Novel subtractive transcription-based amplification of mRNA (STAR) method and its application in search of rare and differentially expressed genes in AD brains

BACKGROUND: Alzheimer's disease (AD) is a complex disorder that involves multiple biological processes. Many genes implicated in these processes may be present in low abundance in the human brain. DNA microarray analysis identifies changed genes that are expressed at high or moderate levels. Complementary to this approach, we described here a novel technology designed specifically to isolate rare and novel genes previously undetectable by other methods. We have used this method to identify differentially expressed genes in brains affected by AD. Our method, termed Subtractive Transcription-based Amplification of mRNA (STAR), is a combination of subtractive RNA/DNA hybridization and RNA amplification, which allows the removal of non-differentially expressed transcripts and the linear amplification of the differentially expressed genes. RESULTS: Using the STAR technology we have identified over 800 differentially expressed sequences in AD brains, both up- and down- regulated, compared to age-matched controls. Over 55% of the sequences represent genes of unknown function and roughly half of them were novel and rare discoveries in the human brain. The expression changes of nearly 80 unique genes were further confirmed by qRT-PCR and the association of additional genes with AD and/or neurodegeneration was established using an in-house literature mining tool (LitMiner). CONCLUSION: The STAR process significantly amplifies unique and rare sequences relative to abundant housekeeping genes and, as a consequence, identifies genes not previously linked to AD. This method also offers new opportunities to study the subtle changes in gene expression that potentially contribute to the development and/or progression of AD

Precision medicine driven by cancer systems biology

Molecular insights from genome and systems biology are influencing how cancer is diagnosed and treated. We critically evaluate big data challenges in precision medicine. The melanoma research community has identified distinct subtypes involving chronic sun-induced damage and the mitogen-activated protein kinase driver pathway. In addition, despite low mutation burden, non-genomic mitogen-activated protein kinase melanoma drivers are found in membrane receptors, metabolism, or epigenetic signaling with the ability to bypass central mitogen-activated protein kinase molecules and activating a similar program of mitogenic effectors. Mutation hotspots, structural modeling, UV signature, and genomic as well as non-genomic mechanisms of disease initiation and progression are taken into consideration to identify resistance mutations and novel drug targets. A comprehensive precision medicine profile of a malignant melanoma patient illustrates future rational drug targeting strategies. Network analysis emphasizes an important role of epigenetic and metabolic master regulators in oncogenesis. Co-occurrence of driver mutations in signaling, metabolic, and epigenetic factors highlights how cumulative alterations of our genomes and epigenomes progressively lead to uncontrolled cell proliferation. Precision insights have the ability to identify independent molecular pathways suitable for drug targeting. Synergistic treatment combinations of orthogonal modalities including immunotherapy, mitogen-activated protein kinase inhibitors, epigenetic inhibitors, and metabolic inhibitors have the potential to overcome immune evasion, side effects, and drug resistance

Search for flavour-changing neutral-current interactions of a top quark and a gluon in pp collisions at √s=13 TeV with the ATLAS detector

A search is presented for the production of a single top quark via left-handed flavour-changing neutral-current (FCNC) interactions of a top quark, a gluon and an up or charm quark. Two production processes are considered: u+ g→ t and c+ g→ t. The analysis is based on proton–proton collision data taken at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC. The data set corresponds to an integrated luminosity of 139 fb- 1. Events with exactly one electron or muon, exactly one b-tagged jet and missing transverse momentum are selected, resembling the decay products of a singly produced top quark. Neural networks based on kinematic variables differentiate between events from the two signal processes and events from background processes. The measured data are consistent with the background-only hypothesis, and limits are set on the production cross-sections of the signal processes: σ(u+g→t)×B(t→Wb)×B(W→ℓν)<3.0pb and σ(c+g→t)×B(t→Wb)×B(W→ℓν)<4.7pb at the 95% confidence level, with B(W→ ℓν) = 0.325 being the sum of branching ratios of all three leptonic decay modes of the W boson. Based on the framework of an effective field theory, the cross-section limits are translated into limits on the strengths of the tug and tcg couplings occurring in the theory: |CuGut|/Λ2<0.057TeV- 2 and |CuGct|/Λ2<0.14TeV- 2. These bounds correspond to limits on the branching ratios of FCNC-induced top-quark decays: B(t→ u+ g) < 0.61 × 10 - 4 and B(t→ c+ g) < 3.7 × 10 - 4

Measurement of the energy response of the ATLAS calorimeter to charged pions from W±→ τ±(→ π±ντ) ντ events in Run 2 data

The energy response of the ATLAS calorimeter is measured for single charged pions with transverse momentum in the range 10 < pT< 300 GeV. The measurement is performed using 139 fb - 1 of LHC proton–proton collision data at s=13 TeV taken in Run 2 by the ATLAS detector. Charged pions originating from τ-lepton decays are used to provide a sample of high-pT isolated particles, where the composition is known, to test an energy regime that has not previously been probed by in situ single-particle measurements. The calorimeter response to single-pions is observed to be overestimated by ∼ 2 % across a large part of the pT spectrum in the central region and underestimated by ∼ 4 % in the endcaps in the ATLAS simulation. The uncertainties in the measurements are ≲ 1 % for 15 < pT< 185 GeV in the central region. To investigate the source of the discrepancies, the width of the distribution of the ratio of calorimeter energy to track momentum, the energies per layer and response in the hadronic calorimeter are also compared between data and simulation