Synthesis, antitubercular activity and mechanism of resistance of highly effective thiacetazone analogues

Defining the pharmacological target(s) of currently used drugs and developing new analogues with greater potency are both important aspects of the search for agents that are effective against drug-sensitive and drug-resistant Mycobacterium tuberculosis. Thiacetazone (TAC) is an anti-tubercular drug that was formerly used in conjunction with isoniazid, but removed from the antitubercular chemotherapeutic arsenal due to toxic side effects. However, several recent studies have linked the mechanisms of action of TAC to mycolic acid metabolism and TAC-derived analogues have shown increased potency against M. tuberculosis. To obtain new insights into the molecular mechanisms of TAC resistance, we isolated and analyzed 10 mutants of M. tuberculosis that were highly resistant to TAC. One strain was found to be mutated in the methyltransferase MmaA4 at Gly101, consistent with its lack of oxygenated mycolic acids. All remaining strains harbored missense mutations in either HadA (at Cys61) or HadC (at Val85, Lys157 or Thr123), which are components of the bhydroxyacyl-ACP dehydratase complex that participates in the mycolic acid elongation step. Separately, a library of 31 new TAC analogues was synthesized and evaluated against M. tuberculosis. Two of these compounds, 15 and 16, exhibited minimal inhibitory concentrations 10-fold lower than the parental molecule, and inhibited mycolic acid biosynthesis in a dose-dependent manner. Moreover, overexpression of HadAB HadBC or HadABC in M. tuberculosis led to high level resistance to these compounds, demonstrating that their mode of action is similar to that of TAC. In summary, this study uncovered new mutations associated with TAC resistance and also demonstrated that simple structural optimization of the TAC scaffold was possible and may lead to a new generation of TAC-derived drug candidates for the potential treatment of tuberculosis as mycolic acid inhibitors

A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems

International audienceSzeliski et al. published an influential study in 2006 on energy minimization methods for Markov Random Fields (MRF). This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically , the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different car-dinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2,453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types

The Absence of MIST1 Leads to Increased Ethanol Sensitivity and Decreased Activity of the Unfolded Protein Response in Mouse Pancreatic Acinar Cells

Background: Alcohol abuse is a leading cause of pancreatitis in humans. However, rodent models suggest that alcohol only sensitizes the pancreas to subsequent insult, indicating that additional factors play a role in alcohol-induced pancreatic injury. The goal of this study was to determine if an absence of MIST1, a transcription factor required for complete differentiation of pancreatic acinar cells in mice, increased the sensitivity to alcohol. Methods: Two to four month-old mice lacking MIST1 (Mist1 2/2) or congenic C57 Bl6 mice were placed on a Lieber-DeCarli diet (36 % of total kcal from ethanol and fat), a control liquid diet (36 % kcal from fat) or a regular breeding chow diet (22% kcal from fat). After six weeks, pancreatic morphology was assessed. Biochemical and immunofluorescent analysis was used to assess mediators of the unfolded protein response (UPR). Results: Ethanol-fed Mist1 2/2 mice developed periductal accumulations of inflammatory cells that did not appear in wild type or control-fed Mist1 2/2 mice. Wild type mice fed diets high in ethanol or fat showed enhancement of the UPR based on increased accumulation of peIF2a and spliced XBP1. These increases were not observed in Mist1 2/2 pancreatic tissue, which had elevated levels of UPR activity prior to diet exposure. Indeed, exposure to ethanol resulted in a reduction of UPR activity in Mist1 2/2 mice. Conclusions: Our findings suggest that an absence of MIST1 increases the sensitivity to ethanol that correlated wit

Data mining of high density genomic variant data for prediction of Alzheimer's disease risk

<p>Abstract</p> <p>Background</p> <p>The discovery of genetic associations is an important factor in the understanding of human illness to derive disease pathways. Identifying multiple interacting genetic mutations associated with disease remains challenging in studying the etiology of complex diseases. And although recently new single nucleotide polymorphisms (SNPs) at genes implicated in immune response, cholesterol/lipid metabolism, and cell membrane processes have been confirmed by genome-wide association studies (GWAS) to be associated with late-onset Alzheimer's disease (LOAD), a percentage of AD heritability continues to be unexplained. We try to find other genetic variants that may influence LOAD risk utilizing data mining methods.</p> <p>Methods</p> <p>Two different approaches were devised to select SNPs associated with LOAD in a publicly available GWAS data set consisting of three cohorts. In both approaches, single-locus analysis (logistic regression) was conducted to filter the data with a less conservative p-value than the Bonferroni threshold; this resulted in a subset of SNPs used next in multi-locus analysis (random forest (RF)). In the second approach, we took into account prior biological knowledge, and performed sample stratification and linkage disequilibrium (LD) in addition to logistic regression analysis to preselect loci to input into the RF classifier construction step.</p> <p>Results</p> <p>The first approach gave 199 SNPs mostly associated with genes in calcium signaling, cell adhesion, endocytosis, immune response, and synaptic function. These SNPs together with <it>APOE and GAB2 </it>SNPs formed a predictive subset for LOAD status with an average error of 9.8% using 10-fold cross validation (CV) in RF modeling. Nineteen variants in LD with <it>ST5, TRPC1, ATG10, ANO3, NDUFA12, and NISCH </it>respectively, genes linked directly or indirectly with neurobiology, were identified with the second approach. These variants were part of a model that included <it>APOE </it>and <it>GAB2 </it>SNPs to predict LOAD risk which produced a 10-fold CV average error of 17.5% in the classification modeling.</p> <p>Conclusions</p> <p>With the two proposed approaches, we identified a large subset of SNPs in genes mostly clustered around specific pathways/functions and a smaller set of SNPs, within or in proximity to five genes not previously reported, that may be relevant for the prediction/understanding of AD.</p

Promoter DNA Methylation of Oncostatin M receptor-β as a Novel Diagnostic and Therapeutic Marker in Colon Cancer

In addition to genetic changes, the occurrence of epigenetic alterations is associated with accumulation of both genetic and epigenetic events that promote the development and progression of human cancer. Previously, we reported a set of candidate genes that comprise part of the emerging “cancer methylome”. In the present study, we first tested 23 candidate genes for promoter methylation in a small number of primary colon tumor tissues and controls. Based on these results, we then examined the methylation frequency of Oncostatin M receptor-β (OSMR) in a larger number of tissue and stool DNA samples collected from colon cancer patients and controls. We found that OSMR was frequently methylated in primary colon cancer tissues (80%, 80/100), but not in normal tissues (4%, 4/100). Methylation of OSMR was also detected in stool DNA from colorectal cancer patients (38%, 26/69) (cut-off in TaqMan-MSP, 4). Detection of other methylated markers in stool DNA improved sensitivity with little effect on specificity. Promoter methylation mediated silencing of OSMR in cell lines, and CRC cells with low OSMR expression were resistant to growth inhibition by Oncostatin M. Our data provide a biologic rationale for silencing of OSMR in colon cancer progression and highlight a new therapeutic target in this disease. Moreover, detection and quantification of OSMR promoter methylation in fecal DNA is a highly specific diagnostic biomarker for CRC

Direct Visualization by Cryo-EM of the Mycobacterial Capsular Layer: A Labile Structure Containing ESX-1-Secreted Proteins

The cell envelope of mycobacteria, a group of Gram positive bacteria, is composed of a plasma membrane and a Gram-negative-like outer membrane containing mycolic acids. In addition, the surface of the mycobacteria is coated with an ill-characterized layer of extractable, non-covalently linked glycans, lipids and proteins, collectively known as the capsule, whose occurrence is a matter of debate. By using plunge freezing cryo-electron microscopy technique, we were able to show that pathogenic mycobacteria produce a thick capsule, only present when the cells were grown under unperturbed conditions and easily removed by mild detergents. This detergent-labile capsule layer contains arabinomannan, α-glucan and oligomannosyl-capped glycolipids. Further immunogenic and proteomic analyses revealed that Mycobacterium marinum capsule contains high amounts of proteins that are secreted via the ESX-1 pathway. Finally, cell infection experiments demonstrated the importance of the capsule for binding to cells and dampening of pro-inflammatory cytokine response. Together, these results show a direct visualization of the mycobacterial capsular layer as a labile structure that contains ESX-1-secreted proteins

A mutation in Nischarin causes otitis media via LIMK1 and NF-κB pathways

Otitis media (OM), inflammation of the middle ear (ME), is a common cause of conductive hearing impairment. Despite the importance of the disease, the aetiology of chronic and recurrent forms of middle ear inflammatory disease remains poorly understood. Studies of the human population suggest that there is a significant genetic component predisposing to the development of chronic OM, although the underlying genes are largely unknown. Using N-ethyl-N-nitrosourea mutagenesis we identified a recessive mouse mutant, edison, that spontaneously develops a conductive hearing loss due to chronic OM. The causal mutation was identified as a missense change, L972P, in the Nischarin (NISCH) gene. edison mice develop a serous or granulocytic effusion, increasingly macrophage and neutrophil rich with age, along with a thickened, inflamed mucoperiosteum. We also identified a second hypomorphic allele, V33A, with only modest increases in auditory thresholds and reduced incidence of OM. NISCH interacts with several proteins, including ITGA5 that is thought to have a role in modulating VEGF-induced angiogenesis and vascularization. We identified a significant genetic interaction between Nisch and Itga5; mice heterozygous for Itga5-null and homozygous for edison mutations display a significantly increased penetrance and severity of chronic OM. In order to understand the pathological mechanisms underlying the OM phenotype, we studied interacting partners to NISCH along with downstream signalling molecules in the middle ear epithelia of edison mouse. Our analysis implicates PAK1 and RAC1, and downstream signalling in LIMK1 and NF-κB pathways in the development of chronic OM

Automatically Selecting Inference Algorithms for Discrete Energy Minimisation

Minimisation of discrete energies defined over factors is an important problem in computer vision, and a vast number of MAP inference algorithms have been proposed. Different inference algorithms perform better on factor graph models (GMs) from different underlying problem classes, and in general it is difficult to know which algorithm will yield the lowest energy for a given GM. To mitigate this difficulty, survey papers advise the practitioner on what algorithms perform well on what classes of models. We take the next step forward, and present a technique to automatically select the best inference algorithm for an input GM. We validate our method experimentally on an extended version of the OpenGM2 benchmark, containing a diverse set of vision problems. On average, our method selects an inference algorithm yielding labellings with 96% of variables the same as the best available algorithm