The environmental contribution to gene expression profiles

Personhood Begins at Birth: The Rational Foundation for Abortion Policy in a Secular State

The struggle over legal abortion access in the United States is a religious controversy, not a scientific debate. Religious activists who believe that meaningful individual life (i.e., "personhood") begins at a specific "moment-of-conception" are attempting to pass laws that force this view upon all pregnant persons, irrespective of their medical circumstances, individual preferences, or personal religious beliefs. This paper argues that such actions promote a constitutionally prohibited "establishment of religion." Abortion policy in a secular state must be based upon scientifically accurate biology, not unprovable theological presuppositions. The scientific facts regarding human pregnancy do not support the position that personhood begins with fertilization-at which point a pregnancy does not yet even exist. Abortion policy should regard the embryo/fetus as part of the pregnant individual's body until delivery. We argue that individual "personhood" only begins when the latent potentialities of the fetal nervous system are actualized in the newborn after delivery. The paper argues that instantiating non-scientific beliefs concerning embryonic/fetal "personhood" into the law as the basis for abortion policy establishes a state-sponsored religion. The protection of religious liberty requires that abortion be decriminalized. Abortion should be treated like any other medical procedure and regulated similarly. To protect both religious freedom and sound medical practice, individual legal personhood should be recognized as beginning only at birth.

Transgenerational effects of triazole fungicides on gene expression and egg compounds in non-exposed offspring: A case study using Red-Legged Partridges (Alectoris rufa)

Triazole fungicides are widely used to treat cereal seeds before sowing. Granivorous birds like the Red-legged Partridge (Alectoris rufa) have high exposure risk because they ingest treated seeds that remain on the field surface. As triazole fungicides can act as endocrine disruptors, affecting sterol synthesis and reproduction in birds several months after exposure, we hypothesized that these effects could also impact subsequent generations of exposed birds. To test this hypothesis, we exposed adult partridges (F0) to seeds treated at commercial doses with four different formulations containing triazoles as active ingredients (flutriafol, prothioconazole, tebuconazole, and a mixture of the latter two), simulating field exposure during late autumn sowing. During the subsequent reproductive season, two to four months after exposure, we examined compound allocation of steroid hormones, cholesterol, vitamins, and carotenoids in eggs laid by exposed birds (F1), as well as the expression of genes encoding enzymes involved in sterol biosynthesis in one-day-old chicks of this F1. One year later, F1 animals were paired again to investigate the expression of the same genes in the F2 chicks. We found changes in the expression of some genes for all treatments and both generations. Additionally, we observed an increase in estrone levels in eggs from partridges treated with flutriafol compared to controls, a decrease in tocopherol levels in partridges exposed to the mixture of tebuconazole and prothioconazole, and an increase in retinol levels in partridges exposed to prothioconazole. Despite sample size limitations, this study provides novel insights into the mechanisms of action of the previously observed effects of triazole fungicide-treated seeds on avian reproduction with evidence that the effects can persist beyond the exposure windows, affecting unexposed offspring of partridges fed with treated seeds. The results highlight the importance of considering long-term chronic effects when assessing pesticide risks to wild birds.

Decreased mRNA expression of NR1H3 and ABCA1 in pulmonary tuberculosis patients from population of Punjab, India

BACKGROUND

Mycobacterium tuberculosis (MTB) is the causative organism of tuberculosis. Cholesterol is a crucial carbon source required for the survival of MTB in host cells. Transcription factor NR1H3 along with its important target genes ABCA1 and ApoE play important role in removal of extra cholesterol from cells. Changes in the gene expression of NR1H3, ABCA1 and ApoE can affect cholesterol homeostasis and thus the survival of MTB in host cells.Therefore, the present study was designed to analyze the mRNA expression of NR1H3, ABCA1 and ApoE in pulmonary TB (PTB) patients from the population of Punjab, India.

METHODS AND RESULTS

In this study, mRNA expression of the transcription factor NR1H3 and its target genes ABCA1 and ApoE was analyzed in 89 subjects, including 41 PTB patients and 48 healthy controls (HCs) by real-time quantitative PCR. It was found that the mRNA expression of both NR1H3 and ABCA1 genes was significantly lower in TB patients than in HCs (p < 0.001). Even after sex-wise stratification of the subjects, mRNA expression of NR1H3 and ABCA1 was found to be down-regulated in both male and female TB patients. No significant difference was observed in expression of ApoE (p = 0.98).

CONCLUSIONS

The present study found that the mRNA expression of NR1H3 and ABCA1 is down-regulated in TB patients from Punjab state of India.

imply: improving cell-type deconvolution accuracy using personalized reference profiles

Using computational tools, bulk transcriptomics can be deconvoluted to estimate the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, ignoring person-to-person heterogeneity. Here, we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. Simulation studies demonstrate reduced bias compared with existing methods. Real data analyses on longitudinal consortia show disparities in cell type proportions are associated with several disease phenotypes in Type 1 diabetes and Parkinson's disease. imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/ .

Comparative Transcriptome Analysis of High- and Low-Growth Genotypes of Eucalyptus urophylla in Response to Long-Term Nitrogen Deficiency

Nutrients play important roles in the growth and development of most plant species. However, in perennial trees, the function of nutrients in different genotypes is poorly understood. Three different nutrient levels (low, sufficient, and high nutrient levels) were applied to two contrasting Eucalyptus urophylla cultivars (a high-growth cultivar ZQUA44 and a low-growth cultivar ZQUB15), and growth and expression levels were analyzed. Although the growth traits of both genotypes under nutrient starvation treatment were much lower than under abundant nutrients, tree height, crown width, and biomass of different ZQUA44 tissues were much higher than those of ZQUB15 at all three nutrient levels. Differentially expressed genes (DEGs) clustered into six subclusters based on their expression patterns, and functional annotation showed that the DEGs involved in glutathione metabolism and flavonoid biosynthesis may be responsible for nutrient starvation across different genotypes, while the DEGs involved in carotenoid biosynthesis and starch and sucrose metabolism may have a range of functions in different genotypes. The DEGs encoding the MYB-related family may be responsible for nutrient deficiency in all genotypes, while B3 may have different functions in different genotypes. Our results demonstrate that different genotypes may form different pathways to coordinate plant survival when they face abiotic stresses.

imply: improving cell-type deconvolution accuracy using personalized reference profiles

Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, which ignores person-to-person heterogeneity. Here we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. imply can borrow information across repeatedly measured samples for each subject, and obtain precise cell type proportion estimations. Simulation studies demonstrate reduced bias in cell type abundance estimation compared with existing methods. Real data analyses on large longitudinal consortia show more realistic deconvolution results that align with biological facts. Our results suggest that disparities in cell type proportions are associated with several disease phenotypes in type 1 diabetes and Parkinson's disease. Our proposed tool imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/.

Environmentally robust cis-regulatory changes underlie rapid climatic adaptation

Changes in gene expression are thought to play a major role in adaptive evolution. While it is known that gene expression is highly sensitive to the environment, very few studies have determined the influence of genetic and environmental effects on adaptive gene expression differences in natural populations. Here, we utilize allele-specific expression to characterize cis and trans gene regulatory divergence in temperate and tropical house mice in two metabolic tissues under two thermal conditions. First, we show that gene expression divergence is pervasive between populations and across thermal conditions, with roughly 5 to 10% of genes exhibiting genotype-by-environment interactions. Second, we found that most expression divergence was due to cis-regulatory changes that were stable across temperatures. In contrast, patterns of expression plasticity were largely attributable to trans-effects, which showed greater sensitivity to temperature. Nonetheless, we found a small subset of temperature-dependent cis-regulatory changes, thereby identifying loci underlying expression plasticity. Finally, we performed scans for selection in wild house mice to identify genomic signatures of rapid adaptation. Genomic outliers were enriched in genes with evidence for cis-regulatory divergence. Notably, these genes were associated with phenotypes that affected body weight and metabolism, suggesting that cis-regulatory changes are a possible mechanism for adaptive body size evolution between populations. Our results show that gene expression plasticity, largely controlled in trans, may facilitate the colonization of new environments, but that evolved changes in gene expression are largely controlled in cis, illustrating the genetic and nongenetic mechanisms underlying the establishment of populations in new environments.

Efforts to Minimise the Bacterial Genome as a Free-Living Growing System

Exploring the minimal genetic requirements for cells to maintain free living is an exciting topic in biology. Multiple approaches are employed to address the question of the minimal genome. In addition to constructing the synthetic genome in the test tube, reducing the size of the wild-type genome is a practical approach for obtaining the essential genomic sequence for living cells. The well-studied Escherichia coli has been used as a model organism for genome reduction owing to its fast growth and easy manipulation. Extensive studies have reported how to reduce the bacterial genome and the collections of genomic disturbed strains acquired, which were sufficiently reviewed previously. However, the common issue of growth decrease caused by genetic disturbance remains largely unaddressed. This mini-review discusses the considerable efforts made to improve growth fitness, which was decreased due to genome reduction. The proposal and perspective are clarified for further accumulated genetic deletion to minimise the Escherichia coli genome in terms of genome reduction, experimental evolution, medium optimization, and machine learning.

Identifying novel regulatory effects for clinically relevant genes through the study of the Greek population

BACKGROUND

Expression quantitative trait loci (eQTL) studies provide insights into regulatory mechanisms underlying disease risk. Expanding studies of gene regulation to underexplored populations and to medically relevant tissues offers potential to reveal yet unknown regulatory variants and to better understand disease mechanisms. Here, we performed eQTL mapping in subcutaneous (S) and visceral (V) adipose tissue from 106 Greek individuals (Greek Metabolic study, GM) and compared our findings to those from the Genotype-Tissue Expression (GTEx) resource.

RESULTS

We identified 1,930 and 1,515 eGenes in S and V respectively, over 13% of which are not observed in GTEx adipose tissue, and that do not arise due to different ancestry. We report additional context-specific regulatory effects in genes of clinical interest (e.g. oncogene ST7) and in genes regulating responses to environmental stimuli (e.g. MIR21, SNX33). We suggest that a fraction of the reported differences across populations is due to environmental effects on gene expression, driving context-specific eQTLs, and suggest that environmental effects can determine the penetrance of disease variants thus shaping disease risk. We report that over half of GM eQTLs colocalize with GWAS SNPs and of these colocalizations 41% are not detected in GTEx. We also highlight the clinical relevance of S adipose tissue by revealing that inflammatory processes are upregulated in individuals with obesity, not only in V, but also in S tissue.

CONCLUSIONS

By focusing on an understudied population, our results provide further candidate genes for investigation regarding their role in adipose tissue biology and their contribution to disease risk and pathogenesis.

Variation in gene expression along an elevation gradient of Rhododendron sanguineum var. haemaleum assessed in a comparative transcriptomic analysis

Selection along environmental gradients may play a vital role in driving adaptive evolution. Nevertheless, genomic variation and genetic adaptation along environmental clines remains largely unknown in plants in alpine ecosystems. To close this knowledge gap, we assayed transcriptomic profiles of late flower bud and early leaf bud of Rhododendron sanguineum var. haemaleum from four different elevational belts between 3,000 m and 3,800 m in the Gaoligong Mountains. By comparing differences in gene expression of these samples, a gene co-expression network (WGCNA) was constructed to identify candidate genes related to elevation. We found that the overall gene expression patterns are organ-specific for the flower and leaf. Differentially expressed unigenes were identified in these organs. In flowers, these were mainly related to terpenoid metabolism (RsHMGR, RsTPS), while in leaves mainly related to anthocyanin biosynthesis (RsCHS, RsF3'5'H). Terpenoids are the main components of flower scent (fragrance) likely attracting insects for pollination. In response to fewer pollinators at higher elevation zone, it seems relatively less scent is produced in flower organs to reduce energy consumption. Secondary metabolites in leaves such as anthocyanins determine the plants' alternative adaptive strategy to extreme environments, such as selective pressures of insect herbivory from environmental changes and substrate competition in biosynthesis pathways at high elevations. Our findings indicated that the gene expression profiles generated from flower and leaf organs showed parallel expression shifts but with different functionality, suggesting the existence of flexibility in response strategies of plants exposed to heterogeneous environments across elevational gradients. The genes identified here are likely to be involved in the adaptation of the plants to these varying mountainous environments. This study thus contributes to our understanding of the molecular mechanisms of adaptation in response to environmental change.

How can childhood maltreatment affect post-traumatic stress disorder in adult: Results from a composite null hypothesis perspective of mediation analysis

Background

A greatly growing body of literature has revealed the mediating role of DNA methylation in the influence path from childhood maltreatment to psychiatric disorders such as post-traumatic stress disorder (PTSD) in adult. However, the statistical method is challenging and powerful mediation analyses regarding this issue are lacking.

Methods

To study how the maltreatment in childhood alters long-lasting DNA methylation changes which further affect PTSD in adult, we here carried out a gene-based mediation analysis from a perspective of composite null hypothesis in the Grady Trauma Project (352 participants and 16,565 genes) with childhood maltreatment as exposure, multiple DNA methylation sites as mediators, and PTSD or its relevant scores as outcome. We effectively addressed the challenging issue of gene-based mediation analysis by taking its composite null hypothesis testing nature into consideration and fitting a weighted test statistic.

Results

We discovered that childhood maltreatment could substantially affected PTSD or PTSD-related scores, and that childhood maltreatment was associated with DNA methylation which further had significant roles in PTSD and these scores. Furthermore, using the proposed mediation method, we identified multiple genes within which DNA methylation sites exhibited mediating roles in the influence path from childhood maltreatment to PTSD-relevant scores in adult, with 13 for Beck Depression Inventory and 6 for modified PTSD Symptom Scale, respectively.

Conclusion

Our results have the potential to confer meaningful insights into the biological mechanism for the impact of early adverse experience on adult diseases; and our proposed mediation methods can be applied to other similar analysis settings.

Gene Functional Networks from Time Expression Profiles: A Constructive Approach Demonstrated in Chili Pepper (Capsicum annuum L.)

Gene co-expression networks are powerful tools to understand functional interactions between genes. However, large co-expression networks are difficult to interpret and do not guarantee that the relations found will be true for different genotypes. Statistically verified time expression profiles give information about significant changes in expressions through time, and genes with highly correlated time expression profiles, which are annotated in the same biological process, are likely to be functionally connected. A method to obtain robust networks of functionally related genes will be useful to understand the complexity of the transcriptome, leading to biologically relevant insights. We present an algorithm to construct gene functional networks for genes annotated in a given biological process or other aspects of interest. We assume that there are genome-wide time expression profiles for a set of representative genotypes of the species of interest. The method is based on the correlation of time expression profiles, bound by a set of thresholds that assure both, a given false discovery rate, and the discard of correlation outliers. The novelty of the method consists in that a gene expression relation must be repeatedly found in a given set of independent genotypes to be considered valid. This automatically discards relations particular to specific genotypes, assuring a network robustness, which can be set a priori. Additionally, we present an algorithm to find transcription factors candidates for regulating hub genes within a network. The algorithms are demonstrated with data from a large experiment studying gene expression during the development of the fruit in a diverse set of chili pepper genotypes. The algorithm is implemented and demonstrated in a new version of the publicly available R package "Salsa" (version 1.0).

Genetic diversity and gene expression diversity shape the adaptive pattern of the aquatic plant Batrachium bungei along an altitudinal gradient on the Qinghai-Tibet plateau

It is an intriguing issue of evolutionary biology how genetic diversity and gene expression diversity shape the adaptive patterns. Comparative transcriptomic studies of wild populations in extreme environments provide critical insights into the relative contribution of genetic and expressive components. In this study, we analyzed the genetic diversity and gene expression diversity of 20 populations of the aquatic plant Batrachium bungei along elevations ranging from 2690 to 4896 m on the Qinghai-Tibet plateau (QTP). Based on single nucleotide polymorphisms (SNPs) and gene expression data from 100 individuals of B. bungei, we found that variation in genetic sequence was more sensitive to detect weak differentiation than gene expression. Using 292,613 high-quality SNPs, we documented a significant phylogeographical structure, a low within-population genetic diversity, and a high inter-population genetic differentiation in B. bungei populations. Analysis of relationship between geographic distance, genetic distance, and gene expression similarity showed that geographic isolation shaped gene flow patterns but not gene expression patterns. We observed a negative relationship between genetic diversity and gene expression diversity within and among B. bungei populations, and we demonstrated that as environmental conditions worsen with increasing altitude, genetic diversity played an increased role in maintaining the stability of populations, while the corresponding role of gene expression diversity decreased. These results suggested that genetic diversity and gene expression diversity might act as a complementary mechanism contributing to the long-term survival of B. bungei in extreme environments.

Human variation in population-wide gene expression data predicts gene perturbation phenotype

Population-scale datasets of healthy individuals capture genetic and environmental factors influencing gene expression. The expression variance of a gene of interest (GOI) can be exploited to set up a quasi loss- or gain-of-function "in population" experiment. We describe here an approach, huva (human variation), taking advantage of population-scale multi-layered data to infer gene function and relationships between phenotypes and expression. Within a reference dataset, huva derives two experimental groups with LOW or HIGH expression of the GOI, enabling the subsequent comparison of their transcriptional profile and functional parameters. We demonstrate that this approach robustly identifies the phenotypic relevance of a GOI allowing the stratification of genes according to biological functions, and we generalize this concept to almost 16,000 genes in the human transcriptome. Additionally, we describe how huva predicts monocytes to be the major cell type in the pathophysiology of STAT1 mutations, evidence validated in a clinical cohort.

Environmental factors have a major effect in shaping the gene expression of Siberian larch in the Altai Mountains of China

The differentiation of gene expression is an important link between genotype and phenotype and has important contributions to species adaptation and ecosystem evolution. As a major component of the world's forests, boreal forests play an important role in regulating the global climate, and the phenology of tree species has been and is undergoing changes during global warming. Here, to understand the impact of global warming on gene expression in boreal forest species, we used PacBio and Illumina sequencing methods to study the transcriptome of natural populations of Siberian larch (Larix sibirica Ledeb.) from the Altai Mountains in Xinjiang, China. We found that populations in this area had low genetic differentiation, but individuals were genetically clustered together when they had close geographic distance. Environmental factors, especially temperature, dominated differential gene expression of Siberian larch, while the contribution of genetic variation is relatively small. We speculate that Siberian larch adapts to changes in temperature and precipitation by altering its own gene expression. These results not only predict the tolerance of boreal forests to higher temperatures in the future, but also inform forest management strategies under global climate change.

Multivariate genomic and transcriptomic determinants of imaging-derived personalized therapeutic needs in Parkinson's disease

Due to the marked interpersonal neuropathologic and clinical heterogeneity of Parkinson's disease (PD), current interventions are not personalized and fail to benefit all patients. Furthermore, we continue to lack well-established methods and clinical tests to tailor interventions at the individual level in PD. Here, we identify the genetic determinants of individual-tailored treatment needs derived from longitudinal multimodal neuroimaging data in 294 PD patients (PPMI data). Advanced multivariate statistical analysis revealed that both genomic and blood transcriptomic data significantly explain (P < 0.01, FWE-corrected) the interindividual variability in therapeutic needs associated with dopaminergic, functional, and structural brain reorganization. We confirmed a high overlap between the identified highly predictive molecular pathways and determinants of levodopa clinical responsiveness, including well-known (Wnt signaling, angiogenesis, dopaminergic activity) and recently discovered (immune markers, gonadotropin-releasing hormone receptor) pathways/components. In addition, the observed strong correspondence between the identified genomic and baseline-transcriptomic determinants of treatment needs/response supports the genome's active role at the time of patient evaluation (i.e., beyond individual genetic predispositions at birth). This study paves the way for effectively combining genomic, transcriptomic and neuroimaging data for implementing successful individually tailored interventions in PD and extending our pathogenetic understanding of this multifactorial and heterogeneous disorder.

Loneliness: An Immunometabolic Syndrome

Loneliness has been defined as an agonizing encounter, experienced when the need for human intimacy is not met adequately, or when a person’s social network does not match their preference, either in number or attributes. This definition helps us realize that the cause of loneliness is not merely being alone, but rather not being in the company we desire. With loneliness being introduced as a measurable, distinct psychological experience, it has been found to be associated with poor health behaviors, heightened stress response, and inadequate physiological repairing activity. With these three major pathways of pathogenesis, loneliness can do much harm; as it impacts both immune and metabolic regulation, altering the levels of inflammatory cytokines, growth factors, acute-phase reactants, chemokines, immunoglobulins, antibody response against viruses and vaccines, and immune cell activity; and affecting stress circuitry, glycemic control, lipid metabolism, body composition, metabolic syndrome, cardiovascular function, cognitive function and mental health, respectively. Taken together, there are too many immunologic and metabolic manifestations associated with the construct of loneliness, and with previous literature showcasing loneliness as a distinct psychological experience and a health determinant, we propose that loneliness, in and of itself, is not just a psychosocial phenomenon. It is also an all-encompassing complex of systemic alterations that occur with it, expanding it into a syndrome of events, linked through a shared network of immunometabolic pathology. This review aims to portray a detailed picture of loneliness as an “immunometabolic syndrome”, with its multifaceted pathology.

A systematic review of datasets that can help elucidate relationships among gene expression, race, and immunohistochemistry-defined subtypes in breast cancer

Scholarly requirements have led to a massive increase of transcriptomic data in the public domain, with millions of samples available for secondary research. We identified gene-expression datasets representing 10,214 breast-cancer patients in public databases. We focused on datasets that included patient metadata on race and/or immunohistochemistry (IHC) profiling of the ER, PR, and HER-2 proteins. This review provides a summary of these datasets and describes findings from 32 research articles associated with the datasets. These studies have helped to elucidate relationships between IHC, race, and/or treatment options, as well as relationships between IHC status and the breast-cancer intrinsic subtypes. We have also identified broad themes across the analysis methodologies used in these studies, including breast cancer subtyping, deriving predictive biomarkers, identifying differentially expressed genes, and optimizing data processing. Finally, we discuss limitations of prior work and recommend future directions for reusing these datasets in secondary analyses.

Phenotypic and Transcriptomic Responses to Stress Differ According to Population Geography in an Invasive Species

Adaptation to rapid environmental changes must occur within a short-time scale. In this context, studies of invasive species may provide insights into the underlying mechanisms of rapid adaptation as these species have repeatedly encountered and adapted to novel environmental conditions. We investigated how invasive and noninvasive genotypes of Drosophila suzukii deal with oxidative stress at the phenotypic and molecular levels. We also studied the impact of transposable element (TE) insertions on the gene expression in response to stress. Our results show that flies from invasive areas (France and the United States) live longer in natural conditions than the ones from native Japanese areas. As expected, lifespan for all genotypes was significantly reduced following exposure to paraquat, but this reduction varied among genotypes (genotype-by-environment interaction) with invasive genotypes appearing more affected by exposure than noninvasive ones. A transcriptomic analysis of genotypes upon paraquat treatment detected many genes differentially expressed (DE). Although a small core set of genes were DE in all genotypes following paraquat exposure, much of the response of each genotype was unique. Moreover, we showed that TEs were not activated after oxidative stress and DE genes were significantly depleted of TEs. In conclusion, it is likely that transcriptomic changes are involved in the rapid adaptation to local environments. We provide new evidence that in the decade since the invasion from Asia, the sampled genotypes in Europe and the United States of D. suzukii diverged from the ones from the native area regarding their phenotypic and genomic response to oxidative stress.

Asexual reproduction and vegetative growth of Bionectria ochroleuca in response to temperature and photoperiod

Growth and reproduction are two essential life-history traits for fungi. Understanding life-history strategies provides insight into the environmental adaption of species. Here, we investigated the colonial morphology, vegetative growth, and asexual reproduction of the ascomycete fungus Bionectria ochroleuca in response to a variety of environmental conditions. We demonstrated that the increased temperature from 15 to 25°C induced mycelial growth and conidiation in B. ochroleuca. We also found that the optimal temperatures for mycelial growth and conidial formation in this fungus species were 25 and 30°C, respectively. However, as the temperature increased from 25 to 30°C, mycelial growth was suppressed, but the total number of conidia was significantly increased. The shift in light-dark cycles dramatically changed the morphological features of the colonies and affected both vegetative growth and asexual reproduction. Under incubation environments of alternating light and dark (16:8 and 8:16 light:dark cycles), conidiophores and conidia in the colonies formed dense-sparse rings and displayed synchronous wave structures. When the light duration was prolonged in the sequence of 0, 8, 16, and 24 hr per day, mycelial growth was suppressed, but conidiation was promoted. Together, our results indicate that temperature and light period may trigger a trade-off between vegetative growth and asexual reproduction in B. ochroleuca.

Molecular mapping of quantitative disease resistance loci for soybean partial resistance to Phytophthora sansomeana

KEY MESSAGE

Two soybean QDRL were identified with additive interaction to P. sansomeana isolate MPS17-22. Further analyses uncovered four interaction patterns between the two QDRL and seven additional P. sansomeana isolates. Phytophthora sansomeana is a recently recognized species that contributes to root rot in soybean. Previous studies indicated that P. sansomeana is widely distributed among soybean growing regions and has a much wider host range than P. sojae, a well-known pathogen of soybean. Unlike P. sojae, no known disease resistance genes have been documented that can effectively control P. sansomeana. Therefore, it is important to identify resistance that can be quickly integrated into future soybean varieties. E13901 is an improved soybean line that confers partial resistance to P. sansomeana. A mapping population of 228 F4:5 families was developed from a cross between E13901 and a susceptible improved soybean variety E13390. Using a composite interval mapping method, two quantitative disease resistance loci (QDRL) were identified on Chromosomes 5 (designated qPsan5.1) and 16 (designated qPsan16.1), respectively. qPsan5.1 was mapped at 54.71 cM between Gm05_32565157_T_C and Gm05_32327497_T_C. qPsan5.1 was contributed by E13390 and explained about 6% of the disease resistance variation. qPsan16.1 was located at 39.01 cM between Gm16_35700223_G_T and Gm16_35933600/ Gm16_35816475. qPsan16.1 was from E13901 and could explain 5.5% of partial disease resistance. Further analysis indicated an additive interaction of qPsan5.1 and qPsan16.1 against P. sansomeana isolate MPS17-22. Marker assisted resistance spectrum analysis and progeny tests verified the two QDRL and their interaction patterns with other P. sansomeana isolates. Both QDRL can be quickly integrated into soybean varieties using marker assisted selection.

A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions

Genetic effects on gene expression and splicing can be modulated by cellular and environmental factors; yet interactions between genotypes, cell type, and treatment have not been comprehensively studied together. We used an induced pluripotent stem cell system to study multiple cell types derived from the same individuals and exposed them to a large panel of treatments. Cellular responses involved different genes and pathways for gene expression and splicing and were highly variable across contexts. For thousands of genes, we identified variable allelic expression across contexts and characterized different types of gene-environment interactions, many of which are associated with complex traits. Promoter functional and evolutionary features distinguished genes with elevated allelic imbalance mean and variance. On average, half of the genes with dynamic regulatory interactions were missed by large eQTL mapping studies, indicating the importance of exploring multiple treatments to reveal previously unrecognized regulatory loci that may be important for disease.

Efficient and effective control of confounding in eQTL mapping studies through joint differential expression and Mendelian randomization analyses

MOTIVATION

Identifying cis-acting genetic variants associated with gene expression levels-an analysis commonly referred to as expression quantitative trait loci (eQTLs) mapping-is an important first step toward understanding the genetic determinant of gene expression variation. Successful eQTL mapping requires effective control of confounding factors. A common method for confounding effects control in eQTL mapping studies is the probabilistic estimation of expression residual (PEER) analysis. PEER analysis extracts PEER factors to serve as surrogates for confounding factors, which is further included in the subsequent eQTL mapping analysis. However, it is computationally challenging to determine the optimal number of PEER factors used for eQTL mapping. In particular, the standard approach to determine the optimal number of PEER factors examines one number at a time and chooses a number that optimizes eQTLs discovery. Unfortunately, this standard approach involves multiple repetitive eQTL mapping procedures that are computationally expensive, restricting its use in large-scale eQTL mapping studies that being collected today.

RESULTS

Here, we present a simple and computationally scalable alternative, Effect size Correlation for COnfounding determination (ECCO), to determine the optimal number of PEER factors used for eQTL mapping studies. Instead of performing repetitive eQTL mapping, ECCO jointly applies differential expression analysis and Mendelian randomization analysis, leading to substantial computational savings. In simulations and real data applications, we show that ECCO identifies a similar number of PEER factors required for eQTL mapping analysis as the standard approach but is two orders of magnitude faster. The computational scalability of ECCO allows for optimized eQTL discovery across 48 GTEx tissues for the first time, yielding an overall 5.89% power gain on the number of eQTL harboring genes (eGenes) discovered as compared to the previous GTEx recommendation that does not attempt to determine tissue-specific optimal number of PEER factors.

AVAILABILITYAND IMPLEMENTATION

Our method is implemented in the ECCO software, which, along with its GTEx mapping results, is freely available at www.xzlab.org/software.html. All R scripts used in this study are also available at this site.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Altered immunoemotional regulatory system in COVID-19: From the origins to opportunities

The emergence of the novel coronavirus (SARS-CoV-2) and the worldwide spread of the coronavirus disease (COVID-19) have led to social regulations that caused substantial changes in manners of daily life. The subsequent loneliness and concerns of the pandemic during social distancing, quarantine, and lockdown are psychosocial stressors that negatively affect the immune system. These effects occur through mechanisms controlled by the sympathetic nervous system (SNS) and the hypothalamic-pituitary-adrenocortical (HPA) axis that alter immune regulation, namely the conserved transcriptional response to adversity (CTRA), which promotes inflammation and diminishes antiviral responses, leading to inadequate protection against viral disease. Unhealthy eating habits, physical inactivity, sleep disturbances, and mental health consequences of COVID-19 add on to the pathological effects of loneliness, making immunity against this ferocious virus an even tougher fight. Therefore, social isolation, with its unintended consequences, has inherently paradoxical effects on immunity in relation to viral disease. Though this paradox can present a challenge, its acknowledgment can serve as an opportunity to address the associated issues and find ways to mitigate the adverse effects. In this review, we aim to explore, in detail, the pathological effects of the new social norms on immunity and present suggested methods to improve our physical, psychological, and healthcare abilities to fight viral infection in the context of the COVID-19 pandemic.

Stress-induced nuclear condensation of NELF drives transcriptional downregulation

In response to stress, human cells coordinately downregulate transcription and translation of housekeeping genes. To downregulate transcription, the negative elongation factor (NELF) is recruited to gene promoters impairing RNA polymerase II elongation. Here we report that NELF rapidly forms nuclear condensates upon stress in human cells. Condensate formation requires NELF dephosphorylation and SUMOylation induced by stress. The intrinsically disordered region (IDR) in NELFA is necessary for nuclear NELF condensation and can be functionally replaced by the IDR of FUS or EWSR1 protein. We find that biomolecular condensation facilitates enhanced recruitment of NELF to promoters upon stress to drive transcriptional downregulation. Importantly, NELF condensation is required for cellular viability under stressful conditions. We propose that stress-induced NELF condensates reported here are nuclear counterparts of cytosolic stress granules. These two stress-inducible condensates may drive the coordinated downregulation of transcription and translation, likely forming a critical node of the stress survival strategy.

Local Rather than Global H3K27me3 Dynamics Are Associated with Differential Gene Expression in Verticillium dahliae

Differential growth conditions typically trigger global transcriptional responses in filamentous fungi. Such fungal responses to environmental cues involve epigenetic regulation, including chemical histone modifications. It has been proposed that conditionally expressed genes, such as those that encode secondary metabolites but also effectors in pathogenic species, are often associated with a specific histone modification, lysine27 methylation of H3 (H3K27me3). However, thus far, no analyses on the global H3K27me3 profiles have been reported under differential growth conditions in order to assess if H3K27me3 dynamics govern differential transcription. Using chromatin immunoprecipitation sequencing (ChIP-seq) and RNA sequencing data from the plant-pathogenic fungus Verticillium dahliae grown in three in vitro cultivation media, we now show that a substantial number of the identified H3K27me3 domains globally display stable profiles among these growth conditions. However, we observe local quantitative differences in H3K27me3 ChIP-seq signals that are associated with a subset of differentially transcribed genes between media. Comparing the in vitro results to expression during plant infection suggests that in planta-induced genes may require chromatin remodeling to achieve expression. Overall, our results demonstrate that some loci display H3K27me3 dynamics associated with concomitant transcriptional variation, but many differentially expressed genes are associated with stable H3K27me3 domains. Thus, we conclude that while H3K27me3 is required for transcriptional repression, it does not appear that transcriptional activation requires the global erasure of H3K27me3. We propose that the H3K27me3 domains that do not undergo dynamic methylation may contribute to transcription through other mechanisms or may serve additional genomic regulatory functions. IMPORTANCE In many organisms, including filamentous fungi, epigenetic mechanisms that involve chemical and physical modifications of DNA without changing the genetic sequence have been implicated in transcriptional responses upon developmental or environmental cues. In fungi, facultative heterochromatin that can decondense to allow transcription in response to developmental changes or environmental stimuli is characterized by the trimethylation of lysine 27 on histone H3 (H3K27me3), and H3K27me3 has been implicated in transcriptional regulation, although the precise mechanisms and functions remain enigmatic. Based on ChIP and RNA sequencing data, we show for the soilborne broad-host-range vascular wilt plant-pathogenic fungus Verticillium dahliae that although some loci display H3K27me3 dynamics that can contribute to transcriptional variation, other loci do not show such a dependence. Thus, although we recognize that H3K27me3 is required for transcriptional repression, we also conclude that this mark is not a conditionally responsive global regulator of differential transcription upon responses to environmental cues.

Proteomic Analysis of the Protective Effect of Early Heat Exposure against Chronic Heat Stress in Broilers

Simple Summary

Heat stress affects the livestock industry, especially in poultry. Screening for metabolic changes after early and chronic heat exposure in poultry would be beneficial in resolving the production issues. In this study, we identified differentially expressed proteins that affected early heat exposure during chronic heat stress. Chronic heat stress affected 277 proteins, of which 95 differed in expression by early heat exposure. Differentially expressed proteins were related to actin metabolism and also involved in carbohydrate and carbon metabolism. According to our results, early heat exposed liver of broilers activates the different physiological mechanisms for protection from later heat stress.

Abstract

The increasing trend of global warming has affected the livestock industry through the heat stress, especially in poultry. Therefore, a better understanding of the mechanisms of heat stress in poultry would be helpful for maintaining the poultry production. Three groups were designed to determine early heat stress effects during chronic heat stress: CC, raised at a comfortable temperature; CH, chronic heat exposure at 35 °C for 21–35 days continuously; and HH, early heat exposure at 40 °C for 24 h at 5 days old with 35 °C temperature for 21–35 days continuously. In this study, proteome analysis was carried out to identify differentially expressed proteins in the liver tissue of broilers under chronic and early heat exposure. There were eight differentially expressed proteins from early heat stress during chronic heat exposure, which were related to actin metabolism. According to KEGG (Kyoto encyclopedia of genes and genomes) analysis, the proteins involved in carbohydrate metabolism were expressed to promote the metabolism of carbohydrates under chronic heat stress. Early heat reduced the heat stress-induced expression changes of select proteins. Our study has shown that early heat exposure suggests that the liver of broilers has various physiological mechanisms for regulating homeostasis to aid heat resistance.

Genetic Polymorphisms of MMP1, MMP9, COL1A1, and COL1A2 in Polish Patients with Thoracic Aortopathy

BACKGROUND

The pathogenesis of thoracic aortopathy is complex, and much evidence suggests the influence of genetic factors. Some genes with polymorphisms are widely considered critical factors in the initiation and development of aortic aneurysm. The aim of our study was to analyze the association of genetic polymorphisms of MMP1 rs1799750 (c.-1607G>GG), MMP9 rs3918242 (c.-1562C>T), COL1A1 rs1800012 (c.1245G>T), and COL1A2 rs42524 (c.1645G>C) with predisposition to thoracic aortopathy in Polish patients and with clinical characteristics of these patients.

METHODS

The study was carried out with 96 patients with thoracic aortopathy (47 patients with ascending aortic aneurysm and 49 patients with thoracic aortic dissection) and 61 control subjects without thoracic aortopathy. The MMP1, MMP9, COL1A1, and COL1A2 polymorphisms were determined by PCR-RFLP.

RESULTS

No significant differences in the frequency distributions of MMP1, MMP9, COL1A1, and COL1A2 genotypes or alleles were found (1) between the control group and patients with ascending aortic aneurysm (AsAA), (2) between the control group and patients with thoracic aortic dissection (TAD), or (3) between AsAA and TAD patients. Multivariate logistic regression analysis revealed that MMP1 and MMP9 polymorphisms were associated with the degree of aortic valve regurgitation.

CONCLUSION

The results of our study did not support associations between MMP1, MMP9, COL1A1, and COL1A2 genetic variants with the risk of thoracic artery disease in Polish patients. However, rs1799750 MMP1 and rs3918242 MMP9 seem to be associated with the degree of aortic regurgitation.

Restoring ancestral phenotypes is a general pattern in gene expression evolution during adaptation to new environments in Tribolium castaneum

Plasticity and evolution are two processes allowing populations to respond to environmental changes, but how both are related and impact each other remains controversial. We studied plastic and evolutionary responses in gene expression of Tribolium castaneum after exposure of the beetles to new environments that differed from ancestral conditions in temperature, humidity or both. Using experimental evolution with 10 replicated lines per condition, we were able to demonstrate adaptation after 20 generations. We measured whole-transcriptome gene expression with RNA-sequencing to infer evolutionary and plastic changes. We found more evidence for changes in mean expression (shift in the intercept of reaction norms) in adapted lines than for changes in plasticity (shifts in slopes). Plasticity was mainly preserved in selected lines and was responsible for a large part of the phenotypic divergence in expression between ancestral and new conditions. However, we found that genes with the largest evolutionary changes in expression also evolved reduced plasticity and often showed expression levels closer to the ancestral stage. Results obtained in the three different conditions were similar, suggesting that restoration of ancestral expression levels during adaptation is a general evolutionary pattern. With a larger sample in the most stressful condition, we were able to detect a positive correlation between the proportion of genes with reversion of the ancestral plastic response and mean fitness per selection line.

Transcriptional Control of Quality Differences in the Lipid-Based Cuticle Barrier in Drosophila suzukii and Drosophila melanogaster

Cuticle barrier efficiency in insects depends largely on cuticular lipids. To learn about the evolution of cuticle barrier function, we compared the basic properties of the cuticle inward and outward barrier function in adults of the fruit flies Drosophila suzukii and Drosophila melanogaster that live on fruits sharing a similar habitat. At low air humidity, D. suzukii flies desiccate faster than D. melanogaster flies. We observed a general trend indicating that in this respect males are less robust than females in both species. Xenobiotics penetration occurs at lower temperatures in D. suzukii than in D. melanogaster. Likewise, D. suzukii flies are more susceptible to contact insecticides than D. melanogaster flies. Thus, both the inward and outward barriers of D. suzukii are less efficient. Consistently, D. suzukii flies have less cuticular hydrocarbons (CHC) that participate as key components of the cuticle barrier. Especially, the relative amounts of branched and desaturated CHCs, known to enhance desiccation resistance, show reduced levels in D. suzukii. Moreover, the expression of snustorr (snu) that encodes an ABC transporter involved in barrier construction and CHC externalization, is strongly suppressed in D. suzukii. Hence, species-specific genetic programs regulate the quality of the lipid-based cuticle barrier in these two Drosophilae. Together, we conclude that the weaker inward and outward barriers of D. suzukii may be partly explained by differences in CHC composition and by a reduced Snu-dependent transport rate of CHCs to the surface. In turn, this suggests that snu is an ecologically adjustable and therefore relevant gene in cuticle barrier efficiency.

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.

Additive and mostly adaptive plastic responses of gene expression to multiple stress in Tribolium castaneum

Gene expression is known to be highly responsive to the environment and important for adjustment of metabolism but there is also growing evidence that differences in gene regulation contribute to species divergence and differences among locally adapted populations. However, most studies so far investigated populations when divergence had already occurred. Selection acting on expression levels at the onset of adaptation to an environmental change has not been characterized. Understanding the mechanisms is further complicated by the fact that environmental change is often multivariate, meaning that organisms are exposed to multiple stressors simultaneously with potentially interactive effects. Here we use a novel approach by combining fitness and whole-transcriptome data in a large-scale experiment to investigate responses to drought, heat and their combination in Tribolium castaneum. We found that fitness was reduced by both stressors and their combined effect was almost additive. Expression data showed that stressor responses were acting independently and did not interfere physiologically. Since we measured expression and fitness within the same individuals, we were able to estimate selection on gene expression levels. We found that variation in fitness can be attributed to gene expression variation and that selection pressures were environment dependent and opposite between control and stress conditions. We could further show that plastic responses of expression were largely adaptive, i.e. in the direction that should increase fitness.

Genome-Wide Genotype-Expression Relationships Reveal Both Copy Number and Single Nucleotide Differentiation Contribute to Differential Gene Expression between Stickleback Ecotypes

Repeated and independent emergence of trait divergence that matches habitat differences is a sign of parallel evolution by natural selection. Yet, the molecular underpinnings that are targeted by adaptive evolution often remain elusive. We investigate this question by combining genome-wide analyses of copy number variants (CNVs), single nucleotide polymorphisms (SNPs), and gene expression across four pairs of lake and river populations of the three-spined stickleback (Gasterosteus aculeatus). We tested whether CNVs that span entire genes and SNPs occurring in putative cis-regulatory regions contribute to gene expression differences between sticklebacks from lake and river origins. We found 135 gene CNVs that showed a significant positive association between gene copy number and gene expression, suggesting that CNVs result in dosage effects that can fuel phenotypic variation and serve as substrates for habitat-specific selection. Copy number differentiation between lake and river sticklebacks also contributed to expression differences of two immune-related genes in immune tissues, cathepsin A and GIMAP7. In addition, we identified SNPs in cis-regulatory regions (eSNPs) associated with the expression of 1,865 genes, including one eSNP upstream of a carboxypeptidase gene where both the SNP alleles differentiated and the gene was differentially expressed between lake and river populations. Our study highlights two types of mutations as important sources of genetic variation involved in the evolution of gene expression and in potentially facilitating repeated adaptation to novel environments.

Variable prediction accuracy of polygenic scores within an ancestry group

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

Variable prediction accuracy of polygenic scores within an ancestry group

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

Assessing the Impact of Sample Heterogeneity on Transcriptome Analysis of Human Diseases Using MDP Webtool

Transcriptome analyses have increased our understanding of the molecular mechanisms underlying human diseases. Most approaches aim to identify significant genes by comparing their expression values between healthy subjects and a group of patients with a certain disease. Given that studies normally contain few samples, the heterogeneity among individuals caused by environmental factors or undetected illnesses can impact gene expression analyses. We present a systematic analysis of sample heterogeneity in a variety of gene expression studies relating to inflammatory and infectious diseases and show that novel immunological insights may arise once heterogeneity is addressed. The perturbation score of samples is quantified using nonperturbed subjects (i.e., healthy subjects) as a reference group. Such a score allows us to detect outlying samples and subgroups of diseased patients and even assess the molecular perturbation of single cells infected with viruses. We also show how removal of outlying samples can improve the "signal" of the disease and impact detection of differentially expressed genes. The method is made available via the mdp Bioconductor R package and as a user-friendly webtool, webMDP, available at http://mdp.sysbio.tools.

Predicting the decision making chemicals used for bacterial growth

Predicting the contribution of media components to bacterial growth was first initiated by introducing machine learning to high-throughput growth assays. A total of 1336 temporal growth records corresponding to 225 different media, which were composed of 13 chemical components, were generated. The growth rate and saturated density of each growth curve were automatically calculated with the newly developed data processing program. To identify the decision making factors related to growth among the 13 chemicals, big datasets linking the growth parameters to the chemical combinations were subjected to decision tree learning. The results showed that the only carbon source, glucose, determined bacterial growth, but it was not the first priority. Instead, the top decision making chemicals in relation to the growth rate and saturated density were ammonium and ferric ions, respectively. Three chemical components (NH₄⁺, Mg²⁺ and glucose) commonly appeared in the decision trees of the growth rate and saturated density, but they exhibited different mechanisms. The concentration ranges for fast growth and high density were overlapped for glucose but distinguished for NH₄⁺ and Mg²⁺. The results suggested that these chemicals were crucial in determining the growth speed and growth maximum in either a universal use or a trade-off manner. This differentiation might reflect the diversity in the resource allocation mechanisms for growth priority depending on the environmental restrictions. This study provides a representative example for clarifying the contribution of the environment to population dynamics through an innovative viewpoint of employing modern data science within traditional microbiology to obtain novel findings.

Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives

High-throughput measurements of molecular phenotypes provide an unprecedented opportunity to model cellular processes and their impact on disease. These highly structured datasets are usually strongly confounded, creating false positives and reducing power. This has motivated many approaches based on principal components analysis (PCA) to estimate and correct for confounders, which have become indispensable elements of association tests between molecular phenotypes and both genetic and nongenetic factors. Here, we show that these correction approaches induce a bias, and that it persists for large sample sizes and replicates out-of-sample. We prove this theoretically for PCA by deriving an analytic, deterministic, and intuitive bias approximation. We assess other methods with realistic simulations, which show that perturbing any of several basic parameters can cause false positive rate (FPR) inflation. Our experiments show the bias depends on covariate and confounder sparsity, effect sizes, and their correlation. Surprisingly, when the covariate and confounder have [Formula: see text], standard two-step methods all have [Formula: see text]-fold FPR inflation. Our analysis informs best practices for confounder correction in genomic studies, and suggests many false discoveries have been made and replicated in some differential expression analyses.

Emerging concepts and challenges in implementing the exposome paradigm in allergic diseases and asthma: a Practall document

Exposome research can improve the understanding of the mechanistic connections between exposures and health to help mitigate adverse health outcomes across the life span. The exposomic approach provides a risk profile instead of single predictors and thus is particularly applicable to allergic diseases and asthma. Under the PRACTALL collaboration between the European Academy of Allergy and Clinical Immunology (EAACI) and the American Academy of Allergy, Asthma, and Immunology (AAAAI), we evaluated the current concepts and the unmet needs on the role of the exposome in allergic diseases and asthma.

Gene regulatory divergence between locally adapted ecotypes in their native habitats

Local adaptation is a key driver of ecological specialization and the formation of new species. Despite its importance, the evolution of gene regulatory divergence among locally adapted populations is poorly understood, especially how that divergence manifests in nature. Here, we evaluate gene expression divergence and allele-specific gene expression responses for locally adapted coastal perennial and inland annual accessions of the yellow monkeyflower, Mimulus guttatus, in a field reciprocal transplant experiment. Overall, 6765 (73%) of surveyed genes were differentially expressed between coastal and inland habitats, while 7213 (77%) were differentially expressed between the coastal perennial and inland annual accessions. Cis-regulatory variation was pervasive, affecting 79% (5532) of differentially expressed genes. We detected trans effects for 52% (3611) of differentially expressed genes. Expression plasticity of alleles across habitats (G × E interactions) appears to be relatively common (affecting 18% of transcripts) and could minimize fitness trade-offs at loci that contribute to local adaptation. We also found evidence that at least one chromosomal inversion may act as supergene by holding together haplotypes of differentially expressed genes, but this pattern depends on habitat context. Our results highlight multiple key patterns regarding the relationship between gene expression and the evolution of locally adapted populations.

Integrated profiling of phenotype and blood transcriptome for stress vulnerability and depression

Etiology of depression and its vulnerability remains elusive. Using a latent profile analysis on dimensional personality traits, we previously identified 3 different phenotypes in the general population, namely stress-resilient, -vulnerable, and -resistant groups. Here we performed microarray-based blood gene expression profiling of these 3 groups (n = 20 for each group) in order to identify genes involved in stress vulnerability as it relates to the risk of depression. Identified differentially expressed genes among the groups were most markedly enriched in ribosome-related pathways. These ribosomal genes, which included ribosomal protein L17 (RPL17) and ribosomal protein L34 (RPL34), were upregulated in relation to the stress vulnerability. Protein-protein interaction and correlational co-expression analyses of the differentially expressed genes/non-coding RNAs consistently showed that functional networks involving ribosomes were affected. The significant upregulation of RPL17 and RPL34 was also observed in depressed patients compared to healthy controls, as confirmed in 2 independent case-control datasets by using pooled microarray data and qPCR experiments (total number of subjects was 122 and 166, respectively). Moreover, the upregulation of RPL17 and RPL34 was most marked in DSM-IV major depressive disorder, followed by in bipolar disorder, and then in schizophrenia, suggesting some diagnostic specificity of these markers as well as their general roles in stress vulnerability. These results suggest that ribosomal genes, particularly RPL17 and RPL34, can play integral roles in stress vulnerability and depression across nonclinical and clinical conditions. This study presents an opportunity to understand how multiple psychological traits and underlying molecular mechanisms interact to render individuals vulnerable to depression.

Low-stress Microfluidic Density-gradient Centrifugation for Blood Cell Sorting

Density gradient centrifugation exploits density differences between different blood cells to accomplish separation of peripheral blood mononuclear cells (PBMCs) from polymorphonuclear (PNM) cells, and erythrocytes or red blood cells (RBCs). While density gradient centrifugation offers a label-free alternative avoiding the use of harsh lysis buffers for blood cell isolation, it is a time-consuming and labor-intensive process during which blood cells are subject to high-levels of centrifugal force that can artifactually activate cells. To provide a low-stress alternative to this elegant method, we miniaturized and automated this process using microfluidics to ensure continuous PBMCs isolation from whole blood while avoiding the exposure to high-levels of centrifugal stress in a simple flow-through format. Within this device, a density gradient is established by exploiting laminar flow within microfluidic channels to layer a thin stream of blood over a larger stream of Ficoll. Using this approach we demonstrate successful isolation of PBMCs from whole blood with preservation of monocytes and different lymphocyte subpopulations similar to that seen with conventional density gradient centrifugation. Evaluation of activation status of PBMCs isolated using this technique shows that our approach achieves minimal isolation process induced activation of cells in comparison to conventional lysis or density gradient centrifugation. This simple, automated microfluidic density gradient centrifugation technique can potentially serve as tool for rapid and activation-free technique for isolation of PBMCs from whole blood for point-of-care applications.

Incipient and Subclinical Tuberculosis: a Clinical Review of Early Stages and Progression of Infection

Tuberculosis (TB) is the leading infectious cause of mortality worldwide, due in part to a limited understanding of its clinical pathogenic spectrum of infection and disease. Historically, scientific research, diagnostic testing, and drug treatment have focused on addressing one of two disease states: latent TB infection or active TB disease. Recent research has clearly demonstrated that human TB infection, from latent infection to active disease, exists within a continuous spectrum of metabolic bacterial activity and antagonistic immunological responses. This revised understanding leads us to propose two additional clinical states: incipient and subclinical TB. The recognition of incipient and subclinical TB, which helps divide latent and active TB along the clinical disease spectrum, provides opportunities for the development of diagnostic and therapeutic interventions to prevent progression to active TB disease and transmission of TB bacilli. In this report, we review the current understanding of the pathogenesis, immunology, clinical epidemiology, diagnosis, treatment, and prevention of both incipient and subclinical TB, two emerging clinical states of an ancient bacterium.

Roles of Female and Male Genotype in Post-Mating Responses in Drosophila melanogaster

Mating induces a multitude of changes in female behavior, physiology, and gene expression. Interactions between female and male genotype lead to variation in post-mating phenotypes and reproductive success. So far, few female molecules responsible for these interactions have been identified. Here, we used Drosophila melanogaster from 5 geographically dispersed populations to investigate such female × male genotypic interactions at the female transcriptomic and phenotypic levels. Females from each line were singly-mated to males from the same 5 lines, for a total of 25 combinations. Reproductive output and refractoriness to re-mating were assayed in females from the 25 mating combinations. Female × male genotypic interactions resulted in significant differences in these post-mating phenotypes. To assess whether female × male genotypic interactions affect the female post-mating transcriptome, next-generation RNA sequencing was performed on virgin and mated females at 5 to 6 h post-mating. Seventy-seven genes showed strong variation in mating-induced expression changes in a female × male genotype-dependent manner. These genes were enriched for immune response and odorant-binding functions, and for expression exclusively in the head. Strikingly, variation in post-mating transcript levels of a gene encoding a spermathecal endopeptidase was correlated with short-term egg production. The transcriptional variation found in specific functional classes of genes might be a read-out of female × male compatibility at a molecular level. Understanding the roles these genes play in the female post-mating response will be crucial to better understand the evolution of post-mating responses and related conflicts between the sexes.

Gene-by-environment interactions in urban populations modulate risk phenotypes

Uncovering the interaction between genomes and the environment is a principal challenge of modern genomics and preventive medicine. While theoretical models are well defined, little is known of the G × E interactions in humans. We used an integrative approach to comprehensively assess the interactions between 1.6 million data points, encompassing a range of environmental exposures, health, and gene expression levels, coupled with whole-genome genetic variation. From ∼1000 individuals of a founder population in Quebec, we reveal a substantial impact of the environment on the transcriptome and clinical endophenotypes, overpowering that of genetic ancestry. Air pollution impacts gene expression and pathways affecting cardio-metabolic and respiratory traits, when controlling for genetic ancestry. Finally, we capture four expression quantitative trait loci that interact with the environment (air pollution). Our findings demonstrate how the local environment directly affects disease risk phenotypes and that genetic variation, including less common variants, can modulate individual’s response to environmental challenges.

Individuals with different genotypes may respond differently to environmental variation. Here, Favé et al. find substantial impacts of different environment exposures on the transcriptome and clinical endophenotypes when controlling for genetic ancestry by analyzing data from ∼1000 individuals from a founder population in Quebec.

Codominant grasses differ in gene expression under experimental climate extremes in native tallgrass prairie

Extremes in climate, such as heat waves and drought, are expected to become more frequent and intense with forecasted climate change. Plant species will almost certainly differ in their responses to these stressors. We experimentally imposed a heat wave and drought in the tallgrass prairie ecosystem near Manhattan, Kansas, USA to assess transcriptional responses of two ecologically important C4 grass species, Andropogon gerardii and Sorghastrum nutans. Based on previous research, we expected that S. nutans would regulate more genes, particularly those related to stress response, under high heat and drought. Across all treatments, S. nutans showed greater expression of negative regulatory and catabolism genes while A. gerardii upregulated cellular and protein metabolism. As predicted, S. nutans showed greater sensitivity to water stress, particularly with downregulation of non-coding RNAs and upregulation of water stress and catabolism genes. A. gerardii was less sensitive to drought, although A. gerardii tended to respond with upregulation in response to drought versus S. nutans which downregulated more genes under drier conditions. Surprisingly, A. gerardii only showed minimal gene expression response to increased temperature, while S. nutans showed no response. Gene functional annotation suggested that these two species may respond to stress via different mechanisms. Specifically, A. gerardii tends to maintain molecular function while S. nutans prioritizes avoidance. Sorghastrum nutans may strategize abscisic acid response and catabolism to respond rapidly to stress. These results have important implications for success of these two important grass species under a more variable and extreme climate forecast for the future.