Douglas B. Kell

Bio: Douglas B. Kell is an academic researcher from University of Liverpool. The author has contributed to research in topics: Dielectric & Systems biology. The author has an hindex of 111, co-authored 634 publications receiving 50335 citations. Previous affiliations of Douglas B. Kell include Max Planck Society & University of Wales.

A brain-permeable inhibitor of the neurodegenerative disease target kynurenine 3-monooxygenase prevents accumulation of neurotoxic metabolites

Abstract: Dysregulation of the kynurenine pathway (KP) leads to imbalances in neuroactive metabolites associated with the pathogenesis of several neurodegenerative disorders, including Huntington's disease (HD). Inhibition of the enzyme kynurenine 3-monooxygenase (KMO) in the KP normalises these metabolic imbalances and ameliorates neurodegeneration and related phenotypes in several neurodegenerative disease models. KMO is thus a promising candidate drug target for these disorders, but known inhibitors are not brain permeable. Here, 19 new KMO inhibitors have been identified. One of these (1) is neuroprotective in a Drosophila HD model but is minimally brain penetrant in mice. The prodrug variant (1b) crosses the blood-brain barrier, releases 1 in the brain, thereby lowering levels of 3-hydroxykynurenine, a toxic KP metabolite linked to neurodegeneration. Prodrug 1b will advance development of targeted therapies against multiple neurodegenerative and neuroinflammatory diseases in which KP likely plays a role, including HD, Alzheimer's disease, and Parkinson's disease.

Proteomics of fibrin amyloid microclots in long COVID/post-acute sequelae of COVID-19 (PASC) shows many entrapped pro-inflammatory molecules that may also contribute to a failed fibrinolytic system

Abstract: Post-acute sequelae of COVID-19 (PASC), also now known as long COVID, has become a major global health and economic burden. Previously, we provided evidence that there is a significant insoluble fibrin amyloid microclot load in the circulation of individuals with long COVID, and that these microclots entrap a substantial number of inflammatory molecules, including those that might prevent clot breakdown. Scientifically, the most challenging aspect of this debilitating condition is that traditional pathology tests such as a serum CRP (C-reactive protein) may not show any significant abnormal inflammatory markers, albeit these tests measure only the soluble inflammatory molecules. Elevated, or abnormal soluble biomarkers such as IL-6, D-Dimer or fibrinogen indicate an increased risk for thrombosis or a host immune response in COVID-19. The absence of biomarkers in standard pathology tests, result in a significant amount of confusion for patients and clinicians, as patients are extremely sick or even bed-ridden but with no regular identifiable reason for their disease. Biomarkers that are currently available cannot detect the molecules present in the microclots we identified and are therefore unable to confirm their presence or the mechanisms that drive their formation.Here we analysed the protein content of double-digested microclots of 99 long COVID patients and 29 healthy controls. The patients suffering from long COVID reported their symptoms through a questionnaire completed by themselves or their attending physician.Our long COVID cohort's symptoms were found to be in line with global findings, where the most prevalent symptoms were constant fatigue (74%,) cognitive impairment (71%) and depression and anxiety (30%). Our most noteworthy findings were a reduced level of plasma Kallikrein compared to our controls, an increased level of platelet factor 4 (PF4) von Willebrand factor (VWF), and a marginally increased level of α-2 antiplasmin (α-2-AP). We also found a significant presence of antibodies entrapped inside these microclots.Our results confirm the presence of pro-inflammatory molecules that may also contribute to a failed fibrinolysis phenomenon, which could possibly explain why individuals with long COVID suffer from chronic fatigue, dyspnoea, or cognitive impairment. In addition, significant platelet hyperactivation was noted. Hyperactivation will result in the granular content of platelets being shed into the circulation, including PF4. Overall, our results provide further evidence of both a failed fibrinolytic system in long COVID/PASC and the entrapment of many proteins whose presence might otherwise go unrecorded. These findings might have significant implications for individuals with pre-existing comorbidities, including cardiovascular disease and type 2 diabetes.

Analysis of aptamer sequence activity relationships

Abstract: DNA sequences that can bind selectively and specifically to target molecules are known as aptamers. Normally such binding analyses are performed using soluble aptamers. However, there is much to be gained by using an on-chip or microarray format, where a large number of aptameric DNA sequences can be interrogated simultaneously. To calibrate the system, known thrombin binding aptamers (TBAs) have been mutated systematically, producing large populations that allow exploration of key structural aspects of the overall binding motif. The ability to discriminate between background noise and low affinity binding aptamers can be problematic on arrays, and we use the mutated sequences to establish appropriate experimental conditions and their limitations for two commonly used fluorescence-based detection methods. Having optimized experimental conditions, high-density oligonucleotide microarrays were used to explore the entire loop–sequence–functionality relationship creating a detailed model based on over 40 000 analyses, describing key features for quadruplex-forming sequences.

Interactions among oscillatory pathways in NF-kappa B signaling.

Abstract: Background: Sustained stimulation with tumour necrosis factor alpha (TNF-alpha) induces substantial oscillations— observed at both the single cell and population levels—in the nuclear factor kappa B (NF-kappa B) system. Although the mechanism has not yet been elucidated fully, a core system has been identified consisting of a negative feedback loop involving NF-kappa B (RelA:p50 hetero-dimer) and its inhibitor I-kappa B-alpha. Many authors have suggested that this core oscillator should couple to other oscillatory pathways. Results: First we analyse single-cell data from experiments in which the NF-kappa B system is forced by short trains of strong pulses of TNF-alpha. Power spectra of the ratio of nuclear-to-cytoplasmic concentration of NF-kappa B suggest that the cells’ responses are entrained by the pulsing frequency. Using a recent model of the NF-kappa B system due to Caroline Horton, we carried out extensive numerical simulations to analyze the response frequencies induced by trains of pulses of TNF-alpha stimulation having a wide range of frequencies and amplitudes. These studies suggest that for sufficiently weak stimulation, various nonlinear resonances should be observable. To explore further the possibility of probing alternative feedback mechanisms, we also coupled the model to sinusoidal signals with a wide range of strengths and frequencies. Our results show that, at least in simulation, frequencies other than those of the forcing and the main NF-kappa B oscillator can be excited via sub- and superharmonic resonance, producing quasiperiodic and even chaotic dynamics. Conclusions: Our numerical results suggest that the entrainment phenomena observed in pulse-stimulated experiments is a consequence of the high intensity of the stimulation. Computational studies based on current models suggest that resonant interactions between periodic pulsatile forcing and the system’s natural frequencies may become evident for sufficiently weak stimulation. Further simulations suggest that the nonlinearities of the NF-kappa B feedback oscillator mean that even sinusoidally modulated forcing can induce a rich variety of nonlinear interactions.

On the current-voltage relationships of energy-transducing membranes: phosphorylating membrane vesicles from Paracoccus denitrificans [proceedings].

Abstract: The reducing species is actually the unstable semi-reduced dRFIH· radical, which decays spontaneously to an inactive dimer (Massey & Hemmerich, 1978). Thus reduction cannot proceed beyond the stage where the second-order Pd0 reduction becomes slower than the second-order dimerization, and in the dark the solution contains no extra reducing equivalents. On cooling, the Soret band of the cytochrome shifts from 410nm (Fe2+-RH) toward 417 nm (Fe3+-RH, low spin) (Fig. 2). This reversible shift follows perfectly heating ~cool­ ing cycles, and clearly indicates the reverse electron transfer from Fe+ -RH to Pd0 as another possibility of uncoupling. The various possibilities opened by the combination of subzero temperatures and light-activation can be tested on other sites of the cytochrome P-450 cycle and other multi-component electron-transport chains.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products

Abstract: We have developed a simple and highly efficient method to disrupt chromosomal genes in Escherichia coli in which PCR primers provide the homology to the targeted gene(s). In this procedure, recombination requires the phage lambda Red recombinase, which is synthesized under the control of an inducible promoter on an easily curable, low copy number plasmid. To demonstrate the utility of this approach, we generated PCR products by using primers with 36- to 50-nt extensions that are homologous to regions adjacent to the gene to be inactivated and template plasmids carrying antibiotic resistance genes that are flanked by FRT (FLP recognition target) sites. By using the respective PCR products, we made 13 different disruptions of chromosomal genes. Mutants of the arcB, cyaA, lacZYA, ompR-envZ, phnR, pstB, pstCA, pstS, pstSCAB-phoU, recA, and torSTRCAD genes or operons were isolated as antibiotic-resistant colonies after the introduction into bacteria carrying a Red expression plasmid of synthetic (PCR-generated) DNA. The resistance genes were then eliminated by using a helper plasmid encoding the FLP recombinase which is also easily curable. This procedure should be widely useful, especially in genome analysis of E. coli and other bacteria because the procedure can be done in wild-type cells.

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal

Abstract: The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.