bluuebunny/crossref_metadata_2025_split huggingface dataset:
Three-Level Prediction of Protein Function by Combining Profile-Sequence Search, Profile-Profile Search, and Domain Co-Occurrence Networks
Authors: Wang Zheng , Cao Renzhi , Cheng Jianlin 
Published: December 2019

Abstract
Predicting protein function from sequence is useful for biochemical experiment design, mutagenesis analysis, protein engineering, protein design, biological pathway analysis, drug design, disease diagnosis, and genome annotation as a vast number of protein sequences with unknown function are routinely being generated by DNA, RNA and protein sequencing in the genomic era. However, despite significant progresses in the last several years, the accuracy of protein function prediction still needs to be improved in order to be used effectively in practice, particularly when little or no homology exists between a target protein and proteins with annotated function. Here, we developed a method that integrated profile-sequence alignment, profile-profile alignment, and Domain Co-Occurrence Networks (DCN) to predict protein function at different levels of complexity, ranging from obvious homology, to remote homology, to no homology. We tested the method blindingly in the 2011 Critical Assessment of Function Annotation (CAFA). Our experiments demonstrated that our three-level prediction method effectively increased the recall of function prediction while maintaining a reasonable precision. Particularly, our method can predict function terms defined by the Gene Ontology more accurately than three standard baseline methods in most situations, handle multi-domain proteins naturally, and make ab initio function prediction when no homology exists. These results show that our approach can combine complementary strengths of most widely used BLAST-based function prediction methods, rarely used in function prediction but more sensitive profile-profile comparison-based homology detection methods, and non-homology-based domain co-occurrence networks, to effectively extend the power of function prediction from high homology, to low homology, to no homology (ab initio cases).
Prediction of conformational epitopes with the use of a knowledge-based energy function and geometrically related neighboring residue characteristics
Authors: Lo Ying-Tsang , Pai Tun-Wen , Wu Wei-Kuo , Chang Hao-Teng 
Published: December 2019

Abstract

Background
A conformational epitope (CE) in an antigentic protein is composed of amino acid residues that are spatially near each other on the antigen's surface but are separated in sequence; CEs bind their complementary paratopes in B-cell receptors and/or antibodies. CE predication is used during vaccine design and in immuno-biological experiments. Here, we develop a novel system, CE-KEG, which predicts CEs based on knowledge-based energy and geometrical neighboring residue contents. The workflow applied grid-based mathematical morphological algorithms to efficiently detect the surface atoms of the antigens. After extracting surface residues, we ranked CE candidate residues first according to their local average energy distributions. Then, the frequencies at which geometrically related neighboring residue combinations in the potential CEs occurred were incorporated into our workflow, and the weighted combinations of the average energies and neighboring residue frequencies were used to assess the sensitivity, accuracy, and efficiency of our prediction workflow.

Results
We prepared a database containing 247 antigen structures and a second database containing the 163 non-redundant antigen structures in the first database to test our workflow. Our predictive workflow performed better than did algorithms found in the literature in terms of accuracy and efficiency. For the non-redundant dataset tested, our workflow achieved an average of 47.8% sensitivity, 84.3% specificity, and 80.7% accuracy according to a 10-fold cross-validation mechanism, and the performance was evaluated under providing top three predicted CE candidates for each antigen.

Conclusions
Our method combines an energy profile for surface residues with the frequency that each geometrically related amino acid residue pair occurs to identify possible CEs in antigens. This combination of these features facilitates improved identification for immuno-biological studies and synthetic vaccine design. CE-KEG is available at http://cekeg.cs.ntou.edu.tw.
Quasi-cellular systems: stochastic simulation analysis at nanoscale range
Authors: Calviello Lorenzo , Stano Pasquale , Mavelli Fabio , Luisi Pier Luigi , Marangoni Roberto 
Published: December 2019

Abstract

Background
The wet-lab synthesis of the simplest forms of life (minimal cells) is a challenging aspect in modern synthetic biology. Quasi-cellular systems able to produce proteins directly from DNA can be obtained by encapsulating the cell-free transcription/translation system PURESYSTEM™(PS) in liposomes. It is possible to detect the intra-vesicle protein production using DNA encoding for GFP and monitoring the fluorescence emission over time. The entrapment of solutes in small-volume liposomes is a fundamental open problem. Stochastic simulation is a valuable tool in the study of biochemical reaction at nanoscale range. QDC (Quick Direct-Method Controlled), a stochastic simulation software based on the well-known Gillespie's SSA algorithm, was used. A suitable model formally describing the PS reactions network was developed, to predict, from inner species concentrations (very difficult to measure in small-volumes), the resulting fluorescence signal (experimentally observable).

Results
Thanks to suitable features specific of QDC, we successfully formalized the dynamical coupling between the transcription and translation processes that occurs in the real PS, thus bypassing the concurrent-only environment of Gillespie's algorithm. Simulations were firstly performed for large liposomes (2.67µm of diameter) entrapping the PS to synthetize GFP. By varying the initial concentrations of the three main classes of molecules involved in the PS (DNA, enzymes, consumables), we were able to stochastically simulate the time-course of GFP-production. The sigmoid fit of the GFP-production curves allowed us to extract three quantitative parameters which are significantly dependent on the various initial states. Then we extended this study for small-volume liposomes (575 nm of diameter), where it is more complex to infer the intra-vesicle composition, due to the expected anomalous entrapment phenomena. We identified almost two extreme states that are forecasted to give rise to significantly different experimental observables.

Conclusions
The present work is the first one describing in the detail the stochastic behavior of the PS. Thanks to our results, an experimental approach is now possible, aimed at recording the GFP production kinetics in very small micro-emulsion droplets or liposomes, and inferring, by using the simulation as a reverse-engineering procedure, the internal solutes distribution, and shed light on the still unknown forces driving the entrapment phenomenon.
A nonparametric Bayesian approach for clustering bisulfate-based DNA methylation profiles
Authors: Zhang Lin , Meng Jia , Liu Hui , Huang Yufei 
Published: December 2019

Abstract

Background
DNA methylation occurs in the context of a CpG dinucleotide. It is an important epigenetic modification, which can be inherited through cell division. The two major types of methylation include hypomethylation and hypermethylation. Unique methylation patterns have been shown to exist in diseases including various types of cancer. DNA methylation analysis promises to become a powerful tool in cancer diagnosis, treatment and prognostication. Large-scale methylation arrays are now available for studying methylation genome-wide. The Illumina methylation platform simultaneously measures cytosine methylation at more than 1500 CpG sites associated with over 800 cancer-related genes. Cluster analysis is often used to identify DNA methylation subgroups for prognosis and diagnosis. However, due to the unique non-Gaussian characteristics, traditional clustering methods may not be appropriate for DNA and methylation data, and the determination of optimal cluster number is still problematic.

Method
A Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation expressions as an infinite number of beta mixture distribution. The model allows automatic learning of the relevant parameters such as the cluster mixing proportion, the parameters of beta distribution for each cluster, and especially the number of potential clusters. Since the model is high dimensional and analytically intractable, we proposed a Gibbs sampling "no-gaps" solution for computing the posterior distributions, hence the estimates of the parameters.

Result
The proposed algorithm was tested on simulated data as well as methylation data from 55 Glioblastoma multiform (GBM) brain tissue samples. To reduce the computational burden due to the high data dimensionality, a dimension reduction method is adopted. The two GBM clusters yielded by DPBMM are based on data of different number of loci (P-value < 0.1), while hierarchical clustering cannot yield statistically significant clusters.
The parasite specific substitution matrices improve the annotation of apicomplexan proteins
Authors: Ali Jamshaid , Thummala Shashi Rekha , Ranjan Akash 
Published: December 2019

Abstract

Background
A number of apicomplexan genomes have been sequenced successfully in recent years and this would help in understanding the biology of apicomplexan parasites. The members of the phylum Apicomplexa are important protozoan parasites (Plasmodium, Toxoplasma and Cryptosporidium etc) that cause some of the deadly diseases in humans and animals. In our earlier studies, we have shown that the standard BLOSUM matrices are not suitable for compositionally biased apicomplexan proteins. So we developed a novel series (SMAT and PfFSmat60) of substitution matrices which performed better in comparison to standard BLOSUM matrices and developed ApicoAlign, a sequence search and alignment tool for apicomplexan proteins. In this study, we demonstrate the higher specificity of these matrices and make an attempt to improve the annotation of apicomplexan kinases and proteases.

Results
The ROC curves proved that SMAT80 performs best for apicomplexan proteins followed by compositionally adjusted BLOSUM62 (PSI-BLAST searches), BLOSUM90 and BLOSUM62 matrices in terms of detecting true positives. The poor E-values and/or bit scores given by SMAT80 matrix for the experimentally identified coccidia-specific oocyst wall proteins against hematozoan (non-coccidian) parasites further supported the higher specificity of the same. SMAT80 uniquely detected (missed by BLOSUM) orthologs for 1374 apicomplexan hypothetical proteins against SwissProt database and predicted 70 kinases and 17 proteases. Further analysis confirmed the conservation of functional residues of kinase domain in one of the SMAT80 detected kinases. Similarly, one of the SMAT80 detected proteases was predicted to be a rhomboid protease.

Conclusions
The parasite specific substitution matrices have higher specificity for apicomplexan proteins and are helpful in detecting the orthologs missed by BLOSUM matrices and thereby improve the annotation of apicomplexan proteins which are hypothetical or with unknown function.
Investigating the relationship of DNA methylation with mutation rate and allele frequency in the human genome
Authors: Xia Junfeng , Han Leng , Zhao Zhongming 
Published: December 2019

Abstract

Background
DNA methylation, which mainly occurs at CpG dinucleotides, is a dynamic epigenetic regulation mechanism in most eukaryotic genomes. It is already known that methylated CpG dinucleotides can lead to a high rate of C to T mutation at these sites. However, less is known about whether and how the methylation level causes a different mutation rate, especially at the single-base resolution.

Results
In this study, we used genome-wide single-base resolution methylation data to perform a comprehensive analysis of the mutation rate of methylated cytosines from human embryonic stem cell. Through the analysis of the density of single nucleotide polymorphisms, we first confirmed that the mutation rate in methylated CpG sites is greater than that in unmethylated CpG sites. Then, we showed that among methylated CpG sites, the mutation rate is markedly increased in low-intermediately (20-40% methylation level) to intermediately methylated CpG sites (40-60% methylation level) of the human genome. This mutation pattern was observed regardless of DNA strand direction and the sequence coverage over the site on which the methylation level was calculated. Moreover, this highly non-random mutation pattern was found more apparent in intergenic and intronic regions than in promoter regions and CpG islands. Our investigation suggested this pattern appears primarily in autosomes rather than sex chromosomes. Further analysis based on human-chimpanzee divergence confirmed these observations. Finally, we observed a significant correlation between the methylation level and cytosine allele frequency.

Conclusions
Our results showed a high mutation rate in low-intermediately to intermediately methylated CpG sites at different scales, from the categorized genomic region, whole chromosome, to the whole genome level, thereby providing the first supporting evidence of mutation rate variation at human methylated CpG sites using the genome-wide sing-base resolution methylation data.
Steps to ensure accuracy in genotype and SNP calling from Illumina sequencing data
Authors: Liu Qi , Guo Yan , Li Jiang , Long Jirong , Zhang Bing , Shyr Yu 
Published: December 2019

Abstract

Background
Accurate calling of SNPs and genotypes from next-generation sequencing data is an essential prerequisite for most human genetics studies. A number of computational steps are required or recommended when translating the raw sequencing data into the final calls. However, whether each step does contribute to the performance of variant calling and how it affects the accuracy still remain unclear, making it difficult to select and arrange appropriate steps to derive high quality variants from different sequencing data. In this study, we made a systematic assessment of the relative contribution of each step to the accuracy of variant calling from Illumina DNA sequencing data.

Results
We found that the read preprocessing step did not improve the accuracy of variant calling, contrary to the general expectation. Although trimming off low-quality tails helped align more reads, it introduced lots of false positives. The ability of markup duplication, local realignment and recalibration, to help eliminate false positive variants depended on the sequencing depth. Rearranging these steps did not affect the results. The relative performance of three popular multi-sample SNP callers, SAMtools, GATK, and GlfMultiples, also varied with the sequencing depth.

Conclusions
Our findings clarify the necessity and effectiveness of computational steps for improving the accuracy of SNP and genotype calls from Illumina sequencing data and can serve as a general guideline for choosing SNP calling strategies for data with different coverage.
Delayed neuronal cell death in brainstem after transient brainstem ischemia in gerbils
Authors: Cao Fang , Hata Ryuji , Zhu Pengxiang , Takeda Shoichiro , Yoshida Tadashi , Hakuba Nobuhiro , Sakanaka Masahiro , Gyo Kiyofumi 
Published: December 2019

Abstract

Background
Because of the lack of reproducible brainstem ischemia models in rodents, the temporal profile of ischemic lesions in the brainstem after transient brainstem ischemia has not been evaluated intensively. Previously, we produced a reproducible brainstem ischemia model of Mongolian gerbils. Here, we showed the temporal profile of ischemic lesions after transient brainstem ischemia.

Results
Brainstem ischemia was produced by occlusion of the bilateral vertebral arteries just before their entry into the transverse foramina of the cervical vertebrae of Mongolian gerbils. Animals were subjected to brainstem ischemia for 15 min, and then reperfused for 0 d (just after ischemia), 1 d, 3 d and 7 d (n = 4 in each group). Sham-operated animals (n = 4) were used as control. After deep anesthesia, the gerbils were perfused with fixative for immunohistochemical investigation. Ischemic lesions were detected by immunostaining for microtubule-associated protein 2 (MAP2). Just after 15-min brainstem ischemia, ischemic lesions were detected in the lateral vestibular nucleus and the ventral part of the spinal trigeminal nucleus, and these ischemic lesions disappeared one day after reperfusion in all animals examined. However, 3 days and 7 days after reperfusion, ischemic lesions appeared again and clusters of ionized calcium-binding adapter molecule-1(IBA-1)-positive cells were detected in the same areas in all animals.

Conclusion
These results suggest that delayed neuronal cell death took place in the brainstem after transient brainstem ischemia in gerbils.
Effects of aluminum on nucleoli in root tip cells and selected physiological and biochemical characters in Allium cepa var. agrogarumL
Authors: Qin Rong , Jiao Yunqiu , Zhang Shanshan , Jiang Wusheng , Liu Donghua 
Published: December 2019

Abstract

Background
Increased Al concentration causes reduction of mitotic activity, induction of nucleolar alteration, increase of the production of ROS and alteration of several antioxidant enzyme activities in plant cells. Allium cepa is an excellent plant and a useful biomarker for environmental monitoring. Limited information is available about the effects of Al on nucleoli, antioxidant enzyme system, contents of MDA and soluble protein in A. cepa. Therefore, we carried out the investigation in order to better understand the effects of Al on the growth, nucleoli in root tip cells and selected physiological and biochemical characters.

Results
The results showed that the root growth exposed to 50 μM Al was inhibited significantly. 50 μM Al could induce some particles of argyrophilic proteins scattered in the nuclei and extruded from the nucleoli into the cytoplasm. The nucleolus did not disaggregate normally and still remained its characteristic structure during metaphase. Nucleolar reconstruction was inhibited. 50 μM Al induced high activities of SOD and POD in leaves and roots significantly (P < 0.05) when compared with control, whereas the level of CAT was low significantly (P < 0.05). At 50 μM Al the content of MDA in leaves was high significantly (P < 0.05) at 9th day and in roots increased (P < 0.05) with prolonging the treatment time during 6-12 days. The soluble protein content in leaves treated with 50 μM Al was high significantly (P < 0.05) at 6th day and increased with prolonging the treatment time.

Conclusions
We suggest that variations in nucleoli and the alterations of antioxidant enzyme activities, MDA and soluble protein contents in Allium cepa can serve as useful biomarkers, which can provide valuable information for monitoring and forecasting effects of exposure to Al in real scenarios conditions. Among the antioxidant enzymes SOD and POD appear to play a key role in the antioxidant defense mechanism under Al toxicity condition. Data from MDA concentration show that Al indirectly produces superoxide radicals, resulting in increased lipid peroxidative products and oxidative stress.
Tumour suppressor function of MDA-7/IL-24 in human breast cancer
Authors: Patani Neill , Douglas-Jones Anthony , Mansel Robert , Jiang Wen , Mokbel Kefah 
Published: December 2019

Abstract

Introduction
Melanoma differentiation associated gene-7 (MDA-7), also known as interleukin (IL)-24, is a tumour suppressor gene associated with differentiation, growth and apoptosis. However, the mechanisms underlying its anti-neoplastic activity, tumour-specificity and efficacy across a spectrum of human cancers have yet to be fully elucidated. In this study, the biological impact of MDA-7 on the behavior of breast cancer (BC) cells is evaluated. Furthermore, mRNA expression of MDA-7 is assessed in a cohort of women with BC and correlated with established pathological parameters and clinical outcome.

Methods
The human BC cell line MDA MB-231 was used to evaluate the in-vitro impact of recombinant human (rh)-MDA-7 on cell growth and motility, using a growth assay, wounding assay and electric cell impedance sensing (ECIS). Localisation of MDA-7 in mammary tissues was assessed with standard immuno-histochemical methodology. BC tissues (n = 127) and normal tissues (n = 33) underwent RNA extraction and reverse transcription, MDA-7 transcript levels were determined using real-time quantitative PCR. Transcript levels were analyzed against tumour size, grade, oestrogen receptor (ER) status, nodal involvement, TNM stage, Nottingham Prognostic Index (NPI) and clinical outcome over a 10 year follow-up period.

Results
Exposure to rh-MDA-7 significantly reduced wound closure rates for human BC cells in-vitro. The ECIS model demonstrated a significantly reduced motility and migration following rh-MDA-7 treatment (p = 0.024). Exposure to rh-MDA-7 was only found to exert a marginal effect on growth. Immuno-histochemical staining of human breast tissues revealed substantially greater MDA-7 positivity in normal compared to cancer cells. Significantly lower MDA-7 transcript levels were identified in those predicted to have a poorer prognosis by the NPI (p = 0.049) and those with node positive tumours. Significantly lower expression was also noted in tumours from patients who died of BC compared to those who remained disease free (p = 0.035). Low levels of MDA-7 were significantly correlated with a shorter disease free survival (mean = 121.7 vs. 140.4 months, p = 0.0287) on Kaplan-Meier survival analysis.

Conclusion
MDA-7 significantly inhibits the motility and migration of human BC cells in-vitro. MDA-7 expression is substantially reduced in malignant breast tissue and low transcript levels are significantly associated with unfavourable pathological parameters, including nodal positivity; and adverse clinical outcomes including poor prognosis and shorter disease free survival. MDA-7 offers utility as a prognostic marker and potential for future therapeutic strategies.
Validating an infrared thermal switch as a novel access technology
Authors: Memarian Negar , Venetsanopoulos Anastasios N , Chau Tom 
Published: December 2019

Abstract

Background
Recently, a novel single-switch access technology based on infrared thermography was proposed. The technology exploits the temperature differences between the inside and surrounding areas of the mouth as a switch trigger, thereby allowing voluntary switch activation upon mouth opening. However, for this technology to be clinically viable, it must be validated against a gold standard switch, such as a chin switch, that taps into the same voluntary motion.

Methods
In this study, we report an experiment designed to gauge the concurrent validity of the infrared thermal switch. Ten able-bodied adults participated in a series of 3 test sessions where they simultaneously used both an infrared thermal and conventional chin switch to perform multiple trials of a number identification task with visual, auditory and audiovisual stimuli. Participants also provided qualitative feedback about switch use. User performance with the two switches was quantified using an efficiency measure based on mutual information.

Results
User performance (p = 0.16) and response time (p = 0.25) with the infrared thermal switch were comparable to those of the gold standard. Users reported preference for the infrared thermal switch given its non-contact nature and robustness to changes in user posture.

Conclusions
Thermal infrared access technology appears to be a valid single switch alternative for individuals with disabilities who retain voluntary mouth opening and closing.