BackgroundMitochondrial genomes provide a rich source of molecular variation of proven and widespread utility in molecular ecology, population genetics and evolutionary biology. The tapeworm genus Taenia includes a diversity of tapeworm parasites of significant human and veterinary importance. Here we add complete sequences of the mt genomes of T. multiceps, T. hydatigena and T. pisiformis, to a data set of 4 published mtDNAs in the same genus. Seven complete mt genomes of Taenia species are used to compare and contrast variation within and between genomes in the genus, to estimate a phylogeny for the genus, and to develop novel molecular markers as part of an extended mitochondrial toolkit.ResultsThe complete circular mtDNAs of T. multiceps, T. hydatigena and T. pisiformis were 13,693, 13,492 and 13,387 bp in size respectively, comprising the usual complement of flatworm genes. Start and stop codons of protein coding genes included those found commonly amongst other platyhelminth mt genomes, but the much rarer initiation codon GTT was inferred for the gene atp6 in T. pisiformis. Phylogenetic analysis of mtDNAs offered novel estimates of the interrelationships of Taenia. Sliding window analyses showed nad6, nad5, atp6, nad3 and nad2 are amongst the most variable of genes per unit length, with the highest peaks in nucleotide diversity found in nad5. New primer pairs capable of amplifying fragments of variable DNA in nad1, rrnS and nad5 genes were designed in silico and tested as possible alternatives to existing mitochondrial markers for Taenia.ConclusionsWith the availability of complete mtDNAs of 7 Taenia species, we have shown that analysis of amino acids provides a robust estimate of phylogeny for the genus that differs markedly from morphological estimates or those using partial genes; with implications for understanding the evolutionary radiation of important Taenia. Full alignment of the nucleotides of Taenia mtDNAs and sliding window analysis suggests numerous alternative gene regions are likely to capture greater nucleotide variation than those currently pursued as molecular markers. New PCR primers developed from a comparative mitogenomic analysis of Taenia species, extend the use of mitochondrial markers for molecular ecology, population genetics and diagnostics.

BackgroundWith the accumulation of increasing omics data, a key goal of systems biology is to construct networks at different cellular levels to investigate cellular machinery of the cell. However, there is currently no satisfactory method to construct an integrated cellular network that combines the gene regulatory network and the signaling regulatory pathway.ResultsIn this study, we integrated different kinds of omics data and developed a systematic method to construct the integrated cellular network based on coupling dynamic models and statistical assessments. The proposed method was applied to S. cerevisiae stress responses, elucidating the stress response mechanism of the yeast. From the resulting integrated cellular network under hyperosmotic stress, the highly connected hubs which are functionally relevant to the stress response were identified. Beyond hyperosmotic stress, the integrated network under heat shock and oxidative stress were also constructed and the crosstalks of these networks were analyzed, specifying the significance of some transcription factors to serve as the decision-making devices at the center of the bow-tie structure and the crucial role for rapid adaptation scheme to respond to stress. In addition, the predictive power of the proposed method was also demonstrated.ConclusionsWe successfully construct the integrated cellular network which is validated by literature evidences. The integration of transcription regulations and protein-protein interactions gives more insight into the actual biological network and is more predictive than those without integration. The method is shown to be powerful and flexible and can be used under different conditions and for different species. The coupling dynamic models of the whole integrated cellular network are very useful for theoretical analyses and for further experiments in the fields of network biology and synthetic biology.

BackgroundLung cancer is the leading cause of cancer deaths worldwide. Many studies have investigated the carcinogenic process and identified the biomarkers for signature classification. However, based on the research dedicated to this field, there is no highly sensitive network-based method for carcinogenesis characterization and diagnosis from the systems perspective.MethodsIn this study, a systems biology approach integrating microarray gene expression profiles and protein-protein interaction information was proposed to develop a network-based biomarker for molecular investigation into the network mechanism of lung carcinogenesis and diagnosis of lung cancer. The network-based biomarker consists of two protein association networks constructed for cancer samples and non-cancer samples.ResultsBased on the network-based biomarker, a total of 40 significant proteins in lung carcinogenesis were identified with carcinogenesis relevance values (CRVs). In addition, the network-based biomarker, acting as the screening test, proved to be effective in diagnosing smokers with signs of lung cancer.ConclusionsA network-based biomarker using constructed protein association networks is a useful tool to highlight the pathways and mechanisms of the lung carcinogenic process and, more importantly, provides potential therapeutic targets to combat cancer.

The determination of the mutation load, a total number of nonsynonymous point mutations, by whole-exome sequencing was shown to be useful in predicting the treatment responses to cancer immunotherapy. However, this technique is expensive and time-consuming, which hampers its application in clinical practice. Therefore, the objective of this study was to construct a mutation load estimation model for lung adenocarcinoma, using a small set of genes, as a predictor of the immunotherapy treatment response. Using the somatic mutation data downloaded from The Cancer Genome Atlas (TCGA) database, a computational framework was developed. The estimation model consisted of only 24 genes, used to estimate the mutation load in the independent validation cohort precisely (R2 = 0.7626). Additionally, the estimated mutation load can be used to identify the patients with durable clinical benefits, with 85% sensitivity, 93% specificity, and 89% accuracy, indicating that the model can serve as a predictive biomarker for cancer immunotherapy treatment response. Furthermore, our analyses demonstrated the necessity of the cancer-specific models by the constructed melanoma and colorectal models. Since most genes in the lung adenocarcinoma model are not currently included in the sequencing panels, a customized targeted sequencing panel can be designed with the selected model genes to assess the mutation load, instead of whole-exome sequencing or the currently used panel-based methods. Consequently, the cost and time required for the assessment of mutation load may be considerably decreased, which indicates that the presented model is a more cost-effective approach to cancer immunotherapy response prediction in clinical practice.

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