Disease-gene discovery by integration of 3D gene expression and transcription factor binding affinities

Random forest-integrated analysis in AD and LATE brain transcriptome-wide data to identify disease-specific gene expression

Alzheimer's disease (AD) is a complex neurodegenerative disorder that affects thinking, memory, and behavior. Limbic-predominant age-related TDP-43 encephalopathy (LATE) is a recently identified common neurodegenerative disease that mimics the clinical symptoms of AD. The development of drugs to prevent or treat these neurodegenerative diseases has been slow, partly because the genes associated with these diseases are incompletely understood. A notable hindrance from data analysis perspective is that, usually, the clinical samples for patients and controls are highly imbalanced, thus rendering it challenging to apply most existing machine learning algorithms to directly analyze such datasets. Meeting this data analysis challenge is critical, as more specific disease-associated gene identification may enable new insights into underlying disease-driving mechanisms and help find biomarkers and, in turn, improve prospects for effective treatment strategies. In order to detect disease-associated genes based on imbalanced transcriptome-wide data, we proposed an integrated multiple random forests (IMRF) algorithm. IMRF is effective in differentiating putative genes associated with subjects having LATE and/or AD from controls based on transcriptome-wide data, thereby enabling effective discrimination between these samples. Various forms of validations, such as cross-domain verification of our method over other datasets, improved and competitive classification performance by using identified genes, effectiveness of testing data with a classifier that is completely independent from decision trees and random forests, and relationships with prior AD and LATE studies on the genes linked to neurodegeneration, all testify to the effectiveness of IMRF in identifying genes with altered expression in LATE and/or AD. We conclude that IMRF, as an effective feature selection algorithm for imbalanced data, is promising to facilitate the development of new gene biomarkers as well as targets for effective strategies of disease prevention and treatment.

Comparative analysis of a novel disease phenotype network based on clinical manifestations

Systems approaches to analyzing disease phenotype networks in combination with protein functional interaction networks have great potential in illuminating disease pathophysiological mechanisms. While many genetic networks are readily available, disease phenotype networks remain largely incomplete. In this study, we built a large-scale Disease Manifestation Network (DMN) from 50,543 highly accurate disease-manifestation semantic relationships in the United Medical Language System (UMLS). Our new phenotype network contains 2305 nodes and 373,527 weighted edges to represent the disease phenotypic similarities. We first compared DMN with the networks representing genetic relationships among diseases, and demonstrated that the phenotype clustering in DMN reflects common disease genetics. Then we compared DMN with a widely-used disease phenotype network in previous gene discovery studies, called mimMiner, which was extracted from the textual descriptions in Online Mendelian Inheritance in Man (OMIM). We demonstrated that DMN contains different knowledge from the existing phenotype data source. Finally, a case study on Marfan syndrome further proved that DMN contains useful information and can provide leads to discover unknown disease causes. Integrating DMN in systems approaches with mimMiner and other data offers the opportunities to predict novel disease genetics. We made DMN publicly available at nlp/case.edu/public/data/DMN.

Stem cell characteristics in glioblastoma are maintained by the ecto-nucleotidase E-NPP1

Glioblastomas are highly aggressive brain tumours and are characterised by substantial cellular heterogeneity within a single tumour. A sub-population of glioblastoma stem-like cells (GSCs) that shares properties with neural precursor cells has been described, exhibiting resistance to therapy and therefore being considered responsible for the high recurrence rate in glioblastoma. To elucidate the underlying cellular processes we investigated the role of phosphatases in the GSC phenotype, using an in vitro phosphatome-wide RNA interference screen. We identified a set of genes, the knockdown of which induces a significant decrease in the glioma stem cell marker CD133, indicating a role in the glioblastoma stem-like phenotype. Among these genes, the ecto-nucleotidase ENPP1 (ectonucleotide pyrophosphatase/phosphodiesterase 1) was found to be highly expressed in GSCs compared with normal brain and neural stem cells. Knockdown of ENPP1 in cultured GSCs resulted in an overall downregulation of stem cell-associated genes, induction of differentiation into astrocytic cell lineage, impairment of sphere formation, in addition to increased cell death, accumulation of cells in G1/G0 cell cycle phase and sensitisation to chemotherapeutic treatment. Genome-wide gene expression analysis and nucleoside and nucleotide profiling revealed that knockdown of ENPP1 affects purine and pyrimidine metabolism, suggesting a link between ENPP1 expression and a balanced nucleoside-nucleotide pool in GSCs. The phenotypic changes in E-NPP1-deficient GSCs are assumed to be a consequence of decreased transcriptional function of E2F1. Together, these results reveal that E-NPP1, by acting upstream of E2F1, is indispensable for the maintenance of GSCs in vitro and hence required to keep GSCs in an undifferentiated, proliferative state.

Progress and challenges in the computational prediction of gene function using networks: 2012-2013 update

In an opinion published in 2012, we reviewed and discussed our studies of how gene network-based guilt-by-association (GBA) is impacted by confounds related to gene multifunctionality. We found such confounds account for a significant part of the GBA signal, and as a result meaningfully evaluating and applying computationally-guided GBA is more challenging than generally appreciated. We proposed that effort currently spent on incrementally improving algorithms would be better spent in identifying the features of data that do yield novel functional insights. We also suggested that part of the problem is the reliance by computational biologists on gold standard annotations such as the Gene Ontology. In the year since, there has been continued heavy activity in GBA-based research, including work that contributes to our understanding of the issues we raised. Here we provide a review of some of the most relevant recent work, or which point to new areas of progress and challenges.