An NF-κB Transcription-Factor-Dependent Lineage-Specific Transcriptional Program Promotes Regulatory T Cell Identity and Function.

Both conventional T (Tconv) cells and regulatory T (Treg) cells are activated through ligation of the T cell receptor (TCR) complex, leading to the induction of the transcription factor NF-κB. In Tconv cells, NF-κB regulates expression of genes essential for T cell activation, proliferation, and function. However the role of NF-κB in Treg function remains unclear. We conditionally deleted canonical NF-κB members p65 and c-Rel in developing and mature Treg cells and found they have unique but partially redundant roles. c-Rel was critical for thymic Treg development while p65 was essential for mature Treg identity and maintenance of immune tolerance. Transcriptome and NF-κB p65 binding analyses demonstrated a lineage specific, NF-κB-dependent transcriptional program, enabled by enhanced chromatin accessibility. These dual roles of canonical NF-κB in Tconv and Treg cells highlight the functional plasticity of the NF-κB signaling pathway and underscores the need for more selective strategies to therapeutically target NF-κB.

A pan-cancer analysis implicates human NKIRAS1 as a tumor-suppressor gene.

The NF-κB family of transcription factors and the Ras family of small GTPases are important mediators of proproliferative signaling that drives tumorigenesis and carcinogenesis. The κB-Ras proteins were previously shown to inhibit both NF-κB and Ras activation through independent mechanisms, implicating them as tumor suppressors with potentially broad relevance to human cancers. In this study, we have used two mouse models to establish the relevance of the κB-Ras proteins for tumorigenesis. Additionally, we have utilized a pan-cancer bioinformatics analysis to explore the role of the κB-Ras proteins in human cancers. Surprisingly, we find that the genes encoding κB-Ras 1 (NKIRAS1) and κB-Ras 2 (NKIRAS2) are rarely down-regulated in tumor samples with oncogenic Ras mutations. Reduced expression of human NKIRAS1 alone is associated with worse prognosis in at least four cancer types and linked to a network of genes implicated in tumorigenesis. Our findings provide direct evidence that loss of NKIRAS1 in human tumors that do not carry oncogenic RAS mutations is associated with worse clinical outcomes.

Kernel lysine content does not increase in some maize opaque2 mutants.

The recessive mutant allele of the opaque2 gene (o2) alters the endosperm protein pattern and increases the kernel lysine content of maize (Zea mays L.). In this study, sequencing results showed that the o2 mutant was successfully introgressed into 12 elite normal maize inbred lines by marker assisted selection (MAS). The average genetic similarity between these normal inbred lines and their o2 near-isogenic lines (NILs) was more than 95%. Kernel lysine content increased significantly in most of o2 NILs lines relative to normal elite inbreds, but remained unchanged in the genetic backgrounds Dan598o2 and Liao2345o2. Moreover, the kernel characteristics of these two o2 NILs did not differ from the other inbred lines. The results of lysine content analysis in the F1 hybrids between Liao2345o2 and Dan598o2 and other o2 NILs demonstrated that gene(s) other than opaque2 may control kernel lysine content in these two o2 NILs. The results of zein analysis showed that 22-kD α-zein synthesis was reduced or absent, and the 19-kD α-zein synthesis was greatly reduced compared with the recurrent parents in most o2 NILs except for Dan598o2 and Liao2345o2. Our results indicate that gene(s) other than opaque2 may play more important roles in zein synthesis and kernel lysine content in some maize genetic backgrounds.

The panorama of physiological responses and gene expression of whole plant of maize inbred line YQ7-96 at the three-leaf stage under water deficit and re-watering.

Changes in water potential, growth elongation, photosynthesis of three-leaf-old seedlings of maize inbred line YQ7-96 under water deficit (WD) for 0.5, 1 and 2 h and re-watering (RW) for 24 h were characterized. Gene expression was analyzed using cDNA microarray covering 11,855 maize unigenes. As for whole maize plant, the expression of WD-regulated genes was characterized by up-regulation. The expression of WD-regulated genes was categorized into eight different patterns, respectively, in leaves and roots. Newly found and WD-affected cellular processes were metabolic process, amino acid and derivative metabolic process and cell death. A great number of the analyzed genes were found to be regulated specifically by RW and commonly by both WD and RW, respectively, in leaves. It is therefore concluded that (1) whole maize plant tolerance to WD, as well as growth recovery from WD, depends at least in part on transcriptional coordination between leaves and roots; (2) WD exerts effects on the maize, especially on basal metabolism; (3) WD could probably affect CO(2) uptake and partitioning, and transport of fixed carbons; (4) WD could likely influence nuclear activity and genome stability; and (5) maize growth recovery from WD is likely involved in some specific signaling pathways related to RW-specific responsive genes.

Average trapping time on weighted directed Koch network.

Numerous recent studies have focused on random walks on undirected binary scale-free networks. However, random walks with a given target node on weighted directed networks remain less understood. In this paper, we first introduce directed weighted Koch networks, in which any pair of nodes is linked by two edges with opposite directions, and weights of edges are controlled by a parameter θ . Then, to evaluate the transportation efficiency of random walk, we derive an exact solution for the average trapping time (ATT), which agrees well with the corresponding numerical solution. We show that leading behaviour of ATT is function of the weight parameter θ and that the ATT can grow sub-linearly, linearly and super-linearly with varying θ . Finally, we introduce a delay parameter p to modify the transition probability of random walk, and provide a closed-form solution for ATT, which still coincides with numerical solution. We show that in the closed-form solution, the delay parameter p can change the coefficient of ATT, but cannot change the leading behavior. We also show that desired ATT or trapping efficiency can be obtained by setting appropriate weight parameter and delay parameter simultaneously. Thereby, this work advance the understanding of random walks on directed weighted scale-free networks.

Discrimination of outer membrane proteins by a new measure of information discrepancy.

Discriminating outer membrane proteins for globular proteins (GPs) and other types of membrane proteins from genomic sequences is an important and hot topic. In this paper, a measure based on information discrepancy is proposed and applied to the discrimination of outer membrane proteins. It differs from previous methods which are based on amino acid composition. Our approach focuses on the comparison of subsequence distributions and takes into account the effect of residue order in protein primary structures. As a result, the new approach outperforms all previous methods on the same benchmark datasets. In particular, we show that the proposed approach has correctly identified the outer membrane proteins at an accuracy of 99% for the training set of 337 proteins and has correctly excluded the GPs at an accuracy of 86% in a non-redundant dataset of 668 proteins. Furthermore, this method is able to correctly exclude alpha-helical membrane proteins at an accuracy of 100%.

Hybrid method to solve HP model on 3D lattice and to probe protein stability upon amino acid mutations.

BACKGROUND: Predicting protein structure from amino acid sequence is a prominent problem in computational biology. The long range interactions (or non-local interactions) are known as the main source of complexity for protein folding and dynamics and play the dominant role in the compact architecture. Some simple but exact model, such as HP model, captures the pain point for this difficult problem and has important implications to understand the mapping between protein sequence and structure. RESULTS: In this paper, we formulate the biological problem into optimization model to study the hydrophobic-hydrophilic model on 3D square lattice. This is a combinatorial optimization problem and known as NP-hard. Particle swarm optimization is utilized as the heuristic framework to solve the hard problem. To avoid premature in computation, we incorporated the Tabu search strategy. In addition, a pulling strategy was designed to accelerate the convergence of algorithm based on the characteristic of native protein structure. Together a novel hybrid method combining particle swarm optimization, Tabu strategy, and pulling strategy can fold the amino acid sequences on 3D square lattice efficiently. Promising results are reported in several examples by comparing with existing methods. This allows us to use this tool to study the protein stability upon amino acid mutation on 3D lattice. In particular, we evaluate the effect of single amino acid mutation and double amino acids mutation via 3D HP lattice model and some useful insights are derived. CONCLUSION: We propose a novel hybrid method to combine several heuristic strategies to study HP model on 3D lattice. The results indicate that our hybrid method can predict protein structure more accurately and efficiently. Furthermore, it serves as a useful tools to probe the protein stability on 3D lattice and provides some biological insights.

Extended particle swarm optimisation method for folding protein on triangular lattice.

In this study, the authors studied the protein structure prediction problem by the two-dimensional hydrophobic-polar model on triangular lattice. Particularly the non-compact conformation was modelled to fold the amino acid sequence into a relatively larger triangular lattice, which is more biologically realistic and significant than the compact conformation. Then protein structure prediction problem was abstracted to match amino acids to lattice points. Mathematically, the problem was formulated as an integer programming and they transformed the biological problem into an optimisation problem. To solve this problem, classical particle swarm optimisation algorithm was extended by the single point adjustment strategy. Compared with square lattice, conformations on triangular lattice are more flexible in several benchmark examples. They further compared the authors' algorithm with hybrid of hill climbing and genetic algorithm. The results showed that their method was more effective in finding solution with lower energy and less running time.

Sparse electrocardiogram signals recovery based on solving a row echelon-like form of system.

The study of biology and medicine in a noise environment is an evolving direction in biological data analysis. Among these studies, analysis of electrocardiogram (ECG) signals in a noise environment is a challenging direction in personalized medicine. Due to its periodic characteristic, ECG signal can be roughly regarded as sparse biomedical signals. This study proposes a two-stage recovery algorithm for sparse biomedical signals in time domain. In the first stage, the concentration subspaces are found in advance. Then by exploiting these subspaces, the mixing matrix is estimated accurately. In the second stage, based on the number of active sources at each time point, the time points are divided into different layers. Next, by constructing some transformation matrices, these time points form a row echelon-like system. After that, the sources at each layer can be solved out explicitly by corresponding matrix operations. It is noting that all these operations are conducted under a weak sparse condition that the number of active sources is less than the number of observations. Experimental results show that the proposed method has a better performance for sparse ECG signal recovery problem.

Network-based drug repositioning.

Network-based computational biology, with the emphasis on biomolecular interactions and omics-data integration, has had success in drug development and created new directions such as drug repositioning and drug combination. Drug repositioning, i.e., revealing a drug's new roles, is increasingly attracting much attention from the pharmaceutical community to tackle the problems of high failure rate and long-term development in drug discovery. While drug combination or drug cocktails, i.e., combining multiple drugs against diseases, mainly aims to alleviate the problems of the recurrent emergence of drug resistance and also reveal their synergistic effects. In this paper, we unify the two topics to reveal new roles of drug interactions from a network perspective by treating drug combination as another form of drug repositioning. In particular, first, we emphasize that rationally repositioning drugs in the large scale is driven by the accumulation of various high-throughput genome-wide data. These data can be utilized to capture the interplay among targets and biological molecules, uncover the resulting network structures, and further bridge molecular profiles and phenotypes. This motivates many network-based computational methods on these topics. Second, we organize these existing methods into two categories, i.e., single drug repositioning and drug combination, and further depict their main features by three data sources. Finally, we discuss the merits and shortcomings of these methods and pinpoint some future topics in this promising field.

Drug repositioning framework by incorporating functional information.

As a shortcut for drug development, drug repositioning draws more and more attention in pharmaceutical industry to identify new indications for marketed drugs or drugs failed in late clinical trial phase. At the same time, the abundant high-throughput data pushes the computationally repositioning drugs a hot topic in the area of systems biology. Here, the authors propose a general framework for repositioning drug by incorporating various functional information. The framework starts with the identification of differentially expressed gene sets under disease state and drug treatment. Then the disease and drug are associated by the overlap of these two gene sets via biological function. The authors provide two strategies to assess the functional overlap. In the first strategy, functional relevance are evaluated by leveraging genes' lethality information to reveal drug's potential of curing diseases. In the second strategy, biological process perturbation profiles are identified by mapping differentially expressed genes into pathways and gene ontology (GO) terms. Their associations are assessed and used to rank drugs' potential of curing diseases. The preliminary results on prostate cancer demonstrate that our new framework improves the drug repositioning efficiency and various function information could complement each other. Importantly, the new framework will enhance the biological interpretation and rationale of drug repositioning and provide insights into drug action mechanisms.

Comprehensive characterization of human genome variation by high coverage whole-genome sequencing of forty four Caucasians.

Whole genome sequencing studies are essential to obtain a comprehensive understanding of the vast pattern of human genomic variations. Here we report the results of a high-coverage whole genome sequencing study for 44 unrelated healthy Caucasian adults, each sequenced to over 50-fold coverage (averaging 65.8×). We identified approximately 11 million single nucleotide polymorphisms (SNPs), 2.8 million short insertions and deletions, and over 500,000 block substitutions. We showed that, although previous studies, including the 1000 Genomes Project Phase 1 study, have catalogued the vast majority of common SNPs, many of the low-frequency and rare variants remain undiscovered. For instance, approximately 1.4 million SNPs and 1.3 million short indels that we found were novel to both the dbSNP and the 1000 Genomes Project Phase 1 data sets, and the majority of which (∼96%) have a minor allele frequency less than 5%. On average, each individual genome carried ∼3.3 million SNPs and ∼492,000 indels/block substitutions, including approximately 179 variants that were predicted to cause loss of function of the gene products. Moreover, each individual genome carried an average of 44 such loss-of-function variants in a homozygous state, which would completely "knock out" the corresponding genes. Across all the 44 genomes, a total of 182 genes were "knocked-out" in at least one individual genome, among which 46 genes were "knocked out" in over 30% of our samples, suggesting that a number of genes are commonly "knocked-out" in general populations. Gene ontology analysis suggested that these commonly "knocked-out" genes are enriched in biological process related to antigen processing and immune response. Our results contribute towards a comprehensive characterization of human genomic variation, especially for less-common and rare variants, and provide an invaluable resource for future genetic studies of human variation and diseases.

A systems biology approach to identify effective cocktail drugs.

BACKGROUND: Complex diseases, such as Type 2 Diabetes, are generally caused by multiple factors, which hamper effective drug discovery. To combat these diseases, combination regimens or combination drugs provide an alternative way, and are becoming the standard of treatment for complex diseases. However, most of existing combination drugs are developed based on clinical experience or test-and-trial strategy, which are not only time consuming but also expensive. RESULTS: In this paper, we presented a novel network-based systems biology approach to identify effective drug combinations by exploiting high throughput data. We assumed that a subnetwork or pathway will be affected in the networked cellular system after a drug is administrated. Therefore, the affected subnetwork can be used to assess the drug's overall effect, and thereby help to identify effective drug combinations by comparing the subnetworks affected by individual drugs with that by the combination drug. In this work, we first constructed a molecular interaction network by integrating protein interactions, protein-DNA interactions, and signaling pathways. A new model was then developed to detect subnetworks affected by drugs. Furthermore, we proposed a new score to evaluate the overall effect of one drug by taking into account both efficacy and side-effects. As a pilot study we applied the proposed method to identify effective combinations of drugs used to treat Type 2 Diabetes. Our method detected the combination of Metformin and Rosiglitazone, which is actually Avandamet, a drug that has been successfully used to treat Type 2 Diabetes. CONCLUSIONS: The results on real biological data demonstrate the effectiveness and efficiency of the proposed method, which can not only detect effective cocktail combination of drugs in an accurate manner but also significantly reduce expensive and tedious trial-and-error experiments.

Identifying responsive functional modules from protein-protein interaction network.

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.