Identifying metabolites from protein identifiers with P2M

The identification of metabolites from complex biological samples often involves matching experimental mass spectrometry data to signatures of compounds derived from massive chemical databases. However, misidentifications may result due to the complexity of potential chemical space that leads to databases containing compounds with nearly identical structures. Prior knowledge of compounds that may be enzymatically consumed or produced by an organism can help reduce misidentifications by restricting initial database searching to compounds that are likely to be present in a biological system. While databases such as UniProt allow for the identification of small molecules that may be consumed or generated by enzymes encoded in an organism's genome, currently no tool exists for identifying SMILES strings of metabolites associated with protein identifiers and expanding R-containing substructures to fully defined, biologically relevant chemical structures. Here we present Proteome2Metabolome (P2M), a tool that performs these tasks using external database querying behind a simple command line interface. Beyond mass spectrometry based applications, P2M can be generally used to identify biologically relevant chemical structures likely to be observed in a biological system

Linear combinations of docking affinities explain quantitative differences in RTK signaling

Receptor tyrosine kinases (RTKs) process extracellular cues by activating a broad array of signaling proteins. Paradoxically, they often use the same proteins to elicit diverse and even opposing phenotypic responses. Binary, ‘on–off' wiring diagrams are therefore inadequate to explain their differences. Here, we show that when six diverse RTKs are placed in the same cellular background, they activate many of the same proteins, but to different quantitative degrees. Additionally, we find that the relative phosphorylation levels of upstream signaling proteins can be accurately predicted using linear models that rely on combinations of receptor-docking affinities and that the docking sites for phosphoinositide 3-kinase (PI3K) and Shc1 provide much of the predictive information. In contrast, we find that the phosphorylation levels of downstream proteins cannot be predicted using linear models. Taken together, these results show that information processing by RTKs can be segmented into discrete upstream and downstream steps, suggesting that the challenging task of constructing mathematical models of RTK signaling can be parsed into separate and more manageable layers

Dangerous Skyrmions in Little Higgs Models

Skyrmions are present in many models of electroweak symmetry breaking where the Higgs is a pseudo-Goldstone boson of some strongly interacting sector. They are stable, composite objects whose mass lies in the range 10-100 TeV and can be naturally abundant in the universe due to their small annihilation cross-section. They represent therefore good dark matter candidates. We show however in this work that the lightest skyrmion states are electrically charged in most of the popular little Higgs models, and hence should have been directly or indirectly observed in nature already. The charge of the skyrmion under the electroweak gauge group is computed in a model-independent way and is related to the presence of anomalies in the underlying theory via the Wess-Zumino-Witten term.Comment: 31 pages, 4 figures; v2: minor changes, one reference added, version to appear in JHEP; v3: erratum added, conclusions unchange

Rational Design of Pathogen-Mimicking Amphiphilic Materials as Nanoadjuvants

An opportunity exists today for cross-cutting research utilizing advances in materials science, immunology, microbial pathogenesis, and computational analysis to effectively design the next generation of adjuvants and vaccines. This study integrates these advances into a bottom-up approach for the molecular design of nanoadjuvants capable of mimicking the immune response induced by a natural infection but without the toxic side effects. Biodegradable amphiphilic polyanhydrides possess the unique ability to mimic pathogens and pathogen associated molecular patterns with respect to persisting within and activating immune cells, respectively. The molecular properties responsible for the pathogen-mimicking abilities of these materials have been identified. The value of using polyanhydride nanovaccines was demonstrated by the induction of long-lived protection against a lethal challenge of Yersinia pestis following a single administration ten months earlier. This approach has the tantalizing potential to catalyze the development of next generation vaccines against diseases caused by emerging and re-emerging pathogens

Evaluating the performance of malaria genomics for inferring changes in transmission intensity using transmission modelling

AbstractAdvances in genetic sequencing and accompanying methodological approaches have resulted in pathogen genetics being used in the control of infectious diseases. To utilise these methodologies for malaria we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment. Here we develop an individual-based transmission model to simulate malaria parasite genetics parameterised using estimated relationships between complexity of infection and age from 5 regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterise the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The best performing model successfully predicted malaria prevalence with mean absolute error of 0.055, suggesting genetic tools could be used for monitoring the impact of malaria interventions.</jats:p

Mapping interactions with the chaperone network reveals factors that protect against tau aggregation.

A network of molecular chaperones is known to bind proteins ('clients') and balance their folding, function and turnover. However, it is often unclear which chaperones are critical for selective recognition of individual clients. It is also not clear why these key chaperones might fail in protein-aggregation diseases. Here, we utilized human microtubule-associated protein tau (MAPT or tau) as a model client to survey interactions between ~30 purified chaperones and ~20 disease-associated tau variants (~600 combinations). From this large-scale analysis, we identified human DnaJA2 as an unexpected, but potent, inhibitor of tau aggregation. DnaJA2 levels were correlated with tau pathology in human brains, supporting the idea that it is an important regulator of tau homeostasis. Of note, we found that some disease-associated tau variants were relatively immune to interactions with chaperones, suggesting a model in which avoiding physical recognition by chaperone networks may contribute to disease

Combined proteome and transcriptome analyses for the discovery of urinary biomarkers for urothelial carcinoma

Background: Proteomic discovery of cancer biomarkers in body fluids is challenging because of their low abundance in a complex background. Altered gene expression in tumours may not reflect protein levels in body fluids. We have tested combining gene expression profiling of tumours with proteomic analysis of cancer cell line secretomes as a strategy to discover urinary biomarkers for bladder cancer. Methods: We used shotgun proteomics to identify proteins secreted by three bladder cancer cell lines. Secreted proteins with high mRNA levels in bladder tumours relative to normal urothelium were assayed by ELISA in urine samples from 642 patients. Results: Midkine and HAI-1 were significantly increased in bladder cancer patients, with the highest levels in invasive disease (area under the receiver operating characteristic curve 0.89 vs non-cancer). The urinary concentration of both proteins was too high to be explained by bladder cancer associated haematuria and most likely arises by direct tumour secretion. Conclusions: This ‘dual-omic’ strategy identified tumour secreted proteins whose urine concentrations are increased significantly by bladder cancer. Combined secretome-transcriptome analysis may be more useful than direct proteomic analysis of body fluids for biomarker discovery in both bladder cancer and other tumour type