Gene expression variability within and between human populations and implications toward disease susceptibility

A robust model for cell type-specific interindividual variation in single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) has been widely used to characterize cell types based on their average gene expression profiles. However, most studies do not consider cell type-specific variation across donors. Modelling this cell type-specific inter-individual variation could help elucidate cell type-specific biology and inform genes and cell types underlying complex traits. We therefore develop a new model to detect and quantify cell type-specific variation across individuals called CTMM (Cell Type-specific linear Mixed Model). We use extensive simulations to show that CTMM is powerful and unbiased in realistic settings. We also derive calibrated tests for cell type-specific interindividual variation, which is challenging given the modest sample sizes in scRNA-seq. We apply CTMM to scRNA-seq data from human induced pluripotent stem cells to characterize the transcriptomic variation across donors as cells differentiate into endoderm. We find that almost 100% of transcriptome-wide variability between donors is differentiation stage-specific. CTMM also identifies individual genes with statistically significant stage-specific variability across samples, including 85 genes that do not have significant stage-specific mean expression. Finally, we extend CTMM to partition interindividual covariance between stages, which recapitulates the overall differentiation trajectory. Overall, CTMM is a powerful tool to illuminate cell type-specific biology in scRNA-seq.

Polymorphisms of CCR5, IL-6, IFN-γ and IL-10 genes in Cuban HIV/AIDS patients

INTRODUCTION

Genetic studies have shown associations of several single nucleotide polymorphisms (SNP) with different rates of progression and variation in susceptibility to HIV infection. This study aimed to estimate the frequency of ccr5Δ32, IL-6-174G/C, IFN-γ+874T/A and IL-10-1082A/G polymorphisms in Cuban HIV-infected patients and a group of sero-discordant couples to assess their influence on risk and disease progression.

METHODS

A cross-sectional study was carried out on 120 subjects registered at the Institute of Tropical Medicine «Pedro Kour» (IPK) and the Ameijeiras Hospital from June 2018 until December 2019. The amplification of fragments of the ccr5, IL-6, IFN-γ and IL-10 genes was performed by polymerase chain reaction followed by identification of polymorphisms using the restriction fragment length polymorphism analysis for IL-6 with the restriction enzymes Nla III. Amplification Refractory Mutation System was used for IFN-γ and IL-10 genes.

RESULTS

The allelic and genotypic distributions of the genes ccr5, IL-6, IFN-γ and IL-10 did not differ significantly between the two groups. Cell counts and plasma viral load values did not differ significantly between genotypes of the ccr5, IL-6, IFN-γ and IL-10 genes. Only the IL-6 GC genotype was associated with higher viral load values. The combination of alleles of the four considered SNPs showed a highly significant increase in the risk of HIV infection for one of them, but with a very low frequency (<1%).

CONCLUSION

This study contributes to evaluating the frequency of these polymorphisms and their influence on biomarkers of the progression of HIV infection in the Cuban HIV-population.

Direct inference and control of genetic population structure from RNA sequencing data

RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations and the Geuvadis study, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data.

Outlining the Clinical Profile of TCIRG1 14 Variants including 5 Novels with Overview of ARO Phenotype and Ethnic Impact in 20 Egyptian Families

TCIRG1 gene mutations underlie osteopetrosis, a rare genetic disorder impacting osteoclast function with consequent brittle bones prone to fracture, in spite of being characterized by increased bone density. The disorder is known to exhibit marked genetic heterogeneity, has no treatment, and is lethal in most instances. There are reports of ethnic variations affecting bone mineral density and variants' expression as diverse phenotypes even within individuals descending from the same pedigree. We herein focus on one of osteopetrosis's three types: the autosomal recessive malignant form (MIM 259700) (ARO) that is almost always associated with severe clinical symptoms. We reviewed the results of about 1800 Egyptian exomes and we did not detect similar variants within our Egyptian dataset and secondary neurological deficit. We studied twenty Egyptian families: sixteen ARO patients, ten carrier parents with at least one ARO affected sib, and two fetuses. They were all subjected to thorough evaluation and TCIRG1 gene sequencing. Our results of twenty-eight individuals descending from twenty Egyptian pedigrees with at least one ARO patient, expand the phenotype as well as genotype spectrum of recessive mutations in the TCIRG1 gene by five novel pathogenic variants. Identifying TCIRG1 gene mutations in Egyptian patients with ARO allowed the provision of proper genetic counseling, carrier detection, and prenatal diagnosis starting with two families included herein. It also could pave the way to modern genomic therapeutic approaches.

Gene expression variability in long-term survivors of childhood cancer and cancer-free controls in response to ionizing irradiation

BACKGROUND

Differential expression analysis is usually adjusted for variation. However, most studies that examined the expression variability (EV) have used computations affected by low expression levels and did not examine healthy tissue. This study aims to calculate and characterize an unbiased EV in primary fibroblasts of childhood cancer survivors and cancer-free controls (N0) in response to ionizing radiation.

METHODS

Human skin fibroblasts of 52 donors with a first primary neoplasm in childhood (N1), 52 donors with at least one second primary neoplasm (N2 +), as well as 52 N0 were obtained from the KiKme case-control study and exposed to a high (2 Gray) and a low dose (0.05 Gray) of X-rays and sham- irradiation (0 Gray). Genes were then classified as hypo-, non-, or hyper-variable per donor group and radiation treatment, and then examined for over-represented functional signatures.

RESULTS

We found 22 genes with considerable EV differences between donor groups, of which 11 genes were associated with response to ionizing radiation, stress, and DNA repair. The largest number of genes exclusive to one donor group and variability classification combination were all detected in N0: hypo-variable genes after 0 Gray (n = 49), 0.05 Gray (n = 41), and 2 Gray (n = 38), as well as hyper-variable genes after any dose (n = 43). While after 2 Gray positive regulation of cell cycle was hypo-variable in N0, (regulation of) fibroblast proliferation was over-represented in hyper-variable genes of N1 and N2+. In N2+, 30 genes were uniquely classified as hyper-variable after the low dose and were associated with the ERK1/ERK2 cascade. For N1, no exclusive gene sets with functions related to the radiation response were detected in our data.

CONCLUSION

N2+ showed high degrees of variability in pathways for the cell fate decision after genotoxic insults that may lead to the transfer and multiplication of DNA-damage via proliferation, where apoptosis and removal of the damaged genome would have been appropriate. Such a deficiency could potentially lead to a higher vulnerability towards side effects of exposure to high doses of ionizing radiation, but following low-dose applications employed in diagnostics, as well.

A robust model for cell type-specific interindividual variation in single-cell RNA sequencing data

The development of single-cell RNA sequencing (scRNA-seq) offers opportunities to characterize cellular heterogeneity at unprecedented resolution. Although scRNA-seq has been widely used to identify and characterize gene expression variation across cell types and cell states based on their average gene expression profiles, most studies ignore variation across individual donors. Modelling this inter-individual variation could improve statistical power to detect cell type-specific biology and inform the genes and cell types that underlying complex traits. We therefore develop a new model to detect and quantify cell type-specific variation across individuals called CTMM (Cell Type-specific linear Mixed Model). CTMM operates on cell type-specific pseudobulk expression and is fit with efficient methods that scale to hundreds of samples. We use extensive simulations to show that CTMM is powerful and unbiased in realistic settings. We also derive calibrated tests for cell type-specific interindividual variation, which is challenging given the modest sample sizes in scRNA-seq data. We apply CTMM to scRNA-seq data from human induced pluripotent stem cells to characterize the transcriptomic variation across donors as cells differentiate into endoderm. We find that almost 100% of transcriptome-wide variability between donors is differentiation stage-specific. CTMM also identifies individual genes with statistically significant stage-specific variability across samples, including 61 genes that do not have significant stage-specific mean expression. Finally, we extend CTMM to partition interindividual covariance between stages, which recapitulates the overall differentiation trajectory. Overall, CTMM is a powerful tool to characterize a novel dimension of cell type-specific biology in scRNA-seq.

Endogenous miRNA-Based Innate-Immunity against SARS-CoV-2 Invasion of the Brain

The severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2), the causative agent of COVID-19, possesses an unusually large positive-sense, single-stranded viral RNA (ssvRNA) genome of about ~29,903 nucleotides (nt). In many respects, this ssvRNA resembles a very large, polycistronic messenger RNA (mRNA) possessing a 5'-methyl cap (m7GpppN), a 3'- and 5'-untranslated region (3'-UTR, 5'-UTR), and a poly-adenylated (poly-A+) tail. As such, the SARS-CoV-2 ssvRNA is susceptible to targeting by small non-coding RNA (sncRNA) and/or microRNA (miRNA), as well as neutralization and/or inhibition of its infectivity via the human body's natural complement of about ~2650 miRNA species. Depending on host cell and tissue type, in silico analysis, RNA sequencing, and molecular-genetic investigations indicate that, remarkably, almost every single human miRNA has the potential to interact with the primary sequence of SARS-CoV-2 ssvRNA. Individual human variation in host miRNA abundance, speciation, and complexity among different human populations and additional variability in the cell and tissue distribution of the SARS-CoV-2 angiotensin converting enzyme-2 (ACE2) receptor (ACE2R) appear to further contribute to the molecular-genetic basis for the wide variation in individual host cell and tissue susceptibility to COVID-19 infection. In this paper, we review recently described aspects of the miRNA and ssvRNA ribonucleotide sequence structure in this highly evolved miRNA-ssvRNA recognition and signaling system and, for the first time, report the most abundant miRNAs in the control superior temporal lobe neocortex (STLN), an anatomical area involved in cognition and targeted by both SARS-CoV-2 invasion and Alzheimer's disease (AD). We further evaluate important factors involving the neurotropic nature of SARS-CoV-2 and miRNAs and ACE2R distribution in the STLN that modulate significant functional deficits in the brain and CNS associated with SARS-CoV-2 infection and COVID-19's long-term neurological effects.

Comparative studies of genomic and epigenetic factors influencing transcriptional variation in two insect species

Different genes show different levels of expression variability. For example, highly expressed genes tend to exhibit less expression variability. Genes whose promoters have TATA box and initiator motifs tend to have increased expression variability. On the other hand, DNA methylation of transcriptional units, or gene body DNA methylation, is associated with reduced gene expression variability in many species. Interestingly, some insect lineages, most notably Diptera including the canonical model insect Drosophila melanogaster, have lost DNA methylation. Therefore, it is of interest to determine whether genomic features similarly influence gene expression variability in lineages with and without DNA methylation. We analyzed recently generated large-scale data sets in D. melanogaster and honey bee (Apis mellifera) to investigate these questions. Our analysis shows that increased gene expression levels are consistently associated with reduced expression variability in both species, while the presence of TATA box is consistently associated with increased gene expression variability. In contrast, initiator motifs and gene lengths have weak effects limited to some data sets. Importantly, we show that a sequence characteristics indicative of gene body DNA methylation is strongly and negatively associate with gene expression variability in honey bees, while it shows no such association in D. melanogaster. These results suggest the evolutionary loss of DNA methylation in some insect lineages has reshaped the molecular mechanisms concerning the regulation of gene expression variability.

Molecular Characterization and Genotype-Phenotype Correlation of G6PD Mutations in Five Ethnicities of Northern Vietnam

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is the most common enzyme disorder and is caused by G6PD gene mutations. To date, more than 400 variants in the G6PD gene have been discovered, and about 160 identified variants are associated with a significant decrease in the G6PD enzyme activity. However, the molecular characterization and epidemiological study of G6PD deficiency are still limited in Vietnam. Therefore, we conducted this study to determine the G6PD variants among the Vietnamese populations and evaluate their correlation to G6PD enzyme activity. A total of 339 patients (302 males and 37 females) were enrolled in this study. The G6PD variants were identified by Sanger sequencing. Our results indicate that males are more severely deficient in G6PD than females. This enzyme activity in males (1.27 ± 1.06 IU/g·Hb) is significantly lower than in females (2.98 ± 1.57 IU/g·Hb) (p < 0.0001). The enzyme activity of the heterozygous-homozygous females and heterozygous females-hemizygous males was found to be significantly different (p < 0.05), which is interpreted due to random X-inactivation. For G6PD molecular characteristics, Viangchan (c.871G>A), Canton (c.1376G>T) and Kaiping (c.1388G>A) variants were the most dominant, accounting for 24.48%, 17.70%, and 22.42%, respectively, whereas the highest frequency of complex variants was observed in Viangchan/Silent with 20.35%. In terms of G6PD activity, the Union variant presented the lowest mean value (1.03 IU/g·Hb) compared to the other variants (p < 0.05). Computational analysis using Polyphen-2 tool investigated that all variants were relative to G6PD deficiency and separated the levels as benign and damaged. The result will establish effective methods to screen G6PD variants in Vietnam.

MicroRNA biomarkers in frontotemporal dementia and to distinguish from Alzheimer's disease and amyotrophic lateral sclerosis

Frontotemporal lobar degeneration describes a group of progressive brain disorders that primarily are associated with atrophy of the prefrontal and anterior temporal lobes. Frontotemporal lobar degeneration is considered to be equivalent to frontotemporal dementia. Frontotemporal dementia is characterized by progressive impairments in behavior, executive function, and language. There are two main clinical subtypes: behavioral-variant frontotemporal dementia and primary progressive aphasia. The early diagnosis of frontotemporal dementia is critical for developing management strategies and interventions for these patients. Without validated biomarkers, the clinical diagnosis depends on recognizing all the core or necessary neuropsychiatric features, but misdiagnosis often occurs due to overlap with a range of neurologic and psychiatric disorders. In the studies reviewed a very large number of microRNAs were found to be dysregulated but with limited overlap between individual studies. Measurement of specific miRNAs singly or in combination, or as miRNA pairs (as a ratio) in blood plasma, serum, or cerebrospinal fluid enabled frontotemporal dementia to be discriminated from healthy controls, Alzheimer's disease, and amyotrophic lateral sclerosis. Furthermore, upregulation of miR-223-3p and downregulation of miR-15a-5p, which occurred both in blood serum and cerebrospinal fluid, distinguished behavioral-variant frontotemporal dementia from healthy controls. Downregulation of miR-132-3p in frontal and temporal cortical tissue distinguished frontotemporal lobar degeneration and frontotemporal dementia, respectively, from healthy controls. Possible strong miRNA biofluid biomarker contenders for behavioral-variant frontotemporal dementia are miR-223-3p, miR-15a-5p, miR-22-3p in blood serum and cerebrospinal fluid, and miR-124 in cerebrospinal fluid. No miRNAs were identified able to distinguish between behavioral-variant frontotemporal dementia and primary progressive aphasia subtypes. Further studies are warranted on investigating miRNA expression in biofluids and frontal/temporal cortical tissue to validate and extend these findings.

Identification of human long noncoding RNAs associated with nonalcoholic fatty liver disease and metabolic homeostasis

A growing number of long noncoding RNAs (lncRNAs) have emerged as vital metabolic regulators. However, most human lncRNAs are nonconserved and highly tissue specific, vastly limiting our ability to identify human lncRNA metabolic regulators (hLMRs). In this study, we established a pipeline to identify putative hLMRs that are metabolically sensitive, disease relevant, and population applicable. We first progressively processed multilevel human transcriptome data to select liver lncRNAs that exhibit highly dynamic expression in the general population, show differential expression in a nonalcoholic fatty liver disease (NAFLD) population, and respond to dietary intervention in a small NAFLD cohort. We then experimentally demonstrated the responsiveness of selected hepatic lncRNAs to defined metabolic milieus in a liver-specific humanized mouse model. Furthermore, by extracting a concise list of protein-coding genes that are persistently correlated with lncRNAs in general and NAFLD populations, we predicted the specific function for each hLMR. Using gain- and loss-of-function approaches in humanized mice as well as ectopic expression in conventional mice, we validated the regulatory role of one nonconserved hLMR in cholesterol metabolism by coordinating with an RNA-binding protein, PTBP1, to modulate the transcription of cholesterol synthesis genes. Our work overcame the heterogeneity intrinsic to human data to enable the efficient identification and functional definition of disease-relevant human lncRNAs in metabolic homeostasis.

Increased gene expression variability in BRCA1-associated and basal-like breast tumours

Purpose

Inherited variants in the cancer susceptibility genes, BRCA1 and BRCA2 account for up to 5% of breast cancers. Multiple gene expression studies have analysed gene expression patterns that maybe associated with BRCA12 pathogenic variant status; however, results from these studies lack consensus. These studies have focused on the differences in population means to identified genes associated with BRCA1/2-carriers with little consideration for gene expression variability, which is also under genetic control and is a feature of cellular function.

Methods

We measured differential gene expression variability in three of the largest familial breast cancer datasets and a 2116 breast cancer meta-cohort. Additionally, we used RNA in situ hybridisation to confirm expression variability of EN1 in an independent cohort of more than 500 breast tumours.

Results

BRCA1-associated breast tumours exhibited a 22.8% (95% CI 22.3–23.2) increase in transcriptome-wide gene expression variability compared to BRCAx tumours. Additionally, 40 genes were associated with BRCA1-related breast cancers that had ChIP-seq data suggestive of enriched EZH2 binding. Of these, two genes (EN1 and IGF2BP3) were significantly variable in both BRCA1-associated and basal-like breast tumours. RNA in situ analysis of EN1 supported a significant (p = 6.3 × 10⁻⁰⁴) increase in expression variability in BRCA1-associated breast tumours.

Conclusion

Our novel results describe a state of increased gene expression variability in BRCA1-related and basal-like breast tumours. Furthermore, genes with increased variability may be driven by changes in DNA occupancy of epigenetic effectors. The variation in gene expression is replicable and led to the identification of novel associations between genes and disease phenotypes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-021-06328-y.

Prioritizing and characterizing functionally relevant genes across human tissues

Knowledge of genes that are critical to a tissue's function remains difficult to ascertain and presents a major bottleneck toward a mechanistic understanding of genotype-phenotype links. Here, we present the first machine learning model-FUGUE-combining transcriptional and network features, to predict tissue-relevant genes across 30 human tissues. FUGUE achieves an average cross-validation auROC of 0.86 and auPRC of 0.50 (expected 0.09). In independent datasets, FUGUE accurately distinguishes tissue or cell type-specific genes, significantly outperforming the conventional metric based on tissue-specific expression alone. Comparison of tissue-relevant transcription factors across tissue recapitulate their developmental relationships. Interestingly, the tissue-relevant genes cluster on the genome within topologically associated domains and furthermore, are highly enriched for differentially expressed genes in the corresponding cancer type. We provide the prioritized gene lists in 30 human tissues and an open-source software to prioritize genes in a novel context given multi-sample transcriptomic data.

Variation of microRNA expression in the human placenta driven by population identity and sex of the newborn

BACKGROUND

Analysis of lymphocyte cell lines revealed substantial differences in the expression of mRNA and microRNA (miRNA) among human populations. The extent of such population-associated differences in actual human tissues remains largely unexplored. The placenta is one of the few solid human tissues that can be collected in substantial numbers in a controlled manner, enabling quantitative analysis of transient biomolecules such as RNA transcripts. Here, we analyzed microRNA (miRNA) expression in human placental samples derived from 36 individuals representing four genetically distinct human populations: African Americans, European Americans, South Asians, and East Asians. All samples were collected at the same hospital following a unified protocol, thus minimizing potential biases that might influence the results.

RESULTS

Sequence analysis of the miRNA fraction yielded 938 annotated and 70 novel miRNA transcripts expressed in the placenta. Of them, 82 (9%) of annotated and 11 (16%) of novel miRNAs displayed quantitative expression differences among populations, generally reflecting reported genetic and mRNA-expression-based distances. Several co-expressed miRNA clusters stood out from the rest of the population-associated differences in terms of miRNA evolutionary age, tissue-specificity, and disease-association characteristics. Among three non-environmental influenced demographic parameters, the second largest contributor to miRNA expression variation after population was the sex of the newborn, with 32 miRNAs (3% of detected) exhibiting significant expression differences depending on whether the newborn was male or female. Male-associated miRNAs were evolutionarily younger and correlated inversely with the expression of target mRNA involved in neuron-related functions. In contrast, both male and female-associated miRNAs appeared to mediate different types of hormonal responses. Demographic factors further affected reported imprinted expression of 66 placental miRNAs: the imprinting strength correlated with the mother's weight, but not height.

CONCLUSIONS

Our results showed that among 12 assessed demographic variables, population affiliation and fetal sex had a substantial influence on miRNA expression variation among human placental samples. The effect of newborn-sex-associated miRNA differences further led to expression inhibition of the target genes clustering in specific functional pathways. By contrast, population-driven miRNA differences might mainly represent neutral changes with minimal functional impacts.

Natural variation of the cardiac transcriptome in humans

We investigated the gene-expression variation among humans by analysing previously published mRNA-seq and ribosome footprint profiling of heart left-ventricles from healthy donors. We ranked the genes according to their coefficient of variation values and found that the top 5% most variable genes had special features compared to the rest of the genome, such as lower mRNA levels and shorter half-lives coupled to increased translation efficiency. We observed that these genes are mostly involved with immune response and have a pleiotropic effect on disease phenotypes, indicating that asymptomatic conditions contribute to the gene expression diversity of healthy individuals.

CRELD1 modulates homeostasis of the immune system in mice and humans

CRELD1 is a pivotal factor for heart development, the function of which is unknown in adult life. We here provide evidence that CRELD1 is an important gatekeeper of immune system homeostasis. Exploiting expression variance in large human cohorts contrasting individuals with the lowest and highest CRELD1 expression levels revealed strong phenotypic, functional and transcriptional differences, including reduced CD4⁺ T cell numbers. These findings were validated in T cell-specific Creld1-deficient mice. Loss of Creld1 was associated with simultaneous overactivation and increased apoptosis, resulting in a net loss of T cells with age. Creld1 was transcriptionally and functionally linked to Wnt signaling. Collectively, gene expression variance in large human cohorts combined with murine genetic models, transcriptomics and functional testing defines CRELD1 as an important modulator of immune homeostasis.

Gene expression variability in human and chimpanzee populations share common determinants

Inter-individual variation in gene expression has been shown to be heritable and is often associated with differences in disease susceptibility between individuals. Many studies focused on mapping associations between genetic and gene regulatory variation, yet much less attention has been paid to the evolutionary processes that shape the observed differences in gene regulation between individuals in humans or any other primate. To begin addressing this gap, we performed a comparative analysis of gene expression variability and expression quantitative trait loci (eQTLs) in humans and chimpanzees, using gene expression data from primary heart samples. We found that expression variability in both species is often determined by non-genetic sources, such as cell-type heterogeneity. However, we also provide evidence that inter-individual variation in gene regulation can be genetically controlled, and that the degree of such variability is generally conserved in humans and chimpanzees. In particular, we found a significant overlap of orthologous genes associated with eQTLs in both species. We conclude that gene expression variability in humans and chimpanzees often evolves under similar evolutionary pressures.

Investigating population-scale allelic differential expression in wild populations of Oithona similis (Cyclopoida, Claus, 1866)

Acclimation allowed by variation in gene or allele expression in natural populations is increasingly understood as a decisive mechanism, as much as adaptation, for species evolution. However, for small eukaryotic organisms, as species from zooplankton, classical methods face numerous challenges. Here, we propose the concept of allelic differential expression at the population-scale (psADE) to investigate the variation in allele expression in natural populations. We developed a novel approach to detect psADE based on metagenomic and metatranscriptomic data from environmental samples. This approach was applied on the widespread marine copepod, Oithona similis, by combining samples collected during the Tara Oceans expedition (2009-2013) and de novo transcriptome assemblies. Among a total of 25,768 single nucleotide variants (SNVs) of O. similis, 572 (2.2%) were affected by psADE in at least one population (FDR < 0.05). The distribution of SNVs under psADE in different populations is significantly shaped by population genomic differentiation (Pearson r = 0.87, p = 5.6 × 10-30), supporting a partial genetic control of psADE. Moreover, a significant amount of SNVs (0.6%) were under both selection and psADE (p < .05), supporting the hypothesis that natural selection and psADE tends to impact common loci. Population-scale allelic differential expression offers new insights into the gene regulation control in populations and its link with natural selection.

Single-Cell Expression Variability Implies Cell Function

As single-cell RNA sequencing (scRNA-seq) data becomes widely available, cell-to-cell variability in gene expression, or single-cell expression variability (scEV), has been increasingly appreciated. However, it remains unclear whether this variability is functionally important and, if so, what are its implications for multi-cellular organisms. Here, we analyzed multiple scRNA-seq data sets from lymphoblastoid cell lines (LCLs), lung airway epithelial cells (LAECs), and dermal fibroblasts (DFs) and, for each cell type, selected a group of homogenous cells with highly similar expression profiles. We estimated the scEV levels for genes after correcting the mean-variance dependency in that data and identified 465, 466, and 364 highly variable genes (HVGs) in LCLs, LAECs, and DFs, respectively. Functions of these HVGs were found to be enriched with those biological processes precisely relevant to the corresponding cell type’s function, from which the scRNA-seq data used to identify HVGs were generated—e.g., cytokine signaling pathways were enriched in HVGs identified in LCLs, collagen formation in LAECs, and keratinization in DFs. We repeated the same analysis with scRNA-seq data from induced pluripotent stem cells (iPSCs) and identified only 79 HVGs with no statistically significant enriched functions; the overall scEV in iPSCs was of negligible magnitude. Our results support the “variation is function” hypothesis, arguing that scEV is required for cell type-specific, higher-level system function. Thus, quantifying and characterizing scEV are of importance for our understating of normal and pathological cellular processes.

Human gene expression variability and its dependence on methylation and aging

BACKGROUND

Phenotypic variability of human populations is partly the result of gene polymorphism and differential gene expression. As such, understanding the molecular basis for diversity requires identifying genes with both high and low population expression variance and identifying the mechanisms underlying their expression control. Key issues remain unanswered with respect to expression variability in human populations. The role of gene methylation as well as the contribution that age, sex and tissue-specific factors have on expression variability are not well understood.

RESULTS

Here we used a novel method that accounts for sampling error to classify human genes based on their expression variability in normal human breast and brain tissues. We find that high expression variability is almost exclusively unimodal, indicating that variance is not the result of segregation into distinct expression states. Genes with high expression variability differ markedly between tissues and we find that genes with high population expression variability are likely to have age-, but not sex-dependent expression. Lastly, we find that methylation likely has a key role in controlling expression variability insofar as genes with low expression variability are likely to be non-methylated.

CONCLUSIONS

We conclude that gene expression variability in the human population is likely to be important in tissue development and identity, methylation, and in natural biological aging. The expression variability of a gene is an important functional characteristic of the gene itself and the classification of a gene as one with Hyper-Variability or Hypo-Variability in a human population or in a specific tissue should be useful in the identification of important genes that functionally regulate development or disease.

Gene expression variability: the other dimension in transcriptome analysis

Transcriptome sequencing is a powerful technique to study molecular changes that underlie the differences in physiological conditions and disease progression. A typical question that is posed in such studies is finding genes with significant changes between sample groups. In this respect expression variability is regarded as a nuisance factor that is primarily of technical origin and complicates the data analysis. However, it is becoming apparent that the biological variation in gene expression might be an important molecular phenotype that can affect physiological parameters. In this review we explore the recent literature on technical and biological variability in gene expression, sources of expression variability, (epi-)genetic hallmarks, and evolutionary constraints in genes with robust and variable gene expression. We provide an overview of recent findings on effects of external cues, such as diet and aging, on expression variability and on other biological phenomena that can be linked to it. We discuss metrics and tools that were developed for quantification of expression variability and highlight the importance of future studies in this direction. To assist the adoption of expression variability analysis, we also provide a detailed description and computer code, which can easily be utilized by other researchers. We also provide a reanalysis of recently published data to highlight the value of the analysis method.

The relationships between urinary phthalate metabolites, reproductive hormones and semen parameters in men attending in vitro fertilization clinic

Evidence from previous studies has shown that phthalates may play a role in male reproductive function; however, results are still inconclusive, and the mechanism remains unclear. In this study, we first assessed whether exposure to phthalates is associated with altered reproductive hormones and semen parameters in 599 men attending an in vitro fertilization clinic. Secondly, we evaluated whether reproductive hormones could play a mediating role in the association between phthalates and sperm parameters. Eight phthalate metabolites were measured in two different spot urine samples: mono‑n‑butyl phthalate, mono-isobutyl phthalate (MiBP), monoethyl phthalate (MEP), monobenzyl phthalate, and four oxidative metabolites of di‑(2‑ethylhexyl) phthalate (DEHP) [i.e., mono‑(2‑ethylhexyl) phthalate (MEHP), mono‑(2‑ethyl‑5‑hydroxyhexyl) phthalate (MEHHP), mono‑(2‑ethyl‑5‑oxohexyl) phthalate (MEOHP), and mono‑(2‑ethyl‑5‑carboxypentyl) phthalate (MECPP)]. Semen parameters (concentration, volume, motility, and morphology) and reproductive hormones, i.e., follicle-stimulating hormone (FSH), luteinizing hormone (LH), thyroid-stimulating hormone, estradiol (E₂), testosterone (TEST) and prolactin (PROL) were also determined and considered the main study outcomes. Separate multivariate linear regression was used to assess associations between levels of each urinary phthalate metabolite, molar sum of DEHP metabolites (∑DEHP), percentage of MEHP to ∑DEHP (%MEHP), and each outcome (natural log-transformed). Inverse associations were observed between TEST and MiBP (β = -0.099), FSH and MEHHP (β = -0.087), and PROL and MEOHP (β = -0.102), while a positive relationship was seen between E₂ and MEP (β = 0.098). %MEHP was associated positively with FSH (β = 0.118) and LH (β = 0.099), but negatively with TEST/LH (β = -0.086) and TEST/E₂ (β = -0.109). Sperm concentration was associated positively with MECPP (β = 0.131), MEHHP (β = 0.117), MEOHP (β = 0.107) and ∑DEHP (β = 0.111), but negatively with %MEHP (β = -0.135). All p-values were <0.05. Sobel's test indicated that FSH mediated significantly up to 60% of the positive relationship between sperm concentration and MEHHP, while FSH and LH mediated respectively 15 and 12% of the inverse association between sperm concentration and %MEHP. Further research on this topic is warranted.

Male increase in brain gene expression variability is linked to genetic risk for schizophrenia

Schizophrenia shows substantial sex differences in age of onset, course, and treatment response, but the biological basis of these effects is incompletely understood. Here we show that during human development, males show a regionally specific decrease in brain expression similarity compared to females. The genes modulating this effect were significantly co-expressed with schizophrenia risk genes during prefrontal cortex brain development in the fetal period as well as during early adolescence. This suggests a genetic contribution to a mechanism through which developmental abnormalities manifest with psychosis during adolescence. It further supports sex differences in brain expression variability as a factor underlying the well-established sex differences in schizophrenia.

Epigenetic and Transcriptional Variability Shape Phenotypic Plasticity

Epigenetic and transcriptional variability contribute to the vast diversity of cellular and organismal phenotypes and are key in human health and disease. In this review, we describe different types, sources, and determinants of epigenetic and transcriptional variability, enabling cells and organisms to adapt and evolve to a changing environment. We highlight the latest research and hypotheses on how chromatin structure and the epigenome influence gene expression variability. Further, we provide an overview of challenges in the analysis of biological variability. An improved understanding of the molecular mechanisms underlying epigenetic and transcriptional variability, at both the intra- and inter-individual level, provides great opportunity for disease prevention, better therapeutic approaches, and personalized medicine.

Validation of microRNAs in Cerebrospinal Fluid as Biomarkers for Different Forms of Dementia in a Multicenter Study

MicroRNAs (miRNAs) regulate translational inhibition of proteins, but are also detected in body fluids, including cerebrospinal fluid (CSF), where they may serve as disease-specific biomarkers. Previously, we showed differential expression of miR-146a, miR-29a, and miR-125b in the CSF of Alzheimer's disease (AD) patients versus controls. In this study, we aim to confirm these findings by using larger, independent sample cohorts of AD patients and controls from three different centers. Furthermore, we aim to identify confounding factors that possibly arise using such a multicenter approach. The study was extended by including patients diagnosed with mild cognitive impairment due to AD, frontotemporal dementia and dementia with Lewy bodies. Previous results of decreased miR-146a levels in AD patients compared to controls were confirmed in one center. When samples from all three centers were combined, several confounding factors were identified. After controlling for these factors, we did not identify differences in miRNA levels between the different groups. However, we provide suggestions to circumvent various pitfalls when measuring miRNAs in CSF to improve future studies.

HEROD: a human ethnic and regional specific omics database

Motivation

Genetic and gene expression variations within and between populations and across geographical regions have substantial effects on the biological phenotypes, diseases, and therapeutic response. The development of precision medicines can be facilitated by the OMICS studies of the patients of specific ethnicity and geographic region. However, there is an inadequate facility for broadly and conveniently accessing the ethnic and regional specific OMICS data.

Results

Here, we introduced a new free database, HEROD, a human ethnic and regional specific OMICS database. Its first version contains the gene expression data of 53 070 patients of 169 diseases in seven ethnic populations from 193 cities/regions in 49 nations curated from the Gene Expression Omnibus (GEO), the ArrayExpress Archive of Functional Genomics Data (ArrayExpress), the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Geographic region information of curated patients was mainly manually extracted from referenced publications of each original study. These data can be accessed and downloaded via keyword search, World map search, and menu-bar search of disease name, the international classification of disease code, geographical region, location of sample collection, ethnic population, gender, age, sample source organ, patient type (patient or healthy), sample type (disease or normal tissue) and assay type on the web interface.

Availability and implementation

The HEROD database is freely accessible at http://bidd2.nus.edu.sg/herod/index.php. The database and web interface are implemented in MySQL, PHP and HTML with all major browsers supported.

Contact

phacyz@nus.edu.sg.

How heterogeneous susceptibility and recovery rates affect the spread of epidemics on networks

In this paper, an extended heterogeneous SIR model is proposed, which generalizes the heterogeneous mean-field theory. Different from the traditional heterogeneous mean-field model only taking into account the heterogeneity of degree, our model considers not only the heterogeneity of degree but also the heterogeneity of susceptibility and recovery rates. Then, we analytically study the basic reproductive number and the final epidemic size. Combining with numerical simulations, it is found that the basic reproductive number depends on the mean of distributions of susceptibility and disease course when both of them are independent. If the mean of these two distributions is identical, increasing the variance of susceptibility may block the spread of epidemics, while the corresponding increase in the variance of disease course has little effect on the final epidemic size. It is also shown that positive correlations between individual susceptibility, course of disease and the square of degree make the population more vulnerable to epidemic and avail to the epidemic prevalence, whereas the negative correlations make the population less vulnerable and impede the epidemic prevalence.

Gene expression variability and the analysis of large-scale RNA-seq studies with the MDSeq

Rapidly decreasing cost of next-generation sequencing has led to the recent availability of large-scale RNA-seq data, that empowers the analysis of gene expression variability, in addition to gene expression means. In this paper, we present the MDSeq, based on the coefficient of dispersion, to provide robust and computationally efficient analysis of both gene expression means and variability on RNA-seq counts. The MDSeq utilizes a novel reparametrization of the negative binomial to provide flexible generalized linear models (GLMs) on both the mean and dispersion. We address challenges of analyzing large-scale RNA-seq data via several new developments to provide a comprehensive toolset that models technical excess zeros, identifies outliers efficiently, and evaluates differential expressions at biologically interesting levels. We evaluated performances of the MDSeq using simulated data when the ground truths are known. Results suggest that the MDSeq often outperforms current methods for the analysis of gene expression mean and variability. Moreover, the MDSeq is applied in two real RNA-seq studies, in which we identified functionally relevant genes and gene pathways. Specifically, the analysis of gene expression variability with the MDSeq on the GTEx human brain tissue data has identified pathways associated with common neurodegenerative disorders when gene expression means were conserved.

Universal attenuators and their interactions with feedback loops in gene regulatory networks

Using a combination of mathematical modelling, statistical simulation and large-scale data analysis we study the properties of linear regulatory chains (LRCs) within gene regulatory networks (GRNs). Our modelling indicates that downstream genes embedded within LRCs are highly insulated from the variation in expression of upstream genes, and thus LRCs act as attenuators. This observation implies a progressively weaker functionality of LRCs as their length increases. When analyzing the preponderance of LRCs in the GRNs of Escherichia coli K12 and several other organisms, we find that very long LRCs are essentially absent. In both E. coli and M. tuberculosis we find that four-gene LRCs are intimately linked to identical feedback loops that are involved in potentially chaotic stress response, indicating that the dynamics of these potentially destabilising motifs are strongly restrained under homeostatic conditions. The same relationship is observed in a human cancer cell line (K562), and we postulate that four-gene LRCs act as ‘universal attenuators’. These findings suggest a role for long LRCs in dampening variation in gene expression, thereby protecting cell identity, and in controlling dramatic shifts in cell-wide gene expression through inhibiting chaos-generating motifs.

Rapid, broad-scale gene expression evolution in experimentally harvested fish populations

Gene expression changes potentially play an important role in adaptive evolution under human-induced selection pressures, but this has been challenging to demonstrate in natural populations. Fishing exhibits strong selection pressure against large body size, thus potentially inducing evolutionary changes in life history and other traits that may be slowly reversible once fishing ceases. However, there is a lack of convincing examples regarding the speed and magnitude of fisheries-induced evolution, and thus, the relevant underlying molecular-level effects remain elusive. We use wild-origin zebrafish (Danio rerio) as a model for harvest-induced evolution. We experimentally demonstrate broad-scale gene expression changes induced by just five generations of size-selective harvesting, and limited genetic convergence following the cessation of harvesting. We also demonstrate significant allele frequency changes in genes that were differentially expressed after five generations of size-selective harvesting. We further show that nine generations of captive breeding induced substantial gene expression changes in control stocks likely due to inadvertent selection in the captive environment. The large extent and rapid pace of the gene expression changes caused by both harvest-induced selection and captive breeding emphasizes the need for evolutionary enlightened management towards sustainable fisheries.

An improved and explicit surrogate variable analysis procedure by coefficient adjustment

Unobserved environmental, demographic and technical factors canadversely affect the estimation and testing of the effects ofprimary variables. Surrogate variable analysis, proposed to tacklethis problem, has been widely used in genomic studies. To estimatehidden factors that are correlated with the primary variables,surrogate variable analysis performs principal component analysiseither on a subset of features or on all features, but weightingeach differently. However, existing approaches may fail to identifyhidden factors that are strongly correlated with the primaryvariables, and the extra step of feature selection and weightcalculation makes the theoretical investigation of surrogatevariable analysis challenging. In this paper, we propose an improvedsurrogate variable analysis, using all measured features, that has anatural connection with restricted least squares, which allows us tostudy its theoretical properties. Simulation studies and real-dataanalysis show that the method is competitive with state-of-the-artmethods.

Collective effect of personal behavior induced preventive measures and differential rate of transmission on spread of epidemics

In the present work, the effect of personal behavior induced preventive measures is studied on the spread of epidemics over scale free networks that are characterized by the differential rate of disease transmission. The role of personal behavior induced preventive measures is parameterized in terms of variable λ, which modulates the number of concurrent contacts a node makes with the fraction of its neighboring nodes. The dynamics of the disease is described by a non-linear Susceptible Infected Susceptible model based upon the discrete time Markov Chain method. The network mean field approach is generalized to account for the effect of non-linear coupling between the aforementioned factors on the collective dynamics of nodes. The upper bound estimates of the disease outbreak threshold obtained from the mean field theory are found to be in good agreement with the corresponding non-linear stochastic model. From the results of parametric study, it is shown that the epidemic size has inverse dependence on the preventive measures (λ). It has also been shown that the increase in the average degree of the nodes lowers the time of spread and enhances the size of epidemics.

Genome-wide analysis of differential transcriptional and epigenetic variability across human immune cell types

Background

A healthy immune system requires immune cells that adapt rapidly to environmental challenges. This phenotypic plasticity can be mediated by transcriptional and epigenetic variability.

Results

We apply a novel analytical approach to measure and compare transcriptional and epigenetic variability genome-wide across CD14⁺CD16⁻ monocytes, CD66b⁺CD16⁺ neutrophils, and CD4⁺CD45RA⁺ naïve T cells from the same 125 healthy individuals. We discover substantially increased variability in neutrophils compared to monocytes and T cells. In neutrophils, genes with hypervariable expression are found to be implicated in key immune pathways and are associated with cellular properties and environmental exposure. We also observe increased sex-specific gene expression differences in neutrophils. Neutrophil-specific DNA methylation hypervariable sites are enriched at dynamic chromatin regions and active enhancers.

Conclusions

Our data highlight the importance of transcriptional and epigenetic variability for the key role of neutrophils as the first responders to inflammatory stimuli. We provide a resource to enable further functional studies into the plasticity of immune cells, which can be accessed from: http://blueprint-dev.bioinfo.cnio.es/WP10/hypervariability.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1156-8) contains supplementary material, which is available to authorized users.

Microenvironmental Gene Expression Plasticity Among Individual Drosophila melanogaster

Differences in phenotype among genetically identical individuals exposed to the same environmental condition are often noted in genetic studies. Despite this commonplace observation, little is known about the causes of this variability, which has been termed microenvironmental plasticity. One possibility is that stochastic or technical sources of variance produce these differences. A second possibility is that this variation has a genetic component. We have explored gene expression robustness in the transcriptomes of 730 individual Drosophila melanogaster of 16 fixed genotypes, nine of which are infected with Wolbachia. Three replicates of flies were grown, controlling for food, day/night cycles, humidity, temperature, sex, mating status, social exposure, and circadian timing of RNA extraction. Despite the use of inbred genotypes, and carefully controlled experimental conditions, thousands of genes were differentially expressed, revealing a unique and dynamic transcriptional signature for each individual fly. We found that 23% of the transcriptome was differentially expressed among individuals, and that the variability in gene expression among individuals is influenced by genotype. This transcriptional variation originated from specific gene pathways, suggesting a plastic response to the microenvironment; but there was also evidence of gene expression differences due to stochastic fluctuations. These observations reveal previously unappreciated genetic sources of variability in gene expression among individuals, which has implications for complex trait genetics and precision medicine.

Aluminum, the genetic apparatus of the human CNS and Alzheimer's disease (AD)

The genomes of eukaryotes orchestrate their expression to ensure an effective, homeostatic and functional gene signaling program, and this includes fundamentally altered patterns of transcription during aging, development, differentiation and disease. These actions constitute an extremely complex and intricate process as genetic operations such as transcription involve the very rapid translocation and polymerization of ribonucleotides using RNA polymerases, accessory transcription protein complexes and other interrelated chromatin proteins and genetic factors. As both free ribonucleotides and polymerized single-stranded RNA chains, ribonucleotides are highly charged with phosphate, and this genetic system is extremely vulnerable to disruption by a large number of electrostatic forces, and primarily by cationic metals such as aluminum. Aluminum has been shown by independent researchers to be particularly genotoxic to the genetic apparatus, and it has become reasonably clear that aluminum disturbs genetic signaling programs in the CNS that bear a surprising resemblance to those observed in Alzheimer's disease (AD) brain. This paper will focus on a discussion of two molecular-genetic aspects of aluminum genotoxicity: (1) the observation that micro-RNA (miRNA)-mediated global gene expression patterns in aluminum-treated transgenic animal models of AD (Tg-AD) strongly resemble those found in AD; and (2) the concept of "human biochemical individuality" and the hypothesis that individuals with certain gene expression patterns may be especially sensitive and perhaps predisposed to aluminum genotoxicity.

Topologically inferring pathway activity toward precise cancer classification via integrating genomic and metabolomic data: prostate cancer as a case

Precise cancer classification is a central challenge in clinical cancer research such as diagnosis, prognosis and metastasis prediction. Most existing cancer classification methods based on gene or metabolite biomarkers were limited to single genomics or metabolomics, and lacked integration and utilization of multiple ‘omics’ data. The accuracy and robustness of these methods when applied to independent cohorts of patients must be improved. In this study, we propose a directed random walk-based method to evaluate the topological importance of each gene in a reconstructed gene–metabolite graph by integrating information from matched gene expression profiles and metabolomic profiles. The joint use of gene and metabolite information contributes to accurate evaluation of the topological importance of genes and reproducible pathway activities. We constructed classifiers using reproducible pathway activities for precise cancer classification and risk metabolic pathway identification. We applied the proposed method to the classification of prostate cancer. Within-dataset experiments and cross-dataset experiments on three independent datasets demonstrated that the proposed method achieved a more accurate and robust overall performance compared to several existing classification methods. The resulting risk pathways and topologically important differential genes and metabolites provide biologically informative models for prostate cancer prognosis and therapeutic strategies development.

Variability of Gene Expression Identifies Transcriptional Regulators of Early Human Embryonic Development

An analysis of gene expression variability can provide an insightful window into how regulatory control is distributed across the transcriptome. In a single cell analysis, the inter-cellular variability of gene expression measures the consistency of transcript copy numbers observed between cells in the same population. Application of these ideas to the study of early human embryonic development may reveal important insights into the transcriptional programs controlling this process, based on which components are most tightly regulated. Using a published single cell RNA-seq data set of human embryos collected at four-cell, eight-cell, morula and blastocyst stages, we identified genes with the most stable, invariant expression across all four developmental stages. Stably-expressed genes were found to be enriched for those sharing indispensable features, including essentiality, haploinsufficiency, and ubiquitous expression. The stable genes were less likely to be associated with loss-of-function variant genes or human recessive disease genes affected by a DNA copy number variant deletion, suggesting that stable genes have a functional impact on the regulation of some of the basic cellular processes. Genes with low expression variability at early stages of development are involved in regulation of DNA methylation, responses to hypoxia and telomerase activity, whereas by the blastocyst stage, low-variability genes are enriched for metabolic processes as well as telomerase signaling. Based on changes in expression variability, we identified a putative set of gene expression markers of morulae and blastocyst stages. Experimental validation of a blastocyst-expressed variability marker demonstrated that HDDC2 plays a role in the maintenance of pluripotency in human ES and iPS cells. Collectively our analyses identified new regulators involved in human embryonic development that would have otherwise been missed using methods that focus on assessment of the average expression levels; in doing so, we highlight the value of studying expression variability for single cell RNA-seq data.

Identification of Critical Elements for Regulation of Inorganic Pyrophosphatase (PPA1) in MCF7 Breast Cancer Cells

Cytosolic inorganic pyrophosphatase plays an important role in the cellular metabolism by hydrolyzing inorganic pyrophosphate (PPi) formed as a by-product of various metabolic reactions. Inorganic pyrophosphatases are known to be associated with important functions related to the growth and development of various organisms. In humans, the expression of inorganic pyrophosphatase (PPA1) is deregulated in different types of cancer and is involved in the migration and invasion of gastric cancer cells and proliferation of ovarian cancer cells. However, the transcriptional regulation of the gene encoding PPA1 is poorly understood. To gain insights into PPA1 gene regulation, a 1217 bp of its 5'-flanking region was cloned and analyzed. The 5'-deletion analysis of the promoter revealed a 266 bp proximal promoter region exhibit most of the transcriptional activity and upon sequence analysis, three putative Sp1 binding sites were found to be present in this region. Binding of Sp1 to the PPA1 promoter was confirmed by Electrophoretic mobility shift assay (EMSA) and Chromatin immunoprecipitation (ChIP) assay. Importance of these binding sites was verified by site-directed mutagenesis and overexpression of Sp1 transactivates PPA1 promoter activity, upregulates protein expression and increases chromatin accessibility. p300 binds to the PPA1 promoter and stimulates Sp1 induced promoter activity. Trichostatin A (TSA), a histone deacetylase (HDAC) inhibitor induces PPA1 promoter activity and protein expression and HAT activity of p300 was important in regulation of PPA1 expression. These results demonstrated that PPA1 is positively regulated by Sp1 and p300 coactivates Sp1 induced PPA1 promoter activity and histone acetylation/deacetylation may contribute to a local chromatin remodeling across the PPA1 promoter. Further, knockdown of PPA1 decreased colony formation and viability of MCF7 cells.

MicroRNA-29a Is a Candidate Biomarker for Alzheimer's Disease in Cell-Free Cerebrospinal Fluid

The identification of reliable biomarkers for Alzheimer's disease (AD) remains challenging. Recently, abnormal levels of microRNAs (miRNAs) miR-27a, miR-29a, miR-29b, and miR-125b in cerebrospinal fluid (CSF) of AD patients were reported. We aimed to confirm the biomarker potential of these miRNAs for AD diagnosis. Additionally, we examined the influence of blood contamination on CSF miRNA levels as potential confounding factor. We studied expression levels of the four miRNAs by quantitative PCR in CSF samples of AD patients and non-demented controls, and in blood-spiked CSF. Levels of miR-29a, but not of the other three miRNAs, were increased by a factor of 2.2 in CSF of AD patients. Spiking of small amounts of blood into CSF revealed that miR-27a and miR-29a, but not miR-125b levels were strongly influenced by the number of blood cells in the sample. In conclusion, miR-29a may be a candidate biomarker for AD, but only when used in cell-free CSF.

Analysis of RNA from Alzheimer's Disease Post-mortem Brain Tissues

Alzheimer's disease (AD) is a uniquely human, age-related central nervous system (CNS) disorder for which there is no adequate experimental model. While well over 100 transgenic murine models of AD (TgAD) have been developed that recapitulate many of the neuropathological features of AD, key pathological features of AD such as progressive neuronal atrophy, neuron cell loss, and neurofibrillary tangle (NFT) formation have not been observed in any TgAD model to date. To more completely analyze and understand the neuropathology, altered neuro-inflammatory and innate-immune signaling pathways, and the complex molecular-genetics and epigenetics of AD, it is therefore necessary to rigorously examine short post-mortem interval (PMI) human brain tissues to gain a deeper and more thorough insight into the neuropathological mechanisms that characterize the AD process. This perspective-methods paper will highlight some important recent findings on the utilization of short PMI tissues in sporadic (idiopathic; of unknown origin) AD research with focus on the extraction and quantification of RNA, and in particular microRNA (miRNA) and messenger RNA (mRNA) and analytical strategies, drawing on the authors' combined 125 years of laboratory experience into this investigative research area. We sincerely hope that new investigators in the field of "gene expression analysis in neurological disease" will benefit from the observations presented here and incorporate these recent findings and observations into their future experimental planning and design.

Integration of pathway structure information into a reweighted partial Cox regression approach for survival analysis on high-dimensional gene expression data

Accurately predicting the risk of cancer relapse or death is important for clinical utility.

Brain gene expression patterns differentiate mild cognitive impairment from normal aged and Alzheimer's disease

Mild cognitive impairment (MCI) represents a cognitive state intermediate between normal aging and early Alzheimer's disease (AD). To investigate if the molecular signature of MCI parallels the clinical picture, we use microarrays to extensively profile gene expression in 4 cortical brain regions (entorhinal cortex, hippocampus, superior frontal gyrus, post-central gyrus) using the postmortem tissue from cognitively normal aged controls, MCI, and AD cases. Our data reveal that gene expression patterns in MCI are not an extension of aging, and for the most part, are not intermediate between aged controls and AD. Functional enrichment analysis of significant genes revealed prominent upregulation in MCI brains of genes associated with anabolic and biosynthetic pathways (notably transcription, protein biosynthesis, protein trafficking, and turnover) as well as mitochondrial energy generation. In addition, many synaptic genes showed altered expression in MCI, predominantly upregulation, including genes for central components of the vesicle fusion machinery at the synapse, synaptic vesicle trafficking, neurotransmitter receptors, and synaptic structure and stabilization. These data suggest that there is a rebalancing of synaptic transmission in the MCI brain. To investigate if synaptic gene expression levels in MCI were related to cognitive function, Pearson correlation coefficient between the Mini Mental State Examination (MMSE) and region-specific messenger RNA expression were computed for MCI cases. A number of synaptic genes showed strong significant correlations (r > 0.8, p < 0.01) most notably in the entorhinal cortex, with fewer in the hippocampus, and very few in neocortical regions. The synaptic genes with highly significant correlations were predominantly related to synaptic transmission and plasticity, and myelin composition. Unexpectedly, we found that gene expression changes that facilitate synaptic excitability and plasticity were overwhelmingly associated with poorer MMSE, and conversely that gene expression changes that inhibit plasticity were positively associated with MMSE. These data suggest that there are excessive excitability and apparent plasticity in limbic brain regions in MCI, that is associated with impaired synaptic and cognitive function. Such changes would be predicted to contribute to increased excitability, in turn leading to greater metabolic demand and ultimately progressive degeneration and AD, if not controlled.

Genetic architecture of ethanol-responsive transcriptome variation in Saccharomyces cerevisiae strains

Natural variation in gene expression is pervasive within and between species, and it likely explains a significant fraction of phenotypic variation between individuals. Phenotypic variation in acute systemic responses can also be leveraged to reveal physiological differences in how individuals perceive and respond to environmental perturbations. We previously found extensive variation in the transcriptomic response to acute ethanol exposure in two wild isolates and a common laboratory strain of Saccharomyces cerevisiae. Many expression differences persisted across several modules of coregulated genes, implicating trans-acting systemic differences in ethanol sensing and/or response. Here, we conducted expression QTL mapping of the ethanol response in two strain crosses to identify the genetic basis for these differences. To understand systemic differences, we focused on "hotspot" loci that affect many transcripts in trans. Candidate causal regulators contained within hotspots implicate upstream regulators as well as downstream effectors of the ethanol response. Overlap in hotspot targets revealed additive genetic effects of trans-acting loci as well as "epi-hotspots," in which epistatic interactions between two loci affected the same suites of downstream targets. One epi-hotspot implicated interactions between Mkt1p and proteins linked to translational regulation, prompting us to show that Mkt1p localizes to P bodies upon ethanol stress in a strain-specific manner. Our results provide a glimpse into the genetic architecture underlying natural variation in a stress response and present new details on how yeast respond to ethanol stress.

Beyond network structure: How heterogeneous susceptibility modulates the spread of epidemics

The compartmental models used to study epidemic spreading often assume the same susceptibility for all individuals, and are therefore, agnostic about the effects that differences in susceptibility can have on epidemic spreading. Here we show that–for the SIS model–differential susceptibility can make networks more vulnerable to the spread of diseases when the correlation between a node's degree and susceptibility are positive, and less vulnerable when this correlation is negative. Moreover, we show that networks become more likely to contain a pocket of infection when individuals are more likely to connect with others that have similar susceptibility (the network is segregated). These results show that the failure to include differential susceptibility to epidemic models can lead to a systematic over/under estimation of fundamental epidemic parameters when the structure of the networks is not independent from the susceptibility of the nodes or when there are correlations between the susceptibility of connected individuals.

Over-expression of Arabidopsis AtCHR23 chromatin remodeling ATPase results in increased variability of growth and gene expression

BACKGROUND

Plants are sessile organisms that deal with their -sometimes adverse- environment in well-regulated ways. Chromatin remodeling involving SWI/SNF2-type ATPases is thought to be an important epigenetic mechanism for the regulation of gene expression in different developmental programs and for integrating these programs with the response to environmental signals. In this study, we report on the role of chromatin remodeling in Arabidopsis with respect to the variability of growth and gene expression in relationship to environmental conditions.

RESULTS

Already modest (2-fold) over-expression of the AtCHR23 ATPase gene in Arabidopsis results in overall reduced growth compared to the wild-type. Detailed analyses show that in the root, the reduction of growth is due to reduced cell elongation. The reduced-growth phenotype requires sufficient light and is magnified by applying deliberate abiotic (salt, osmotic) stress. In contrast, the knockout mutation of AtCHR23 does not lead to such visible phenotypic effects. In addition, we show that over-expression of AtCHR23 increases the variability of growth in populations of genetically identical plants. These data indicate that accurate and controlled expression of AtCHR23 contributes to the stability or robustness of growth. Detailed RNAseq analyses demonstrate that upon AtCHR23 over-expression also the variation of gene expression is increased in a subset of genes that associate with environmental stress. The larger variation of gene expression is confirmed in individual plants with the help of independent qRT-PCR analysis.

CONCLUSIONS

Over-expression of AtCHR23 gives Arabidopsis a phenotype that is markedly different from the growth arrest phenotype observed upon over-expression of AtCHR12, the paralog of AtCHR23, in response to abiotic stress. This demonstrates functional sub-specialization of highly similar ATPases in Arabidopsis. Over-expression of AtCHR23 increases the variability of growth among genetically identical individuals in a way that is consistent with increased variability of expression of a distinct subset of genes that associate with environmental stress. We propose that ATCHR23-mediated chromatin remodeling is a potential component of a buffer system in plants that protects against environmentally-induced phenotypic and transcriptional variation.