Profiling the peripheral blood T cell receptor repertoires of gastric cancer patients

Cancer driven by somatic mutations may express neoantigens that can trigger T-cell immune responses. Since T-cell receptor (TCR) repertoires play critical roles in anti-tumor immune responses for oncology, next-generation sequencing (NGS) was used to profile the hypervariable complementarity-determining region 3 (CDR3) of the TCR-beta chain in peripheral blood samples from 68 gastric cancer patients and 49 healthy controls. We found that most hyper-expanded CDR3 are individual-specific, and the gene usage of TRBV3-1 is more frequent in the tumor group regardless of tumor stage than in the healthy control group. We identified 374 hyper-expanded tumor-specific CDR3, which may play a vital role in anti-tumor immune responses. The patients with stage IV gastric cancer have higher EBV-specific CDR3 abundance than the control. In conclusion, analysis of the peripheral blood TCR repertoires may provide the biomarker for gastric cancer prognosis and guide future immunotherapy

Instance-wise weighted nonnegative matrix factorization for aggregating partitions with locally reliable clusters

IJCAI-15: Buenos Aires, Argentina, 25–31 July 2015We address an ensemble clustering problem, where reliable clusters are locally embedded in given multiple partitions. We propose a new nonnegative matrix factorization (NMF)-based method, in which locally reliable clusters are explicitly considered by using instance-wise weights over clusters. Our method factorizes the input cluster assignment matrix into two matrices H and W, which are optimized by iteratively 1) updating H and W while keeping the weight matrix constant and 2) updating the weight matrix while keeping H and W constant, alternatively. The weights in the second step were updated by solving a convex problem, which makes our algorithm significantly faster than existing NMF-based ensemble clustering methods. We empirically proved that our method outperformed a lot of cutting-edge ensemble clustering methods by using a variety of datasets

A Robust Convex Formulations for Ensemble Clustering.

International Joint Conference on Artificial Intelligence , New York City , United States , 9-16 July .We formulate ensemble clustering as a regularization problem over nuclear norm and cluster-wise group norm, and present an efficient optimization algorithm, which we call Robust Convex Ensemble Clustering (RCEC). A key feature of RCEC allows to remove anomalous cluster assignments obtained from component clustering methods by using the group-norm regularization. Moreover, the proposed method is convex and can find the globally optimal solution. We first showed that using synthetic data experiments, RCEC could learn stable cluster assignments from the input matrix including anomalous clusters. We then showed that RCEC outperformed state-of-the-art ensemble clustering methods by using real-world data sets

AiProAnnotator

Best student paper of the conferenceAnnotating genes/proteins is a vital issue in biology. Particularly we focus on human proteins and medical annotation, which both are important. The most proper data for our annotation is human phenotype ontology (HPO), which are sparse but reliable (well-curated). Existing approaches for this problem are feature-based or network-based. The feature-based approach can incorporate a variety of information, by which this approach is more appropriate for noisy data than reliable data, while the network-based approach is not necessarily useful for sparse data. Low-rank approximation is very powerful for both sparse and reliable data. We thus propose to use matrix factorization to approximate the input annotation matrix (proteins × HPO terms) by factorized low-rank matrices. We further incorporate network information, i.e. protein-protein network (PPN) and network from HPO (NHPO), into the framework of matrix factorization as graph regularization over the two low-rank matrices. That is, the input annotation matrix is factorized into two low-rank factor matrices so that they can be smooth over PPN and NHPO. We call our software of implementing the above method “AiProAnnotator”, which in this paper has been empirically examined using the latest HPO data extensively under various experimental settings, including performance comparison under cross-validation, computation time and case studies, etc. Experimental results showed the high predictive performance and time efficiency of AiProAnnotator clearly.Peer reviewe

SR protein 9G8 modulates splicing of tau exon 10 via its proximal downstream intron, a clustering region for frontotemporal dementia mutations

The microtubule-associated protein tau is important to normal neuronal function in the mammalian nervous system. Aggregated tau is the major component of neurofibrillary tangles (NFTs), present in several neurodegenerative diseases, including Alzheimer\u27s and frontotemporal dementia with Parkinsonism (FTDP). Splicing misregulation of adult-specific exon 10 results in expression of abnormal ratios of tau isoforms, leading to FTDP. Positions +3 to +16 of the intron downstream of exon 10 define a clustering region for point mutations that are found in FTDP. The serine/arginine-rich (SR) factor 9G8 strongly inhibits inclusion of tau exon 10. In this study, we established that 9G8 binds directly to this clustering region, requires a wild-type residue at position +14 to inhibit exon inclusion, and RNAi constructs against 9G8 increase exon 10 inclusion. These results indicate that 9G8 plays a key role in regulation of exon 10 splicing and imply a pathogenic role in neurodegenerative diseases

A Robust Convex Formulation for Ensemble Clustering

We formulate ensemble clustering as a regularization problem over nuclear norm and cluster-wise group norm, and present an efficient optimization algorithm, which we call Robust Convex Ensemble Clustering (RCEC). A key feature of RCEC allows to remove anomalous cluster assignments obtained from component clustering methods by using the group-norm regularization. Moreover, the proposed method is convex and can find the globally optimal solution. We first showed that using synthetic data experiments, RCEC could learn stable cluster assignments from the input matrix including anomalous clusters. We then showed that RCEC outperformed state-of-the-art ensemble clustering methods by using real-world data sets.Peer reviewe

An SRp75/hnRNPG Complex Interacting with hnRNPE2 Regulates the 5\u27 Splice Site of Tau Exon 10, Whose Misregulation Causes Frontotemporal Dementia

Tau is a neuronal-specific microtubule-associated protein that plays an important role in establishing neuronal polarity and maintaining the axonal cytoskeleton. Aggregated tau is the major component of neurofibrillary tangles (NFTs), structures present in the brains of people affected by neurodegenerative diseases called tauopathies. Tauopathies include Alzheimer\u27s disease (AD), frontotemporal dementia with Parkinsonism (FTDP-17), the early onset dementia observed in Down syndrome (DS; trisomy 21) and the dementia component of myotonic dystrophy type 1 (DM1). Splicing misregulation of adult-specific exon 10, which codes for a microtubule binding domain, results in expression of abnormal ratios of tau isoforms, leading to FTDP-17. Positions 3 to 19 of the intron downstream of exon 10 define a hotspot of splicing regulation: the region diverges between humans and rodents, and point mutations within it result in tauopathies. In this study, we investigated three regulators of exon 10 splicing: serine/arginine-rich protein SRp75 and heterogeneous nuclear ribonucleoproteins hnRNPG and hnRNPE2. SRp75 and hnRNPG inhibit splicing of exon 10 whereas hnRNPE2 activates it. Using co-transfections, co-immunoprecipitations and RNAi we discovered that SRp75 binds to the proximal downstream intron of tau exon 10 at the FTDP-17 hotspot region; and that hnRNPG and hnRNPE2 interact with SRp75. Thus, increased exon 10 inclusion in FTDP mutants may arise from weakened SRp75 binding. This work provides insights into the splicing regulation of the tau gene and into possible strategies for correcting the imbalance in tauopathies caused by changes in the ratio of exon 10

Tau exons 2 and 10, which are misregulated in neurodegenerative diseases, are partly regulated by silencers which bind a SRp30c.SRp55 complex that either recruits or antagonizes htra2beta1

Tau is a microtubule-associated protein whose transcript undergoes complex regulated splicing in the mammalian nervous system. Exon 2 modulates the tau N-terminal domain, which interacts with the axonal membrane. Exon 10 codes for a microtubule binding domain, increasing the affinity of tau for microtubules. Both exons are excluded from fetal brain, but their default behavior is inclusion, suggesting that silencers are involved in their regulation. Exon 2 is significantly reduced in myotonic dystrophy type 1, whose symptoms include dementia. Mutations that affect exon 10 splicing cause frontotemporal dementia (FTDP). In this study, we investigated three regulators of exon 2 and 10 splicing: serine/arginine-rich (SR) proteins SRp55, SRp30c, and htra2beta1. The first two inhibit both exons; htra2beta1 inhibits exon 2 but activates exon 10. By deletion analysis, we identified splicing silencers located at the 5\u27 end of each exon. Furthermore, we demonstrated that SRp30c and SRp55 bind to both silencers and to each other. In exon 2, htra2beta1 binds to the inhibitory heterodimer through its RS1 domain but not to exon 2, whereas in exon 10 the heterodimer may sterically interfere with htra2beta1 binding to a purine-rich enhancer (defined by FTDP mutation E10-Delta5 = Delta280K) directly downstream of the silencer. Increased exon 10 inclusion in FTDP mutant ENH (N279K) may arise from abolishing SRp30c binding. Also, htra2beta3, a naturally occurring variant of htra2beta1, no longer inhibits exon 2 splicing but can partially rescue splicing of exon 10 in FTDP mutation E10-Delta5. This work provides interesting insights into the splicing regulation of the tau gene

DrugE-Rank

Motivation: Identifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug-target interactions of new candidate drugs or targets. Methods: Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used ascomponents of ensemble learning. Results: The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs.Peer reviewe