Identifying periodically expressed transcripts in microarray time series data

Time-/dose- series transcriptome data analysis and traditional Chinese medicine treatment of pneumoconiosis

Pneumoconiosis' pathogenesis is still unclear and specific drugs for its treatment are lacking. Analysis of series transcriptome data often uses a single comparison method, and there are few reports on using such data to predict the treatment of pneumoconiosis with traditional Chinese medicine (TCM). Here, we proposed a new method for analyzing series transcriptomic data, series difference analysis (SDA), and applied it to pneumoconiosis. By comparison with 5 gene sets including existing pneumoconiosis-related genes and gene set functional enrichment analysis, we demonstrated that the new method was not inferior to two existing traditional analysis methods. Furthermore, based on the TCM-drug target interaction network, we predicted the TCM corresponding to the common pneumoconiosis-related genes obtained by multiple methods, and combined them with the high-frequency TCM for its treatment obtained through literature mining to form a new TCM formula for it. After feeding it to pneumoconiosis modeling mice for two months, compared with the untreated group, the coat color, mental state and tissue sections of the mice in the treated group were markedly improved, indicating that the new TCM formula has a certain efficacy. Our study provides new insights into method development for series transcriptomic data analysis and treatment of pneumoconiosis.

Light regulation of rhodopsin distribution during outer segment renewal in murine rod photoreceptors

Vision under dim light relies on primary cilia elaborated by rod photoreceptors in the retina. This specialized sensory structure, called the rod outer segment (ROS), comprises hundreds of stacked, membranous discs containing the light-sensitive protein rhodopsin, and the incorporation of new discs into the ROS is essential for maintaining the rod's health and function. ROS renewal appears to be primarily regulated by extrinsic factors (light); however, results vary depending on different model organisms. We generated two independent transgenic mouse lines where rhodopsin's fate is tracked by a fluorescently labeled rhodopsin fusion protein (Rho-Timer) and show that rhodopsin incorporation into nascent ROS discs appears to be regulated by both external lighting cues and autonomous retinal clocks. Live-cell imaging of the ROS isolated from mice exposed to six unique lighting conditions demonstrates that ROS formation occurs in a periodic manner in cyclic light, constant darkness, and artificial light/dark cycles. This alternating bright/weak banding of Rho-Timer along the length of the ROS relates to inhomogeneities in rhodopsin density and potential points of structural weakness. In addition, we reveal that prolonged dim ambient light exposure impacts not only the rhodopsin content of new discs but also that of older discs, suggesting a dynamic interchange of material between new and old discs. Furthermore, we show that rhodopsin incorporation into the ROS is greatly altered in two autosomal recessive retinitis pigmentosa mouse models, potentially contributing to the pathogenesis. Our findings provide insights into how extrinsic (light) and intrinsic (retinal clocks and genetic mutation) factors dynamically regulate mammalian ROS renewal.

Detecting Rhythmic Gene Expression in Single Cell Transcriptomics

An autonomous, environmentally-synchronizable circadian rhythm is a ubiquitous feature of life on Earth. In multicellular organisms, this rhythm is generated by a transcription-translation feedback loop present in nearly every cell that drives daily expression of thousands of genes in a tissue-dependent manner. Identifying the genes that are under circadian control can elucidate the mechanisms by which physiological processes are coordinated in multicellular organisms. Today, transcriptomic profiling at the single-cell level provides an unprecedented opportunity to understand the function of cell-level clocks. However, while many cycling detection algorithms have been developed to identify genes under circadian control in bulk transcriptomic data, it is not known how best to adapt these algorithms to single-cell RNAseq data. Here, we benchmark commonly used circadian detection methods on their reliability and efficiency when applied to single cell RNAseq data. Our results provide guidance on adapting existing cycling detection methods to the single-cell domain, and elucidate opportunities for more robust and efficient rhythm detection in single-cell data. We also propose a subsampling procedure combined with harmonic regression as an efficient, reliable strategy to detect circadian genes in the single-cell setting.

Modulating signalling lifetime to optimise a prototypical animal opsin for optogenetic applications

Animal opsins are light activated G-protein-coupled receptors, capable of optogenetic control of G-protein signalling for research or therapeutic applications. Animal opsins offer excellent photosensitivity, but their temporal resolution can be limited by long photoresponse duration when expressed outside their native cellular environment. Here, we explore methods for addressing this limitation for a prototypical animal opsin (human rod opsin) in HEK293T cells. We find that the application of the canonical rhodopsin kinase (GRK1)/visual arrestin signal termination mechanism to this problem is complicated by a generalised suppressive effect of GRK1 expression. This attenuation can be overcome using phosphorylation-independent mutants of arrestin, especially when these are tethered to the opsin protein. We further show that point mutations targeting the Schiff base stability of the opsin can also reduce signalling lifetime. Finally, we apply one such mutation (E122Q) to improve the temporal fidelity of restored visual responses following ectopic opsin expression in the inner retina of a mouse model of retinal degeneration (rd1). Our results reveal that these two strategies (targeting either arrestin binding or Schiff-base hydrolysis) can produce more time-delimited opsin signalling under heterologous expression and establish the potential of this approach to improve optogenetic performance.

Circadian regulation of liver metabolism: experimental approaches in human, rodent, and cellular models

Circadian rhythms are endogenous oscillations with approximately a 24-h period that allow organisms to anticipate the change between day and night. Disruptions that desynchronize or misalign circadian rhythms are associated with an increased risk of cardiometabolic disease. This review focuses on the liver circadian clock as relevant to the risk of developing metabolic diseases including nonalcoholic fatty liver disease (NAFLD), insulin resistance, and type 2 diabetes (T2D). Many liver functions exhibit rhythmicity. Approximately 40% of the hepatic transcriptome exhibits 24-h rhythms, along with rhythms in protein levels, posttranslational modification, and various metabolites. The liver circadian clock is critical for maintaining glucose and lipid homeostasis. Most of the attention in the metabolic field has been directed toward diet, exercise, and rather little to modifiable risks due to circadian misalignment or disruption. Therefore, the aim of this review is to systematically analyze the various approaches that study liver circadian pathways, targeting metabolic liver diseases, such as diabetes, nonalcoholic fatty liver disease, using human, rodent, and cell biology models.NEW & NOTEWORTHY Over the past decade, there has been an increased interest in understanding the intricate relationship between circadian rhythm and liver metabolism. In this review, we have systematically searched the literature to analyze the various experimental approaches utilizing human, rodent, and in vitro cellular approaches to dissect the link between liver circadian rhythms and metabolic disease.

Climate-driven tree growth and mortality in the Black Forest, Germany-Long-term observations

Episodic tree mortality can be caused by various reasons. This study describes climate-driven tree mortality and tree growth in the Black Forest mountain range in Germany. It is based on a 68-year consistent data series describing the annual mortality of all trees growing in a forest area of almost 250 thousand ha. The study excludes mortality caused by storm, snow and ice, and fire. The sequence of the remaining mortality, the so-called "desiccated trees," is analyzed and compared with the sequence of the climatic water balance during the growing season and the annual radial growth of Norway spruce in the Black Forest. The annual radial growth series covers 121 years and the climatic water balance series 140 years. These unique time series enable a quantitative assessment of multidecadal drought and heat impacts on growth and mortality of forest trees on a regional spatial scale. Data compiled here suggest that the mortality of desiccated trees in the Black Forest during the last 68 years is driven by the climatic water balance. Decreasing climatic water balance coincided with an increase in tree mortality and growth decline. Consecutive hot and dry summers enhance mortality and growth decline as a consequence of drought legacies lasting several years. The sensitivity of tree growth and mortality to changes in the climatic water balance increases with the decreasing trend of the climatic water balance. The findings identify the climatic water balance as the main driver of mortality and growth variation during the 68-year observation period on a landscape-scale including a variety of different sites. They suggest that bark beetle population dynamics modify mortality rates. They as well provide evidence that the mortality during the last 140 years never was as high as in the most recent years.

Amniotes co-opt intrinsic genetic instability to protect germ-line genome integrity

Unlike PIWI-interacting RNA (piRNA) in other species that mostly target transposable elements (TEs), >80% of piRNAs in adult mammalian testes lack obvious targets. However, mammalian piRNA sequences and piRNA-producing loci evolve more rapidly than the rest of the genome for unknown reasons. Here, through comparative studies of chickens, ducks, mice, and humans, as well as long-read nanopore sequencing on diverse chicken breeds, we find that piRNA loci across amniotes experience: (1) a high local mutation rate of structural variations (SVs, mutations ≥ 50 bp in size); (2) positive selection to suppress young and actively mobilizing TEs commencing at the pachytene stage of meiosis during germ cell development; and (3) negative selection to purge deleterious SV hotspots. Our results indicate that genetic instability at pachytene piRNA loci, while producing certain pathogenic SVs, also protects genome integrity against TE mobilization by driving the formation of rapid-evolving piRNA sequences.

Growth rate-associated transcriptome reorganization in response to genomic, environmental, and evolutionary interruptions

The genomic, environmental, and evolutionary interruptions caused the changes in bacterial growth, which were stringently associated with changes in gene expression. The growth and gene expression changes remained unclear in response to these interruptions that occurred combinative. As a pilot study, whether and how bacterial growth was affected by the individual and dual interruptions of genome reduction, environmental stress, and adaptive evolution were investigated. Growth assay showed that the presence of the environmental stressors, i.e., threonine and chloramphenicol, significantly decreased the growth rate of the wild-type Escherichia coli, whereas not that of the reduced genome. It indicated a canceling effect in bacterial growth due to the dual interruption of the genomic and environmental changes. Experimental evolution of the reduced genome released the canceling effect by improving growth fitness. Intriguingly, the transcriptome architecture maintained a homeostatic chromosomal periodicity regardless of the genomic, environmental, and evolutionary interruptions. Negative epistasis in transcriptome reorganization was commonly observed in response to the dual interruptions, which might contribute to the canceling effect. It was supported by the changes in the numbers of differentially expressed genes (DEGs) and the enriched regulons and functions. Gene network analysis newly constructed 11 gene modules, one out of which was correlated to the growth rate. Enrichment of DEGs in these modules successfully categorized them into three types, i.e., conserved, responsive, and epistatic. Taken together, homeostasis in transcriptome architecture was essential to being alive, and it might be attributed to the negative epistasis in transcriptome reorganization and the functional differentiation in gene modules. The present study directly connected bacterial growth fitness with transcriptome reorganization and provided a global view of how microorganisms responded to genomic, environmental, and evolutionary interruptions for survival from wild nature.

Chronic sleep loss disrupts rhythmic gene expression in Drosophila

Genome-wide profiling of rhythmic gene expression has offered new avenues for studying the contribution of circadian clock to diverse biological processes. Sleep has been considered one of the most important physiological processes that are regulated by the circadian clock, however, the effects of chronic sleep loss on rhythmic gene expression remain poorly understood. In the present study, we exploited Drosophila sleep mutants insomniac 1 (inc 1 ) and wide awake D2 (wake D2 ) as models for chronic sleep loss. We profiled the transcriptomes of head tissues collected from 4-week-old wild type flies, inc 1 and wake D2 at timepoints around the clock. Analysis of gene oscillation revealed a substantial loss of rhythmicity in inc 1 and wake D2 compared to wild type flies, with most of the affected genes common to both mutants. The disruption of gene oscillation was not due to changes in average gene expression levels. We also identified a subset of genes whose loss of rhythmicity was shared among animals with chronic sleep loss and old flies, suggesting a contribution of aging to chronic, sleep-loss-induced disruption of gene oscillation.

Identification of differentially expressed genes and their major pathways among the patient with COVID-19, cystic fibrosis, and chronic kidney disease

The SARS-CoV-2 virus causes Coronavirus disease, an infectious disease. The majority of people who are infected with this virus will have mild to moderate respiratory symptoms. Multiple studies have proved that there is a substantial pathophysiological link between COVID-19 disease and patients having comorbidities such as cystic fibrosis and chronic kidney disease. In this study, we attempted to identify differentially expressed genes as well as genes that intersected among them in order to comprehend their compatibility. Gene expression profiling indicated that 849 genes were mutually exclusive and functional analysis was done within the context of gene ontology and key pathways involvement. Three genes (PRPF31, FOXN2, and RIOK3) were commonly upregulated in the analysed datasets of three disease categories. These genes could be potential biomarkers for patients with COVID-19 and cystic fibrosis, and COVID-19 and chronic kidney disease. Further extensive analyses have been performed to describe how these genes are regulated by various transcription factors and microRNAs. Then, our analyses revealed six hub genes (PRPF31, FOXN2, RIOK3, UBC, HNF4A, and ELAVL). As they were involved in the interaction between COVID-19 and the patient with CF and CKD, they could help researchers identify potential therapeutic molecules. Some drugs have been predicted based on the upregulated genes, which may have a significant impact on reducing the burden of these diseases in the future.

Dynamic switching of lateral inhibition spatial patterns

Hes genes are transcriptional repressors activated by Notch. In the developing mouse neural tissue, HES5 expression oscillates in neural progenitors (Manning et al. 2019 Nat. Commun.10, 1–19 (doi:10.1038/s41467-019-10734-8)) and is spatially organized in small clusters of cells with synchronized expression (microclusters). Furthermore, these microclusters are arranged with a spatial periodicity of three–four cells in the dorso-ventral axis and show regular switching between HES5 high/low expression on a longer time scale and larger amplitude than individual temporal oscillators (Biga et al. 2021 Mol. Syst. Biol.17, e9902 (doi:10.15252/msb.20209902)). However, our initial computational modelling of coupled HES5 could not explain these features of the experimental data. In this study, we provide theoretical results that address these issues with biologically pertinent additions. Here, we report that extending Notch signalling to non-neighbouring progenitor cells is sufficient to generate spatial periodicity of the correct size. In addition, introducing a regular perturbation of Notch signalling by the emerging differentiating cells induces a temporal switching in the spatial pattern, which is longer than an individual cell’s periodicity. Thus, with these two new mechanisms, a computational model delivers outputs that closely resemble the complex tissue-level HES5 dynamics. Finally, we predict that such dynamic patterning spreads out differentiation events in space, complementing our previous findings whereby the local synchronization controls the rate of differentiation.

Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious respiratory ailment known as new coronavirus disease (COVID-19). Despite the fact that the prevalence of COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and how patients with COVID-19 become so unwell. Detecting biomarkers for COVID-19 using peripheral blood mononuclear cells (PBMCs) may aid in drug development and treatment. This research aimed to find blood cell transcripts that represent levels of gene expression associated with COVID-19 progression. Through the development of a bioinformatics pipeline, two RNA-Seq transcriptomic datasets and one microarray dataset were studied and discovered 102 significant differentially expressed genes (DEGs) that were shared by three datasets derived from PBMCs. To identify the roles of these DEGs, we discovered disease-gene association networks and signaling pathways, as well as we performed gene ontology (GO) studies and identified hub protein. Identified significant gene ontology and molecular pathways improved our understanding of the pathophysiology of COVID-19, and our identified blood-based hub proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, and TOP2A could be used for the development of therapeutic intervention. In COVID-19 subjects, we discovered effective putative connections between pathological processes in the transcripts blood cells, suggesting that blood cells could be used to diagnose and monitor the disease’s initiation and progression as well as developing drug therapeutics.

Subthalamic Peak Beta Ratio Is Asymmetric in Glucocerebrosidase Mutation Carriers With Parkinson's Disease: A Pilot Study

Introduction: Up to 27% of individuals undergoing subthalamic nucleus deep brain stimulation (STN-DBS) have a genetic form of Parkinson's disease (PD). Glucocerebrosidase (GBA) mutation carriers, compared to sporadic PD, present with a more aggressive disease, less asymmetry, and fare worse on cognitive outcomes with STN-DBS. Evaluating STN intra-operative local field potentials provide the opportunity to assess and compare symmetry between GBA and non-GBA mutation carriers with PD; thus, providing insight into genotype and STN physiology, and eligibility for and programming of STN-DBS. The purpose of this pilot study was to test differences in left and right STN resting state beta power in non-GBA and GBA mutation carriers with PD. Materials and Methods: STN (left and right) resting state local field potentials were recorded intraoperatively from 4 GBA and 5 non-GBA patients with PD while off medication. Peak beta power expressed as a ratio to total beta power (peak beta ratio) was compared between STN hemispheres and groups while co-varying for age, age of disease onset, and disease severity. Results: Peak beta ratio was significantly different between the left and the right STN for the GBA group (p < 0.01) but not the non-GBA group (p = 0.56) after co-varying for age, age of disease onset, and disease severity. Discussion: Peak beta ratio in GBA mutation carriers was more asymmetric compared with non-mutation carriers and this corresponded with the degree of clinical asymmetry as measured by rating scales. This finding suggests that GBA mutation carriers have a physiologic signature that is distinct from that found in sporadic PD.

Coupled protein synthesis and ribosome-guided piRNA processing on mRNAs

PIWI-interacting small RNAs (piRNAs) protect the germline genome and are essential for fertility. piRNAs originate from transposable element (TE) RNAs, long non-coding RNAs, or 3´ untranslated regions (3´UTRs) of protein-coding messenger genes, with the last being the least characterized of the three piRNA classes. Here, we demonstrate that the precursors of 3´UTR piRNAs are full-length mRNAs and that post-termination 80S ribosomes guide piRNA production on 3´UTRs in mice and chickens. At the pachytene stage, when other co-translational RNA surveillance pathways are sequestered, piRNA biogenesis degrades mRNAs right after pioneer rounds of translation and fine-tunes protein production from mRNAs. Although 3´UTR piRNA precursor mRNAs code for distinct proteins in mice and chickens, they all harbor embedded TEs and produce piRNAs that cleave TEs. Altogether, we discover a function of the piRNA pathway in fine-tuning protein production and reveal a conserved piRNA biogenesis mechanism that recognizes translating RNAs in amniotes.

TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts in Circadian Time-Series Data

MOTIVATION

The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues. The recent revolution in high-throughput transcriptomics, coupled with the significant implications of the circadian clock for human health, has sparked an interest in circadian profiling studies to discover genes under circadian control.

RESULT

We present TimeCycle: a topology-based rhythm detection method designed to identify cycling transcripts. For a given time-series, the method reconstructs the state space using time-delay embedding, a data transformation technique from dynamical systems theory. In the embedded space, Takens' theorem proves that the dynamics of a rhythmic signal will exhibit circular patterns. The degree of circularity of the embedding is calculated as a persistence score using persistent homology, an algebraic method for discerning the topological features of data. By comparing the persistence scores to a bootstrapped null distribution, cycling genes are identified. Results in both synthetic and biological data highlight TimeCycle's ability to identify cycling genes across a range of sampling schemes, number of replicates, and missing data. Comparison to competing methods highlights their relative strengths, providing guidance as to the optimal choice of cycling detection method.

AVAILABILITY

A fully documented open-source R package implementing TimeCycle is available at: https://nesscoder.github.io/TimeCycle/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PredCRG: A computational method for recognition of plant circadian genes by employing support vector machine with Laplace kernel

BACKGROUND

Circadian rhythms regulate several physiological and developmental processes of plants. Hence, the identification of genes with the underlying circadian rhythmic features is pivotal. Though computational methods have been developed for the identification of circadian genes, all these methods are based on gene expression datasets. In other words, we failed to search any sequence-based model, and that motivated us to deploy the present computational method to identify the proteins encoded by the circadian genes.

RESULTS

Support vector machine (SVM) with seven kernels, i.e., linear, polynomial, radial, sigmoid, hyperbolic, Bessel and Laplace was utilized for prediction by employing compositional, transitional and physico-chemical features. Higher accuracy of 62.48% was achieved with the Laplace kernel, following the fivefold cross- validation approach. The developed model further secured 62.96% accuracy with an independent dataset. The SVM also outperformed other state-of-art machine learning algorithms, i.e., Random Forest, Bagging, AdaBoost, XGBoost and LASSO. We also performed proteome-wide identification of circadian proteins in two cereal crops namely, Oryza sativa and Sorghum bicolor, followed by the functional annotation of the predicted circadian proteins with Gene Ontology (GO) terms.

CONCLUSIONS

To the best of our knowledge, this is the first computational method to identify the circadian genes with the sequence data. Based on the proposed method, we have developed an R-package PredCRG ( https://cran.r-project.org/web/packages/PredCRG/index.html ) for the scientific community for proteome-wide identification of circadian genes. The present study supplements the existing computational methods as well as wet-lab experiments for the recognition of circadian genes.

Identification of biomarkers and pathways for the SARS-CoV-2 infections that make complexities in pulmonary arterial hypertension patients

This study aimed to identify significant gene expression profiles of the human lung epithelial cells caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We performed a comparative genomic analysis to show genomic observations between SARS-CoV and SARS-CoV-2. A phylogenetic tree has been carried for genomic analysis that confirmed the genomic variance between SARS-CoV and SARS-CoV-2. Transcriptomic analyses have been performed for SARS-CoV-2 infection responses and pulmonary arterial hypertension (PAH) patients' lungs as a number of patients have been identified who faced PAH after being diagnosed with coronavirus disease 2019 (COVID-19). Gene expression profiling showed significant expression levels for SARS-CoV-2 infection responses to human lung epithelial cells and PAH lungs as well. Differentially expressed genes identification and integration showed concordant genes (SAA2, S100A9, S100A8, SAA1, S100A12 and EDN1) for both SARS-CoV-2 and PAH samples, including S100A9 and S100A8 genes that showed significant interaction in the protein-protein interactions network. Extensive analyses of gene ontology and signaling pathways identification provided evidence of inflammatory responses regarding SARS-CoV-2 infections. The altered signaling and ontology pathways that have emerged from this research may influence the development of effective drugs, especially for the people with preexisting conditions. Identification of regulatory biomolecules revealed the presence of active promoter gene of SARS-CoV-2 in Transferrin-micro Ribonucleic acid (TF-miRNA) co-regulatory network. Predictive drug analyses provided concordant drug compounds that are associated with SARS-CoV-2 infection responses and PAH lung samples, and these compounds showed significant immune response against the RNA viruses like SARS-CoV-2, which is beneficial in therapeutic development in the COVID-19 pandemic.

Single-molecule long-read sequencing reveals a conserved intact long RNA profile in sperm

Sperm contributes diverse RNAs to the zygote. While sperm small RNAs have been shown to impact offspring phenotypes, our knowledge of the sperm transcriptome, especially the composition of long RNAs, has been limited by the lack of sensitive, high-throughput experimental techniques that can distinguish intact RNAs from fragmented RNAs, known to abound in sperm. Here, we integrate single-molecule long-read sequencing with short-read sequencing to detect sperm intact RNAs (spiRNAs). We identify 3440 spiRNA species in mice and 4100 in humans. The spiRNA profile consists of both mRNAs and long non-coding RNAs, is evolutionarily conserved between mice and humans, and displays an enrichment in mRNAs encoding for ribosome. In sum, we characterize the landscape of intact long RNAs in sperm, paving the way for future studies on their biogenesis and functions. Our experimental and bioinformatics approaches can be applied to other tissues and organisms to detect intact transcripts.

Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.

Computationally enhanced quantitative phase microscopy reveals autonomous oscillations in mammalian cell growth

The fine balance of growth and division is a fundamental property of the physiology of cells, and one of the least understood. Its study has been thwarted by difficulties in the accurate measurement of cell size and the even greater challenges of measuring growth of a single cell over time. We address these limitations by demonstrating a computationally enhanced methodology for quantitative phase microscopy for adherent cells, using improved image processing algorithms and automated cell-tracking software. Accuracy has been improved more than twofold and this improvement is sufficient to establish the dynamics of cell growth and adherence to simple growth laws. It is also sufficient to reveal unknown features of cell growth, previously unmeasurable. With these methodological and analytical improvements, in several cell lines we document a remarkable oscillation in growth rate, occurring throughout the cell cycle, coupled to cell division or birth yet independent of cell cycle progression. We expect that further exploration with this advanced tool will provide a better understanding of growth rate regulation in mammalian cells.

An Optimal Time for Treatment-Predicting Circadian Time by Machine Learning and Mathematical Modelling

Tailoring medical interventions to a particular patient and pathology has been termed personalized medicine. The outcome of cancer treatments is improved when the intervention is timed in accordance with the patient's internal time. Yet, one challenge of personalized medicine is how to consider the biological time of the patient. Prerequisite for this so-called chronotherapy is an accurate characterization of the internal circadian time of the patient. As an alternative to time-consuming measurements in a sleep-laboratory, recent studies in chronobiology predict circadian time by applying machine learning approaches and mathematical modelling to easier accessible observables such as gene expression. Embedding these results into the mathematical dynamics between clock and cancer in mammals, we review the precision of predictions and the potential usage with respect to cancer treatment and discuss whether the patient's internal time and circadian observables, may provide an additional indication for individualized treatment timing. Besides the health improvement, timing treatment may imply financial advantages, by ameliorating side effects of treatments, thus reducing costs. Summarizing the advances of recent years, this review brings together the current clinical standard for measuring biological time, the general assessment of circadian rhythmicity, the usage of rhythmic variables to predict biological time and models of circadian rhythmicity.

Cell-Cycle-Associated Expression Patterns Predict Gene Function in Mycobacteria

Although the major events in prokaryotic cell cycle progression are likely to be coordinated with transcriptional and metabolic changes, these processes remain poorly characterized. Unlike many rapidly growing bacteria, DNA replication and cell division are temporally resolved in mycobacteria, making these slow-growing organisms a potentially useful system to investigate the prokaryotic cell cycle. To determine whether cell-cycle-dependent gene regulation occurs in mycobacteria, we characterized the temporal changes in the transcriptome of synchronously replicating populations of Mycobacterium tuberculosis (Mtb). By enriching for genes that display a sinusoidal expression pattern, we discover 485 genes that oscillate with a period consistent with the cell cycle. During cytokinesis, the timing of gene induction could be used to predict the timing of gene function, as mRNA abundance was found to correlate with the order in which proteins were recruited to the developing septum. Similarly, the expression pattern of primary metabolic genes could be used to predict the relative importance of these pathways for different cell cycle processes. Pyrimidine synthetic genes peaked during DNA replication, and their depletion caused a filamentation phenotype that phenocopied defects in this process. In contrast, the inosine monophasphate dehydrogenase dedicated to guanosine synthesis, GuaB2, displayed the opposite expression pattern and its depletion perturbed septation. Together, these data imply obligate coordination between primary metabolism and cell division and identify periodically regulated genes that can be related to specific cell biological functions.

Correlated chromosomal periodicities according to the growth rate and gene expression

Linking genetic information to population fitness is crucial to understanding living organisms. Despite the abundant knowledge of the genetic contribution to growth, the overall patterns/features connecting genes, their expression, and growth remain unclear. To reveal the quantitative and direct connections, systematic growth assays of single-gene knockout Escherichia coli strains under both rich and poor nutritional conditions were performed; subsequently, the resultant growth rates were associated with the original expression levels of the knockout genes in the parental genome. Comparative analysis of growth and the transcriptome identified not only the nutritionally differentiated fitness cost genes but also a significant correlation between the growth rates of the single-gene knockout strains and the original expression levels of these knockout genes in the parental strain, regardless of the nutritional variation. In addition, the coordinated chromosomal periodicities of the wild-type transcriptome and the growth rates of the strains lacking the corresponding genes were observed. The common six-period periodicity was somehow attributed to the essential genes, although the underlying mechanism remains to be addressed. The correlated chromosomal periodicities associated with the gene expression-growth dataset were highly valuable for bacterial growth prediction and discovering the working principles governing minimal genetic information.

A Methodology for Forecasting Dissolved Oxygen in Urban Streams

Real-time monitoring of river water quality is at the forefront of a proactive urban water management strategy to meet the global challenge of vital freshwater resource sustainability. The concentration of dissolved oxygen (DO) is a primary indicator of the health state of the aquatic habitats, and its modeling is crucial for river water quality management. This paper investigates the importance of the choices of different techniques for preprocessing and stochastic modeling for developing a simple and reliable linear stochastic model for forecasting DO in urban rivers. We describe several methods of evaluation, preprocessing, and modeling for the DO parameter time series in the Credit River, Ontario, Canada, to achieve the optimum data preprocessing and input selection techniques and consequently obtain the optimum performance of the stochastic models as an effective river management tool. The Manly normalization and standardization (Std) methods were chosen for preprocessing the time series. Modeling the preprocessed time series using the stochastic autoregressive integrated moving average (ARIMA) model resulted in very accurate forecasts with a negligible difference from sole normalization and spectral analysis (Sf) methods.

Genome-wide circadian regulation: A unique system for computational biology

Circadian rhythms are 24-hour oscillations affecting an organism at multiple levels from gene expression all the way to tissues and organs. They have been observed in organisms across the kingdom of life, spanning from cyanobacteria to humans. In mammals, the master circadian pacemaker is located in the hypothalamic suprachiasmatic nuclei (SCN) in the brain where it synchronizes the peripheral oscillators that exist in other tissues. This system regulates the circadian activity of a large part of the transcriptome and recent findings indicate that almost every cell in the body has this clock at the molecular level. In this review, we briefly summarize the different factors that can influence the circadian transcriptome, including light, temperature, and food intake. We then summarize recently identified general principles governing genome-scale circadian regulation, as well as future lines of research. Genome-scale circadian activity represents a fascinating study model for computational biology. For this purpose, systems biology methods are promising exploratory tools to decode the global regulatory principles of circadian regulation.

Global transcriptome analysis reveals circadian control of splicing events in Arabidopsis thaliana

The circadian clock of Arabidopsis thaliana controls many physiological and molecular processes, allowing plants to anticipate daily changes in their environment. However, developing a detailed understanding of how oscillations in mRNA levels are connected to oscillations in co/post-transcriptional processes, such as splicing, has remained a challenge. Here we applied a combined approach using deep transcriptome sequencing and bioinformatics tools to identify novel circadian-regulated genes and splicing events. Using a stringent approach, we identified 300 intron retention, eight exon skipping, 79 alternative 3' splice site usage, 48 alternative 5' splice site usage, and 350 multiple (more than one event type) annotated events under circadian regulation. We also found seven and 721 novel alternative exonic and intronic events. Depletion of the circadian-regulated splicing factor AtSPF30 homologue resulted in the disruption of a subset of clock-controlled splicing events. Altogether, our global circadian RNA-seq coupled with an in silico, event-centred, splicing analysis tool offers a new approach for studying the interplay between the circadian clock and the splicing machinery at a global scale. The identification of many circadian-regulated splicing events broadens our current understanding of the level of control that the circadian clock has over this co/post-transcriptional regulatory layer.

The highly conserved chromosomal periodicity of transcriptomes and the correlation of its amplitude with the growth rate in Escherichia coli

The growth rate, representing the fitness of a bacterial population, is determined by the transcriptome. Chromosomal periodicity, which is known as the periodic spatial pattern of a preferred chromosomal distance in microbial genomes, is a representative overall feature of the transcriptome; however, whether and how it is associated with the bacterial growth rate are unknown. To address these questions, we analysed a total of 213 transcriptomes of multiple Escherichia coli strains growing in an assortment of culture conditions varying in terms of temperature, nutrition level and osmotic pressure. Intriguingly, Fourier transform analyses of the transcriptome identified a common chromosomal periodicity of transcriptomes, which was independent of the variation in genomes and environments. In addition, fitting of the data to a theoretical model, we found that the amplitudes of the periodic transcriptomes were significantly correlated with the growth rates. These results indicated that the amplitude of periodic transcriptomes is a parameter representing the global pattern of gene expression in correlation with the bacterial growth rate. Thus, our study provides a novel parameter for evaluating the adaptiveness of a growing bacterial population and quantitatively predicting the growth dynamics according to the global expression pattern.

Changes of Conformation in Albumin with Temperature by Molecular Dynamics Simulations

This work presents the analysis of the conformation of albumin in the temperature range of 300K–312K, i.e., in the physiological range. Using molecular dynamics simulations, we calculate values of the backbone and dihedral angles for this molecule. We analyze the global dynamic properties of albumin treated as a chain. In this range of temperature, we study parameters of the molecule and the conformational entropy derived from two angles that reflect global dynamics in the conformational space. A thorough rationalization, based on the scaling theory, for the subdiffusion Flory–De Gennes type exponent of 0.4 unfolds in conjunction with picking up the most appreciable fluctuations of the corresponding statistical-test parameter. These fluctuations coincide adequately with entropy fluctuations, namely the oscillations out of thermodynamic equilibrium. Using Fisher’s test, we investigate the conformational entropy over time and suggest its oscillatory properties in the corresponding time domain. Using the Kruscal–Wallis test, we also analyze differences between the root mean square displacement of a molecule at various temperatures. Here we show that its values in the range of 306K–309K are different than in another temperature. Using the Kullback–Leibler theory, we investigate differences between the distribution of the root mean square displacement for each temperature and time window.

Methods detecting rhythmic gene expression are biologically relevant only for strong signal

The nycthemeral transcriptome embodies all genes displaying a rhythmic variation of their mRNAs periodically every 24 hours, including but not restricted to circadian genes. In this study, we show that the nycthemeral rhythmicity at the gene expression level is biologically functional and that this functionality is more conserved between orthologous genes than between random genes. We used this conservation of the rhythmic expression to assess the ability of seven methods (ARSER, Lomb Scargle, RAIN, JTK, empirical-JTK, GeneCycle, and meta2d) to detect rhythmic signal in gene expression. We have contrasted them to a naive method, not based on rhythmic parameters. By taking into account the tissue-specificity of rhythmic gene expression and different species comparisons, we show that no method is strongly favored. The results show that these methods designed for rhythm detection, in addition to having quite similar performances, are consistent only among genes with a strong rhythm signal. Rhythmic genes defined with a standard p-value threshold of 0.01 for instance, could include genes whose rhythmicity is biologically irrelevant. Although these results were dependent on the datasets used and the evolutionary distance between the species compared, we call for caution about the results of studies reporting or using large sets of rhythmic genes. Furthermore, given the analysis of the behaviors of the methods on real and randomized data, we recommend using primarily ARS, empJTK, or GeneCycle, which verify expectations of a classical distribution of p-values. Experimental design should also take into account the circumstances under which the methods seem more efficient, such as giving priority to biological replicates over the number of time-points, or to the number of time-points over the quality of the technique (microarray vs RNAseq). GeneCycle, and to a lesser extent empirical-JTK, might be the most robust method when applied to weakly informative datasets. Finally, our analyzes suggest that rhythmic genes are mainly highly expressed genes.

To be active, genes have to be transcribed to RNA. For some genes, the transcription rate follows a circadian rhythm with a periodicity of approximately 24 hours; we call these genes “rhythmic”. In this study, we compared methods designed to detect rhythmic genes in gene expression data. The data are measures of the number of RNA molecules for each gene, given at several time-points, usually spaced 2 to 4 hours, over one or several periods of 24 hours. There are many such methods, but it is not known which ones work best to detect genes whose rhythmic expression is biologically functional. We compared these methods using a reference group of evolutionarily conserved rhythmic genes. We compared data from baboon, mouse, rat, zebrafish, fly, and mosquitoes. Surprisingly, no method was particularly effective. Furthermore, we found that only very strong rhythmic signals were relevant with each method. More precisely, when we use a usual cut-off to define rhythmic genes, the group of genes considered as rhythmic contains many genes whose rhythmicity cannot be confirmed to be biologically relevant. We also show that rhythmic genes mainly contain highly expressed genes. Finally, based on our results, we provide recommendations on which methods to use and how, and suggestions for future experimental designs.

Order restricted inference in chronobiology

This paper is motivated by applications in oscillatory systems where researchers are typically interested in discovering components of those systems that display rhythmic temporal patterns. The contributions of this paper are twofold. First, a methodology is developed based on a circular signal plus error model that is defined using order restrictions. This mathematical formulation of rhythmicity is simple, easily interpretable and very flexible, with the latter property derived from the nonparametric formulation of the signal. Second, we address various commonly encountered problems in the analysis of oscillatory systems data. Specifically, we propose a methodology for (a) detecting rhythmic signals in an oscillatory system and (b) estimating the unknown sampling time that occurs when tissues are obtained from subjects whose time of death is unknown. The proposed methodology is computationally efficient, outperforms the existing methods, and is broadly applicable to address a wide range of questions related to oscillatory systems.

Ribosomes guide pachytene piRNA formation on long intergenic piRNA precursors

PIWI-interacting RNAs (piRNAs) are a class of small non-coding RNAs essential for fertility. In adult mouse testes, most piRNAs are derived from long single-stranded RNAs lacking annotated open reading frames (ORFs). The mechanisms underlying how piRNA sequences are defined during the cleavages of piRNA precursors remain elusive. Here, we show that 80S ribosomes translate the 5'-proximal short ORFs (uORFs) of piRNA precursors. The MOV10L1/Armitage RNA helicase then facilitates the translocation of ribosomes into the uORF downstream regions (UDRs). The ribosome-bound UDRs are targeted by piRNA processing machinery, with the processed ribosome-protected regions becoming piRNAs. The dual modes of interaction between ribosomes and piRNA precursors underlie the distinct piRNA biogenesis requirements at uORFs and UDRs. Ribosomes also mediate piRNA processing in roosters and green lizards, implying that this mechanism is evolutionarily conserved in amniotes. Our results uncover a function for ribosomes on non-coding regions of RNAs and reveal the mechanisms underlying how piRNAs are defined.

An Application of the Bayesian Periodicity Test to Identify Diurnal Rhythm Genes in the Brain

Biological systems are extremely dynamic and many aspects of cellular processes show rhythmic circadian patterns. Extracting such information from large expression data is challenging. In this work, we present a modified application of the Empirical Bayes periodicity test to identify genes with diurnal rhythmic behavior in two brain regions. The hypothalamus and amygdala gene expression data were generated from 100 BXD recombinant inbred mice during the day hours. Brain samples were collected over the course of two days. We first filtered the transcripts based on rank correlation at matched time points between day-1 and day-2. We then applied the proposed test of periodicity to identify diurnal rhythm genes in the full cohort and gender-specific sub-cohorts. In hypothalamus, at a Benjamini-Hochberg false discovery rate (BH-FDR) of 0.01, we identified 15 transcripts with cyclic behavior in the full cohort, none, and 53 transcripts in the female and male cohort, respectively. Similarly, in amygdala, we identified 58 diurnal rhythm genes in the full cohort, and 1 and 28 in the female and male cohorts, respectively. In conclusion, we present a modified version of the empirical Bayes periodicity test to detect periodic expression patterns. Our results demonstrate that this approach can capture cyclic patterns from relatively noisy expression data sets.

A Composite Mode Differential Gene Regulatory Architecture based on Temporal Expression Profiles

Exploring the complex interactive mechanism in a Gene Regulatory Network (GRN) developed using transcriptome data obtained from standard microarray and/or RNA-seq experiments helps us to understand the triggering factors in cancer research. The Transcription Factor (TF) genes generate protein complexes which affect the transcription of various target genes. However, considering the mode of regulation in a time frame such transcriptional activities are dependent on some specific activation time points only. It is also crucial to check whether the regulating capabilities are uniform across varied stages, especially when periodicity is a big issue. In this context, we propose an algorithm called RIFT which helps to monitor the temporal differential regulatory pattern of a Differentially Expressed (DE) target gene either by a TF gene or a group of TF genes from a large time series (TS) data. We have tested our algorithm on HeLa cell cycle data and compared the result with its most advanced state of the art counterpart proposed so far. As our algorithm yields up stringent mode and target specific significant valid TF genes for a DE gene, we can expect to have new forms of genetic interactions.

The impact of pelvic lateral rotation on hindlimb kinematics and stride length in the red-legged running frog, Kassina maculata

Some frog species, such as Kassina maculata (red-legged running frog), use an asynchronous walking/running gait as their primary locomotor mode. Prior comparative anatomy work has suggested that lateral rotation of the pelvis improves walking performance by increasing hindlimb stride length; however, this hypothesis has never been tested. Using non-invasive methods, experimental high-speed video data collected from eight animals were used to create two three-dimensional kinematic models. These models, each fixed to alternative local anatomical reference frames, were used to investigate the hypothesis that lateral rotation of the mobile ilio-sacral joint in the anuran pelvis plays a propulsive role in walking locomotion by increasing hindlimb stride length. All frogs used a walking gait (duty factor greater than 0.5) despite travelling over a range of speeds (0.04-0.23 m s^-1). The hindlimb joint motions throughout a single stride were temporally synchronized with lateral rotation of the pelvis. The pelvis itself, on average, underwent an angular excursion of 12.71° (±4.39°) with respect to the body midline during lateral rotation. However, comparison between our two kinematic models demonstrated that lateral rotation of the pelvis only increases the cranio-caudal excursion of the hindlimb modestly. Thus, we propose that pelvic lateral rotation is not a stride length augmenting mechanism in K. maculata.

Autonomic impairment of patients in coma with different Glasgow coma score assessed with heart rate variability

PRIMARY OBJECTIVE

The objective of this study is to assess the functional state of the autonomic nervous system in healthy individuals and in individuals in coma using measures of heart rate variability (HRV) and to evaluate its efficiency in predicting mortality.

DESIGN AND METHODS

Retrospective group comparison study of patients in coma classified into two subgroups, according to their Glasgow coma score, with a healthy control group. HRV indices were calculated from 7 min of artefact-free electrocardiograms using the Hilbert-Huang method in the spectral range 0.02-0.6 Hz. A special procedure was applied to avoid confounding factors. Stepwise multiple regression logistic analysis (SMLRA) and ROC analysis evaluated predictions.

RESULTS

Progressive reduction of HRV was confirmed and was associated with deepening of coma and a mortality score model that included three spectral HRV indices of absolute power values of very low, low and very high frequency bands (0.4-0.6 Hz). The SMLRA model showed sensitivity of 95.65%, specificity of 95.83%, positive predictive value of 95.65%, and overall efficiency of 95.74%.

CONCLUSIONS

HRV is a reliable method to assess the integrity of the neural control of the caudal brainstem centres on the hearts of patients in coma and to predict patient mortality.

Ultradian Rhythms in the Transcriptome of Neurospora crassa

In many organisms, the circadian clock drives rhythms in the transcription of clock-controlled genes that can be either circadian (∼24-hr period) or ultradian (<24-hr period). Ultradian rhythms with periods that are a fraction of 24 hr are termed harmonics. Several harmonic transcripts were discovered in the mouse liver, but their functional significance remains unclear. Using a model-based analysis, we report for the first time ∼7-hr third harmonic transcripts in Neurospora crassa, a well-established fungal circadian model organism. Several third harmonic genes are regulated by female fertility 7 (FF-7), whose transcript itself is third harmonic. The knockout of circadian output regulator CSP1 superimposes circadian rhythms on the third harmonic genes, whereas the knockout of stress response regulator MSN1 converts third harmonic rhythms to second harmonic rhythms. The 460 ∼7-hr genes are co-regulated in two anti-phasic groups in multiple genotypes and include kinases, chromatin remodelers, and homologs of harmonic genes in the mouse liver.

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Coexisting harmonic ∼7-hr and circadian rhythms in fungal clock model organism

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Knockout of output regulator CSP1 imposes circadian rhythms over ∼7-hr rhythms

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Third harmonic rhythms are a part of key cellular processes and mediated by FF-7

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7-hr genes are co-regulated in two anti-phasic clusters across genotypes and laboratories

On detection of periodicity in C-reactive protein (CRP) levels

C-reactive protein (CRP) is an acute-phase plasma protein that can be used as a biomarker for activation of the immune system. A spectral analysis of CRP level over time for patients with gynaecological tumours has been reported by Madondo et al., using a periodogram method, suggesting that there is no significant periodicity in the data. In our study, we investigate the impact of low sample number on periodogram analysis, for non-uniform sampling intervals—we conclude that data of Madondo et al. cannot rule out periodic behaviour. The search for patterns (periodic or otherwise) in the CRP time-series is of interest for providing a cue for the optimal times at which cancer therapies are best administered. In this paper we show (i) there is no evidence to rule out periodicity in CRP levels, and (ii) we provide a prescription for the minimum data sample rate required in future experiments for improved testing of a periodic CRP signal hypothesis. The analysis we provide may be used for establishing periodicity in any short time-series signal that is observed without a priori information.

Gene Regulatory Network Inference from Perturbed Time-Series Expression Data via Ordered Dynamical Expansion of Non-Steady State Actors

The reconstruction of gene regulatory networks from gene expression data has been the subject of intense research activity. A variety of models and methods have been developed to address different aspects of this important problem. However, these techniques are narrowly focused on particular biological and experimental platforms, and require experimental data that are typically unavailable and difficult to ascertain. The more recent availability of higher-throughput sequencing platforms, combined with more precise modes of genetic perturbation, presents an opportunity to formulate more robust and comprehensive approaches to gene network inference. Here, we propose a step-wise framework for identifying gene-gene regulatory interactions that expand from a known point of genetic or chemical perturbation using time series gene expression data. This novel approach sequentially identifies non-steady state genes post-perturbation and incorporates them into a growing series of low-complexity optimization problems. The governing ordinary differential equations of this model are rooted in the biophysics of stochastic molecular events that underlie gene regulation, delineating roles for both protein and RNA-mediated gene regulation. We show the successful application of our core algorithms for network inference using simulated and real datasets.

MICOP: Maximal information coefficient-based oscillation prediction to detect biological rhythms in proteomics data

BACKGROUND

Circadian rhythms comprise oscillating molecular interactions, the disruption of the homeostasis of which would cause various disorders. To understand this phenomenon systematically, an accurate technique to identify oscillating molecules among omics datasets must be developed; however, this is still impeded by many difficulties, such as experimental noise and attenuated amplitude.

RESULTS

To address these issues, we developed a new algorithm named Maximal Information Coefficient-based Oscillation Prediction (MICOP), a sine curve-matching method. The performance of MICOP in labeling oscillation or non-oscillation was compared with four reported methods using Mathews correlation coefficient (MCC) values. The numerical experiments were performed with time-series data with (1) mimicking of molecular oscillation decay, (2) high noise and low sampling frequency and (3) one-cycle data. The first experiment revealed that MICOP could accurately identify the rhythmicity of decaying molecular oscillation (MCC > 0.7). The second experiment revealed that MICOP was robust against high-level noise (MCC > 0.8) even upon the use of low-sampling-frequency data. The third experiment revealed that MICOP could accurately identify the rhythmicity of noisy one-cycle data (MCC > 0.8). As an application, we utilized MICOP to analyze time-series proteome data of mouse liver. MICOP identified that novel oscillating candidates numbered 14 and 30 for C57BL/6 and C57BL/6 J, respectively.

CONCLUSIONS

In this paper, we presented MICOP, which is an MIC-based algorithm, for predicting periodic patterns in large-scale time-resolved protein expression profiles. The performance test using artificially generated simulation data revealed that the performance of MICOP for decaying data was superior to that of the existing widely used methods. It can reveal novel findings from time-series data and may contribute to biologically significant results. This study suggests that MICOP is an ideal approach for detecting and characterizing oscillations in time-resolved omics data sets.

Turbulent flow reduces oxygen consumption in the labriform swimming shiner perch, Cymatogaster aggregata

Fish swimming energetics are often measured in laboratory environments which attempt to minimize turbulence, though turbulent flows are common in the natural environment. To test whether the swimming energetics and kinematics of shiner perch, Cymatogaster aggregata (a labriform swimmer), were affected by turbulence, two flow conditions were constructed in a swim-tunnel respirometer. A low-turbulence flow was created using a common swim-tunnel respirometry setup with a flow straightener and fine-mesh grid to minimize velocity fluctuations. A high-turbulence flow condition was created by allowing large velocity fluctuations to persist without a flow straightener or fine grid. The two conditions were tested with particle image velocimetry to confirm significantly different turbulence properties throughout a range of mean flow speeds. Oxygen consumption rate of the swimming fish increased with swimming speed and pectoral fin beat frequency in both flow conditions. Higher turbulence also caused a greater positional variability in swimming individuals (versus low-turbulence flow) at medium and high speeds. Surprisingly, fish used less oxygen in high-turbulence compared with low-turbulence flow at medium and high swimming speeds. Simultaneous measurements of swimming kinematics indicated that these reductions in oxygen consumption could not be explained by specific known flow-adaptive behaviours such as Kármán gaiting or entraining. Therefore, fish in high-turbulence flow may take advantage of the high variability in turbulent energy through time. These results suggest that swimming behaviour and energetics measured in the lab in straightened flow, typical of standard swimming respirometers, might differ from that of more turbulent, semi-natural flow conditions.

Daily variation of gene expression in diverse rat tissues

Circadian information is maintained in mammalian tissues by a cell-autonomous network of transcriptional feedback loops that have evolved to optimally regulate tissue-specific functions. An analysis of daily gene expression in different tissues, as well as an evaluation of inter-tissue circadian variability, is crucial for a systems-level understanding of this transcriptional circuitry. Affymetrix gene chip measurements of liver, muscle, adipose, and lung tissues were obtained from a rich time series light/dark experiment, involving 54 normal rats sacrificed at 18 time points within the 24-hr cycle. Our analysis revealed a high degree of circadian regulation with a variable distribution of phases among the four tissues. Interestingly, only a small number of common genes maintain circadian activity in all tissues, with many of them consisting of "core-clock" components with synchronous rhythms. Our results suggest that inter-tissue circadian variability is a critical component of homeostatic body function and is mediated by diverse signaling pathways that ultimately lead to highly tissue-specific transcription regulation.

Effect of the lymphocyte-to-monocyte ratio on the clinical outcome of chemotherapy administration in advanced melanoma patients

Skin cancer affects more individuals in the USA than any other malignancy and malignant melanoma is particularly deadly because of its metastatic potential. Melanoma has been recognized as one of the most immunogenic malignancies; therefore, understanding the mechanisms of tumor-immune interaction is key for developing more efficient treatments. As the tumor microenvironment shows an immunosuppressive action, immunotherapeutic agents promoting endogenous immune response to cancer have been tested (interleukin-2, anticytotoxic-T-lymphocyte-associated antigen 4, and antiprogrammed cell death protein 1 monoclonal antibodies) as well as combinations of cytotoxic chemotherapy agents and inhibitors of angiogenesis (taxol/carboplatin/avastin). However, clinical outcomes are variable, with only a minority of patients achieving durable complete responses. The variability of immune homeostasis, which may be more active or more tolerant at any given time, in cancer patients and the interaction of the immune system with the tumor could explain the inconsistency in clinical outcomes among these patients. Recently, the role of the lymphocyte-to-monocyte-ratio (LMR) in the peripheral blood has been investigated and has been proven to be an independent predictor of survival in different hematological malignancies and in solid tumors. In melanoma, our group has validated the significance of LMR as a predictor of relapse after resection of advanced melanoma. In this study, we examined the dynamics in the immune system of patients with advanced melanoma by performing serial multiday concentration measurements of cytokines and immune cell subsets in the peripheral blood. The analysis of outcomes of chemotherapy administration as related to LMR on the day of treatment initiation showed that progression-free survival was improved in the patients who received chemotherapy on the day when LMR was elevated.

Identification of human circadian genes based on time course gene expression profiles by using a deep learning method

Circadian genes express periodically in an approximate 24-h period and the identification and study of these genes can provide deep understanding of the circadian control which plays significant roles in human health. Although many circadian gene identification algorithms have been developed, large numbers of false positives and low coverage are still major problems in this field. In this study we constructed a novel computational framework for circadian gene identification using deep neural networks (DNN) - a deep learning algorithm which can represent the raw form of data patterns without imposing assumptions on the expression distribution. Firstly, we transformed time-course gene expression data into categorical-state data to denote the changing trend of gene expression. Two distinct expression patterns emerged after clustering of the state data for circadian genes from our manually created learning dataset. DNN was then applied to discriminate the aperiodic genes and the two subtypes of periodic genes. In order to assess the performance of DNN, four commonly used machine learning methods including k-nearest neighbors, logistic regression, naïve Bayes, and support vector machines were used for comparison. The results show that the DNN model achieves the best balanced precision and recall. Next, we conducted large scale circadian gene detection using the trained DNN model for the remaining transcription profiles. Comparing with JTK_CYCLE and a study performed by Möller-Levet et al. (doi: https://doi.org/10.1073/pnas.1217154110), we identified 1132 novel periodic genes. Through the functional analysis of these novel circadian genes, we found that the GTPase superfamily exhibits distinct circadian expression patterns and may provide a molecular switch of circadian control of the functioning of the immune system in human blood. Our study provides novel insights into both the circadian gene identification field and the study of complex circadian-driven biological control. 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.

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

Analysis of Nonstationary Time Series for Biological Rhythms Research

This article is part of a Journal of Biological Rhythms series exploring analysis and statistics topics relevant to researchers in biological rhythms and sleep research. The goal is to provide an overview of the most common issues that arise in the analysis and interpretation of data in these fields. In this article on time series analysis for biological rhythms, we describe some methods for assessing the rhythmic properties of time series, including tests of whether a time series is indeed rhythmic. Because biological rhythms can exhibit significant fluctuations in their period, phase, and amplitude, their analysis may require methods appropriate for nonstationary time series, such as wavelet transforms, which can measure how these rhythmic parameters change over time. We illustrate these methods using simulated and real time series.

Domestic chickens activate a piRNA defense against avian leukosis virus

PIWI-interacting RNAs (piRNAs) protect the germ line by targeting transposable elements (TEs) through the base-pair complementarity. We do not know how piRNAs co-evolve with TEs in chickens. Here we reported that all active TEs in the chicken germ line are targeted by piRNAs, and as TEs lose their activity, the corresponding piRNAs erode away. We observed de novo piRNA birth as host responds to a recent retroviral invasion. Avian leukosis virus (ALV) has endogenized prior to chicken domestication, remains infectious, and threatens poultry industry. Domestic fowl produce piRNAs targeting ALV from one ALV provirus that was known to render its host ALV resistant. This proviral locus does not produce piRNAs in undomesticated wild chickens. Our findings uncover rapid piRNA evolution reflecting contemporary TE activity, identify a new piRNA acquisition modality by activating a pre-existing genomic locus, and extend piRNA defense roles to include the period when endogenous retroviruses are still infectious.

DOI:http://dx.doi.org/10.7554/eLife.24695.001

Viruses called retroviruses can infect animal cells and merge their genetic information with those of the animal causing damage to the animal’s genetic blueprints. Once retroviruses are integrated into a cell they can sometimes get passed down through the generations over the centuries. Almost half of the human genetic code, for example, is made from ancient retroviruses and other foreign sequences. Over time many of these ancient viruses lost the ability to infect other cells and became trapped within cells but they can still jump out and damage the animal’s genetic code under certain circumstances. These trapped foreign sequences are called transposable elements.

Animal cells produce molecules called piRNAs to shut down transposable elements. Most piRNAs are produced from genetic information that originally came from integrated retroviruses and that has been hijacked to defend the cell, a similar strategy as Crisper system in bacteria. Domestic chickens produce piRNAs against a virus called avian leukosis virus (or ALV for short) – which commonly infects domestic fowl. The virus also infected the wild ancestors of chickens, known as red jungle fowl, but these birds do not produce piRNAs. This provides an ideal setting to study the evolution of piRNAs in an animal that is not too distantly related to humans (chickens and humans both have backbones, and are therefore both warm-blooded vertebrates).

Sun et al. examined cells from the testicles of domestic chickens and red jungle fowl as an example of the role of piRNAs in protecting genetic information in vertebrates. The investigation revealed that piRNAs against all previously trapped viruses in the chicken’s genetic code are produced in chickens to stop them from causing more damage. Sun et al. also observed the creation of piRNAs in chickens in response to ALV that had not yet become trapped in the chicken’s genetic code. Importantly, the piRNAs could control these retroviruses while they were still infectious.

The experiments also revealed that piRNAs against ALV are produced from a single copy of ALV that is found in both domestic and wild chickens. The results showed that cells can produce new piRNAs using these pre-existing viral copies within their own genetics. This illustrates that production of piRNA from existing genetic material can be activated in response to certain cues.

Further work will seek to discover how existing genetic information becomes a source of piRNAs. In the United States, 8 billion domestic chickens are consumed each year, and a better understanding of how these birds defend themselves against viral infections could increase the productivity of the poultry industry around the world. Moreover, because other viruses trapped in the chicken’s genetic code are related to similar viruses in humans, future discoveries made in this area could help to guide research that will benefit human health as well.

DOI:http://dx.doi.org/10.7554/eLife.24695.002

Trypanosoma brucei metabolism is under circadian control

The Earth's rotation forced life to evolve under cyclic day and night environmental changes. To anticipate such daily cycles, prokaryote and eukaryote free-living organisms evolved intrinsic clocks that regulate physiological and behavioural processes. Daily rhythms have been observed in organisms living within hosts, such as parasites. Whether parasites have intrinsic molecular clocks or whether they simply respond to host rhythmic physiological cues remains unknown. Here, we show that Trypanosoma brucei, the causative agent of human sleeping sickness, has an intrinsic circadian clock that regulates its metabolism in two different stages of the life cycle. We found that, in vitro, ∼10% of genes in T. brucei are expressed with a circadian rhythm. The maximum expression of these genes occurs at two different phases of the day and may depend on a post-transcriptional mechanism. Circadian genes are enriched in cellular metabolic pathways and coincide with two peaks of intracellular adenosine triphosphate concentration. Moreover, daily changes in the parasite population lead to differences in suramin sensitivity, a drug commonly used to treat this infection. These results demonstrate that parasites have an intrinsic circadian clock that is independent of the host, and which regulates parasite biology throughout the day.