A simple and exact Laplacian clustering of complex networking phenomena: application to gene expression profiles

Resting state of human brain measured by fMRI experiment is governed more dominantly by essential mode as a global signal rather than default mode network

Resting-state of the human brain has been described by a combination of various basis modes including the default mode network (DMN) identified by fMRI BOLD signals in human brains. Whether DMN is the most dominant representation of the resting-state has been under question. Here, we investigated the unexplored yet fundamental nature of the resting-state. In the absence of global signal regression for the analysis of brain-wide spatial activity pattern, the fMRI BOLD spatiotemporal signals during the rest were completely decomposed into time-invariant spatial-expression basis modes (SEBMs) and their time-evolution basis modes (TEBMs). Contrary to our conventional concept above, similarity clustering analysis of the SEBMs from 166 human brains revealed that the most dominant SEBM cluster is an asymmetric mode where the distribution of the sign of the components is skewed in one direction, for which we call essential mode (EM), whereas the second dominant SEBM cluster resembles the spatial pattern of DMN. Having removed the strong 1/f noise in the power spectrum of TEBMs, the genuine oscillatory behavior embedded in TEBMs of EM and DMN-like mode was uncovered around the low-frequency range below 0.2 Hz.

Construction of disease-specific cytokine profiles by associating disease genes with immune responses

The pathogenesis of many inflammatory diseases is a coordinated process involving metabolic dysfunctions and immune response—usually modulated by the production of cytokines and associated inflammatory molecules. In this work, we seek to understand how genes involved in pathogenesis which are often not associated with the immune system in an obvious way communicate with the immune system. We have embedded a network of human protein-protein interactions (PPI) from the STRING database with 14,707 human genes using feature learning that captures high confidence edges. We have found that our predicted Association Scores derived from the features extracted from STRING’s high confidence edges are useful for predicting novel connections between genes, thus enabling the construction of a full map of predicted associations for all possible pairs between 14,707 human genes. In particular, we analyzed the pattern of associations for 126 cytokines and found that the six patterns of cytokine interaction with human genes are consistent with their functional classifications. To define the disease-specific roles of cytokines we have collected gene sets for 11,944 diseases from DisGeNET. We used these gene sets to predict disease-specific gene associations with cytokines by calculating the normalized average Association Scores between disease-associated gene sets and the 126 cytokines; this creates a unique profile of inflammatory genes (both known and predicted) for each disease. We validated our predicted cytokine associations by comparing them to known associations for 171 diseases. The predicted cytokine profiles correlate (p-value<0.0003) with the known ones in 95 diseases. We further characterized the profiles of each disease by calculating an “Inflammation Score” that summarizes different modes of immune responses. Finally, by analyzing subnetworks formed between disease-specific pathogenesis genes, hormones, receptors, and cytokines, we identified the key genes responsible for interactions between pathogenesis and inflammatory responses. These genes and the corresponding cytokines used by different immune disorders suggest unique targets for drug discovery.

The success of anti-TNF treatment in multiple inflammatory diseases suggest that there is a shared cytokine framework that defines highly conserved mechanisms of inflammation. However, clinical trials testing the efficacy of new cytokine inhibitors suggest a more complex set of interacting cytokine mechanisms that are associated with different diseases. In this work, we aim to define the disease-specific role of cytokines that mediate pathogenesis and inflammatory processes, focusing on autoimmune diseases. We hypothesize that specific clinical phenotypes result from the interactions between disease-specific cytokines and disease-related genes (identified through genetics, transcriptomics, and analysis of metabolic dysfunctions), even though they also may share a common cytokine elements and conserved mechanisms of inflammation. We have developed novel network methods that show a robust ability to identify differential associations between characteristic cytokines and genetics factors contributing to pathogenesis. We have validated our methods on 171 well-studied diseases; the predicted associations between cytokines and disease modules correlate with the published data. Our predictions provide the underlying difference of molecular mechanisms that may be responsible for clinical phenotypes.

Computational design of a thermolabile uracil-DNA glycosylase of Escherichia coli

Polymerase chain reaction (PCR) is a powerful tool to diagnose infectious diseases. Uracil DNA glycosylase (UDG) is broadly used to remove carryover contamination in PCR. However, UDG can contribute to false negative results when not inactivated completely, leading to DNA degradation during the amplification step. In this study, we designed novel thermolabile UDG derivatives by supercomputing molecular dynamic simulations and residual network analysis. Based on enzyme activity analysis, thermolability, thermal stability, and biochemical experiments of Escherichia coli-derived UDG and 22 derivatives, we uncovered that the UDG D43A mutant eliminated the false negative problem, demonstrated high efficiency, and offered great benefit for use in PCR diagnosis. We further obtained structural and thermodynamic insights into the role of the D43A mutation, including perturbed protein structure near D43; weakened pairwise interactions of D43 with K42, N46, and R80; and decreased melting temperature and native fraction of the UDG D43A mutant compared with wild-type UDG.

The Graded Change in Connectivity across the Ventromedial Prefrontal Cortex Reveals Distinct Subregions

The functional heterogeneity of the ventromedial prefrontal cortex (vmPFC) suggests it may include distinct functional subregions. To date these have not been well elucidated. Regions with differentiable connectivity (and as a result likely dissociable functions) may be identified using emergent data-driven approaches. However, prior parcellations of the vmPFC have only considered hard splits between distinct regions, although both hard and graded connectivity changes may exist. Here we determine the full pattern of change in structural and functional connectivity across the vmPFC for the first time and extract core distinct regions. Both structural and functional connectivity varied along a dorsomedial to ventrolateral axis from relatively dorsal medial wall regions to relatively lateral basal orbitofrontal cortex. The pattern of connectivity shifted from default mode network to sensorimotor and multimodal semantic connections. This finding extends the classical distinction between primate medial and orbital regions by demonstrating a similar gradient in humans for the first time. Additionally, core distinct regions in the medial wall and orbitofrontal cortex were identified that may show greater correspondence to functional differences than prior hard parcellations. The possible functional roles of the orbitofrontal cortex and medial wall are discussed.

A Ligand-Based Virtual Screening Method Using Direct Quantification of Generalization Ability

Machine learning plays an important role in ligand-based virtual screening. However, conventional machine learning approaches tend to be inefficient when dealing with such problems where the data are imbalanced and features describing the chemical characteristic of ligands are high-dimensional. We here describe a machine learning algorithm LBS (local beta screening) for ligand-based virtual screening. The unique characteristic of LBS is that it quantifies the generalization ability of screening directly by a refined loss function, and thus can assess the risk of over-fitting accurately and efficiently for imbalanced and high-dimensional data in ligand-based virtual screening without the help of resampling methods such as cross validation. The robustness of LBS was demonstrated by a simulation study and tests on real datasets, in which LBS outperformed conventional algorithms in terms of screening accuracy and model interpretation. LBS was then used for screening potential activators of HIV-1 integrase multimerization in an independent compound library, and the virtual screening result was experimentally validated. Of the 25 compounds tested, six were proved to be active. The most potent compound in experimental validation showed an EC₅₀ value of 0.71 µM.

MEK Inhibition Sensitizes Precursor B-Cell Acute Lymphoblastic Leukemia (B-ALL) Cells to Dexamethasone through Modulation of mTOR Activity and Stimulation of Autophagy

Resistance to glucocorticosteroids (GCs) is a major adverse prognostic factor in B-ALL, but the molecular mechanisms leading to GC resistance are not completely understood. Herein, we sought to elucidate the molecular background of GC resistance in B-ALL and characterize the therapeutic potential of targeted intervention in these mechanisms. Using exploratory bioinformatic approaches, we found that resistant cells exhibited significantly higher expression of MEK/ERK (MAPK) pathway components. We found that GC-resistant ALL cell lines had markedly higher baseline activity of MEK and small-molecule MEK1/2 inhibitor selumetinib increased GCs-induced cell death. MEK inhibitor similarly increased in vitro dexamethasone activity in primary ALL blasts from 19 of 22 tested patients. To further confirm these observations, we overexpressed a constitutively active MEK mutant in GC-sensitive cells and found that forced MEK activity induced resistance to dexamethasone. Since recent studies highlight the role GC-induced autophagy upstream of apoptotic cell death, we assessed LC3 processing, MDC staining and GFP-LC3 relocalization in cells incubated with either DEX, SEL or combination of drugs. Unlike either drug alone, only their combination markedly increased these markers of autophagy. These changes were associated with decreased mTOR activity and blocked 4E-BP1 phosphorylation. In cells with silenced beclin-1 (BCN1), required for autophagosome formation, the synergy of DEX and SEL was markedly reduced. Taken together, we show that MEK inhibitor selumetinib enhances dexamethasone toxicity in GC-resistant B-ALL cells. The underlying mechanism of this interaction involves inhibition of mTOR signaling pathway and modulation of autophagy markers, likely reflecting induction of this process and required for cell death. Thus, our data demonstrate that modulation of MEK/ERK pathway is an attractive therapeutic strategy overcoming GC resistance in B-ALL patients.

Uncovering multiloci-ordering by algebraic property of Laplacian matrix and its Fiedler vector

MOTIVATION

The loci-ordering, based on two-point recombination fractions for a pair of loci, is the most important step in constructing a reliable and fine genetic map.

RESULTS

Using the concept from complex graph theory, here we propose a Laplacian ordering approach which uncovers the loci-ordering of multiloci simultaneously. The algebraic property for a Fiedler vector of a Laplacian matrix, constructed from the recombination fraction of the loci-ordering for 26 loci of barley chromosome IV, 846 loci of Arabidopsis thaliana and 1903 loci of Malus domestica, together with the variable threshold uncovers their loci-orders. It offers an alternative yet robust approach for ordering multiloci.

AVAILABILITY AND IMPLEMENTATION

Source code program with data set is available as supplementary data and also in a software category of the website (http://biophysics.dgist.ac.kr)

CONTACT

crkim@pusan.ac.kr or iksoochang@dgist.ac.kr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Knowledge discovery by accuracy maximization

Here we describe KODAMA (knowledge discovery by accuracy maximization), an unsupervised and semisupervised learning algorithm that performs feature extraction from noisy and high-dimensional data. Unlike other data mining methods, the peculiarity of KODAMA is that it is driven by an integrated procedure of cross-validation of the results. The discovery of a local manifold's topology is led by a classifier through a Monte Carlo procedure of maximization of cross-validated predictive accuracy. Briefly, our approach differs from previous methods in that it has an integrated procedure of validation of the results. In this way, the method ensures the highest robustness of the obtained solution. This robustness is demonstrated on experimental datasets of gene expression and metabolomics, where KODAMA compares favorably with other existing feature extraction methods. KODAMA is then applied to an astronomical dataset, revealing unexpected features. Interesting and not easily predictable features are also found in the analysis of the State of the Union speeches by American presidents: KODAMA reveals an abrupt linguistic transition sharply separating all post-Reagan from all pre-Reagan speeches. The transition occurs during Reagan's presidency and not from its beginning.

A unified computational model for revealing and predicting subtle subtypes of cancers

BACKGROUND

Gene expression profiling technologies have gradually become a community standard tool for clinical applications. For example, gene expression data has been analyzed to reveal novel disease subtypes (class discovery) and assign particular samples to well-defined classes (class prediction). In the past decade, many effective methods have been proposed for individual applications. However, there is still a pressing need for a unified framework that can reveal the complicated relationships between samples.

RESULTS

We propose a novel convex optimization model to perform class discovery and class prediction in a unified framework. An efficient algorithm is designed and software named OTCC (Optimization Tool for Clustering and Classification) is developed. Comparison in a simulated dataset shows that our method outperforms the existing methods. We then applied OTCC to acute leukemia and breast cancer datasets. The results demonstrate that our method not only can reveal the subtle structures underlying those cancer gene expression data but also can accurately predict the class labels of unknown cancer samples. Therefore, our method holds the promise to identify novel cancer subtypes and improve diagnosis.

CONCLUSIONS

We propose a unified computational framework for class discovery and class prediction to facilitate the discovery and prediction of subtle subtypes of cancers. Our method can be generally applied to multiple types of measurements, e.g., gene expression profiling, proteomic measuring, and recent next-generation sequencing, since it only requires the similarities among samples as input.

Distance-dependent classification of amino acids by information theory

Reduced amino acid alphabets are useful to understand molecular evolution as they reveal basal, shared properties of amino acids, which the structures and functions of proteins rely on. Several previous studies derived such reduced alphabets and linked them to the origin of life and biotechnological applications. However, all this previous work presupposes that only direct contacts of amino acids in native protein structures are relevant. We show in this work, using information-theoretical measures, that an appropriate alphabet reduction scheme is in fact a function of the maximum distance amino acids interact at. Although for small distances our results agree with previous ones, we show how long-range interactions change the overall picture and prompt for a revised understanding of the protein design process.