An information-flow-based model with dissipation, saturation and direction for active pathway inference

<p>Abstract</p> <p>Background</p> <p>Biological systems process the genetic information and environmental signals through pathways. How to map the pathways systematically and efficiently from high-throughput genomic and proteomic data is a challenging open problem. Previous methods design different heuristics but do not describe explicitly the behaviours of the information flow.</p> <p>Results</p> <p>In this study, we propose new concepts of dissipation, saturation and direction to decipher the information flow behaviours in the pathways and thereby infer the biological pathways from a given source to its target. This model takes into account explicitly the common features of the information transmission and provides a general framework to model the biological pathways. It can incorporate different types of bio-molecular interactions to infer the signal transduction pathways and interpret the expression quantitative trait loci (eQTL) associations. The model is formulated as a linear programming problem and thus is solved efficiently. Experiments on the real data of yeast indicate that the reproduced pathways are highly consistent with the current knowledge.</p> <p>Conclusions</p> <p>Our model explicitly treats the biological pathways as information flows with dissipation, saturation and direction. The effective applications suggest that the three new concepts may be valid to describe the organization rules of biological pathways. The deduced linear programming should be a promising tool to infer the various biological pathways from the high-throughput data.</p

Improving accuracy of protein-protein interaction prediction by considering the converse problem for sequence representation

<p>Abstract</p> <p>Background</p> <p>With the development of genome-sequencing technologies, protein sequences are readily obtained by translating the measured mRNAs. Therefore predicting protein-protein interactions from the sequences is of great demand. The reason lies in the fact that identifying protein-protein interactions is becoming a bottleneck for eventually understanding the functions of proteins, especially for those organisms barely characterized. Although a few methods have been proposed, the converse problem, if the features used extract sufficient and unbiased information from protein sequences, is almost untouched.</p> <p>Results</p> <p>In this study, we interrogate this problem theoretically by an optimization scheme. Motivated by the theoretical investigation, we find novel encoding methods for both protein sequences and protein pairs. Our new methods exploit sufficiently the information of protein sequences and reduce artificial bias and computational cost. Thus, it significantly outperforms the available methods regarding sensitivity, specificity, precision, and recall with cross-validation evaluation and reaches ~80% and ~90% accuracy in <it>Escherichia coli </it>and <it>Saccharomyces cerevisiae </it>respectively. Our findings here hold important implication for other sequence-based prediction tasks because representation of biological sequence is always the first step in computational biology.</p> <p>Conclusions</p> <p>By considering the converse problem, we propose new representation methods for both protein sequences and protein pairs. The results show that our method significantly improves the accuracy of protein-protein interaction predictions.</p

Evaluating the Value of Defensins for Diagnosing Secondary Bacterial Infections in Influenza-Infected Patients

Acute respiratory infections by influenza viruses are commonly causes of severe pneumonia, which can further deteriorate if secondary bacterial infections occur. Although the viral and bacterial agents are quite diverse, defensins, a set of antimicrobial peptides expressed by the host, may provide promising biomarkers that would greatly improve the diagnosis and treatment. We examined the correlations between the gene expression levels of defensins and the viral and bacterial loads in the blood on a longitudinal, precision-medical study of a severe pneumonia patient infected by influenza A H7N9 virus. We found that DEFA5 is positively correlated to the blood load of influenza A H7N9 virus (r = 0.735, p &lt; 0.05, Spearman correlation). DEFB116 and DEFB127 are positively and DEFB108B and DEFB114 are negatively correlated to the bacterial load. Then the diagnostic potential of defensins to discriminate bacterial and viral infections was evaluated on an independent dataset with 61 bacterial pneumonia patients and 39 viral pneumonia patients infected by influenza A viruses and reached 93% accuracy. Expression levels of defensins in the blood may be of important diagnostic values in clinic to indicate viral and bacterial infections

Discovering cooperative biomarkers for heterogeneous complex disease diagnoses

Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases and further facilitate disease diagnosis and therapy. Techniques integrating gene expression profiles and biological networks for the identification of network-based disease biomarkers are receiving increasing interest. The biomarkers for heterogeneous diseases often exhibit strong cooperative effects, which implies that a set of genes may achieve more accurate outcome prediction than any single gene. In this study, we evaluated various biomarker identification methods that consider gene cooperative effects implicitly or explicitly, and proposed the gene cooperation network to explicitly model the cooperative effects of gene combinations. The gene cooperation network- enhanced method, named as MarkRank, achieves superior performance compared with traditional biomarker identification methods in both simulation studies and real data sets. The biomarkers identified by MarkRank not only have a better prediction accuracy but also have stronger topological relationships in the biological network and exhibit high specificity associated with the related diseases. Furthermore, the top genes identified by MarkRank involve crucial biological processes of related diseases and give a good prioritization for known disease genes. In conclusion, MarkRank suggests that explicit modeling of gene cooperative effects can greatly improve biomarker identification for complex diseases, especially for diseases with high heterogeneity

Cytogenetic and genomic characterization of a novel tall wheatgrass‑derived \u3ci\u3eFhb7\u3c/i\u3e allele integrated into wheat B genome

A novel tall wheatgrass-derived (Thinopyrum elongatum, genome EE) Fhb7 allele, designated Fhb7The2, was identified and integrated into the wheat B genome through a small 7B–7E translocation (7BS·7BL–7EL) involving the terminal regions of the long arms. Fhb7The2 conditions significant Type II resistance to Fusarium head blight (FHB) in wheat. Integration of Fhb7The2 into the wheat B genome makes this wild species-derived FHB resistance gene usable for breeding in both common and durum wheat. By contrast, other Fhb7 introgression lines involving wheat chromosome 7D can be utilized only in common wheat breeding programs, not in durum wheat. Additionally, we found that Fhb7The2 does not have the linkage drag of the yellow flour pigment gene that is tightly linked to the decaploid Th. ponticum-derived Fhb7 allele Fhb7Thp. This will further improve the utility of Fhb7The2 in wheat breeding. DNA sequence analysis identified 12 single nucleotide polymorphisms (SNPs) in Fhb7The2, Fhb7Thp, and another Th. elongatum-derived Fhb7 allele Fhb7The1, which led to seven amino acid conversions in Fhb7The2, Fhb7Thp, and Fhb7The1, respectively. However, no significant variation was observed in their predicted protein configuration as a glutathione transferase. Diagnostic DNA markers were developed specifically for Fhb7The2. The 7EL segment containing Fhb7The2 in the translocation chromosome 7BS·7BL–7EL exhibited a monogenic inheritance pattern in the wheat genetic background. This will enhance the efficacy of marker-assisted selection for Fhb7The2 introgression, pyramiding, and deployment in wheat germplasm and varieties

Genomic profiling of plasmablastic lymphoma using array comparative genomic hybridization (aCGH): revealing significant overlapping genomic lesions with diffuse large B-cell lymphoma

<p>Abstract</p> <p>Background</p> <p>Plasmablastic lymphoma (PL) is a subtype of diffuse large B-cell lymphoma (DLBCL). Studies have suggested that tumors with PL morphology represent a group of neoplasms with clinopathologic characteristics corresponding to different entities including extramedullary plasmablastic tumors associated with plasma cell myeloma (PCM). The goal of the current study was to evaluate the genetic similarities and differences among PL, DLBCL (AIDS-related and non AIDS-related) and PCM using array-based comparative genomic hybridization.</p> <p>Results</p> <p>Examination of genomic data in PL revealed that the most frequent segmental gain (> 40%) include: 1p36.11-1p36.33, 1p34.1-1p36.13, 1q21.1-1q23.1, 7q11.2-7q11.23, 11q12-11q13.2 and 22q12.2-22q13.3. This correlated with segmental gains occurring in high frequency in DLBCL (AIDS-related and non AIDS-related) cases. There were some segmental gains and some segmental loss that occurred in PL but not in the other types of lymphoma suggesting that these foci may contain genes responsible for the differentiation of this lymphoma. Additionally, some segmental gains and some segmental loss occurred only in PL and AIDS associated DLBCL suggesting that these foci may be associated with HIV infection. Furthermore, some segmental gains and some segmental loss occurred only in PL and PCM suggesting that these lesions may be related to plasmacytic differentiation.</p> <p>Conclusion</p> <p>To the best of our knowledge, the current study represents the first genomic exploration of PL. The genomic aberration pattern of PL appears to be more similar to that of DLBCL (AIDS-related or non AIDS-related) than to PCM. Our findings suggest that PL may remain best classified as a subtype of DLBCL at least at the genome level.</p

Instability and morphology of polymer films

The fidelity of pattern transfer in lithography relies on how much we can control the integrity of resist films and patterns throughout the process. It is necessary to avoid undesired instability and defects of the resist films in order to ensure the accuracy of the design. This thesis aims to investigate the instability and the surface morphology of polymer films. Spin coating is the most widely used technique to prepare polymer films. Unfortunately, surface patterns are often obtained in spin coated films. Consensus remains elusive till now regarding the formation of these surface patterns. In this thesis, the effects of solvents and substrates on the stability and the surface morphology of polymer thin films have been systematically studied. The stability and the surface morphology were found to depend on the competition of polymer-substrate and solvent-substrate interactions. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) depth profiles were used to determine the distribution of end groups along the depth of the films. A high concentration of bromine end groups at the interface helps to prevent dewetting of films. An increase in the solvent-substrate interactions, which inhibited any contact between the polymer and the substrate, promoted dewetting of films. The addition of nanoparticles was able to suppress dewetting of films. The distribution of the nanoparticles along the depth of the films was obtained by ToF-SIMS depth profiling. The suppression of dewetting was attributed to the surface segregation of the nanoparticles because of their low-surface-tension surface coating. The structure of surface patterns was revealed by ToF-SIMS 3D microanalyses. For the first time, the surface patterns were unambiguously shown to be hollow rather than solid. Moreover, the structural parameters of the surface patterns were estimated from the retrospectively reconstructed depth profiles. In addition, the effects of temperature and plasma treatment on the morphology of polymer films were also investigated in this thesis

ORF8-Related Genetic Evidence for Chinese Horseshoe Bats as the Source of Human Severe Acute Respiratory Syndrome Coronavirus

Several lineage B betacoronaviruses termed severe acute respiratory syndrome (SARS)–like CoVs (SL-CoVs) were identified from Rhinolophus bats in China. These viruses are characterized by a set of unique accessory open reading frames (ORFs) that are located between the M and N genes. Among unique accessory ORFs, ORF8 is most hypervariable. In this study, the ORF8s of all SL-CoVs were classified into 3 types, and, for the first time, it was found that very few SL-CoVs from Rhinolophus sinicus have ORF8s that are identical to that of human SARS-CoV. This finding provides new genetic evidence for Chinese horseshoe bats as the source of human SARS-CoV