Computational Tools for Parsimony Phylogenetic Analysis of Omics Data

Metabolomics bridging proteomics along metabolites/oncometabolites and protein modifications: Paving the way toward integrative multiomics

Systems biology adopted functional and integrative multiomics approaches enable to discover the whole set of interacting regulatory components such as genes, transcripts, proteins, metabolites, and metabolite dependent protein modifications. This interactome build up the midpoint of protein-protein/PTM, protein-DNA/RNA, and protein-metabolite network in a cell. As the key drivers in cellular metabolism, metabolites are precursors and regulators of protein post-translational modifications [PTMs] that affect protein diversity and functionality. The precisely orchestrated core pattern of metabolic networks refer to paradigm 'metabolites regulate PTMs, PTMs regulate enzymes, and enzymes modulate metabolites' through a multitude of feedback and feed-forward pathway loops. The concept represents a flawless PTM-metabolite-enzyme(protein) regulomics underlined in reprogramming cancer metabolism. Immense interconnectivity of those biomolecules in their spectacular network of intertwined metabolic pathways makes integrated proteomics and metabolomics an excellent opportunity, and the central component of integrative multiomics framework. It will therefore be of significant interest to integrate global proteome and PTM-based proteomics with metabolomics to achieve disease related altered levels of those molecules. Thereby, present update aims to highlight role and analysis of interacting metabolites/oncometabolites, and metabolite-regulated PTMs loop which may function as translational monitoring biomarkers along the reprogramming continuum of oncometabolism.

Metabolomics-Based Biosignatures of Prostate Cancer in Patients Following Radiotherapy

Metabolomics offers new promise for research on prostate cancer (PCa) and its personalized treatment. Metabolomic profiling of radiation-treated PCa patients is particularly important to reveal their new metabolomic status, and evaluate the radiation effects. In addition, bioinformatics-integrated metabolomics-based approaches for disease profiling and assessment of therapy could help develop precision biomarkers in a context of PCa. We report mass spectrometry-based untargeted (global) serum metabolomics findings from patients with PCa (n = 55) before and after treatment with stereotactic body radiation therapy (SBRT), and intensity-modulated radiation therapy (IMRT) with SBRT, and using parsimony phylogenetic analysis. Importantly, the radiation-treated serum metabolome of patients represented a unique robust cluster on a cladogram that was distinct from the pre-RT metabolome. The altered radiation responsive serum metabolome was defined by predominant aberrations in the metabolic pathways of nitrogen, pyrimidine, purine, porphyrin, alanine, aspartate, glutamate, and glycerophospholipid. Our findings collectively suggest that global metabolomics integrated with parsimony phylogenetics offer a unique and robust systems biology analytical platform for powerful unbiased determination of radiotherapy (RT)-associated biosignatures in patients with PCa. These new observations call for future translational research for evaluation of metabolomic biomarkers in PCa prognosis specifically, and response to radiation treatment broadly. Radiation metabolomics is an emerging specialty of systems sciences and clinical medicine that warrants further research and educational initiatives.

The first imported case of melioidosis in a patient in central China

Here, we report the first imported case of melioidosis from Laos in central China. COMPACT VITEK2 identification system and PCR, as well as sequencing methods confirmed that the patient was infected by Burkholderia pseudomallei, a bacterial species closely related to an isolate detected in Thailand. These findings are highly valuable for an early diagnosis, treatment and to prevent the spread of this emerging infectious disease in central China.

High-throughput metabolomics for discovering metabolic biomarkers from intestinal tumorigenesis in APC min/+ mice based on liquid chromatography/mass spectrometry

As a major public health concern, colon cancer is one of the most common cancer types, which is also the second cause of cancer death in developed countries and the third most common cancer in other parts of the world. It was reported that patients diagnosed at early stage have a chance to obtain 5-year survival rates at least compared to patients with late stage. Facing the multistep process in intestinal tumorigenesis, there is an urgent need to develop more effective early detection strategies for ameliorating the patient clinical outcome. Metabolomics open up a novel avenue of seeking valuable potential biomarkers for assessing disease severity and prognosticating course by dynamic snapshot of small molecule metabolites. The study aims to provide deeper insights into the discovery, identification and functional pathways analysis of differentially expressed metabolites in intestinal tumorigenesis in APC ^min/+ mice used by the serum metabolomics, and bring about useful information for further effective prevention and treatment of the disease. 17 marker metabolites and related metabolism pathway were identified using non-targeted metabolomics based on liquid chromatography/mass spectrometry (LC/MS) associated with multivariate statistical analysis. The ingenuity pathway analysis platform involved multiple-pathways was applied to metabolic network analysis for further understanding the relationship between functional metabolic pathways and disease.

A modification of the PHYLIP program: A solution for the redundant cluster problem, and an implementation of an automatic bootstrapping on trees inferred from original data

Felsenstein's PHYLIP package of molecular phylogeny tools has been used globally since 1980. The programs are receiving renewed attention because of their character-based user interface, which has the advantage of being scriptable for use with large-scale data studies based on super-computers or massively parallel computing clusters. However, occasionally we found, the PHYLIP Consense program output text file displays two or more divided bootstrap values for the same cluster in its result table, and when this happens the output Newick tree file incorrectly assigns only the last value to that cluster that disturbs correct estimation of a consensus tree. We ascertained the cause of this aberrant behavior in the bootstrapping calculation. Our rewrite of the Consense program source code outputs bootstrap values, without redundancy, in its result table, and a Newick tree file with appropriate, corresponding bootstrap values. Furthermore, we developed an add-on program and shell script, add_bootstrap.pl and fasta2tre_bs.bsh, to generate a Newick tree containing the topology and branch lengths inferred from the original data along with valid bootstrap values, and to actualize the automated inference of a phylogenetic tree containing the originally inferred topology and branch lengths with bootstrap values, from multiple unaligned sequences, respectively. These programs can be downloaded at: https://github.com/ShimadaMK/PHYLIP_enhance/.

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations

The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.

Study of Clinical Survival and Gene Expression in a Sample of Pancreatic Ductal Adenocarcinoma by Parsimony Phylogenetic Analysis

Pancreatic ductal adenocarcinoma (PDAC) is one of the rapidly growing forms of pancreatic cancer with a poor prognosis and less than 5% 5-year survival rate. In this study, we characterized the genetic signatures and signaling pathways related to survival from PDAC, using a parsimony phylogenetic algorithm. We applied the parsimony phylogenetic algorithm to analyze the publicly available whole-genome in silico array analysis of a gene expression data set in 25 early-stage human PDAC specimens. We explain here that the parsimony phylogenetics is an evolutionary analytical method that offers important promise to uncover clonal (driver) and nonclonal (passenger) aberrations in complex diseases. In our analysis, parsimony and statistical analyses did not identify significant correlations between survival times and gene expression values. Thus, the survival rankings did not appear to be significantly different between patients for any specific gene (p > 0.05). Also, we did not find correlation between gene expression data and tumor stage in the present data set. While the present analysis was unable to identify in this relatively small sample of patients a molecular signature associated with pancreatic cancer prognosis, we suggest that future research and analyses with the parsimony phylogenetic algorithm in larger patient samples are worthwhile, given the devastating nature of pancreatic cancer and its early diagnosis, and the need for novel data analytic approaches. The future research practices might want to place greater emphasis on phylogenetics as one of the analytical paradigms, as our findings presented here are on the cusp of this shift, especially in the current era of Big Data and innovation policies advocating for greater data sharing and reanalysis.

A Systems Biology Interpretation of Array Comparative Genomic Hybridization (aCGH) Data through Phylogenetics

Array Comparative Genomic Hybridization (aCGH) is a rapid screening technique to detect gene deletions and duplications, providing an overview of chromosomal aberrations throughout the entire genome of a tumor, without the need for cell culturing. However, the heterogeneity of aCGH data obfuscates existing methods of data analysis. Analysis of aCGH data from a systems biology perspective or in the context of total aberrations is largely absent in the published literature. We present here a novel alternative to the functional analysis of aCGH data using the phylogenetic paradigm that is well-suited to high dimensional datasets of heterogeneous nature, but has not been widely adapted to aCGH data. Maximum parsimony phylogenetic analysis sorts out genetic data through the simplest presentation of the data on a cladogram, a graphical evolutionary tree, thus providing a powerful and efficient method for aCGH data analysis. For example, the cladogram models the multiphasic changes in the cancer genome and identifies shared early mutations in the disease progression, providing a simple yet powerful means of aCGH data interpretation. As such, applying maximum parsimony phylogenetic analysis to aCGH results allows for the differentiation between drivers and passenger genes aberrations in cancer specimens. In addition to offering a novel methodology to analyze aCGH results, we present here a crucial software suite that we wrote to carry out the analysis. In a broader context, we wish to underscore that phylogenetic analysis of aCGH data is a non-parametric method that circumvents the pitfalls and frustrations of standard analytical techniques that rely on parametric statistics. Organizing the data in a cladogram as explained in this research article provides insights into the disease common aberrations, as well as the disease subtypes and their shared aberrations (the synapomorphies) of each subtype. Hence, we report the method and make the software suite publicly and freely available at http://software.phylomcs.com so that researchers can test alternative and innovative approaches to the analysis of aCGH data.