An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation

BACKGROUND PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. METHODS Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. RESULTS In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40. CONCLUSIONS The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer

Metabolomics to unveil and understand phenotypic diversity between pathogen populations

Visceral leishmaniasis is caused by a parasite called Leishmania donovani, which every year infects about half a million people and claims several thousand lives. Existing treatments are now becoming less effective due to the emergence of drug resistance. Improving our understanding of the mechanisms used by the parasite to adapt to drugs and achieve resistance is crucial for developing future treatment strategies. Unfortunately, the biological mechanism whereby Leishmania acquires drug resistance is poorly understood. Recent years have brought new technologies with the potential to increase greatly our understanding of drug resistance mechanisms. The latest mass spectrometry techniques allow the metabolome of parasites to be studied rapidly and in great detail. We have applied this approach to determine the metabolome of drug-sensitive and drug-resistant parasites isolated from patients with leishmaniasis. The data show that there are wholesale differences between the isolates and that the membrane composition has been drastically modified in drug-resistant parasites compared with drug-sensitive parasites. Our findings demonstrate that untargeted metabolomics has great potential to identify major metabolic differences between closely related parasite strains and thus should find many applications in distinguishing parasite phenotypes of clinical relevance

A Method for Structure–Activity Analysis of Quorum-Sensing Signaling Peptides from Naturally Transformable Streptococci

Many species of streptococci secrete and use a competence-stimulating peptide (CSP) to initiate quorum sensing for induction of genetic competence, bacteriocin production, and other activities. These signaling molecules are small, unmodified peptides that induce powerful strain-specific activity at nano-molar concentrations. This feature has provided an excellent opportunity to explore their structure–function relationships. However, CSP variants have also been identified in many species, and each specifically activates its cognate receptor. How such minor changes dramatically affect the specificity of these peptides remains unclear. Structure–activity analysis of these peptides may provide clues for understanding the specificity of signaling peptide–receptor interactions. Here, we use the Streptococcus mutans CSP as an example to describe methods of analyzing its structure–activity relationship. The methods described here may provide a platform for studying quorum-sensing signaling peptides of other naturally transformable streptococci

Label-free integrative pharmacology on-target of drugs at the β2-adrenergic receptor

We describe a label-free integrative pharmacology on-target (iPOT) method to assess the pharmacology of drugs at the β2-adrenergic receptor. This method combines dynamic mass redistribution (DMR) assays using an array of probe molecule-hijacked cells with similarity analysis. The whole cell DMR assays track cell system-based, ligand-directed, and kinetics-dependent biased activities of the drugs, and translates their on-target pharmacology into numerical descriptors which are subject to similarity analysis. We demonstrate that the approach establishes an effective link between the label-free pharmacology and in vivo therapeutic indications of drugs

FlexOracle: predicting flexible hinges by identification of stable domains

<p>Abstract</p> <p>Background</p> <p>Protein motions play an essential role in catalysis and protein-ligand interactions, but are difficult to observe directly. A substantial fraction of protein motions involve hinge bending. For these proteins, the accurate identification of flexible hinges connecting rigid domains would provide significant insight into motion. Programs such as GNM and FIRST have made global flexibility predictions available at low computational cost, but are not designed specifically for finding hinge points.</p> <p>Results</p> <p>Here we present the novel FlexOracle hinge prediction approach based on the ideas that energetic interactions are stronger <it>within </it>structural domains than <it>between </it>them, and that fragments generated by cleaving the protein at the hinge site are independently stable. We implement this as a tool within the Database of Macromolecular Motions, MolMovDB.org. For a given structure, we generate pairs of fragments based on scanning all possible cleavage points on the protein chain, compute the energy of the fragments compared with the undivided protein, and predict hinges where this quantity is minimal. We present three specific implementations of this approach. In the first, we consider only pairs of fragments generated by cutting at a <it>single </it>location on the protein chain and then use a standard molecular mechanics force field to calculate the enthalpies of the two fragments. In the second, we generate fragments in the same way but instead compute their free energies using a knowledge based force field. In the third, we generate fragment pairs by cutting at <it>two </it>points on the protein chain and then calculate their free energies.</p> <p>Conclusion</p> <p>Quantitative results demonstrate our method's ability to predict known hinges from the Database of Macromolecular Motions.</p

Synaptic Defects in the Spinal and Neuromuscular Circuitry in a Mouse Model of Spinal Muscular Atrophy

Spinal muscular atrophy (SMA) is a major genetic cause of death in childhood characterized by marked muscle weakness. To investigate mechanisms underlying motor impairment in SMA, we examined the spinal and neuromuscular circuitry governing hindlimb ambulatory behavior in SMA model mice (SMNΔ7). In the neuromuscular circuitry, we found that nearly all neuromuscular junctions (NMJs) in hindlimb muscles of SMNΔ7 mice remained fully innervated at the disease end stage and were capable of eliciting muscle contraction, despite a modest reduction in quantal content. In the spinal circuitry, we observed a ∼28% loss of synapses onto spinal motoneurons in the lateral column of lumbar segments 3–5, and a significant reduction in proprioceptive sensory neurons, which may contribute to the 50% reduction in vesicular glutamate transporter 1(VGLUT1)-positive synapses onto SMNΔ7 motoneurons. In addition, there was an increase in the association of activated microglia with SMNΔ7 motoneurons. Together, our results present a novel concept that synaptic defects occur at multiple levels of the spinal and neuromuscular circuitry in SMNΔ7 mice, and that proprioceptive spinal synapses could be a potential target for SMA therapy

Metabolomic analysis of human disease and its application to the eye

Metabolomics, the analysis of the metabolite profile in body fluids or tissues, is being applied to the analysis of a number of different diseases as well as being used in following responses to therapy. While genomics involves the study of gene expression and proteomics the expression of proteins, metabolomics investigates the consequences of the activity of these genes and proteins. There is good reason to think that metabolomics will find particular utility in the investigation of inflammation, given the multi-layered responses to infection and damage that are seen. This may be particularly relevant to eye disease, which may have tissue specific and systemic components. Metabolomic analysis can inform us about ocular or other body fluids and can therefore provide new information on pathways and processes involved in these responses. In this review, we explore the metabolic consequences of disease, in particular ocular conditions, and why the data may be usefully and uniquely assessed using the multiplexed analysis inherent in the metabolomic approach

Tracking Subtle Stereotypes of Children with Trisomy 21: From Facial-Feature-Based to Implicit Stereotyping

Background: Stigmatization is one of the greatest obstacles to the successful integration of people with Trisomy 21 (T21 or Down syndrome), the most frequent genetic disorder associated with intellectual disability. Research on attitudes and stereotypes toward these people still focuses on explicit measures subjected to social-desirability biases, and neglects how variability in facial stigmata influences attitudes and stereotyping. Methodology/Principal Findings: The participants were 165 adults including 55 young adult students, 55 non-student adults, and 55 professional caregivers working with intellectually disabled persons. They were faced with implicit association tests (IAT), a well-known technique whereby response latency is used to capture the relative strength with which some groups of people—here photographed faces of typically developing children and children with T21—are automatically (without conscious awareness) associated with positive versus negative attributes in memory. Each participant also rated the same photographed faces (consciously accessible evaluations). We provide the first evidence that the positive bias typically found in explicit judgments of children with T21 is smaller for those whose facial features are highly characteristic of this disorder, compared to their counterparts with less distinctive features and to typically developing children. We also show that this bias can coexist with negative evaluations at the implicit level (with large effect sizes), even among professional caregivers

Harvesting Candidate Genes Responsible for Serious Adverse Drug Reactions from a Chemical-Protein Interactome

Identifying genetic factors responsible for serious adverse drug reaction (SADR) is of critical importance to personalized medicine. However, genome-wide association studies are hampered due to the lack of case-control samples, and the selection of candidate genes is limited by the lack of understanding of the underlying mechanisms of SADRs. We hypothesize that drugs causing the same type of SADR might share a common mechanism by targeting unexpectedly the same SADR-mediating protein. Hence we propose an approach of identifying the common SADR-targets through constructing and mining an in silico chemical-protein interactome (CPI), a matrix of binding strengths among 162 drug molecules known to cause at least one type of SADR and 845 proteins. Drugs sharing the same SADR outcome were also found to possess similarities in their CPI profiles towards this 845 protein set. This methodology identified the candidate gene of sulfonamide-induced toxic epidermal necrolysis (TEN): all nine sulfonamides that cause TEN were found to bind strongly to MHC I (Cw*4), whereas none of the 17 control drugs that do not cause TEN were found to bind to it. Through an insight into the CPI, we found the Y116S substitution of MHC I (B*5703) enhances the unexpected binding of abacavir to its antigen presentation groove, which explains why B*5701, not B*5703, is the risk allele of abacavir-induced hypersensitivity. In conclusion, SADR targets and the patient-specific off-targets could be identified through a systematic investigation of the CPI, generating important hypotheses for prospective experimental validation of the candidate genes

Phosphatase and tensin homologue: a therapeutic target for SMA

Spinal muscular atrophy (SMA) is one of the most common juvenile neurodegenerative diseases, which can be associated with child mortality. SMA is caused by a mutation of ubiquitously expressed gene, Survival Motor Neuron1 (SMN1), leading to reduced SMN protein and the motor neuron death. The disease is incurable and the only therapeutic strategy to follow is to improve the expression of SMN protein levels in motor neurons. Significant numbers of motor neurons in SMA mice and SMA cultures are caspase positive with condensed nuclei, suggesting that these cells are prone to a process of cell death called apoptosis. Searching for other potential molecules or signaling pathways that are neuroprotective for central nervous system (CNS) insults is essential for widening the scope of developmental medicine. PTEN, a Phosphatase and Tensin homologue, is a tumor suppressor, which is widely expressed in CNS. PTEN depletion activates anti-apoptotic factors and it is evident that the pathway plays an important protective role in many neurodegenerative disorders. It functions as a negative regulator of PIP3/AKT pathway and thereby modulates its downstream cellular functions through lipid phosphatase activity. Moreover, previous reports from our group demonstrated that, PTEN depletion using viral vector delivery system in SMN delta7 mice reduces disease pathology, with significant rescue on survival rate and the body weight of the SMA mice. Thus knockdown/depletion/mutation of PTEN and manipulation of PTEN medicated Akt/PKB signaling pathway may represent an important therapeutic strategy to promote motor neuron survival in SMA