Infection and inflammation stimulate expansion of a CD74+ Paneth cell subset to regulate disease progression.

Paneth cells (PCs), a specialized secretory cell type in the small intestine, are increasingly recognized as having an essential role in host responses to microbiome and environmental stresses. Whether and how commensal and pathogenic microbes modify PC composition to modulate inflammation remain unclear. Using newly developed PC-reporter mice under conventional and gnotobiotic conditions, we determined PC transcriptomic heterogeneity in response to commensal and invasive microbes at single cell level. Infection expands the pool of CD74+ PCs, whose number correlates with auto or allogeneic inflammatory disease progressions in mice. Similar correlation was found in human inflammatory disease tissues. Infection-stimulated cytokines increase production of reactive oxygen species (ROS) and expression of a PC-specific mucosal pentraxin (Mptx2) in activated PCs. A PC-specific ablation of MyD88 reduced CD74+ PC population, thus ameliorating pathogen-induced systemic disease. A similar phenotype was also observed in mice lacking Mptx2. Thus, infection stimulates expansion of a PC subset that influences disease progression.

Benchmarking cell-type clustering methods for spatially resolved transcriptomics data.

Spatially resolved transcriptomics technologies enable the measurement of transcriptome information while retaining the spatial context at the regional, cellular or sub-cellular level. While previous computational methods have relied on gene expression information alone for clustering single-cell populations, more recent methods have begun to leverage spatial location and histology information to improve cell clustering and cell-type identification. In this study, using seven semi-synthetic datasets with real spatial locations, simulated gene expression and histology images as well as ground truth cell-type labels, we evaluate 15 clustering methods based on clustering accuracy, robustness to data variation and input parameters, computational efficiency, and software usability. Our analysis demonstrates that even though incorporating the additional spatial and histology information leads to increased accuracy in some datasets, it does not consistently improve clustering compared with using only gene expression data. Our results indicate that for the clustering of spatial transcriptomics data, there are still opportunities to enhance the overall accuracy and robustness by improving information extraction and feature selection from spatial and histology data.

Differential variability analysis of single-cell gene expression data.

The advent of single-cell RNA sequencing (scRNA-seq) technologies has enabled gene expression profiling at the single-cell resolution, thereby enabling the quantification and comparison of transcriptional variability among individual cells. Although alterations in transcriptional variability have been observed in various biological states, statistical methods for quantifying and testing differential variability between groups of cells are still lacking. To identify the best practices in differential variability analysis of single-cell gene expression data, we propose and compare 12 statistical pipelines using different combinations of methods for normalization, feature selection, dimensionality reduction and variability calculation. Using high-quality synthetic scRNA-seq datasets, we benchmarked the proposed pipelines and found that the most powerful and accurate pipeline performs simple library size normalization, retains all genes in analysis and uses denSNE-based distances to cluster medoids as the variability measure. By applying this pipeline to scRNA-seq datasets of COVID-19 and autism patients, we have identified cellular variability changes between patients with different severity status or between patients and healthy controls.

Expression activation of over 70% of Chlamydia trachomatis genes during the first hour of infection.

The obligate intracellular bacterium Chlamydia has a unique developmental cycle that alternates between two contrasting cell types. With a hardy envelope and highly condensed genome, the small elementary body (EB) maintains limited metabolic activities yet survives in extracellular environments and is infectious. After entering host cells, EBs differentiate into larger and proliferating reticulate bodies (RBs). Progeny EBs are derived from RBs in late developmental stages and eventually exit host cells. How expression of the chlamydial genome consisting of nearly 1,000 genes governs the chlamydial developmental cycle is unclear. A previous microarray study identified only 29 Chlamydia trachomatis immediate early genes, defined as genes with increased expression during the first hour postinoculation in cultured cells. In this study, we performed more sensitive RNA sequencing (RNA-Seq) analysis for C. trachomatis cultures with high multiplicities of infection. Remarkably, we observed well over 700 C. trachomatis genes that underwent 2- to 900-fold activation within 1 hour postinoculation. Quantitative reverse transcription real-time PCR analysis was further used to validate the activated expression of a large subset of the genes identified by RNA-Seq. Importantly, our results demonstrate that the immediate early transcriptome is over 20 times more extensive than previously realized. Gene ontology analysis indicates that the activated expression spans all functional categories. We conclude that over 70% of C. trachomatis genes are activated in EBs almost immediately upon entry into host cells, thus implicating their importance in initiating rapid differentiation into RBs and establishing an intracellular niche conducive with chlamydial development and growth.

Analyzing RNA-Seq data from Chlamydia with super broad transcriptomic activation: challenges, solutions, and implications for other systems.

BACKGROUND: RNA sequencing (RNA-Seq) offers profound insights into the complex transcriptomes of diverse biological systems. However, standard differential expression analysis pipelines based on DESeq2 and edgeR encounter challenges when applied to the immediate early transcriptomes of Chlamydia spp., obligate intracellular bacteria. These challenges arise from their reliance on assumptions that do not hold in scenarios characterized by extensive transcriptomic activation and limited repression. RESULTS: Standard analyses using unique chlamydial RNA-Seq reads alone identify nearly 300 upregulated and about 300 downregulated genes, significantly deviating from actual RNA-Seq read trends. By incorporating both chlamydial and host reads or adjusting for total sequencing depth, the revised normalization methods each detected over 700 upregulated genes and 30 or fewer downregulated genes, closely aligned with observed RNA-Seq data. Further validation through qRT-PCR analysis confirmed the effectiveness of these adjusted approaches in capturing the true extent of transcriptomic activation during the immediate early phase of chlamydial infection. CONCLUSIONS: This study highlights the limitations of standard RNA-Seq analysis tools in scenarios with extensive transcriptomic activation, such as in Chlamydia spp. during early infection. Our revised normalization methods, incorporating host reads or total sequencing depth, provide a more accurate representation of gene expression dynamics. These approaches may inform similar adjustments in other systems with unbalanced gene expression dynamics, enhancing the accuracy of transcriptomic analysis.

Metabolomic and Transcriptomic Correlative Analyses in Germ-Free Mice Link Lacticaseibacillus rhamnosus GG-Associated Metabolites to Host Intestinal Fatty Acid Metabolism and ß-Oxidation.

Intestinal microbiota confers susceptibility to diet-induced obesity, yet many probiotic species that synthesize tryptophan (trp) actually attenuate this effect, although the underlying mechanisms are unclear. We monocolonized germ-free mice with a widely consumed probiotic Lacticaseibacillus rhamnosus GG (LGG) under trp-free or -sufficient dietary conditions. We obtained untargeted metabolomics from the mouse feces and serum using liquid chromatography-mass spectrometry and obtained intestinal transcriptomic profiles via bulk-RNA sequencing. When comparing LGG-monocolonized mice with germ-free mice, we found a synergy between LGG and dietary trp in markedly promoting the transcriptome of fatty acid metabolism and ß-oxidation. Upregulation was specific and was not observed in transcriptomes of trp-fed conventional mice and mice monocolonized with Ruminococcus gnavus. Metabolomics showed that fecal and serum metabolites were also modified by LGG-host-trp interaction. We developed an R-Script-based MEtabolome-TRanscriptome Correlation Analysis algorithm and uncovered LGG- and trp-dependent metabolites that were positively or negatively correlated with fatty acid metabolism and ß-oxidation gene networks. This high-throughput metabolome-transcriptome correlation strategy can be used in similar investigations to reveal potential interactions between specific metabolites and functional or disease-related transcriptomic networks.

scAce: an adaptive embedding and clustering method for single-cell gene expression data.

MOTIVATION: Since the development of single-cell RNA sequencing (scRNA-seq) technologies, clustering analysis of single-cell gene expression data has been an essential tool for distinguishing cell types and identifying novel cell types. Even though many methods have been available for scRNA-seq clustering analysis, the majority of them are constrained by the requirement on predetermined cluster numbers or the dependence on selected initial cluster assignment. RESULTS: In this article, we propose an adaptive embedding and clustering method named scAce, which constructs a variational autoencoder to simultaneously learn cell embeddings and cluster assignments. In the scAce method, we develop an adaptive cluster merging approach which achieves improved clustering results without the need to estimate the number of clusters in advance. In addition, scAce provides an option to perform clustering enhancement, which can update and enhance cluster assignments based on previous clustering results from other methods. Based on computational analysis of both simulated and real datasets, we demonstrate that scAce outperforms state-of-the-art clustering methods for scRNA-seq data, and achieves better clustering accuracy and robustness. AVAILABILITY AND IMPLEMENTATION: The scAce package is implemented in python 3.8 and is freely available from https://github.com/sldyns/scAce.

Selecting gene features for unsupervised analysis of single-cell gene expression data.

Single-cell RNA sequencing (scRNA-seq) technologies facilitate the characterization of transcriptomic landscapes in diverse species, tissues, and cell types with unprecedented molecular resolution. In order to evaluate various biological hypotheses using high-dimensional single-cell gene expression data, most computational and statistical methods depend on a gene feature selection step to identify genes with high biological variability and reduce computational complexity. Even though many gene selection methods have been developed for scRNA-seq analysis, there lacks a systematic comparison of the assumptions, statistical models, and selection criteria used by these methods. In this article, we summarize and discuss 17 computational methods for selecting gene features in unsupervised analysis of single-cell gene expression data, with unified notations and statistical frameworks. Our discussion provides a useful summary to help practitioners select appropriate methods based on their assumptions and applicability, and to assist method developers in designing new computational tools for unsupervised learning of scRNA-seq data.

Phitest for analyzing the homogeneity of single-cell populations.

MOTIVATION: Single-cell RNA sequencing technologies facilitate the characterization of transcriptomic landscapes in diverse species, tissues and cell types with unprecedented molecular resolution. In order to better understand animal development, physiology, and pathology, unsupervised clustering analysis is often used to identify relevant cell populations. Although considerable progress has been made in terms of clustering algorithms in recent years, it remains challenging to evaluate the quality of the inferred single-cell clusters, which can greatly impact downstream analysis and interpretation. RESULTS: We propose a bioinformatics tool named Phitest to analyze the homogeneity of single-cell populations. Phitest is able to distinguish between homogeneous and heterogeneous cell populations, providing an objective and automatic method to optimize the performance of single-cell clustering analysis. AVAILABILITY AND IMPLEMENTATION: The PhitestR package is freely available on both Github (https://github.com/Vivianstats/PhitestR) and the Comprehensive R Archive Network (CRAN). There is no new genomic data associated with this article. Published data used in the analysis are described in detail in the Supplementary Data. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Temporal and structural patterns of hepatitis B virus integrations in hepatocellular carcinoma.

Chronic infection of hepatitis B virus (HBV) is the major cause of hepatocellular carcinoma (HCC). Notably, 90% of HBV-positive HCC cases exhibit detectable HBV integrations, hinting at the potential early entanglement of these viral integrations in tumorigenesis and their subsequent oncogenic implications. Nevertheless, the precise chronology of integration events during HCC tumorigenesis, alongside their sequential structural patterns, has remained elusive thus far. In this study, we applied whole-genome sequencing to multiple biopsies extracted from six HBV-positive HCC cases. Through this approach, we identified point mutations and viral integrations, offering a blueprint for the intricate tumor phylogeny of these samples. The emergent narrative paints a rich tapestry of diverse evolutionary trajectories characterizing the analyzed tumors. We uncovered oncogenic integration events in some samples that appear to happen before and during the initiation stage of tumor development based on their locations in reconstituted trajectories. Furthermore, we conducted additional long-read sequencing of selected samples and unveiled integration-bridged chromosome rearrangements and tandem repeats of the HBV sequence within integrations. In summary, this study revealed premalignant oncogenic and sequential complex integrations and highlighted the contributions of HBV integrations to HCC development and genome instability.

AIDE: annotation-assisted isoform discovery with high precision.

Genome-wide accurate identification and quantification of full-length mRNA isoforms is crucial for investigating transcriptional and posttranscriptional regulatory mechanisms of biological phenomena. Despite continuing efforts in developing effective computational tools to identify or assemble full-length mRNA isoforms from second-generation RNA-seq data, it remains a challenge to accurately identify mRNA isoforms from short sequence reads owing to the substantial information loss in RNA-seq experiments. Here, we introduce a novel statistical method, annotation-assisted isoform discovery (AIDE), the first approach that directly controls false isoform discoveries by implementing the testing-based model selection principle. Solving the isoform discovery problem in a stepwise and conservative manner, AIDE prioritizes the annotated isoforms and precisely identifies novel isoforms whose addition significantly improves the explanation of observed RNA-seq reads. We evaluate the performance of AIDE based on multiple simulated and real RNA-seq data sets followed by PCR-Sanger sequencing validation. Our results show that AIDE effectively leverages the annotation information to compensate the information loss owing to short read lengths. AIDE achieves the highest precision in isoform discovery and the lowest error rates in isoform abundance estimation, compared with three state-of-the-art methods Cufflinks, SLIDE, and StringTie. As a robust bioinformatics tool for transcriptome analysis, AIDE enables researchers to discover novel transcripts with high confidence.

Epidemiology of cytomegalovirus infection in pregnancy in Israel: Real-world data from a large healthcare organization.

Congenital cytomegalovirus infection (cCMVi) is the leading cause of nonhereditary sensorineural hearing loss among newborns. Women newly acquiring cytomegalovirus infection (CMVi) during pregnancy have the highest risk of vertical transmission. This study aimed to describe the epidemiology of CMVi in pregnancy in a large healthcare database. A retrospective cohort study was performed using the Maccabi Healthcare Services database (Israel). Women aged 18-44 years old on July 1, 2013 with no record of pregnancy in the prior 6 months were followed through December 31, 2017 for first pregnancy occurrence. Pregnancy outcomes (live birth, spontaneous/therapeutic abortions, stillbirth, and uncertain outcomes) were captured. CMV test results were obtained to assess serostatus at the start of pregnancy (SoP) and primary CMV infection (CMVi) during pregnancy. Associations of demographic and reproductive factors with pCMVi were investigated (multivariable logistic regression). The study included 84 699 pregnant women (median age = 31 years; interquartile range = 28-35). Live birth, fetal loss, and uncertain pregnancy outcomes accounted for 76.8%, 18.2%, and 5.0%, respectively. The seroprevalence of CMV at the start of pregnancy in this cohort was 63.4% (95% confidence interval [CI]: 63.1-63.7). Among seronegative women with available test results (n = 10 657), CMVi incidence was 14.5 per 1000 (95% CI = 12.2-16.7). In multivariate logistic regression models adjusting for maternal age, CMVi was significantly associated with having one or more prior live births (odds ratio [OR]: 3.8 [95% CI: 2.6-5.4]) and having a child less than 6 years of age (OR: 4.3 [95%CI: 3.0-6.1]). One in three pregnant women in Israel is at risk for primary CMVi. This study demonstrates that real-world electronic healthcare data can be leveraged to support clinical management and development of interventions for congenital CMV by identifying women at high risk for CMVi during pregnancy.

EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences.

The availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare chromatin state sequences learned from multi-track epigenomic signals and to identify locally aligned chromatin regions. EpiAlign is a dynamic programming algorithm that novelly incorporates varying lengths and frequencies of chromatin states. We demonstrate the efficacy of EpiAlign through extensive simulations and studies on the real data from the NIH Roadmap Epigenomics project. EpiAlign is able to extract recurrent chromatin state patterns along a single epigenome, and many of these patterns carry cell-type-specific characteristics. EpiAlign can also detect common chromatin state patterns across multiple epigenomes, and it will serve as a useful tool to group and distinguish epigenomic samples based on genome-wide or local chromatin state patterns.

A statistical simulator scDesign for rational scRNA-seq experimental design.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) has revolutionized biological sciences by revealing genome-wide gene expression levels within individual cells. However, a critical challenge faced by researchers is how to optimize the choices of sequencing platforms, sequencing depths and cell numbers in designing scRNA-seq experiments, so as to balance the exploration of the depth and breadth of transcriptome information. RESULTS: Here we present a flexible and robust simulator, scDesign, the first statistical framework for researchers to quantitatively assess practical scRNA-seq experimental design in the context of differential gene expression analysis. In addition to experimental design, scDesign also assists computational method development by generating high-quality synthetic scRNA-seq datasets under customized experimental settings. In an evaluation based on 17 cell types and 6 different protocols, scDesign outperformed four state-of-the-art scRNA-seq simulation methods and led to rational experimental design. In addition, scDesign demonstrates reproducibility across biological replicates and independent studies. We also discuss the performance of multiple differential expression and dimension reduction methods based on the protocol-dependent scRNA-seq data generated by scDesign. scDesign is expected to be an effective bioinformatic tool that assists rational scRNA-seq experimental design and comparison of scRNA-seq computational methods based on specific research goals. AVAILABILITY AND IMPLEMENTATION: We have implemented our method in the R package scDesign, which is freely available at https://github.com/Vivianstats/scDesign. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Student nurses' ethical views on responses to the severe acute respiratory syndrome outbreak.

BACKGROUND: Fifteen years have passed since the outbreak of severe acute respiratory syndrome in Hong Kong. At that time, there were reports of heroic acts among professionals who cared for these patients, whose bravery and professionalism were highly praised. However, there are concerns about changes in new generation of nursing professionals. OBJECTIVE: We aimed to examine the attitude of nursing students, should they be faced with severe acute respiratory syndrome patients during their future work. RESEARCH DESIGN: A questionnaire survey was carried out to examine the attitude among final-year nursing students to three ethical areas, namely, duty of care, resource allocation, and collateral damage. ETHICAL CONSIDERATIONS: This study was carried out in accordance with the requirements and recommendations of the Central Research and Ethics Committee, School of Health Sciences at Caritas Institute of Higher Education. FINDINGS: Complete responses from 102 subjects were analyzed. The overwhelming majority (96.1%) did not agree to participate in the intubation of severe acute respiratory syndrome patients if protective measures, that is, N95 mask and gown, were not available. If there were insufficient N95 masks for all the medical, nursing, and allied health workers in the hospital (resource allocation), 37.3% felt that the distribution of N95 masks should be by casting lot, while the rest disagreed. When asked about collateral damage, more than three-quarters (77.5%) said that severe acute respiratory syndrome patients should be admitted to intensive care unit. There was sex difference in nursing students' attitude toward severe acute respiratory syndrome care during pregnancy and influence of age in understanding intensive care unit care for these patients. Interestingly, 94.1% felt that there should be a separate intensive care unit for severe acute respiratory syndrome patients. CONCLUSION: As infection control practice and isolation facilities improved over the years, relevant knowledge and nursing ethical issues related to infectious diseases should become part of nursing education and training programs, especially in preparation for outbreaks of infectious diseases or distress.

Aflibercept plus FOLFIRI in Asian patients with pretreated metastatic colorectal cancer: a randomized Phase III study.

AIM: To investigate whether the benefit of combining aflibercept with 5-fluorouracil, folinic acid and irinotecan (FOLFIRI) chemotherapy could be confirmed in patients from the Asia-Pacific region (ClinicalTrials.gov: NCT01661270). Patients & methods: Asian patients with oxaliplatin-pretreated metastatic colorectal cancer were randomized to receive aflibercept or placebo, followed by FOLFIRI. The primary end point was progression-free survival. RESULTS: The intention-to-treat population comprised 332 patients. A clinical supply misallocation resulted in 198/332 (60%) patients receiving at least one cycle of misallocated treatment. Nevertheless, the addition of aflibercept to FOLFIRI was shown to improve progression-free survival (hazard ratio: 0.629; 95% CI: 0.488-0.812). Adverse events were in line with expectations. CONCLUSION: The beneficial treatment effect associated with the addition of aflibercept to FOLFIRI was confirmed in Asian patients with pretreated metastatic colorectal cancer.

Relative incidence and individual-level severity of seasonal influenza A H3N2 compared with 2009 pandemic H1N1.

BACKGROUND: Two subtypes of influenza A currently circulate in humans: seasonal H3N2 (sH3N2, emerged in 1968) and pandemic H1N1 (pH1N1, emerged in 2009). While the epidemiological characteristics of the initial wave of pH1N1 have been studied in detail, less is known about its infection dynamics during subsequent waves or its severity relative to sH3N2. Even prior to 2009, few data was available to estimate the risk of severe outcomes following infection with one circulating influenza strain relative to another. METHODS: We analyzed antibodies in quadruples of sera from individuals in Hong Kong collected between July 2009 and December 2011, a period that included three distinct influenza virus epidemics. We estimated infection incidence using these assay data and then estimated rates of severe outcomes per infection using population-wide clinical data. RESULTS: Cumulative incidence of infection was high among children in the first epidemic of pH1N1. There was a change towards the older age group in the age distribution of infections for pH1N1 from the first to the second epidemic, with the age distribution of the second epidemic of pH1N1 more similar to that of sH3N2. We found no serological evidence that individuals were infected in both waves of pH1N1. The risks of excess mortality conditional on infection were higher for sH3N2 than for pH1N1, with age-standardized risk ratios of 2.6 [95% CI: 1.8, 3.7] for all causes and 1.5 [95% CI: 1.0, 2.1] for respiratory causes throughout the study period. CONCLUSIONS: Overall increase in clinical incidence of pH1N1 and higher rates of severity in older adults in post pandemic waves were in line with an age-shift in infection towards the older age groups. The absence of repeated infection is good evidence that waning immunity did not cause the second wave. Despite circulating in humans since 1968, sH3N2 is substantially more severe per infection than the pH1N1 strain. Infection-based estimates of individual-level severity have a role in assessing emerging strains; updating seasonal vaccine components; and optimizing of vaccination programs.

Phenotypically Enriched Genotypic Imputation in Genetic Association Tests.

BACKGROUND: In longitudinal epidemiological studies there may be individuals with rich phenotype data who die or are lost to follow-up before providing DNA for genetic studies. Often, the genotypic and phenotypic data of the relatives are available. Two strategies for analyzing the incomplete data are to exclude ungenotyped subjects from analysis (the complete-case method, CC) and to include phenotyped but ungenotyped individuals in analysis by using relatives' genotypes for genotype imputation (GI). In both strategies, the information in the phenotypic data was not used to handle the missing-genotype problem. METHODS: We propose a phenotypically enriched genotypic imputation (PEGI) method that uses the EM (expectation-maximization)-based maximum likelihood method to incorporate observed phenotypes into genotype imputation. RESULTS: Our simulations with genotypes missing completely at random show that, for a single-nucleotide polymorphism (SNP) with moderate to strong effect on a phenotype, PEGI improves power more than GI without excess type I errors. Using the Framingham Heart Study data set, we compare the ability of the PEGI, GI, and CC to detect the associations between 5 SNPs and age at natural menopause. CONCLUSION: The PEGI method may improve power to detect an association over both CC and GI under many circumstances.

Epigenome overlap measure (EPOM) for comparing tissue/cell types based on chromatin states.

BACKGROUND: The dynamics of epigenomic marks in their relevant chromatin states regulate distinct gene expression patterns, biological functions and phenotypic variations in biological processes. The availability of high-throughput epigenomic data generated by next-generation sequencing technologies allows a data-driven approach to evaluate the similarities and differences of diverse tissue and cell types in terms of epigenomic features. While ChromImpute has allowed for the imputation of large-scale epigenomic information to yield more robust data to capture meaningful relationships between biological samples, widely used methods such as hierarchical clustering and correlation analysis cannot adequately utilize epigenomic data to accurately reveal the distinction and grouping of different tissue and cell types. METHODS: We utilize a three-step testing procedure-ANOVA, t test and overlap test to identify tissue/cell-type- associated enhancers and promoters and to calculate a newly defined Epigenomic Overlap Measure (EPOM). EPOM results in a clear correspondence map of biological samples from different tissue and cell types through comparison of epigenomic marks evaluated in their relevant chromatin states. RESULTS: Correspondence maps by EPOM show strong capability in distinguishing and grouping different tissue and cell types and reveal biologically meaningful similarities between Heart and Muscle, Blood & T-cell and HSC & B-cell, Brain and Neurosphere, etc. The gene ontology enrichment analysis both supports and explains the discoveries made by EPOM and suggests that the associated enhancers and promoters demonstrate distinguishable functions across tissue and cell types. Moreover, the tissue/cell-type-associated enhancers and promoters show enrichment in the disease-related SNPs that are also associated with the corresponding tissue or cell types. This agreement suggests the potential of identifying causal genetic variants relevant to cell-type-specific diseases from our identified associated enhancers and promoters. CONCLUSIONS: The proposed EPOM measure demonstrates superior capability in grouping and finding a clear correspondence map of biological samples from different tissue and cell types. The identified associated enhancers and promoters provide a comprehensive catalog to study distinct biological processes and disease variants in different tissue and cell types. Our results also find that the associated promoters exhibit more cell-type-specific functions than the associated enhancers do, suggesting that the non-associated promoters have more housekeeping functions than the non-associated enhancers.

The contribution of social behaviour to the transmission of influenza A in a human population.

Variability in the risk of transmission for respiratory pathogens can result from several factors, including the intrinsic properties of the pathogen, the immune state of the host and the host's behaviour. It has been proposed that self-reported social mixing patterns can explain the behavioural component of this variability, with simulated intervention studies based on these data used routinely to inform public health policy. However, in the absence of robust studies with biological endpoints for individuals, it is unclear how age and social behaviour contribute to infection risk. To examine how the structure and nature of social contacts influenced infection risk over the course of a single epidemic, we designed a flexible disease modelling framework: the population was divided into a series of increasingly detailed age and social contact classes, with the transmissibility of each age-contact class determined by the average contacts of that class. Fitting the models to serologically confirmed infection data from the 2009 Hong Kong influenza A/H1N1p pandemic, we found that an individual's risk of infection was influenced strongly by the average reported social mixing behaviour of their age group, rather than by their personal reported contacts. We also identified the resolution of social mixing that shaped transmission: epidemic dynamics were driven by intense contacts between children, a post-childhood drop in risky contacts and a subsequent rise in contacts for individuals aged 35-50. Our results demonstrate that self-reported social contact surveys can account for age-associated heterogeneity in the transmission of a respiratory pathogen in humans, and show robustly how these individual-level behaviours manifest themselves through assortative age groups. Our results suggest it is possible to profile the social structure of different populations and to use these aggregated data to predict their inherent transmission potential.