Aligned electrospun fibers for neural patterning

Objectives

To test a 3D approach for neural network formation, alignment, and patterning that is reproducible and sufficiently stable to allow for easy manipulation.

Results

A novel cell culture system was designed by engineering a method for the directional growth of neurons. This uses NG108-15 neuroblastoma x glioma hybrid cells cultured on suspended and aligned electrospun fibers. These fiber networks improved cellular directionality, with alignment angle standard deviations significantly lower on fibers than on regular culture surfaces. Morphological studies found nuclear aspect ratios and cell projection lengths to be unchanged, indicating that cells maintained neural morphology while growing on fibers and forming a 3D network. Furthermore, fibronectin-coated fibers enhanced neurite extensions for all investigated time points. Differentiated neurons exhibited significant increases in average neurite lengths 96 h post plating, and formed neurite extensions parallel to suspended fibers, as visualized through scanning electron microscopy.

Conclusions

The developed model has the potential to serve as the basis for advanced 3D studies, providing an original approach to neural network patterning and setting the groundwork for further investigations into functionality.

     

EphA5 and EphA6: regulation of neuronal and spine morphology

Background

The Eph family of receptor tyrosine kinases plays important roles in neural development. Previous studies have implicated Eph receptors and their ligands, the ephrins, in neuronal migration, axon bundling and guidance to specific targets, dendritic spine formation and neural plasticity. However, specific contributions of EphA5 and EphA6 receptors to the regulation of neuronal cell morphology have not been well studied.

Results

Here we show that deletion of EphA5 and EphA6 results in abnormal Golgi staining patterns of cells in the brain, and abnormal spine morphology.

Conclusion

These observations suggest novel functions of these Eph receptors in the regulation of neuronal and spine structure in brain development and function.

     

Modular and configurable optimal sequence alignment software: <Emphasis Type="Italic">Cola</Emphasis>

Background

The fundamental challenge in optimally aligning homologous sequences is to define a scoring scheme that best reflects the underlying biological processes. Maximising the overall number of matches in the alignment does not always reflect the patterns by which nucleotides mutate. Efficiently implemented algorithms that can be parameterised to accommodate more complex non-linear scoring schemes are thus desirable.

Results

We present Cola, alignment software that implements different optimal alignment algorithms, also allowing for scoring contiguous matches of nucleotides in a nonlinear manner. The latter places more emphasis on short, highly conserved motifs, and less on the surrounding nucleotides, which can be more diverged. To illustrate the differences, we report results from aligning 14,100 sequences from 3' untranslated regions of human genes to 25 of their mammalian counterparts, where we found that a nonlinear scoring scheme is more consistent than a linear scheme in detecting short, conserved motifs.

Conclusions

Cola is freely available under LPGL from https://github.com/nedaz/cola.

     

Deep learning for DNase I hypersensitive sites identification

Background

The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources available on line including experimental datasets and computational tools, the complex language of DHSs remains incompletely understood.

Methods

Here, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) which combined Inception like networks with a gating mechanism for the response of multiple patterns and longterm association in DNA sequences to predict multi-scale DHSs in Arabidopsis, rice and Homo sapiens.

Results

Our method obtains 0.961 area under curve (AUC) on Arabidopsis, 0.969 AUC on rice and 0.918 AUC on Homo sapiens.

Conclusions

Our method provides an efficient and accurate way to identify multi-scale DHSs sequences by deep learning.

     

Core column prediction for protein multiple sequence alignments

Background

In a computed protein multiple sequence alignment, the coreness of a column is the fraction of its substitutions that are in so-called core columns of the gold-standard reference alignment of its proteins. In benchmark suites of protein reference alignments, the core columns of the reference alignment are those that can be confidently labeled as correct, usually due to all residues in the column being sufficiently close in the spatial superposition of the known three-dimensional structures of the proteins. Typically the accuracy of a protein multiple sequence alignment that has been computed for a benchmark is only measured with respect to the core columns of the reference alignment. When computing an alignment in practice, however, a reference alignment is not known, so the coreness of its columns can only be predicted.

Results

We develop for the first time a predictor of column coreness for protein multiple sequence alignments. This allows us to predict which columns of a computed alignment are core, and hence better estimate the alignment’s accuracy. Our approach to predicting coreness is similar to nearest-neighbor classification from machine learning, except we transform nearest-neighbor distances into a coreness prediction via a regression function, and we learn an appropriate distance function through a new optimization formulation that solves a large-scale linear programming problem. We apply our coreness predictor to parameter advising, the task of choosing parameter values for an aligner’s scoring function to obtain a more accurate alignment of a specific set of sequences. We show that for this task, our predictor strongly outperforms other column-confidence estimators from the literature, and affords a substantial boost in alignment accuracy.

     

Feed-forward neural network model for hunger and satiety related VAS score prediction

Background

An artificial neural network approach was chosen to model the outcome of the complex signaling pathways in the gastro-intestinal tract and other peripheral organs that eventually produce the satiety feeling in the brain upon feeding.

Methods

A multilayer feed-forward neural network was trained with sets of experimental data relating concentration-time courses of plasma satiety hormones to Visual Analog Scales (VAS) scores. The network successfully predicted VAS responses from sets of satiety hormone data obtained in experiments using different food compositions.

Results

The correlation coefficients for the predicted VAS responses for test sets having i) a full set of three satiety hormones, ii) a set of only two satiety hormones, and iii) a set of only one satiety hormone were 0.96, 0.96, and 0.89, respectively. The predicted VAS responses discriminated the satiety effects of high satiating food types from less satiating food types both in orally fed and ileal infused forms.

Conclusions

From this application of artificial neural networks, one may conclude that neural network models are very suitable to describe situations where behavior is complex and incompletely understood. However, training data sets that fit the experimental conditions need to be available.

     

Prediction of heme binding residues from protein sequences with integrative sequence profiles

Background

The heme-protein interactions are essential for various biological processes such as electron transfer, catalysis, signal transduction and the control of gene expression. The knowledge of heme binding residues can provide crucial clues to understand these activities and aid in functional annotation, however, insufficient work has been done on the research of heme binding residues from protein sequence information.

Methods

We propose a sequence-based approach for accurate prediction of heme binding residues by a novel integrative sequence profile coupling position specific scoring matrices with heme specific physicochemical properties. In order to select the informative physicochemical properties, we design an intuitive feature selection scheme by combining a greedy strategy with correlation analysis.

Results

Our integrative sequence profile approach for prediction of heme binding residues outperforms the conventional methods using amino acid and evolutionary information on the 5-fold cross validation and the independent tests.

Conclusions

The novel feature of an integrative sequence profile achieves good performance using a reduced set of feature vector elements.

     

DNAAlignEditor: DNA alignment editor tool

Background

With advances in DNA re-sequencing methods and Next-Generation parallel sequencing approaches, there has been a large increase in genomic efforts to define and analyze the sequence variability present among individuals within a species. For very polymorphic species such as maize, this has lead to a need for intuitive, user-friendly software that aids the biologist, often with naïve programming capability, in tracking, editing, displaying, and exporting multiple individual sequence alignments. To fill this need we have developed a novel DNA alignment editor.

Results

We have generated a nucleotide sequence alignment editor (DNAAlignEditor) that provides an intuitive, user-friendly interface for manual editing of multiple sequence alignments with functions for input, editing, and output of sequence alignments. The color-coding of nucleotide identity and the display of associated quality score aids in the manual alignment editing process. DNAAlignEditor works as a client/server tool having two main components: a relational database that collects the processed alignments and a user interface connected to database through universal data access connectivity drivers. DNAAlignEditor can be used either as a stand-alone application or as a network application with multiple users concurrently connected.

Conclusion

We anticipate that this software will be of general interest to biologists and population genetics in editing DNA sequence alignments and analyzing natural sequence variation regardless of species, and will be particularly useful for manual alignment editing of sequences in species with high levels of polymorphism.

     

Coupling effects on turning points of infectious diseases epidemics in scale-free networks

Background

Pandemic is a typical spreading phenomenon that can be observed in the human society and is dependent on the structure of the social network. The Susceptible-Infective-Recovered (SIR) model describes spreading phenomena using two spreading factors; contagiousness (β) and recovery rate (γ). Some network models are trying to reflect the social network, but the real structure is difficult to uncover.

Methods

We have developed a spreading phenomenon simulator that can input the epidemic parameters and network parameters and performed the experiment of disease propagation. The simulation result was analyzed to construct a new marker VRTP distribution. We also induced the VRTP formula for three of the network mathematical models.

Results

We suggest new marker VRTP (value of recovered on turning point) to describe the coupling between the SIR spreading and the Scale-free (SF) network and observe the aspects of the coupling effects with the various of spreading and network parameters. We also derive the analytic formulation of VRTP in the fully mixed model, the configuration model, and the degree-based model respectively in the mathematical function form for the insights on the relationship between experimental simulation and theoretical consideration.

Conclusions

We discover the coupling effect between SIR spreading and SF network through devising novel marker VRTP which reflects the shifting effect and relates to entropy.

     

NMRProcFlow: a graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics

Introduction

Concerning NMR-based metabolomics, 1D spectra processing often requires an expert eye for disentangling the intertwined peaks.

Objectives

The objective of NMRProcFlow is to assist the expert in this task in the best way without requirement of programming skills.

Methods

NMRProcFlow was developed to be a graphical and interactive 1D NMR (¹H & ¹³C) spectra processing tool.

Results

NMRProcFlow (http://nmrprocflow.org), dedicated to metabolic fingerprinting and targeted metabolomics, covers all spectra processing steps including baseline correction, chemical shift calibration and alignment.

Conclusion

Biologists and NMR spectroscopists can easily interact and develop synergies by visualizing the NMR spectra along with their corresponding experimental-factor levels, thus setting a bridge between experimental design and subsequent statistical analyses.

     

Identification of drug repurposing candidates based on a miRNA-mediated drug and pathway network for cardiac hypertrophy and acute myocardial infarction

Background

Cardiac hypertrophy and acute myocardial infarction (AMI) are two common heart diseases worldwide. However, research is needed into the exact pathogenesis and effective treatment strategies for these diseases. Recently, microRNAs (miRNAs) have been suggested to regulate the pathological pathways of heart disease, indicating a potential role in novel treatments.

Results

In our study, we constructed a miRNA-gene-drug network and analyzed its topological features. We also identified some significantly dysregulated miRNA-gene-drug triplets (MGDTs) in cardiac hypertrophy and AMI using a computational method. Then, we characterized the activity score profile features for MGDTs in cardiac hypertrophy and AMI. The functional analyses suggested that the genes in the network held special functions. We extracted an insulin-like growth factor 1 receptor-related subnetwork in cardiac hypertrophy and a vascular endothelial growth factor A-related subnetwork in AMI. Finally, we considered insulin-like growth factor 1 receptor and vascular endothelial growth factor A as two candidate drug targets by utilizing the cardiac hypertrophy and AMI pathways.

Conclusion

These results provide novel insights into the mechanisms and treatment of cardiac hypertrophy and AMI.

     

Split-inducing indels in phylogenomic analysis

Background

Most phylogenetic studies using molecular data treat gaps in multiple sequence alignments as missing data or even completely exclude alignment columns that contain gaps.

Results

Here we show that gap patterns in large-scale, genome-wide alignments are themselves phylogenetically informative and can be used to infer reliable phylogenies provided the gap data are properly filtered to reduce noise introduced by the alignment method. We introduce here the notion of split-inducing indels (splids) that define an approximate bipartition of the taxon set. We show both in simulated data and in case studies on real-life data that splids can be efficiently extracted from phylogenomic data sets.

Conclusions

Suitably processed gap patterns extracted from genome-wide alignment provide a surprisingly clear phylogenetic signal and an allow the inference of accurate phylogenetic trees.

     

A methodology for elucidating regulatory mechanisms leading to changes in lipid profiles

Introduction

It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.

Objectives

This work simplifies this process.

Methods

A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.

Results

The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.

Conclusion

This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.

     

Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk

Background

With the development of sequencing technology, more and more long non-coding RNAs (lncRNAs) have been identified. Some lncRNAs have been confirmed that they play an important role in the process of development through the dosage compensation effect, epigenetic regulation, cell differentiation regulation and other aspects. However, the majority of the lncRNAs have not been functionally characterized. Explore the function of lncRNAs and the regulatory network has become a hot research topic currently.

Methods

In the work, a network-based model named BiRWLGO is developed. The ultimate goal is to predict the probable functions for lncRNAs at large scale. The new model starts with building a global network composed of three networks: lncRNA similarity network, lncRNA-protein association network and protein-protein interaction (PPI) network. After that, it utilizes bi-random walk algorithm to explore the similarities between lncRNAs and proteins. Finally, we can annotate an lncRNA with the Gene Ontology (GO) terms according to its neighboring proteins.

Results

We compare the performance of BiRWLGO with the state-of-the-art models on a manually annotated lncRNA benchmark with known GO terms. The experimental results assert that BiRWLGO outperforms other methods in terms of both maximum F-measure (F_max) and coverage.

Conclusions

BiRWLGO is a relatively efficient method to predict the functions of lncRNA. When protein interaction data is integrated, the predictive performance of BiRWLGO gains a great improvement.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.