inhibitor MLN8237 were highly synergistic and durably suppressed mTOR signaling, resulting in apoptosis and tumor regression in vivo. This signaling map identifies survival factors whose presence limits the efficacy of targeted therapies and reveals new drug combinations that may unlock the full potential of PI3K-AKT-mTOR pathway inhibitors in breast cancer

The glutathione redox system is essential to prevent ferroptosis caused by impaired lipid metabolism in clear cell renal cell carcinoma

The publisher's final edited version of this article is available at Oncogene. 2018 Oct;37(40):5435-5450. doi: 10.1038/s41388-018-0315-z. Epub 2018 Jun 5. Non-commercial use onlyThis study was funded by Cancer Research UK, the German Research Foundation (FOR2314) and the German Cancer Aid (111917)

Computational study of associations between histone modification and protein-DNA binding in yeast genome by integrating diverse information

<p>Abstract</p> <p>Background</p> <p>In parallel with the quick development of high-throughput technologies, <it>in vivo (vitro) </it>experiments for genome-wide identification of protein-DNA interactions have been developed. Nevertheless, a few questions remain in the field, such as how to distinguish true protein-DNA binding (functional binding) from non-specific protein-DNA binding (non-functional binding). Previous researches tackled the problem by integrated analysis of multiple available sources. However, few systematic studies have been carried out to examine the possible relationships between histone modification and protein-DNA binding. Here this issue was investigated by using publicly available histone modification data in yeast.</p> <p>Results</p> <p>Two separate histone modification datasets were studied, at both the open reading frame (ORF) and the promoter region of binding targets for 37 yeast transcription factors. Both results revealed a distinct histone modification pattern between the functional protein-DNA binding sites and non-functional ones for almost half of all TFs tested. Such difference is much stronger at the ORF than at the promoter region. In addition, a protein-histone modification interaction pathway can only be inferred from the functional protein binding targets.</p> <p>Conclusions</p> <p>Overall, the results suggest that histone modification information can be used to distinguish the functional protein-DNA binding from the non-functional, and that the regulation of various proteins is controlled by the modification of different histone lysines such as the protein-specific histone modification levels.</p

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS