Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

Dynamics in zebrafish development define transcriptomic specificity after angiogenesis inhibitor exposure

Testing for developmental toxicity is an integral part of chemical regulations. The applied tests are laborious and costly and require a large number of vertebrate test animals. To reduce animal numbers and associated costs, the zebrafish embryo was proposed as an alternative model. In this study, we investigated the potential of transcriptome analysis in the zebrafish embryo model to support the identification of potential biomarkers for key events in developmental toxicity, using the inhibition of angiogenesis as a proof of principle. Therefore, the effects on the zebrafish transcriptome after exposure to the tyrosine kinase inhibitors, sorafenib (1.3 µM and 2.4 µM) and SU4312 (1 µM, 2 µM, and 5 µM), and the putative vascular disruptor compound rotenone (25 nM and 50 nM) were analyzed. An early (2 hpf-hours post fertilization) and a late (24 hpf) exposure start with a time resolved transcriptome analysis was performed to compare the specificity and sensitivity of the responses with respect to anti-angiogenesis. We also showed that toxicodynamic responses were related to the course of the internal concentrations. To identify differentially expressed genes (DEGs) the time series data were compared by applying generalized additive models (GAMs). We observed mainly unspecific developmental toxicity in the early exposure scenario, while a specific repression of vascular related genes was only partially observed. In contrast, differential expression of vascular-related genes could be identified clearly in the late exposure scenario. Rotenone did not show angiogenesis-specific response on a transcriptomic level, indicating that the observed mild phenotype of angiogenesis inhibition may represent a secondary effect.

Bidirectional f-Divergence-Based Deep Generative Method for Imputing Missing Values in Time-Series Data

Imputing missing values in high-dimensional time-series data remains a significant challenge in statistics and machine learning. Although various methods have been proposed in recent years, many struggle with limitations and reduced accuracy, particularly when the missing rate is high. In this work, we present a novel f-divergence-based bidirectional generative adversarial imputation network, tf-BiGAIN, designed to address these challenges in time-series data imputation. Unlike traditional imputation methods, tf-BiGAIN employs a generative model to synthesize missing values without relying on distributional assumptions. The imputation process is achieved by training two neural networks, implemented using bidirectional modified gated recurrent units, with f-divergence serving as the objective function to guide optimization. Compared to existing deep learning-based methods, tf-BiGAIN introduces two key innovations. First, the use of f-divergence provides a flexible and adaptable framework for optimizing the model across diverse imputation tasks, enhancing its versatility. Second, the use of bidirectional gated recurrent units allows the model to leverage both forward and backward temporal information. This bidirectional approach enables the model to effectively capture dependencies from both past and future observations, enhancing its imputation accuracy and robustness. We applied tf-BiGAIN to analyze two real-world time-series datasets, demonstrating its superior performance in imputing missing values and outperforming existing methods in terms of accuracy and robustness.

Hypothalamic transcriptome response to simulated diel earthen pond hypoxia cycles in channel catfish (Ictalurus punctatus)

Commercial culture of channel catfish (Ictalurus punctatus) occurs in earthen ponds that are characterized by diel swings in dissolved oxygen concentration that can fall to severe levels of hypoxia, which can suppress appetite and lead to suboptimal growth. Given the significance of the hypothalamus in regulating these processes in other fishes, an investigation into the hypothalamus transcriptome was conducted to identify specific genes and expression patterns responding to hypoxia. Channel catfish in normoxic water were compared with catfish subjected to 12 h of hypoxia (20% oxygen saturation; 1.8 mg O2/L; 27°C) followed by 12 h of recovery in normoxia to mimic 24 h in a catfish aquaculture pond. Fish were sampled at 0-, 6-, 12-, 18-, and 24-h timepoints, with the 6- and 12-h samplings occurring during hypoxia. A total of 190 genes were differentially expressed during the experiment, with most occurring during hypoxia and returning to baseline values within 6 h of normoxia. Differentially expressed genes were sorted by function into Gene Ontology biological processes and revealed that most were categorized as "response to hypoxia," "sprouting angiogenesis," and "cellular response to xenobiotic stimulus." The patterns of gene expression reported here suggest that transcriptome responses to hypoxia are broad and quickly reversibly with the onset of normoxia. Although no genes commonly reported to modulate appetite were found to be differentially expressed in this experiment, several candidates were identified for future studies investigating the interplay between hypoxia and appetite in channel catfish, including adm, igfbp1a, igfbp7, and stc2b.NEW & NOTEWORTHY Channel catfish are an economically important species that experience diel episodic periods of hypoxia that can reduce appetite. This is the first study to investigate their transcriptome from the hypothalamus in a simulated 24-h span in a commercial catfish pond, with 12 h of hypoxia and 12 h of normoxia. The research revealed functional groups of genes relating to hypoxia, angiogenesis, and glycolysis as well as individual target genes possibly involved in appetite regulation.

Unveiling gene regulatory networks during cellular state transitions without linkage across time points

Time-stamped cross-sectional data, which lack linkage across time points, are commonly generated in single-cell transcriptional profiling. Many previous methods for inferring gene regulatory networks (GRNs) driving cell-state transitions relied on constructing single-cell temporal ordering. Introducing COSLIR (COvariance restricted Sparse LInear Regression), we presented a direct approach to reconstructing GRNs that govern cell-state transitions, utilizing only the first and second moments of samples between two consecutive time points. Simulations validated COSLIR's perfect accuracy in the oracle case and demonstrated its robust performance in real-world scenarios. When applied to single-cell RT-PCR and RNAseq datasets in developmental biology, COSLIR competed favorably with existing methods. Notably, its running time remained nearly independent of the number of cells. Therefore, COSLIR emerges as a promising addition to GRN reconstruction methods under cell-state transitions, bypassing the single-cell temporal ordering to enhance accuracy and efficiency in single-cell transcriptional profiling.

Mitophagy and mitochondrion-related expression profiles in response to physiological and pathological hypoxia in the corneal epithelium

To study the mitochondrial and cellular responses to physiological and pathological hypoxia, corneal epithelial cells were preconditioned under 21% O2, 8% O2 or 1% O2. The cell survival rate, mitochondrial fluorescence and mitophagy flux were quantified using flow cytometry. After RNA sequencing, gene set enrichment analysis (GSEA) was performed. When the oxygen level decreased from 21% to 8%, mitochondrial fluorescence decreased by 45% (p < 0.001), accompanied by an 80% increase in mitophagy flux (p < 0.001). When the oxygen level dropped to 1%, the cell survival rate and mitochondrial fluorescence decreased, while mitophagy flux further increased (each p < 0.001). Comparison of 1% O2 vs. 21% O2 revealed enrichment of the HYPOXIA hallmark. Most of the significantly enriched mitochondrion-related gene sets were involved in apoptosis. The corresponding foremost leading edge genes belonged to the BCL-2 family. Corneal epithelial cell fate decisions under hypoxia may involve noncanonical pathways of mitophagy.

TrACES of time: Transcriptomic analyses for the contextualization of evidential stains - Identification of RNA markers for estimating time-of-day of bloodstain deposition

Obtaining forensically relevant information beyond who deposited a biological stain on how and under which circumstances it was deposited is a question of increasing importance in forensic molecular biology. In the past few years, several studies have been produced on the potential of gene expression analysis to deliver relevant contextualizing information, e.g. on nature and condition of a stain as well as aspects of stain deposition timing. However, previous attempts to predict the time-of-day of sample deposition were all based on and thus limited by previously described diurnal oscillators. Herein, we newly approached this goal by applying current sequencing technologies and statistical methods to identify novel candidate markers for forensic time-of-day predictions from whole transcriptome analyses. To this purpose, we collected whole blood samples from ten individuals at eight different time points throughout the day, performed whole transcriptome sequencing and applied biostatistical algorithms to identify 81 mRNA markers with significantly differential expression as candidates to predict the time of day. In addition, we performed qPCR analysis to assess the characteristics of a subset of 13 candidate predictors in dried and aged blood stains. While we demonstrated the general possibility of using the selected candidate markers to predict time-of-day of sample deposition, we also observed notable variation between different donors and storage conditions, highlighting the relevance of employing accurate quantification methods in combination with robust normalization procedures.This study's results are foundational and may be built upon when developing a targeted assay for time-of-day predictions from forensic blood samples in the future.

Circadian transcriptome processing and analysis: a workflow for muscle stem cells

Circadian rhythms coordinate biological processes with Earth's 24-h daily light/dark cycle. In the last years, efforts in the field of chronobiology have sought to understand the ways in which the circadian clock controls transcription across tissues and cells. This has been supported by the development of different bioinformatic approaches that allow the identification of 24-h oscillating transcripts. This workflow aims to describe how to isolate muscle stem cells for RNA sequencing analysis from a typical circadian experiment and introduces bioinformatic tools suitable for the analysis of circadian transcriptomes.

Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics

A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery of analyte-responsive promoters. This provides a set of biomarkers that act as a proxy for the transcriptional state referred to as cell state. We construct low-dimensional models of gene expression dynamics and rank genes by their ability to capture the perturbation-specific cell state using a novel observability analysis. Using this ranking, we extract 15 analyte-responsive promoters for the organophosphate malathion in the underutilized host organism Pseudomonas fluorescens SBW25. We develop synthetic genetic reporters from each analyte-responsive promoter and characterize their response to malathion. Furthermore, we enhance malathion reporting through the aggregation of the response of individual reporters with a synthetic consortium approach, and we exemplify the library's ability to be useful outside the lab by detecting malathion in the environment. The engineered host cell, a living malathion sensor, can be optimized for use in environmental diagnostics while the developed machine learning tool can be applied to discover perturbation-inducible gene expression systems in the compendium of host organisms.

Time-Series Transcriptomic Analysis of Contrasting Rice Materials under Heat Stress Reveals a Faster Response in the Tolerant Cultivar

Short-term heat stress can affect the growth of rice (Oryza sativa L.) seedlings, subsequently decreasing yields. Determining the dynamic response of rice seedlings to short-term heat stress is highly important for accelerating research on rice heat tolerance. Here, we observed the seedling characteristics of two contrasting cultivars (T11: heat-tolerant and T15: heat-sensitive) after different durations of 42 °C heat stress. The dynamic transcriptomic changes of the two cultivars were monitored after 0 min, 10 min, 30 min, 1 h, 4 h, and 10 h of stress. The results indicate that several pathways were rapidly responding to heat stress, such as protein processing in the endoplasmic reticulum, glycerophospholipid metabolism, and plant hormone signal transduction. Functional annotation and cluster analysis of differentially expressed genes at different stress times indicate that the tolerant cultivar responded more rapidly and intensively to heat stress compared to the sensitive cultivar. The MAPK signaling pathway was found to be the specific early-response pathway of the tolerant cultivar. Moreover, by combining data from a GWAS and RNA-seq analysis, we identified 27 candidate genes. The reliability of the transcriptome data was verified using RT-qPCR on 10 candidate genes and 20 genes with different expression patterns. This study provides valuable information for short-term thermotolerance response mechanisms active at the rice seedling stage and lays a foundation for breeding thermotolerant varieties via molecular breeding.

Transcriptomic thermal plasticity underlying gonadal development in a turtle with ZZ/ZW sex chromosomes despite canalized genotypic sex determination

Understanding genome-wide responses to environmental conditions during embryogenesis is essential for discerning the evolution of developmental plasticity and canalization, two processes generating phenotypic variation targeted by natural selection. Here, we present the first comparative trajectory analysis of matched transcriptomic developmental time series from two reptiles incubated under identical conditions, a turtle with a ZZ/ZW system of genotypic sex determination (GSD), Apalone spinifera, and a turtle with temperature-dependent sex determination (TSD), Chrysemys picta. Results from our genome-wide, hypervariate gene expression analysis of sexed embryos across five developmental stages revealed that substantial transcriptional plasticity in the developing gonads can persist for >145 Myr, long after the canalization of sex determination via the evolution of sex chromosomes, while some gene-specific thermal sensitivity drifts or evolves anew. Such standing thermosensitivity represents an underappreciated evolutionary potential harbored by GSD species that may be co-opted during future adaptive shifts in developmental programing, such as a GSD to TSD reversal, if favored by ecological conditions. Additionally, we identified novel candidate regulators of vertebrate sexual development in GSD reptiles, including sex-determining candidate genes in a ZZ/ZW turtle.

Toward a hypothesis-free understanding of how phosphorylation dynamically impacts protein turnover

The turnover measurement of proteins and proteoforms has been largely facilitated by workflows coupling metabolic labeling with mass spectrometry (MS), including dynamic stable isotope labeling by amino acids in cell culture (dynamic SILAC) or pulsed SILAC (pSILAC). Very recent studies including ours have integrated themeasurement of post-translational modifications (PTMs) at the proteome level (i.e., phosphoproteomics) with pSILAC experiments in steady state systems, exploring the link between PTMs and turnover at the proteome-scale. An open question in the field is how to exactly interpret these complex datasets in a biological perspective. Here, we present a novel pSILAC phosphoproteomic dataset which was obtained during a dynamic process of cell starvation using data-independent acquisition MS (DIA-MS). To provide an unbiased "hypothesis-free" analysis framework, we developed a strategy to interrogate how phosphorylation dynamically impacts protein turnover across the time series data. With this strategy, we discovered a complex relationship between phosphorylation and protein turnover that was previously underexplored. Our results further revealed a link between phosphorylation stoichiometry with the turnover of phosphorylated peptidoforms. Moreover, our results suggested that phosphoproteomic turnover diversity cannot directly explain the abundance regulation of phosphorylation during cell starvation, underscoring the importance of future studies addressing PTM site-resolved protein turnover.

Prediction of Red Blood Cell Demand for Pediatric Patients Using a Time-Series Model: A Single-Center Study in China

Background

Red blood cells (RBCs) are an essential factor to consider for modern medicine, but planning the future collection of RBCs and supply efforts for coping with fluctuating demands is still a major challenge.

Objectives

This study aimed to explore the feasibility of the time-series model in predicting the clinical demand of RBCs for pediatric patients each month.

Methods

Our study collected clinical RBC transfusion data from years 2014 to 2019 in the National Center for Children's Health (Beijing) in China, with the goal of constructing a time-series, autoregressive integrated moving average (ARIMA) model by fitting the monthly usage of RBCs from 2014 to 2018. Furthermore, the optimal model was used to forecast the monthly usage of RBCs in 2019, and we subsequently compared the data with actual values to verify the validity of the model.

Results

The seasonal multiplicative model SARIMA (0, 1, 1) (1, 1, 0)₁₂ (normalized BIC = 8.740, R² = 0.730) was the best prediction model and could better fit and predict the monthly usage of RBCs for pediatric patients in this medical center in 2019. The model residual sequence was white noise (Ljung-Box Q₍₁₈₎ = 15.127, P > 0.05), and its autocorrelation function (ACF) and partial autocorrelation function (PACF) coefficients also fell within the 95% confidence intervals (CIs). The parameter test results were statistically significant (P < 0.05). 91.67% of the actual values were within the 95% CIs of the forecasted values of the model, and the average relative error of the forecasted and actual values was 6.44%, within 10%.

Conclusions

The SARIMA model can simulate the changing trend in monthly usage of RBCs of pediatric patients in a time-series aspect, which represents a short-term prediction model with high accuracy. The continuously revised SARIMA model may better serve the clinical environments and aid with planning for RBC demand. A clinical study including more data on blood use should be conducted in the future to confirm these results.

A simple guide to de novo transcriptome assembly and annotation

A transcriptome constructed from short-read RNA sequencing (RNA-seq) is an easily attainable proxy catalog of protein-coding genes when genome assembly is unnecessary, expensive or difficult. In the absence of a sequenced genome to guide the reconstruction process, the transcriptome must be assembled de novo using only the information available in the RNA-seq reads. Subsequently, the sequences must be annotated in order to identify sequence-intrinsic and evolutionary features in them (for example, protein-coding regions). Although straightforward at first glance, de novo transcriptome assembly and annotation can quickly prove to be challenging undertakings. In addition to familiarizing themselves with the conceptual and technical intricacies of the tasks at hand and the numerous pre- and post-processing steps involved, those interested must also grapple with an overwhelmingly large choice of tools. The lack of standardized workflows, fast pace of development of new tools and techniques and paucity of authoritative literature have served to exacerbate the difficulty of the task even further. Here, we present a comprehensive overview of de novo transcriptome assembly and annotation. We discuss the procedures involved, including pre- and post-processing steps, and present a compendium of corresponding tools.

Integration of segmented regression analysis with weighted gene correlation network analysis identifies genes whose expression is remodeled throughout physiological aging in mouse tissues

Gene expression alterations occurring with aging have been described for a multitude of species, organs, and cell types. However, most of the underlying studies rely on static comparisons of mean gene expression levels between age groups and do not account for the dynamics of gene expression throughout the lifespan. These studies also tend to disregard the pairwise relationships between gene expression profiles, which may underlie commonly altered pathways and regulatory mechanisms with age. To overcome these limitations, we have combined segmented regression analysis with weighted gene correlation network analysis (WGCNA) to identify high-confidence signatures of aging in the brain, heart, liver, skeletal muscle, and pancreas of C57BL/6 mice in a publicly available RNA-Seq dataset (GSE132040). Functional enrichment analysis of the overlap of genes identified in both approaches showed that immune- and inflammation-related responses are prominently altered in the brain and the liver, while in the heart and the muscle, aging affects amino and fatty acid metabolism, and tissue regeneration, respectively, which reflects an age-related global loss of tissue function. We also explored sexual dimorphism in the aging mouse transcriptome and found the liver and the muscle to have the most pronounced gender differences in gene expression throughout the lifespan, particularly in proteostasis-related pathways. While the data showed little overlap among the age-dysregulated genes between tissues, aging triggered common biological processes in distinct tissues, which we highlight as important features of murine tissue physiological aging.