Roles of microRNAs in cancers and development.

MicroRNAs (miRNAs) are small noncoding RNA molecules thought to play an important role in regulating gene expression. Although knowledge of the biological functions of most miRNAs is still limited, miRNAs are thought to regulate the gene expression in various diseases and embryo development. In this chapter, the roles of miRNAs in human cancers are first examined from the viewpoint of up- and downregulation. Oncogenic miRNAs are involved in the overexpression/upregulation of cancers, whereas suppressive miRNAs are involved in the underexpression/downregulation of cancers. Statistical analysis of the positional nucleotide occurrence features of miRNAs revealed differences between the positional nucleotide occurrences of oncogenic and suppressive miRNAs. A miRNA gene-silencing score was then defined on the basis of the higher and lower levels of the statistical significances of positional nucleotides. Since the miRNA scores were closely related to miRNA frequencies, a method using the scores and nucleotide frequencies to distinguish whether a new miRNA is oncogenic or suppressive is proposed. This chapter also describes the roles of miRNAs in development. As miRNAs can act as cis-regulatory elements in the early embryonic development of Drosophila melanogaster, it is proposed that they mediate signal transduction between genes.

Methods for selecting effective siRNA target sequences using a variety of statistical and analytical techniques.

Short interfering RNA (siRNA) has been widely used for studying gene function in mammalian cells but varies markedly in its gene silencing efficacy. Although many design rules/guidelines for effective siRNAs based on various criteria have been reported recently, there are only a few consistencies among them. This makes it difficult to select effective siRNA sequences in mammalian genes. This chapter first reviews the recently reported siRNA design guidelines and then proposes new methods for selecting effective siRNA sequences from many possible candidates by using decision tree learning, Bayes' theorem, and average silencing probability on the basis of a large number of known effective siRNAs. These methods differ from the previous score-based siRNA design techniques and can predict the probability that a candidate siRNA sequence will be effective. Evaluation of these methods by applying them to recently reported effective and ineffective siRNA sequences for a number of genes indicates that they would be useful for many other genes. They should, therefore, be of general utility for selecting effective siRNA sequences for mammalian genes. The chapter also describes another method using a hidden Markov model to select the optimal functional siRNAs and discusses the frequencies of combinations of two successive nucleotides as an important characteristic of effective siRNA sequences.

Japanese Alzheimer's disease and other complex disorders diagnosis based on mitochondrial SNP haplogroups.

This paper first explains how the relations between Japanese Alzheimer's disease (AD) patients and their mitochondrial SNP frequencies at individual mtDNA positions examined using the radial basis function (RBF) network and a method based on RBF network predictions and that Japanese AD patients are associated with the haplogroups G2a and N9b1. It then describes a method for the initial diagnosis of Alzheimer's disease that is based on the mtSNP haplogroups of the AD patients. The method examines the relations between someone's mtDNA mutations and the mtSNPs of AD patients. As the mtSNP haplogroups thus obtained indicate which nucleotides of mtDNA loci are changed in the Alzheimer's patients, a person's probability of becoming an AD patient can be predicted by comparing those mtDNA mutations with that person's mtDNA mutations. The proposed method can also be used to diagnose diseases such as Parkinson's disease and type 2 diabetes and to identify people likely to become centenarians.

Using decision tree learning to predict the responsiveness of hepatitis C patients to drug treatment.

The recommended treatment for patients with chronic hepatitis C, pegylated interferon α (PEG-IFN-α) plus rebavirin (RBV), does not provide a sustained virologic response in all patients, especially those with hepatitis C virus (HCV) genotype 1. It is therefore important to predict whether or not a new patient with HCV genotype 1 will be cured by the recommended treatment. We propose a prediction method for a new patient using a decision tree learning model based on SNPs evaluated in a genome-wide association study. By the decision tree learning for 142 Japanese patients with HCV genotype 1 (78 with null virologic response and 64 with virologic response), we can predict with high probability (93%) whether or not a new patient with HCV will be helped by the recommended treatment.

Efficient prediction methods for selecting effective siRNA sequences.

Although short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells, its gene silencing efficacy varies markedly and there are only a few consistencies among the recently reported design rules/guidelines for selecting siRNA sequences effective for mammalian genes. Another shortcoming of the previously reported methods is that they cannot estimate the probability that a candidate sequence will silence the target gene. This paper first reviewed the recently reported siRNA design guidelines and clarified the problems concerning the guidelines. It then proposed two prediction methods-Radial Basis Function (RBF) network and decision tree learning-and their combined method for selecting effective siRNA target sequences from many possible candidate sequences. They are quite different from the previous score-based siRNA design techniques and can predict the probability that a candidate siRNA sequence will be effective. The methods imply high estimation accuracy for selecting candidate siRNA sequences.

Mitochondrial haplogroups associated with Japanese Alzheimer's patients.

The relationships between Japanese Alzheimer's disease (AD) patients and their mitochondrial single nucleotide polymorphism (mtSNP) frequencies at individual mtDNA positions of the entire mitochondrial genome are described using the radial basis function (RBF) network and the modified method. Japanese AD patients are associated with the haplogroups G2a, B4c1, and N9b1. In addition, to compare mitochondrial haplogroups of the AD patients with those of other classes of Japanese people, the relationships between four classes of Japanese people (i.e., Japanese centenarians, Parkinson's disease (PD) patients, type 2 diabetic (T2D) patients, and non-obese young males) and their mtSNPs are also described. The four classes of people are associated with following haplogroups: Japanese centenarians-M7b2, D4b2a, and B5b; Japanese PD patients-M7b2, B4e, and B5b; Japanese T2D patients-B5b, M8a1, G, D4, and F1; and Japanese healthy non-obese young males-D4g and D4b1b. The haplogroups of the AD patients are therefore different from those of the other four classes of Japanese people. As the analysis method described in this article can predict a person's mtSNP constitution and the probabilities of becoming an AD patient, centenarian, PD patient, or T2D patient, it may be useful in initial diagnosis of various diseases.

Selecting effective siRNA target sequences by using Bayes' theorem.

Short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells but varies markedly in its gene silencing efficacy. Although many design rules/guidelines for effective siRNAs based on various criteria have been reported recently, there are few consistencies among them. This makes it difficult to select effective siRNA sequences in mammalian genes. Another shortcoming of most previously reported methods is that they cannot estimate the probability that a candidate sequence will silence the target gene. The analytical prediction method proposed in the present study uses Bayes' theorem to select effective siRNA target sequences from many possible candidate sequences. It is quite different from the previous score-based siRNA design techniques and can predict the probability that a candidate siRNA sequence will be effective. The results of evaluating it by applying it to recently reported effective and ineffective siRNA sequences for various genes indicate that it would be useful for many other genes. It should therefore be useful for selecting siRNA sequences effective for mammalian genes.

Mitochondrial haplogroups associated with Japanese centenarians, Alzheimer's patients, Parkinson's patients, type 2 diabetic patients and healthy non-obese young males.

The relationships between five classes of Japanese people (i.e., 96 centenarians, 96 Alzheimer's disease (AD) patients, 96 Parkinson's disease (PD) patients, 96 type 2 diabetic (T2D) patients, and 96 healthy non-obese young males) and their mitochondrial single nucleotide polymorphism (mtSNP) frequencies at individual mtDNA positions of the entire mitochondrial genome were examined using the radial basis function (RBF) network and the modified method. New findings of mitochondrial haplogroups were obtained for individual classes. The five classes of people were associated with the following haplogroups: Japanese centenarians-M7b2, D4b2a, and B5b; Japanese AD patients-G2a, B4c1, and N9b1; Japanese PD patients-M7b2, B4e, and B5b; Japanese T2D patients-B5b, M8a1, G, D4, and F1; and Japanese healthy non-obese young males- D4g and D4b1b. From the points of common haplogroups among the five classes, the centenarians have the common haplogroups M7b2 and B5b with the PD patients and common haplogroup B5b with the T2D patients. In addition, the 112 Japanese semi-supercentenarians (over 105 years old) recently reported were also examined by the method proposed. The results obtained were the haplogroups D4a, B4c1a, M7b2, F1, M1, and B5b. These results are different from the previously reported haplogroup classifications. As the proposed analysis method can predict a person's mtSNP constitution and the probabilities of becoming a centenarian, AD patient, PD patient, or T2D patient, it may be useful in initial diagnosis of various diseases.

Methods for selecting effective siRNA sequences by using statistical and clustering techniques.

Short interfering RNAs (siRNAs) have been widely used for studying gene functions in mammalian cells but vary markedly in their gene-silencing efficacy. Although many design rules/guidelines for effective siRNAs based on various criteria have been reported recently, there are only a few consistencies among them. This makes it difficult to select effective siRNA sequences targeting mammalian genes. This chapter first reviews the reported siRNA design guidelines and clarifies the problems concerning the current guidelines. It then describes the recently reported new scoring methods for selecting effective siRNA sequences by using statistics and clustering techniques such as the self-organizing map (SOM) and the radial basis function (RBF) network. In the proposed three methods, individual scores are defined as a gene degradation measure based on position-specific statistical significances. The effectiveness of the methods was confirmed by evaluating effective and ineffective siRNAs for recently reported genes and comparison with other reported scoring methods. The sizes (values) of these scores are closely correlated with the degree of gene degradation, and the scores can easily be used for selecting high-potential siRNA candidates. The evaluation results indicate that the proposed new methods are useful for selecting siRNA sequences targeting mammalian mRNA sequences.

Coincidence between transcriptome analyses on different microarray platforms using a parametric framework.

A parametric framework for the analysis of transcriptome data is demonstrated to yield coincident results when applied to data acquired using two different microarray platforms. Microarrays are widely employed to acquire transcriptome information, and several platforms of chips are currently in use. However, discrepancies among studies are frequently reported, particularly among those performed using different platforms, casting doubt on the reliability of collected data. The inconsistency among observations can be largely attributed to differences among the analytical frameworks employed for data analysis. The existing frameworks are based on different philosophies and yield different results, but all involve normalization against a standard determined from the data to be analyzed. In the present study, a parametric framework based on a strict model for normalization is applied to data acquired using several slide-glass-type chips and GeneChip. The model is based on a common statistical characteristic of microarray data, and each set of chip data is normalized on the basis of a linear relationship with this model. In the proposed framework, the expressional changes observed and genes selected are coincident between platforms, achieving superior universality of data compared to other frameworks.

Mitochondrial SNPs associated with Japanese centenarians, Alzheimer's patients, and Parkinson's patients.

In this paper we examined the relations between three classes of people (96 Japanese centenarians, 96 Japanese Alzheimer's disease (AD) patients and 96 Japanese Parkinson's disease (PD) patients) and their mitochondrial single nucleotide polymorphism (mtSNP) frequencies at individual mitochondrial DNA (mtDNA) positions of the entire mt-genome by using the radial basis function (RBF) networks. As a result, we got new findings of mtSNPs for representing characteristics of individual classes. These mtSNPs show distinct differences for three classes of people. That is, individual classes of people are characterized by unique mtSNPs. Interestingly, Japanese centenarians are closely associated with haplogroup D4, Japanese AD patients with haplogroup G2a, and Japanese PD patients with haplogroup M7a. These characteristics of mtSNPs are different from those of previously reported works. As the amino acid replacement mtSNPs were at four mtDNA positions, it is indicated that mtSNPs of synonymous nucleotide substitutions as well as those of nonsynonymous nucleotide substitutions may play important roles in mitochondrial functions.

Selecting effective siRNA sequences based on the self-organizing map and statistical techniques.

Short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells but varies markedly in its gene-silencing efficacy. Although many design rules/guidelines for effective siRNAs based on various criteria have been reported recently, there are only a few consistencies among them. This makes it difficult to select effective siRNA sequences in mammalian genes. Here, we propose a new method for selecting effective siRNA target sequences on the basis of the self-organizing map (SOM) technique and statistical significance analyses for a large number of effective siRNAs. In the proposed method, the score is defined as a gene degradation measure. The effectiveness for the proposed method was confirmed by evaluating effective and ineffective siRNAs for recently reported genes (12 genes, 172 siRNA sequences) and comparing with other reported scoring methods. The size (value) of this score is closely correlated with the degree of gene degradation, and the score can easily be used for selecting high-potential siRNA candidates. The evaluation results indicate that the proposed method would be useful for many other genes. It will therefore be useful for selecting siRNA sequences in mammalian genes.

Selecting effective siRNA sequences by using radial basis function network and decision tree learning.

BACKGROUND: Although short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells, its gene silencing efficacy varies markedly and there are only a few consistencies among the recently reported design rules/guidelines for selecting siRNA sequences effective for mammalian genes. Another shortcoming of the previously reported methods is that they cannot estimate the probability that a candidate sequence will silence the target gene. RESULTS: We propose two prediction methods for selecting effective siRNA target sequences from many possible candidate sequences, one based on the supervised learning of a radial basis function (RBF) network and other based on decision tree learning. They are quite different from the previous score-based siRNA design techniques and can predict the probability that a candidate siRNA sequence will be effective. The proposed methods were evaluated by applying them to recently reported effective and ineffective siRNA sequences for various genes (15 genes, 196 siRNA sequences). We also propose the combined prediction method of the RBF network and decision tree learning. As the average prediction probabilities of gene silencing for the effective and ineffective siRNA sequences of the reported genes by the proposed three methods were respectively 65% and 32%, 56.6% and 38.1%, and 68.5% and 28.1%, the methods imply high estimation accuracy for selecting candidate siRNA sequences. CONCLUSION: New prediction methods were presented for selecting effective siRNA sequences. As the proposed methods indicated high estimation accuracy for selecting candidate siRNA sequences, they would be useful for many other genes.

Selecting effective siRNA target sequences for mammalian genes.

Short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells but varies markedly in its gene-silencing efficacy in mammalian genes. The recently reported guidelines for selecting effective siRNA target sequences are not always useful for selecting highly effective siRNA sequences for many other mammalian genes because there are only a few consistencies among them. Hypothesizing that the positional nucleotide occurrence trends play an important role in effective gene-silencing, we examined 361 effective siRNA sequences from 227 different mammalian cDNAs in the literature and found got several nucleotide features different from the ones used in the previous guidelines. Here we first explain the problems concerning the previous guidelines from the qualitative and quantitative points of view. Then after clarifying the requirements for effective siRNA designs, we describe a new method based on a gene degradation measure defined by positional features of specific significant nucleotides. Testing the method on human cyclin B1 confirmed that it selected highly effective gene-silencing sequences and also indicated that it would be useful for other genes. It will therefore be useful for selecting new siRNA target sequences for mammalian genes.

An effective method for selecting siRNA target sequences in mammalian cells.

RNA interference is a gene-silencing phenomenon triggered by dsRNA (double-stranded RNA) and has been widely used for studying gene functions. The short interfering RNA (siRNA) responsible for RNA interference, however, varies markedly in its gene-silencing efficacy. Because this efficacy depends on the selected target sequences, we developed an effective selection method based on the gene degradation measure (priority score) defined by positional features of individual nucleotides. We tested this method experimentally by using it to select new siRNA target sequences in the homo sapiens cyclin B1 gene (CCNB1) and confirmed that it selected highly effective gene-silencing sequences. The proposed method will therefore be useful for selecting new siRNA target sequences in mammalian cells.