Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes

The microarray technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining these data one can identify the dynamics of the gene expression time series. The detection of genes that are periodically expressed is an important step that allows us to study the regulatory mechanisms associated with the circadian cycle. The problem of finding periodicity in biological time series poses many challenges. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, outliers and unevenly sampled time points. Consequently, the method for finding periodicity should preferably be robust against such anomalies in the data. In this paper, we propose a general and robust procedure for identifying genes with a periodic signature at a given significance level. This identification method is based on autoregressive models and the information theory. By using simulated data we show that the suggested method is capable of identifying rhythmic profiles even in the presence of noise and when the number of data points is small. By recourse of our analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis.     

Identifying Genes Involved in Cyclic Processes by Combining Gene Expression Analysis and Prior Knowledge

Based on time series gene expressions, cyclic genes can be recognized via spectral analysis and statistical periodicity detection tests. These cyclic genes are usually associated with cyclic biological processes, for example, cell cycle and circadian rhythm. The power of a scheme is practically measured by comparing the detected periodically expressed genes with experimentally verified genes participating in a cyclic process. However, in the above mentioned procedure the valuable prior knowledge only serves as an evaluation benchmark, and it is not fully exploited in the implementation of the algorithm. In addition, partial data sets are also disregarded due to their nonstationarity. This paper proposes a novel algorithm to identify cyclic-process-involved genes by integrating the prior knowledge with the gene expression analysis. The proposed algorithm is applied on data sets corresponding to Saccharomyces cerevisiae and Drosophila melanogaster, respectively. Biological evidences are found to validate the roles of the discovered genes in cell cycle and circadian rhythm. Dendrograms are presented to cluster the identified genes and to reveal expression patterns. It is corroborated that the proposed novel identification scheme provides a valuable technique for unveiling pathways related to cyclic processes.     

Predicting and analyzing early wake-up associated gene expressions by integrating GWAS and eQTL studies

Circadian rhythms are endogenous 24-hour rhythmic oscillations affecting human behaviors, such as sleep, blood pressure and other biological processes, the disturbance of which lead to circadian rhythm sleep disorders (CRSDs). In this study, based on the data from genome-wide association studies (GWASs) and expression quantitative trait loci (eQTLs), we tried to identify novel gene expression patterns in brain tissues that were associated with early wake-up. First, the maximum-relevance-minimum-redundancy (mRMR) method was adopted to analyze the involved gene expression patterns, yielding a feature list. Second, the incremental feature selection (IFS) method and the Dagging algorithm were applied to extract important gene expression patterns, which yield the best performance for Dagging. As a result, 4374 gene expression patterns were obtained, and they were further used to build an optimal classifier with a good performance of a Matthews's correlation coefficient of 0.933. Furthermore, the most important 49 gene expression patterns were extensively analyzed. Four genes were found to be related to circadian rhythm, as reported in previous studies. As a first attempt in identifying the target genes whose expression levels are associated with sleep-wake rhythms through integrating GWAS and eQTL results, this study can motivate more investigations in this regard.This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.     

Predicting gastrointestinal infection morbidity based on environmental pollutants: Deep learning versus traditional models

Accurate morbidity prediction can contribute greatly to the efficiency of medical services. Gastrointestinal infectious diseases are largely influenced by environmental pollutants, but predicting their morbidity based on pollution indicators is quite difficult because of the complex relationship between the pollutants and the infections. This study presents a deep neural network (DNN) model for estimating the morbidity of gastrointestinal infections based on 129 types of pollutants contained in soil and water. The DNN uses a deep Boltzmann machine (DBM) to model the unknown probabilistic relationship between the pollutants, and employs a Gaussian mixture model (GMM) to output the estimated morbidity. We also propose an evolutionary algorithm for efficiently training the DNN. Experiment on a data set from four counties in central China shows that the proposed model can estimate the morbidity much more accurately than traditional neural network and linear regression models.     

Polar Gini Curve: A Technique to Discover Gene Expression Spatial Patterns from Single-cell RNA-seq Data

In this work, we describe the development of Polar Gini Curve, a method for characterizing cluster markers by analyzing single-cell RNA sequencing (scRNA-seq) data. Polar Gini Curve combines the gene expression and the 2D coordinates ("spatial") information to detect patterns of uniformity in any clustered cells from scRNA-seq data. We demonstrate that Polar Gini Curve can help users characterize the shape and density distribution of cells in a particular cluster, which can be generated during routine scRNA-seq data analysis. To quantify the extent to which a gene is uniformly distributed in a cell cluster space, we combine two polar Gini curves (PGCs)—one drawn upon the cell-points expressing the gene (the"foreground curve") and the other drawn upon all cell-points in the cluster (the"background curve"). We show that genes with highly dissimilar foreground and background curves tend not to uniformly distributed in the cell cluster—thus having spatially divergent gene expression patterns within the cluster. Genes with similar foreground and background curves tend to uniformly distributed in the cell cluster—thus having uniform gene expression patterns within the cluster. Such quantitative attributes of PGCs can be applied to sensitively discover biomarkers across clusters from scRNA-seq data. We demonstrate the performance of the Polar Gini Curve framework in several simulation case studies. Using this framework to analyze a real-world neonatal mouse heart cell dataset, the detected biomarkers may characterize novel subtypes of cardiac muscle cells. The source code and data for Polar Gini Curve could be found at http://discovery.informatics.uab.edu/PGC/ or https://figshare.com/projects/Polar_Gini_Curve/76749.     

Bacterial Competition Reveals Differential Regulation of the pks Genes by Bacillus subtilis

Bacillus subtilis is adaptable to many environments in part due to its ability to produce a broad range of bioactive compounds. One such compound, bacillaene, is a linear polyketide/nonribosomal peptide. The pks genes encode the enzymatic megacomplex that synthesizes bacillaene. The majority of pks genes appear to be organized as a giant operon (>74 kb from pksC-pksR). In previous work (P. D. Straight, M. A. Fischbach, C. T. Walsh, D. Z. Rudner, and R. Kolter, Proc. Natl. Acad. Sci. U. S. A. 104:305–310, 2007, doi:10.1073/pnas.0609073103), a deletion of the pks operon in B. subtilis was found to induce prodiginine production by Streptomyces coelicolor. Here, colonies of wild-type B. subtilis formed a spreading population that induced prodiginine production from Streptomyces lividans, suggesting differential regulation of pks genes and, as a result, bacillaene. While the parent colony showed widespread induction of pks expression among cells in the population, we found the spreading cells uniformly and transiently repressed the expression of the pks genes. To identify regulators that control pks genes, we first determined the pattern of pks gene expression in liquid culture. We next identified mutations in regulatory genes that disrupted the wild-type pattern of pks gene expression. We found that expression of the pks genes requires the master regulator of development, Spo0A, through its repression of AbrB and the stationary-phase regulator, CodY. Deletions of degU, comA, and scoC had moderate effects, disrupting the timing and level of pks gene expression. The observed patterns of expression suggest that complex regulation of bacillaene and other antibiotics optimizes competitive fitness for B. subtilis.     

DeepCPI:A Deep Learning-based Framework for Large-scale in silico Drug Screening

Accurate identification of compound–protein interactions(CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development.Conventional similarity-or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled compound and protein data and often limit their usage to relatively small-scale datasets.In the present study,we propose Deep CPI,a novel general and scalable computational framework that combines effective feature embedding(a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale.Deep CPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unlabeled data.Evaluations of the measured CPIs in large-scale databases,such as Ch EMBL and Binding DB,as well as of the known drug–target interactions from Drug Bank,demonstrated the superior predictive performance of Deep CPI.Furthermore,several interactions among smallmolecule compounds and three G protein-coupled receptor targets(glucagon-like peptide-1 receptor,glucagon receptor,and vasoactive intestinal peptide receptor) predicted using Deep CPI were experimentally validated.The present study suggests that Deep CPI is a useful and powerful tool for drug discovery and repositioning.The source code of Deep CPI can be downloaded from https://github.com/Fangping Wan/Deep CPI.     

Enhancer modularity and the evolution of new traits

ABSTRACT

Animals have modular cis-regulatory regions in their genomes, and expression of a single gene is often regulated by multiple enhancers residing in such a region. In the laboratory, and also in natural populations, loss of an enhancer can result in a loss of gene expression. Although only a few examples have been well characterized to date, some studies have suggested that an evolutionary gain of a new enhancer function can establish a new gene expression domain. Our recent study showed that Drosophila guttifera has more enhancers and additional expression domains of the wingless gene during the pupal stage, compared to D. melanogaster, and that these new features appear to have evolved in the ancestral lineage leading to D. guttifera.¹ Koshikawa S, Giorgianni MW, Vaccaro K, Kassner VA, Yoder JH, Werner T, Carroll SB. Gain of cis-regulatory activities underlies novel domains of wingless gene expression in Drosophila. Proc Natl Acad Sci USA 2015; 112:7524-9; PMID:26034272; http://dx.doi.org/10.1073/pnas.1509022112[Crossref], [PubMed], [Web of Science ®] , [Google Scholar] Gain of a new expression domain of a developmental regulatory gene (toolkit gene), such as wingless, can cause co-option of the expression of its downstream genes to the new domain, resulting in duplication of a preexisting structure at this new body position. Recently, with the advancement of evo-devo studies, we have learned that the developmental regulatory systems are strikingly similar across various animal taxa, in spite of the great diversity of the animals' morphology. Even behind “new” traits, co-options of essential developmental genes from known systems are very common. We previously provided concrete evidence of gains of enhancer activities of a developmental regulatory gene underlying gains of new traits.¹ Koshikawa S, Giorgianni MW, Vaccaro K, Kassner VA, Yoder JH, Werner T, Carroll SB. Gain of cis-regulatory activities underlies novel domains of wingless gene expression in Drosophila. Proc Natl Acad Sci USA 2015; 112:7524-9; PMID:26034272; http://dx.doi.org/10.1073/pnas.1509022112[Crossref], [PubMed], [Web of Science ®] , [Google Scholar] Broad occurrence of this scenario is testable and should be validated in the future.     

Sticky Pi is a high-frequency smart trap that enables the study of insect circadian activity under natural conditions

In the face of severe environmental crises that threaten insect biodiversity, new technologies are imperative to monitor both the identity and ecology of insect species. Traditionally, insect surveys rely on manual collection of traps, which provide abundance data but mask the large intra- and interday variations in insect activity, an important facet of their ecology. Although laboratory studies have shown that circadian processes are central to insects’ biological functions, from feeding to reproduction, we lack the high-frequency monitoring tools to study insect circadian biology in the field. To address these issues, we developed the Sticky Pi, a novel, autonomous, open-source, insect trap that acquires images of sticky cards every 20 minutes. Using custom deep learning algorithms, we automatically and accurately scored where, when, and which insects were captured. First, we validated our device in controlled laboratory conditions with a classic chronobiological model organism, Drosophila melanogaster. Then, we deployed an array of Sticky Pis to the field to characterise the daily activity of an agricultural pest, Drosophila suzukii, and its parasitoid wasps. Finally, we demonstrate the wide scope of our smart trap by describing the sympatric arrangement of insect temporal niches in a community, without targeting particular taxa a priori. Together, the automatic identification and high sampling rate of our tool provide biologists with unique data that impacts research far beyond chronobiology, with applications to biodiversity monitoring and pest control as well as fundamental implications for phenology, behavioural ecology, and ecophysiology. We released the Sticky Pi project as an open community resource on https://doc.sticky-pi.com.

This study presents Sticky Pi, an open-source smart insect trap that automatically identifies and times wild insect capture; it can be deployed in a decentralised manner and at a large scale to monitor and model insect populations in the field, for example, to study the circadian activity patterns within an insect community.     

Detecting periodic patterns in unevenly spaced gene expression time series using Lomb-Scargle periodograms

MOTIVATION: Periodic patterns in time series resulting from biological experiments are of great interest. The commonly used Fast Fourier Transform (FFT) algorithm is applicable only when data are evenly spaced and when no values are missing, which is not always the case in high-throughput measurements. The choice of statistic to evaluate the significance of the periodic patterns for unevenly spaced gene expression time series has not been well substantiated. METHODS: The Lomb-Scargle periodogram approach is used to search time series of gene expression to quantify the periodic behavior of every gene represented on the DNA array. The Lomb-Scargle periodogram analysis provides a direct method to treat missing values and unevenly spaced time points. We propose the combination of a Lomb-Scargle test statistic for periodicity and a multiple hypothesis testing procedure with controlled false discovery rate to detect significant periodic gene expression patterns. RESULTS: We analyzed the Plasmodium falciparum gene expression dataset. In the Quality Control Dataset of 5080 expression patterns, we found 4112 periodic probes. In addition, we identified 243 probes with periodic expression in the Complete Dataset, which could not be examined in the original study by the FFT analysis due to an excessive number of missing values. While most periodic genes had a period of 48 h, some had a period close to 24 h. Our approach should be applicable for detection and quantification of periodic patterns in any unevenly spaced gene expression time-series data.