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Abstract Functional enrichment analysis of genome-wide association study (GWAS)-summary statistics has suggested that CD4+ T-cells play an important role in asthma pathogenesis. Despite this, CD4+ T-cells are under-represented in asthma transcriptome studies. To fill the gap, 3'-RNA-Seq was used to generate gene expression data on CD4+ T-cells (isolated within 2 h from collection) from peripheral blood from participants with well-controlled asthma (n = 32) and healthy controls (n = 11). Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify sets of co-expressed genes (modules) associated with the asthma phenotype. We identified three modules associated with asthma, which are strongly enriched for GWAS-identified asthma genes, antigen processing/presentation and immune response to viral infections. Through integration of publicly available eQTL and GWAS summary statistics (colocalisation), and protein–protein interaction (PPI) data, we identified PTPRC, a potential druggable target, as a putative master regulator of the asthma gene-expression profiles. Using a co-expression network approach, with integration of external genetic and PPI data, we showed that CD4+ T-cells from peripheral blood from asthmatics have different expression profiles, albeit small in magnitude, compared to healthy controls, for sets of genes involved in immune response to viral infections (upregulated) and antigen processing/presentation (downregulated).

Abstract Patterns of human immune responses to viruses and bacteria and how this impacts risk of infections or onset/exacerbation of chronic respiratory diseases are poorly understood. In a population-based birth cohort, we measured peripheral blood mononuclear cell responses (28 cytokines) to respiratory viruses and bacteria, Toll-like receptor ligands and phytohemagglutinin, in 307 children. Cytokine responses were highly variable with > 1000-fold differences between children. Machine learning revealed clear distinction between virus-associated and bacteria-associated stimuli. Cytokines clustered into three functional groups (anti-viral, pro-inflammatory and T-cell derived). To investigate mechanisms potentially explaining such variable responses, we investigated cytokine Quantitative Trait Loci (cQTLs) of IL-6 responses to bacteria and identified nine (eight novel) loci. Our integrative approach describing stimuli, cytokines and children as variables revealed robust immunologically and microbiologically plausible clustering, providing a framework for a greater understanding of host-responses to infection, including novel genetic associations with respiratory disease.

Summary: Dosage compensation, the balancing of X-linked gene expression between sexes and to the autosomes, is critical to an organism’s fitness and survival. In Drosophila, dosage compensation involves hypertranscription of the male X chromosome. Here, we use quantitative live imaging and modeling at single-cell resolution to study X chromosome dosage compensation in Drosophila. We show that the four X chromosome genes studied undergo transcriptional bursting in male and female embryos. Mechanistically, our data reveal that transcriptional upregulation of male X chromosome genes is primarily mediated by a higher RNA polymerase II initiation rate and burst amplitude across the expression domain. In contrast, burst frequency is spatially modulated in nuclei within the expression domain in response to different transcription factor concentrations to tune the transcriptional response. Together, these data show how the local and global regulation of distinct burst parameters can establish the complex transcriptional outputs underpinning developmental patterning.

Regulation of mRNA degradation is critical for a diverse array of cellular processes and developmental cell fate decisions. Many methods for determining mRNA half-lives rely on transcriptional inhibition or metabolic labelling. Here, we use a non-invasive method for estimating half-lives for hundreds of mRNAs in the early Drosophila embryo. This approach uses the intronic and exonic reads from a total RNA-seq time series and Gaussian process regression to model the dynamics of premature and mature mRNAs. We show how regulation of mRNA stability is used to establish a range of mature mRNA dynamics during embryogenesis, despite shared transcription profiles. Using single-molecule imaging, we provide evidence that, for the mRNAs tested, there is a correlation between short half-life and mRNA association with P-bodies. Moreover, we detect an enrichment of mRNA 3' ends in P-bodies in the early embryo, consistent with 5' to 3' degradation occurring in P-bodies for at least a subset of mRNAs. We discuss our findings in relation to recently published data suggesting that the primary function of P-bodies in other biological contexts is mRNA storage.

Abstract The mechanisms explaining excess morbidity and mortality in respiratory infections among males are poorly understood. Innate immune responses are critical in protection against respiratory virus infections. We hypothesised that innate immune responses to respiratory viruses may be deficient in males. We stimulated peripheral blood mononuclear cells from 345 participants at age 16 years in a population-based birth cohort with three live respiratory viruses (rhinoviruses A16 and A1, and respiratory syncytial virus) and two viral mimics (R848 and CpG-A, to mimic responses to SARS-CoV-2) and investigated sex differences in interferon (IFN) responses. IFN-α responses to all viruses and stimuli were 1.34–2.06-fold lower in males than females (P = 0.018 −

Damage to the lung epithelium is a unifying feature of disease caused by the saprophytic fungus Aspergillus fumigatus. However, the mechanistic basis and the regulatory control of such damage is poorly characterized. Previous studies have identified A. fumigatus mediated pathogenesis as occurring at early (≤ 16 hours) or late (>16 hours) phases of the fungal interaction with epithelial cells, and respectively involve direct contact with the host cell or the action of soluble factors produced by mature fungal hyphae. Both early and late phases of epithelial damage have been shown to be subject to genetic regulation by the pH-responsive transcription factor PacC. This study sought to determine whether other transcriptional regulators play a role in modulating epithelial damage. In particular, whether the early and late phases of epithelial damage are governed by same or distinct regulators. Furthermore, whether processes such as spore uptake and hyphal adhesion, that have previously been documented to promote epithelial damage, are governed by the same cohorts of epithelial regulators. Using 479 strains from the recently constructed library of A. fumigatus transcription factor null mutants, two high-throughput screens assessing epithelial cell detachment and epithelial cell lysis were conducted. A total of 17 transcription factor mutants were found to exhibit reproducible deficits in epithelial damage causation. Of these, 10 mutants were defective in causing early phase damage via epithelial detachment and 8 mutants were defective in causing late phase damage via epithelial lysis. Remarkably only one transcription factor, PacC, was required for causation of both phases of epithelial damage. The 17 mutants exhibited varied and often unique phenotypic profiles with respect to fitness, epithelial adhesion, cell wall defects, and rates of spore uptake by epithelial cells. Strikingly, 9 out of 10 mutants deficient in causing early phase damage also exhibited reduced rates of hyphal extension, and culture supernatants of 7 out of 8 mutants deficient in late phase damage were significantly less cytotoxic. Our study delivers the first high-level overview of A. fumigatus regulatory genes governing lung epithelial damage, suggesting highly coordinated genetic orchestration of host-damaging activities that govern epithelial damage in both space and time.

Although genome-wide association studies have identified genetic variation contributing to disease risk, assigning causal genes is challenging. Here, the authors generate ATAC-seq, Hi-C, Capture Hi-C and RNA-seq data in stimulated CD4+ T cells to identify functional enhancers and demonstrate interactions of expression quantitative trait loci with target genes in rheumatoid arthritis.

Abstract Background Oscillatory genes, with periodic expression at the mRNA and/or protein level, have been shown to play a pivotal role in many biological contexts. However, with the exception of the circadian clock and cell cycle, only a few such genes are known. Detecting oscillatory genes from snapshot single-cell experiments is a challenging task due to the lack of time information. Oscope is a recently proposed method to identify co-oscillatory gene pairs using single-cell RNA-seq data. Although promising, the current implementation of Oscope does not provide a principled statistical criterion for selecting oscillatory genes. Results We improve the optimisation scheme underlying Oscope and provide a well-calibrated non-parametric hypothesis test to select oscillatory genes at a given FDR threshold. We evaluate performance on synthetic data and three real datasets and show that our approach is more sensitive than the original Oscope formulation, discovering larger sets of known oscillators while avoiding the need for less interpretable thresholds. We also describe how our proposed pseudo-time estimation method is more accurate in recovering the true cell order for each gene cluster while requiring substantially less computation time than the extended nearest insertion approach. Conclusions OscoNet is a robust and versatile approach to detect oscillatory gene networks from snapshot single-cell data addressing many of the limitations of the original Oscope method.

The single-cell gene expression changes during spinal cord formation and neurogenesis in mice are unclear. Here, the authors use a HES5 reporter to live image, then mathematically model, oscillations in single cells in presumed progenitors and neurons of the developing spinal cord.

Abstract Objective We applied systems biology approaches to investigate circadian rhythmicity in rheumatoid arthritis (RA). Methods We recruited adults (age 16–80 years old) with a clinical diagnosis of RA (active disease [DAS28 > 3.2]). Sleep profiles were determined before inpatient measurements of saliva, serum, and peripheral blood mononuclear leukocytes (PBML). Transcriptome and proteome analyses were carried out by RNA-SEQ and LC-MS/MS. Serum samples were analysed by targeted lipidomics, along with serum from mouse collagen induced-arthritis (CIA). Bioinformatic analysis identified RA-specific gene networks and rhythmic processes differing between healthy and RA. Results RA caused greater time-of-day variation in PBML gene expression, and ex vivo stimulation identified a time-of-day-specific RA transcriptome. We found increased phospho-STAT3 in RA patients, and some targets, including phospho-ATF2, acquired time-of-day variation in RA. Serum ceramides also gained circadian rhythmicity in RA, which was also seen in mouse experimental arthritis, resulting from gain in circadian rhythmicity of hepatic ceramide synthases. Conclusion RA drives a gain in circadian rhythmicity, both in immune cells, and systemically. The coupling of distant timing information to ceramide synthesis and joint inflammation points to a systemic re-wiring of the circadian repertoire. Circadian reprogramming in response to chronic inflammation has implications for inflammatory co-morbidities and time-of-day therapeutics.

Gene expression programs determine cell fate in embryonic development and their dysregulation results in disease. Transcription factors (TFs) control gene expression by binding to enhancers, but how TFs select and activate their target enhancers is still unclear. HOX TFs share conserved homeodomains with highly similar sequence recognition properties, yet they impart the identity of different animal body parts. To understand how HOX TFs control their specific transcriptional programs in vivo, we compared HOXA2 and HOXA3 binding profiles in the mouse embryo. HOXA2 and HOXA3 directly cooperate with TALE TFs and selectively target different subsets of a broad TALE chromatin platform. Binding of HOX and tissue-specific TFs convert low affinity TALE binding into high confidence, tissue-specific binding events, which bear the mark of active enhancers. We propose that HOX paralogs, alone and in combination with tissue-specific TFs, generate tissue-specific transcriptional outputs by modulating the activity of TALE TFs at selected enhancers.

Molecular sequences carry information. Analysis of sequence conservation between homologous loci is a proven approach with which to explore the information content of molecular sequences. This is often done using multiple sequence alignments to support comparisons between homologous loci. These methods therefore rely on sufficient underlying sequence similarity with which to construct a representative alignment. Here we describe a method using a formal metric of information, surprisal, to analyse biological sub-sequences without alignment constraints. We applied our model to the genomes of five different species to reveal similar patterns across a panel of eukaryotes. As the surprisal of a sub-sequence is inversely proportional to its occurrence within the genome, the optimal size of the sub-sequences was selected for each species under consideration. With the model optimized, we found a strong correlation between surprisal and CG dinucleotide usage. The utility of our model was tested by examining the sequences of genes known to undergo splicing. We demonstrate that our model can identify biological features of interest such as known donor and acceptor sites. Analysis across all annotated coding exon junctions in Homo sapiens reveals the information content of coding exons to be greater than the surrounding intron regions, a consequence of increased suppression of the CG dinucleotide in intronic space. Sequences within coding regions proximal to exon junctions exhibited novel patterns within DNA and coding mRNA that are not a function of the encoded amino acid sequence. Our findings are consistent with the presence of secondary information encoding features such as DNA and RNA binding sites, multiplexed through the coding sequence and independent of the information required to define the corresponding amino-acid sequence. We conclude that surprisal provides a complementary methodology with which to locate regions of interest in the genome, particularly in situations that lack an appropriate multiple sequence alignment.

Abstract High-throughput single-cell gene expression experiments can be used to uncover branching dynamics in cell populations undergoing differentiation through pseudotime methods. We develop the branching Gaussian process (BGP), a non-parametric model that is able to identify branching dynamics for individual genes and provide an estimate of branching times for each gene with an associated credible region. We demonstrate the effectiveness of our method on simulated data, a single-cell RNA-seq haematopoiesis study and mouse embryonic stem cells generated using droplet barcoding. The method is robust to high levels of technical variation and dropout, which are common in single-cell data.

We have developed a machine learning approach to predict stimulation-dependent enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. The occupancy of estrogen receptor alpha (ERα), RNA polymerase (Pol II) and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. A Bayesian classifier was developed which uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features to predict interactions. This method was trained using experimentally determined interactions from the same system and was shown to achieve much higher precision than predictions based on the genomic proximity of nearest ERα binding. We use the method to identify a genome-wide confident set of ERα target genes and their regulatory enhancers genome-wide. Validation with publicly available GRO-Seq data demonstrates that our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ERα binding proximity alone.

Multiple biological processes are driven by oscillatory gene expression at different time scales. Pulsatile dynamics are thought to be widespread, and single-cell live imaging of gene expression has lead to a surge of dynamic, possibly oscillatory, data for different gene networks. However, the regulation of gene expression at the level of an individual cell involves reactions between finite numbers of molecules, and this can result in inherent randomness in expression dynamics, which blurs the boundaries between aperiodic fluctuations and noisy oscillators. This underlies a new challenge to the experimentalist because neither intuition nor pre-existing methods work well for identifying oscillatory activity in noisy biological time series. Thus, there is an acute need for an objective statistical method for classifying whether an experimentally derived noisy time series is periodic. Here, we present a new data analysis method that combines mechanistic stochastic modelling with the powerful methods of non-parametric regression with Gaussian processes. Our method can distinguish oscillatory gene expression from random fluctuations of non-oscillatory expression in single-cell time series, despite peak-to-peak variability in period and amplitude of single-cell oscillations. We show that our method outperforms the Lomb-Scargle periodogram in successfully classifying cells as oscillatory or non-oscillatory in data simulated from a simple genetic oscillator model and in experimental data. Analysis of bioluminescent live-cell imaging shows a significantly greater number of oscillatory cells when luciferase is driven by a Hes1 promoter (10/19), which has previously been reported to oscillate, than the constitutive MoMuLV 5' LTR (MMLV) promoter (0/25). The method can be applied to data from any gene network to both quantify the proportion of oscillating cells within a population and to measure the period and quality of oscillations. It is publicly available as a MATLAB package.

Recent studies suggest that cells make stochastic choices with respect to differentiation or division. However, the molecular mechanism underlying such stochasticity is unknown. We previously proposed that the timing of vertebrate neuronal differentiation is regulated by molecular oscillations of a transcriptional repressor, HES1, tuned by a post-transcriptional repressor, miR-9. Here, we computationally model the effects of intrinsic noise on the Hes1/miR-9 oscillator as a consequence of low molecular numbers of interacting species, determined experimentally. We report that increased stochasticity spreads the timing of differentiation in a population, such that initially equivalent cells differentiate over a period of time. Surprisingly, inherent stochasticity also increases the robustness of the progenitor state and lessens the impact of unequal, random distribution of molecules at cell division on the temporal spread of differentiation at the population level. This advantageous use of biological noise contrasts with the view that noise needs to be counteracted.

We consider the problem of detecting and quantifying the periodic component of a function given noise-corrupted observations of a limited number of input/output tuples. Our approach is based on Gaussian process regression, which provides a flexible non-parametric framework for modelling periodic data. We introduce a novel decomposition of the covariance function as the sum of periodic and aperiodic kernels. This decomposition allows for the creation of sub-models which capture the periodic nature of the signal and its complement. To quantify the periodicity of the signal, we derive a periodicity ratio which reflects the uncertainty in the fitted sub-models. Although the method can be applied to many kernels, we give a special emphasis to the Matérn family, from the expression of the reproducing kernel Hilbert space inner product to the implementation of the associated periodic kernels in a Gaussian process toolkit. The proposed method is illustrated by considering the detection of periodically expressed genes in the arabidopsis genome.

An intronic GGGGCC-repeat expansion of C9ORF72 is the most common genetic variant of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia. The mechanism of neurodegeneration is unknown, but a direct effect on RNA processing mediated by RNA foci transcribed from the repeat sequence has been proposed.Gene expression profiling utilised total RNA extracted from motor neurons and lymphoblastoid cell lines derived from human ALS patients, including those with an expansion of C9ORF72, and controls. In lymphoblastoid cell lines, expansion length and the frequency of sense and antisense RNA foci was also examined.Gene level analysis revealed a number of differentially expressed networks and both cell types exhibited dysregulation of a network functionally enriched for genes encoding 'RNA splicing' proteins. There was a significant overlap of these genes with an independently generated list of GGGGCC-repeat protein binding partners. At the exon level, in lymphoblastoid cells derived from C9ORF72-ALS patients splicing consistency was lower than in lines derived from non-C9ORF72 ALS patients or controls; furthermore splicing consistency was lower in samples derived from patients with faster disease progression. Frequency of sense RNA foci showed a trend towards being higher in lymphoblastoid cells derived from patients with shorter survival, but there was no detectable correlation between disease severity and DNA expansion length.Up-regulation of genes encoding predicted binding partners of the C9ORF72 expansion is consistent with an attempted compensation for sequestration of these proteins. A number of studies have analysed changes in the transcriptome caused by C9ORF72 expansion, but to date findings have been inconsistent. As a potential explanation we suggest that dynamic sequestration of RNA processing proteins by RNA foci might lead to a loss of splicing consistency; indeed in our samples measurement of splicing consistency correlates with disease severity.

Gene transcription mediated by RNA polymerase II (pol-II) is a key step in gene expression. The dynamics of pol-II moving along the transcribed region influence the rate and timing of gene expression. In this work, we present a probabilistic model of transcription dynamics which is fitted to pol-II occupancy time course data measured using ChIP-Seq. The model can be used to estimate transcription speed and to infer the temporal pol-II activity profile at the gene promoter. Model parameters are estimated using either maximum likelihood estimation or via Bayesian inference using Markov chain Monte Carlo sampling. The Bayesian approach provides confidence intervals for parameter estimates and allows the use of priors that capture domain knowledge, e.g. the expected range of transcription speeds, based on previous experiments. The model describes the movement of pol-II down the gene body and can be used to identify the time of induction for transcriptionally engaged genes. By clustering the inferred promoter activity time profiles, we are able to determine which genes respond quickly to stimuli and group genes that share activity profiles and may therefore be co-regulated. We apply our methodology to biological data obtained using ChIP-seq to measure pol-II occupancy genome-wide when MCF-7 human breast cancer cells are treated with estradiol (E2). The transcription speeds we obtain agree with those obtained previously for smaller numbers of genes with the advantage that our approach can be applied genome-wide. We validate the biological significance of the pol-II promoter activity clusters by investigating cluster-specific transcription factor binding patterns and determining canonical pathway enrichment. We find that rapidly induced genes are enriched for both estrogen receptor alpha (ERα) and FOXA1 binding in their proximal promoter regions.

Every protein has a biosynthetic cost to the cell based on the synthesis of its constituent amino acids. In order to optimise growth and reproduction, natural selection is expected, where possible, to favour the use of proteins whose constituents are cheaper to produce, as reduced biosynthetic cost may confer a fitness advantage to the organism. Quantifying the cost of amino acid biosynthesis presents challenges, since energetic requirements may change across different cellular and environmental conditions. We developed a systems biology approach to estimate the cost of amino acid synthesis based on genome-scale metabolic models and investigated the effects of the cost of amino acid synthesis on Saccharomyces cerevisiae gene expression and protein evolution. First, we used our two new and six previously reported measures of amino acid cost in conjunction with codon usage bias, tRNA gene number and atomic composition to identify which of these factors best predict transcript and protein levels. Second, we compared amino acid cost with rates of amino acid substitution across four species in the genus Saccharomyces. Regardless of which cost measure is used, amino acid biosynthetic cost is weakly associated with transcript and protein levels. In contrast, we find that biosynthetic cost and amino acid substitution rates show a negative correlation, but for only a subset of cost measures. In the economy of the yeast cell, we find that the cost of amino acid synthesis plays a limited role in shaping transcript and protein expression levels compared to that of translational optimisation. Biosynthetic cost does, however, appear to affect rates of amino acid evolution in Saccharomyces, suggesting that expensive amino acids may only be used when they have specific structural or functional roles in protein sequences. However, as there appears to be no single currency to compute the cost of amino acid synthesis across all cellular and environmental conditions, we conclude that a systems approach is necessary to unravel the full effects of amino acid biosynthetic cost in complex biological systems.

Fungi and oomycetes are the causal agents of many of the most serious diseases of plants. Here we report a detailed comparative analysis of the genome sequences of thirty-six species of fungi and oomycetes, including seven plant pathogenic species, that aims to explore the common genetic features associated with plant disease-causing species. The predicted translational products of each genome have been clustered into groups of potential orthologues using Markov Chain Clustering and the data integrated into the e-Fungi object-oriented data warehouse (http://www.e-fungi.org.uk/). Analysis of the species distribution of members of these clusters has identified proteins that are specific to filamentous fungal species and a group of proteins found only in plant pathogens. By comparing the gene inventories of filamentous, ascomycetous phytopathogenic and free-living species of fungi, we have identified a set of gene families that appear to have expanded during the evolution of phytopathogens and may therefore serve important roles in plant disease. We have also characterised the predicted set of secreted proteins encoded by each genome and identified a set of protein families which are significantly over-represented in the secretomes of plant pathogenic fungi, including putative effector proteins that might perturb host cell biology during plant infection. The results demonstrate the potential of comparative genome analysis for exploring the evolution of eukaryotic microbial pathogenesis.