Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome

We evaluate a version of the recently-proposed classification system named Optimized Dissimilarity Space Embedding (ODSE) that operates in the input space of sequences of generic objects. The ODSE system has been originally presented as a classification system for patterns represented as labeled graphs. However, since ODSE is founded on the dissimilarity space representation of the input data, the classifier can be easily adapted to any input domain where it is possible to define a meaningful dissimilarity measure. Here we demonstrate the effectiveness of the ODSE classifier for sequences by considering an application dealing with the recognition of the solubility degree of the Escherichia coli proteome. Solubility, or analogously aggregation propensity, is an important property of protein molecules, which is intimately related to the mechanisms underlying the chemico-physical process of folding. Each protein of our dataset is initially associated with a solubility degree and it is represented as a sequence of symbols, denoting the 20 amino acid residues. The herein obtained computational results, which we stress that have been achieved with no context-dependent tuning of the ODSE system, confirm the validity and generality of the ODSE-based approach for structured data classification.Comment: 10 pages, 49 reference

Protein O-Mannosylation in the Murine Brain: Occurrence of Mono-O-Mannosyl Glycans and Identification of New Substrates

Protein O-mannosylation is a post-translational modification essential for correct development of mammals. In humans, deficient O-mannosylation results in severe congenital muscular dystrophies often associated with impaired brain and eye development. Although various O-mannosylated proteins have been identified in the recent years, the distribution of O-mannosyl glycans in the mammalian brain and target proteins are still not well defined. In the present study, rabbit monoclonal antibodies directed against the O-mannosylated peptide YAT(α1-Man)AV were generated. Detailed characterization of clone RKU-1-3-5 revealed that this monoclonal antibody recognizes O-linked mannose also in different peptide and protein contexts. Using this tool, we observed that mono-O-mannosyl glycans occur ubiquitously throughout the murine brain but are especially enriched at inhibitory GABAergic neurons and at the perineural nets. Using a mass spectrometry-based approach, we further identified glycoproteins from the murine brain that bear single O-mannose residues. Among the candidates identified are members of the cadherin and plexin superfamilies and the perineural net protein neurocan. In addition, we identified neurexin 3, a cell adhesion protein involved in synaptic plasticity, and inter-alpha-trypsin inhibitor 5, a protease inhibitor important in stabilizing the extracellular matrix, as new O-mannosylated glycoproteins

Temporal tracking of mineralization and transcriptional developments of shell formation during the early life history of pearl oyster Pinctada maxima

Molluscan larval ontogeny is a highly conserved process comprising three principal developmental stages. A characteristic unique to each of these stages is shell design, termed prodissoconch I, prodissoconch II and dissoconch. These shells vary in morphology, mineralogy and microstructure. The discrete temporal transitions in shell biomineralization between these larval stages are utilized in this study to investigate transcriptional involvement in several distinct biomineralization events. Scanning electron microscopy and X-ray diffraction analysis of P. maxima larvae and juveniles collected throughout post-embryonic ontogenesis, document the mineralogy and microstructure of each shelled stage as well as establishing a timeline for transitions in biomineralization. P. maxima larval samples most representative of these biomineralization distinctions and transitions were analyzed for differential gene expression on the microarray platform PmaxArray 1.0. A number of transcripts are reported as differentially expressed in correlation to the mineralization events of P. maxima larval ontogeny. Some of those isolated are known shell matrix genes while others are novel; these are discussed in relation to potential shell formation roles. This interdisciplinary investigation has linked the shell developments of P. maxima larval ontogeny with corresponding gene expression profiles, furthering the elucidation of shell biomineralization

Cold adaptation and replicable microbial community development during long-term low temperature anaerobic digestion treatment of synthetic sewage

The development and, activity of a cold-adapting microbial community was monitored during low temperature anaerobic digestion (LtAD) treatment of wastewater. Two replicate hybrid anaerobic sludge bed-fixed-film reactors treated a synthetic sewage wastewater at 12°C, at organic loading rates of 0.25–1.0 kg Chemical Oxygen Demand (COD) m−3 d−1, over 889 days. The inoculum was obtained from a full-scale AD reactor, which was operated at 37˚C. Both LtAD reactors readily degraded the influent with COD removal efficiencies regularly exceeding 78% for both the total and soluble COD fractions. The biomass from both reactors was sampled temporally and tested for activity against hydrolytic and methanogenic substrates at 12˚C and 37˚C. Data indicated that significantly enhanced low-temperature hydrolytic and methanogenic activity developed in both systems. For example, the hydrolysis rate constant (K) at 12°C had increased 20–30-fold by comparison to the inoculum by day 500. Substrate affinity also increased for hydrolytic substrates at low temperature. Next generation sequencing demonstrated that a shift in community structure occurred over the trial, involving a 1-log-fold change in 25 SEQS (OTU-free approach) from the inoculum. Microbial community structure changes and process performance were replicable in the LtAD reactors

Mammalian myosin I alpha, I beta, and I gamma: new widely expressed genes of the myosin I family.

A polymerase chain reaction strategy was devised to identify new members of the mammalian myosin I family of actin-based motors. Using cellular RNA from mouse granular neurons and PC12 cells, we have cloned and sequenced three 1.2-kb polymerase chain reaction products that correspond to novel mammalian myosin I genes designated MMI alpha, MMI beta, MMI gamma. The pattern of expression for each of the myosin I's is unique: messages are detected in diverse tissues including the brain, lung, kidney, liver, intestine, and adrenal gland. Overlapping clones representing full-length cDNAs for MMI alpha were obtained from mouse brain. These encode a 1,079 amino acid protein containing a myosin head, a domain with five calmodulin binding sites, and a positively charged COOH-terminal tail. In situ hybridization reveals that MMI alpha is highly expressed in virtually all neurons (but not glia) in the postnatal and adult mouse brain and in neuroblasts of the cerebellar external granular layer. Expression varies in different brain regions and undergoes developmental regulation. Myosin I's are present in diverse organisms from protozoa to vertebrates. This and the expression of three novel members of this family in brain and other mammalian tissues suggests that they may participate in critical and fundamental cellular processes

The role of data in model building and prediction: a survey through examples

The goal of Science is to understand phenomena and systems in order to predict their development and gain control over them. In the scientific process of knowledge elaboration, a crucial role is played by models which, in the language of quantitative sciences, mean abstract mathematical or algorithmical representations. This short review discusses a few key examples from Physics, taken from dynamical systems theory, biophysics, and statistical mechanics, representing three paradigmatic procedures to build models and predictions from available data. In the case of dynamical systems we show how predictions can be obtained in a virtually model-free framework using the methods of analogues, and we briefly discuss other approaches based on machine learning methods. In cases where the complexity of systems is challenging, like in biophysics, we stress the necessity to include part of the empirical knowledge in the models to gain the minimal amount of realism. Finally, we consider many body systems where many (temporal or spatial) scales are at play-and show how to derive from data a dimensional reduction in terms of a Langevin dynamics for their slow components

The Role of Data in Model Building and Prediction: A Survey Through Examples

The goal of Science is to understand phenomena and systems in order to predict their development and gain control over them. In the scientific process of knowledge elaboration, a crucial role is played by models which, in the language of quantitative sciences, mean abstract mathematical or algorithmical representations. This short review discusses a few key examples from Physics, taken from dynamical systems theory, biophysics, and statistical mechanics, representing three paradigmatic procedures to build models and predictions from available data. In the case of dynamical systems we show how predictions can be obtained in a virtually model-free framework using the methods of analogues, and we briefly discuss other approaches based on machine learning methods. In cases where the complexity of systems is challenging, like in biophysics, we stress the necessity to include part of the empirical knowledge in the models to gain the minimal amount of realism. Finally, we consider many body systems where many (temporal or spatial) scales are at play and show how to derive from data a dimensional reduction in terms of a Langevin dynamics for their slow components