Exploring the alteration of gut microbiota and brain function in gender-specific Parkinson's disease based on metagenomic sequencing.

Background: The role of the microbiota-gut-brain axis in Parkinson's disease (PD) has received increasing attention. Although gender differences are known to an essential role in the epidemiology and clinical course of PD, there are no studies on the sex specificity of the microbiota-gut-brain axis in the development and progression of PD. Methods: Fresh fecal samples from 24 PD patients (13 males, 11 females) were collected for metagenomic sequencing. The composition and function of the gut microbiota were analyzed by resting-state functional magnetic resonance imaging (fMRI). Gender-dependent differences in brain ALFF values and their correlation with microbiota were further analyzed. Results: The relative abundance of Propionivibrio, Thermosediminibacter, and Flavobacteriaceae_noname was increased in male PD patients. LEfse analysis showed that Verrucomicrobial, Akkermansiaceae, and Akkermansia were dominant in the males. In female patients, the relative abundance of Propionicicella was decreased and Escherichia, Escherichia_coli, and Lachnospiraceae were predominant. The expression of the sesquiterpenoid and triterpenoid biosynthesis pathways was increased in male PD patients and was statistically different from females. Compared to the Male PD patients, female patients showed decreased ALFF values in the left inferior parietal regions, and the relative abundance of Propionivibrio was positively correlated with the regional ALFF values. Conclusion: Our study provides novel clinical evidence of the gender-specific relationship between gut microbiota alterations and brain function in PD patients, highlighting the critical role of the microbiota-gut-brain axis in gender differences in PD.

Alignment-free sequence comparison based on next-generation sequencing reads.

Next-generation sequencing (NGS) technologies have generated enormous amounts of shotgun read data, and assembly of the reads can be challenging, especially for organisms without template sequences. We study the power of genome comparison based on shotgun read data without assembly using three alignment-free sequence comparison statistics, D(2), D(*)(2) and D(s)(2), both theoretically and by simulations. Theoretical formulas for the power of detecting the relationship between two sequences related through a common motif model are derived. It is shown that both D(*)(2) and D(s)(2), outperform D(2) for detecting the relationship between two sequences based on NGS data. We then study the effects of length of the tuple, read length, coverage, and sequencing error on the power of D(*)(2) and D(s)(2). Finally, variations of these statistics, d(2), d(*)(2) and d(s)(2), respectively, are used to first cluster five mammalian species with known phylogenetic relationships, and then cluster 13 tree species whose complete genome sequences are not available using NGS shotgun reads. The clustering results using d(s)(2) are consistent with biological knowledge for the 5 mammalian and 13 tree species, respectively. Thus, the statistic d(s)(2) provides a powerful alignment-free comparison tool to study the relationships among different organisms based on NGS read data without assembly.

Normal and compound poisson approximations for pattern occurrences in NGS reads.

Next generation sequencing (NGS) technologies are now widely used in many biological studies. In NGS, sequence reads are randomly sampled from the genome sequence of interest. Most computational approaches for NGS data first map the reads to the genome and then analyze the data based on the mapped reads. Since many organisms have unknown genome sequences and many reads cannot be uniquely mapped to the genomes even if the genome sequences are known, alternative analytical methods are needed for the study of NGS data. Here we suggest using word patterns to analyze NGS data. Word pattern counting (the study of the probabilistic distribution of the number of occurrences of word patterns in one or multiple long sequences) has played an important role in molecular sequence analysis. However, no studies are available on the distribution of the number of occurrences of word patterns in NGS reads. In this article, we build probabilistic models for the background sequence and the sampling process of the sequence reads from the genome. Based on the models, we provide normal and compound Poisson approximations for the number of occurrences of word patterns from the sequence reads, with bounds on the approximation error. The main challenge is to consider the randomness in generating the long background sequence, as well as in the sampling of the reads using NGS. We show the accuracy of these approximations under a variety of conditions for different patterns with various characteristics. Under realistic assumptions, the compound Poisson approximation seems to outperform the normal approximation in most situations. These approximate distributions can be used to evaluate the statistical significance of the occurrence of patterns from NGS data. The theory and the computational algorithm for calculating the approximate distributions are then used to analyze ChIP-Seq data using transcription factor GABP. Software is available online (www-rcf.usc.edu/∼fsun/Programs/NGS_motif_power/NGS_motif_power.html). In addition, Supplementary Material can be found online (www.liebertonline.com/cmb).

The power of detecting enriched patterns: an HMM approach.

The identification of binding sites of transcription factors (TF) and other regulatory regions, referred to as motifs, located in a set of molecular sequences is of fundamental importance in genomic research. Many computational and experimental approaches have been developed to locate motifs. The set of sequences of interest can be concatenated to form a long sequence of length n. One of the successful approaches for motif discovery is to identify statistically over- or under-represented patterns in this long sequence. A pattern refers to a fixed word W over the alphabet. In the example of interest, W is a word in the set of patterns of the motif. Despite extensive studies on motif discovery, no studies have been carried out on the power of detecting statistically over- or under-represented patterns Here we address the issue of how the known presence of random instances of a known motif affects the power of detecting patterns, such as patterns within the motif. Let N(W)(n) be the number of possibly overlapping occurrences of a pattern W in the sequence that contains instances of a known motif; such a sequence is modeled here by a Hidden Markov Model (HMM). First, efficient computational methods for calculating the mean and variance of N(W)(n) are developed. Second, efficient computational methods for calculating parameters involved in the normal approximation of N(W)(n) for frequent patterns and compound Poisson approximation of N(W)(n) for rare patterns are developed. Third, an easy to use web program is developed to calculate the power of detecting patterns and the program is used to study the power of detection in several interesting biological examples.