Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts

Background

Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations.

Methods

We developed a maximum entropy-based named entity recognizer and a relation recognizer and applied them to a corpus-based approach. We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists. We used it to train the maximum entropy-based named entity recognizer and relation recognizer.

Results

Topic-classified relation recognition achieved 92.1% precision for the relation (an increase of 11.0% from that obtained in a baseline experiment). For all topics, the precision was between 67.6 and 88.1%.

Conclusion

A series of experimental results revealed two important findings: a carefully designed relation recognition system using named entity recognition can improve the performance of relation recognition, and topic-classified relation recognition can be effectively addressed through a corpus-based approach using manual annotation and machine learning techniques.

     

Investigation of the relationship between cell surface hydrophobicity and demulsifying capability of biodemulsifier-producing bacteria

Background

Cell surface hydrophobicity (CSH) is one of the key physicochemical features of biodemulsifier-producing bacteria that influence their demulsification capability maintenance in petroleum contaminated environments.

Methods

In present study, biodemulsifier-producing bacteria were isolated from petroleum contaminated environments using different isolation media and the correlation between their CSH and demulsifying ability was investigated. The demulsifying ability of isolates was measured through demulsification tests on water in kerosene emulsions. The microbial adhesion to the hydrocarbon (MATH) assay was used to denote their CSH.

Results

The evaluation of CSH showed that majority of biodemulsifier producing bacteria have high CSH which indicating a positive correlation between CSH and demulsifying capability.

Conclusions

According to these results it can be concluded that CSH can be used as an indicator for assessment of biodemulsifier-producing bacteria and screening of new isolates for their biodemulsifier production.

     

Identification of protein complexes from multi-relationship protein interaction networks

Background

Protein complexes play an important role in biological processes. Recent developments in experiments have resulted in the publication of many high-quality, large-scale protein-protein interaction (PPI) datasets, which provide abundant data for computational approaches to the prediction of protein complexes. However, the precision of protein complex prediction still needs to be improved due to the incompletion and noise in PPI networks.

Results

There exist complex and diverse relationships among proteins after integrating multiple sources of biological information. Considering that the influences of different types of interactions are not the same weight for protein complex prediction, we construct a multi-relationship protein interaction network (MPIN) by integrating PPI network topology with gene ontology annotation information. Then, we design a novel algorithm named MINE (identifying protein complexes based on Multi-relationship protein Interaction NEtwork) to predict protein complexes with high cohesion and low coupling from MPIN.

Conclusions

The experiments on yeast data show that MINE outperforms the current methods in terms of both accuracy and statistical significance.

     

A protocol for CABS-dock protein–peptide docking driven by side-chain contact information

Background

The characterization of protein–peptide interactions is a challenge for computational molecular docking. Protein–peptide docking tools face at least two major difficulties: (1) efficient sampling of large-scale conformational changes induced by binding and (2) selection of the best models from a large set of predicted structures. In this paper, we merge an efficient sampling technique with external information about side-chain contacts to sample and select the best possible models.

Methods

In this paper we test a new protocol that uses information about side-chain contacts in CABS-dock protein–peptide docking. As shown in our recent studies, CABS-dock enables efficient modeling of large-scale conformational changes without knowledge about the binding site. However, the resulting set of binding sites and poses is in many cases highly diverse and difficult to score.

Results

As we demonstrate here, information about a single side-chain contact can significantly improve the prediction accuracy. Importantly, the imposed constraints for side-chain contacts are quite soft. Therefore, the developed protocol does not require precise contact information and ensures large-scale peptide flexibility in the broad contact area.

Conclusions

The demonstrated protocol provides the extension of the CABS-dock method that can be practically used in the structure prediction of protein–peptide complexes guided by the knowledge of the binding interface.

     

Genetic variation in the TAS2R38 taste receptor contributes to the oral microbiota in North and South European locations: a pilot study

Background

Microbial communities are influenced by environmental factors including host genetics. We investigated the relationship between host bitter taste receptor genotype hTAS2R38 and oral microbiota, together with the influence of geographical location.

Methods

hTAS2R38 polymorphisms and 16S bacterial gene sequencing from oral samples were analyzed from a total of 45 healthy volunteers from different geographical locations.

Results

Genetic variation in the bitter taste receptor TAS2R38 reflected in the microbial composition of oral mucosa in Finnish and Spanish subjects. Multivariate analysis showed significant differences in the microbial composition between country and also dependent on taste genotype. Oral microbiota was shown to be more stable to the geographical location impact among AVI-homozygotes than PAV-homozygotes or heterozygotes (PAV/AVI).

Conclusion

Geographical location and genetic variation in the hTAS2R38 taste receptor impact oral mucosa microbial composition. These findings provide an advance in the knowledge regarding the interactions between taste receptor genes and oral microbiota. This study suggests the role of host-microbiota interactions on the food taste perception in food choices, nutrition, and eating behavior.

     

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.

     

Holm oak decline triggers changes in plant succession and microbial communities,with implications for ecosystem C and N cycling

Background and aims

The occurrence of drought-induced forest die-off events is projected to increase in the future, but we still lack complete understanding of its impact on plant-soil interactions, soil microbial diversity and function. We investigated the effects of holm oak (Quercus ilex) decline (HOD) on soil microbial community and functioning, and how these effects relate to changes in the herbaceous community.

Methods

We selected 30 holm oak trees with different defoliation degrees (healthy, affected and dead) and analyzed soil samples collected under the canopy (holm oak ecotype) and out of the influence (grassland ecotype) of each tree.

Results

HOD increased potential nitrogen (N) mineralization and decreased inorganic N concentrations. These results could be partially explained by changes in the herbaceous composition, an increased herbaceous abundance and changes in soil microbial functional diversity and structure, with HOD favoring bacteria against fungi. Moreover, herbaceous abundance and microbial functional diversity of holm oak and grassland ecotypes converged with HOD.

Conclusions

Our results show that HOD triggers a cascade effect on plant understory and soil microbial communities, as well as a plant succession (savannization) process, where understory species colonize the gaps left by dead holm oaks, with important implications for ecosystem C and N budgets.

     

Normalization and integration of large-scale metabolomics data using support vector regression

Introduction

Untargeted metabolomics studies for biomarker discovery often have hundreds to thousands of human samples. Data acquisition of large-scale samples has to be divided into several batches and may span from months to as long as several years. The signal drift of metabolites during data acquisition (intra- and inter-batch) is unavoidable and is a major confounding factor for large-scale metabolomics studies.

Objectives

We aim to develop a data normalization method to reduce unwanted variations and integrate multiple batches in large-scale metabolomics studies prior to statistical analyses.

Methods

We developed a machine learning algorithm-based method, support vector regression (SVR), for large-scale metabolomics data normalization and integration. An R package named MetNormalizer was developed and provided for data processing using SVR normalization.

Results

After SVR normalization, the portion of metabolite ion peaks with relative standard deviations (RSDs) less than 30 % increased to more than 90 % of the total peaks, which is much better than other common normalization methods. The reduction of unwanted analytical variations helps to improve the performance of multivariate statistical analyses, both unsupervised and supervised, in terms of classification and prediction accuracy so that subtle metabolic changes in epidemiological studies can be detected.

Conclusion

SVR normalization can effectively remove the unwanted intra- and inter-batch variations, and is much better than other common normalization methods.

     

Identity of plant,lichen and moss species connects with microbial abundance and soil functioning in maritime Antarctica

Background and aims

Plant breeding activities shape the rhizosphere microbiome but less is known about the relationship of both with the seed microbiome. We analyzed the composition of bacterial communities of seeds and rhizospheres of Styrian oil pumpkin genotypes in comparison to bulk soil to elucidate specific microbial signatures to support a concept involving plant-microbe interactions in breeding strategies.

Methods

The seed and rhizosphere microbiomes of 14 genotypes of oilseed pumpkin and relatives were analyzed using a 16S rRNA gene amplicon sequencing approach, which was assessed by bioinformatics and statistical methods.

Results

All analyzed microhabitats were characterized by diverse bacterial communities, but the relative proportions of phyla and the overall diversity was different. Seed microbiomes were characterized by the lowest diversity and dominant members of Enterobacteriaceae including potential pathogens (Erwinia, Pectobacterium). Potential plant-beneficial bacteria like Lysobacter, Paenibacillus and Lactococcus contributed to the microbial communities in significant abundances. Interestingly, strong genotype-specific microbiomes were detected for seeds but not for the rhizospheres.

Conclusions

Our study indicates a strong impact of the Cucurbita pepo genotype on the composition of the seed microbiome. This should be considered in breeding of new cultivars that are more capable of exploiting beneficial indigenous microbial communities.

     

Darwinian selection of host and bacteria supports emergence of Lamarckian-like adaptation of the system as a whole

Background

The relatively fast selection of symbiotic bacteria within hosts and the potential transmission of these bacteria across generations of hosts raise the question of whether interactions between host and bacteria support emergent adaptive capabilities beyond those of germ-free hosts.

Results

To investigate possibilities for emergent adaptations that may distinguish composite host-microbiome systems from germ-free hosts, we introduce a population genetics model of a host-microbiome system with vertical transmission of bacteria. The host and its bacteria are jointly exposed to a toxic agent, creating a toxic stress that can be alleviated by selection of resistant individuals and by secretion of a detoxification agent (“detox”). We show that toxic exposure in one generation of hosts leads to selection of resistant bacteria, which in turn, increases the toxic tolerance of the host’s offspring. Prolonged exposure to toxin over many host generations promotes anadditional form of emergent adaptation due to selection of hosts based on detox produced by their bacterial community as a whole (as opposed to properties of individual bacteria).

Conclusions

These findings show that interactions between pure Darwinian selections of host and its bacteria can give rise to emergent adaptive capabilities, including Lamarckian-like adaptation of the host-microbiome system.

Reviewers

This article was reviewed by Eugene Koonin, Yuri Wolf and Philippe Huneman.

     

Effects of phosphorus-mobilizing bacteria on tomato growth and soil microbial activity

Aims

The aim of our study was to clarify whether inoculating a soil with Pseudomonas sp. RU47 (RU47) bacteria would stimulate the enzymatic cleavage of organic P compounds in the rhizosphere and bulk soil, promoting plant growth. Adding either viable or heat treated RU47 cells made it possible to separate direct from indirect effects of the inoculum on P cycling in soil and plants.

Methods

We performed a rhizobox experiment in the greenhouse with tomato plants (Solanum lycopersicum) under low P soil conditions. Three inoculation treatments were conducted, using unselectively grown soil bacteria (bacterial mix), heat treated (HT-RU47) and viable RU47 (RU47) cells, and one not inoculated, optimally P-fertilized treatment. We verified plant growth, nutrient availability, enzyme activities and microbial community structure in soil.

Results

A plant growth promotion effect with improved P uptake was observed in both RU47 treatments. Inoculations of RU47 cells increased microbial phosphatase activity (PA) in the rhizosphere.

Conclusions

Plant growth promotion by RU47 cells is primarily associated with increased microbial PA in soil, while promotion of indigenous Pseudomonads as well as phytohormonal effects appear to be the dominant mechanisms when adding HT-RU47 cells. Thus, using RU47 offers a promising approach for more efficient P fertilization in agriculture.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.

     

Maximizing power generation from dark fermentation effluents in microbial fuel cell by selective enrichment of exoelectrogens and optimization of anodic operational parameters

Objective

To selectively enrich an electrogenic mixed consortium capable of utilizing dark fermentative effluents as substrates in microbial fuel cells and to further enhance the power outputs by optimization of influential anodic operational parameters.

Results

A maximum power density of 1.4 W/m³ was obtained by an enriched mixed electrogenic consortium in microbial fuel cells using acetate as substrate. This was further increased to 5.43 W/m³ by optimization of influential anodic parameters. By utilizing dark fermentative effluents as substrates, the maximum power densities ranged from 5.2 to 6.2 W/m³ with an average COD removal efficiency of 75% and a columbic efficiency of 10.6%.

Conclusion

A simple strategy is provided for selective enrichment of electrogenic bacteria that can be used in microbial fuel cells for generating power from various dark fermentative effluents.

     

Tracing conformational changes in proteins

Background

Many proteins undergo extensive conformational changes as part of their functionality. Tracing these changes is important for understanding the way these proteins function. Traditional biophysics-based conformational search methods require a large number of calculations and are hard to apply to large-scale conformational motions.

Results

In this work we investigate the application of a robotics-inspired method, using backbone and limited side chain representation and a coarse grained energy function to trace large-scale conformational motions. We tested the algorithm on four well known medium to large proteins and we show that even with relatively little information we are able to trace low-energy conformational pathways efficiently. The conformational pathways produced by our methods can be further filtered and refined to produce more useful information on the way proteins function under physiological conditions.

Conclusions

The proposed method effectively captures large-scale conformational changes and produces pathways that are consistent with experimental data and other computational studies. The method represents an important first step towards a larger scale modeling of more complex biological systems.

     

Novel microbial photobioelectrochemical cell using an invasive ultramicroelectrode array and a microfluidic chamber

Objective

To fabricate a novel microbial photobioelectrochemical cell using silicon microfabrication techniques.

Results

High-density photosynthetic cells were immobilized in a microfluidic chamber, and ultra-microelectrodes in a microtip array were inserted into the cytosolic space of the cells to directly harvest photosynthetic electrons. In this way, the microbial photobioelectrochemical cell operated without the aid of electron mediators. Both short circuit current and open circuit voltage of the microbial photobioelectrochemical cell responded to light stimuli, and recorded as high as 250 pA and 45 mV, respectively.

Conclusion

A microbial photobioelectrochemical cell was fabricated with potential use in next-generation photosynthesis-based solar cells and sensors.

     

Phosphorus availability and microbial community in the rhizosphere of intercropped cereal and legume along a P-fertilizer gradient

Background and aims

Positive below-ground interactions (facilitation) should be more pronounced when resources limit crop growth, according to the stress-gradient hypothesis. Our aim was to test this hypothesis for intercropped durum wheat and faba bean along a P-fertilizer gradient.

Methods

A field experiment was conducted in a long-term P-fertilizer trial with three rates of P-fertilization (No, Low and High P). Microbial biomass was assessed by chloroform fumigation-extraction. Quantitative PCR was applied to evaluate the abundance of relevant microbial groups.

Results

Phosphorus availability and microbial biomass systematically increased in the rhizosphere compared to bulk soil. P-fertilization resulted in higher abundance of targeted bacterial phyla, whole bacterial and fungal communities, and depressed mycorrhizal colonization of durum wheat, but not faba bean. Microbial biomass carbon significantly increased in the rhizosphere only in P-fertilized treatments, pointing to P limitation of microbial communities. Intercropping yielded a significant effect on rhizosphere microbial properties only at High P. Microbial biomass P increased in the rhizosphere of intercropped faba bean only at No P level, and was thus the sole finding supporting the stress-gradient hypothesis.

Conclusions

P-fertilization was the main driver of microbial communities in this field trial, and P-fertilizer application modulated the species-specific effect in the intercrop. Plant performance did not validate the stress-gradient hypothesis as positive plant-plant interactions occurred regardless of the level of P-fertilization.

     

PPIRank - an advanced method for ranking protein-protein interations in TAP/MS data

Background

Tandem affinity purification coupled with mass-spectrometry (TAP/MS) analysis is a popular method for the identification of novel endogenous protein-protein interactions (PPIs) in large-scale. Computational analysis of TAP/MS data is a critical step, particularly for high-throughput datasets, yet it remains challenging due to the noisy nature of TAP/MS data.

Results

We investigated several major TAP/MS data analysis methods for identifying PPIs, and developed an advanced method, which incorporates an improved statistical method to filter out false positives from the negative controls. Our method is named PPIRank that stands for PPI rank ing in TAP/MS data. We compared PPIRank with several other existing methods in analyzing two pathway-specific TAP/MS PPI datasets from Drosophila.

Conclusion

Experimental results show that PPIRank is more capable than other approaches in terms of identifying known interactions collected in the BioGRID PPI database. Specifically, PPIRank is able to capture more true interactions and simultaneously less false positives in both Insulin and Hippo pathways of Drosophila Melanogaster.

     

The use of chitooligosaccharide in beer brewing for protection against beer-spoilage bacteria and its influence on beer performance

Objectives

To identify a biological preservative that can protect beer from microbial contamination, which often results in the production of turbidity and off-flavor.

Results

The antimicrobial activity of a chitooligosaccharide against beer-spoilage bacteria and its effect on the fermentation performance of brewer’s yeast was studied. Chitooligosaccharide with an average 2 kDa molecular weight was the best at inhibiting all tested beer-spoilage bacteria. The application of chitooligosaccharide in the brewing process did not influence the fermentation of brewer’s yeast. The change in beer performance induced by the contamination of Lactobacillus brevis could be effectively controlled by application of chitooligosaccharide in the beer brewing process.

Conclusion

The experimental data suggested that chitooligosaccharide should be an excellent preservative to inhibit beer-spoilage bacteria in the brewing process and in the end product.

     

Assembly of seed-associated microbial communities within and across successive plant generations

Background and aims

Seeds are involved in the transmission of microorganisms from one plant generation to another and consequently may act as the initial inoculum source for the plant microbiota. In this work, we assessed the structure and composition of the seed microbiota of radish (Raphanus sativus) across three successive plant generations.

Methods

Structure of seed microbial communities were estimated on individual plants through amplification and sequencing of genes that are markers of taxonomic diversity for bacteria (gyrB) and fungi (ITS1). The relative contribution of dispersal and ecological drift in inter-individual fluctuations were estimated with a neutral community model.

Results

Seed microbial communities of radish display a low heritability across plant generations. Fluctuations in microbial community profiles were related to changes in community membership and composition across plant generations, but also to variation between individual plants. Ecological drift was an important driver of the structure of seed bacterial communities, while dispersal was involved in the assembly of the fungal fraction of the seed microbiota.

Conclusions

These results provide a first glimpse of the governing processes driving the assembly of the seed microbiota.

     

Effect of experimental gingivitis induction and erythritol on the salivary metabolome and functional biochemistry of systemically healthy young adults

Introduction

Understanding the changes occurring in the oral ecosystem during development of gingivitis could help improve prevention and treatment strategies for oral health. Erythritol is a non-caloric polyol proposed to have beneficial effects on oral health.

Objectives

To examine the effect of experimental gingivitis and the effect of erythritol on the salivary metabolome and salivary functional biochemistry.

Methods

In a two-week experimental gingivitis challenge intervention study, non-targeted, mass spectrometry-based metabolomic profiling was performed on saliva samples from 61 healthy adults, collected at five time-points. The effect of erythritol was studied in a randomized, controlled trial setting. Fourteen salivary biochemistry variables were measured with antibody- or enzymatic activity-based assays.

Results

Bacterial amino acid catabolites (cadaverine, N-acetylcadaverine, and α-hydroxyisovalerate) and end-products of bacterial alkali-producing pathways (N-α-acetylornithine and γ-aminobutyrate) increased significantly during the experimental gingivitis. Significant changes were found in a set of 13 salivary metabolite ratios composed of host cell membrane lipids involved in cell signaling, host responses to bacteria, and defense against free radicals. An increase in mevalonate was also observed. There were no significant effects of erythritol. No significant changes were found in functional salivary biochemistry.

Conclusions

The findings underline a dynamic interaction between the host and the oral microbial biofilm during an experimental induction of gingivitis.