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

Creatine and L-carnitine attenuate muscular laminopathy in the LMNA mutation transgenic zebrafish

Lamin A/C gene (LMNA) mutations contribute to severe striated muscle laminopathies, affecting cardiac and skeletal muscles, with limited treatment options. In this study, we delve into the investigations of five distinct LMNA mutations, including three novel variants and two pathogenic variants identified in patients with muscular laminopathy. Our approach employs zebrafish models to comprehensively study these variants. Transgenic zebrafish expressing wild-type LMNA and each mutation undergo extensive morphological profiling, swimming behavior assessments, muscle endurance evaluations, heartbeat measurement, and histopathological analysis of skeletal muscles. Additionally, these models serve as platform for focused drug screening. We explore the transcriptomic landscape through qPCR and RNAseq to unveil altered gene expression profiles in muscle tissues. Larvae of LMNA(L35P), LMNA(E358K), and LMNA(R453W) transgenic fish exhibit reduced swim speed compared to LMNA(WT) measured by DanioVision. All LMNA transgenic adult fish exhibit reduced swim speed compared to LMNA(WT) in T-maze. Moreover, all LMNA transgenic adult fish, except LMNA(E358K), display weaker muscle endurance than LMNA(WT) measured by swimming tunnel. Histochemical staining reveals decreased fiber size in all LMNA mutations transgenic fish, excluding LMNA(WT) fish. Interestingly, LMNA(A539V) and LMNA(E358K) exhibited elevated heartbeats. We recognize potential limitations with transgene overexpression and conducted association calculations to explore its effects on zebrafish phenotypes. Our results suggest lamin A/C overexpression may not directly impact mutant phenotypes, such as impaired swim speed, increased heart rates, or decreased muscle fiber diameter. Utilizing LMNA zebrafish models for drug screening, we identify L-carnitine treatment rescuing muscle endurance in LMNA(L35P) and creatine treatment reversing muscle endurance in LMNA(R453W) zebrafish models. Creatine activates AMPK and mTOR pathways, improving muscle endurance and swim speed in LMNA(R453W) fish. Transcriptomic profiling reveals upstream regulators and affected genes contributing to motor dysfunction, cardiac anomalies, and ion flux dysregulation in LMNA mutant transgenic fish. These findings faithfully mimic clinical manifestations of muscular laminopathies, including dysmorphism, early mortality, decreased fiber size, and muscle dysfunction in zebrafish. Furthermore, our drug screening results suggest L-carnitine and creatine treatments as potential rescuers of muscle endurance in LMNA(L35P) and LMNA(R453W) zebrafish models. Our study offers valuable insights into the future development of potential treatments for LMNA-related muscular laminopathy.

Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression

The majority of gene expression studies focus on the search for genes whose mean expression is different between two or more populations of samples in the so-called "differential expression analysis" approach. However, a difference in variance in gene expression may also be biologically and physiologically relevant. In the classical statistical model used to analyze RNA-sequencing (RNA-seq) data, the dispersion, which defines the variance, is only considered as a parameter to be estimated prior to identifying a difference in mean expression between conditions of interest. Here, we propose to evaluate four recently published methods, which detect differences in both the mean and dispersion in RNA-seq data. We thoroughly investigated the performance of these methods on simulated datasets and characterized parameter settings to reliably detect genes with a differential expression dispersion. We applied these methods to The Cancer Genome Atlas datasets. Interestingly, among the genes with an increased expression dispersion in tumors and without a change in mean expression, we identified some key cellular functions, most of which were related to catabolism and were overrepresented in most of the analyzed cancers. In particular, our results highlight autophagy, whose role in cancerogenesis is context-dependent, illustrating the potential of the differential dispersion approach to gain new insights into biological processes and to discover new biomarkers.

Metabolic and epigenetic dysfunctions underlie the arrest of in vitro fertilized human embryos in a senescent-like state

Around 60% of in vitro fertilized (IVF) human embryos irreversibly arrest before compaction between the 3- to 8-cell stage, posing a significant clinical problem. The mechanisms behind this arrest are unclear. Here, we show that the arrested embryos enter a senescent-like state, marked by cell cycle arrest, the down-regulation of ribosomes and histones and down-regulation of MYC and p53 activity. The arrested embryos can be divided into 3 types. Type I embryos fail to complete the maternal-zygotic transition, and Type II/III embryos have low levels of glycolysis and either high (Type II) or low (Type III) levels of oxidative phosphorylation. Treatment with the SIRT agonist resveratrol or nicotinamide riboside (NR) can partially rescue the arrested phenotype, which is accompanied by changes in metabolic activity. Overall, our data suggests metabolic and epigenetic dysfunctions underlie the arrest of human embryos.

Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability

There is increasing evidence that changes in the variability or overall distribution of gene expression are important both in normal biology and in diseases, particularly cancer. Genes whose expression differs in variability or distribution without a difference in mean are ignored by traditional differential expression-based analyses. Using a Bayesian hierarchical model that provides tests for both differential variability and differential distribution for bulk RNA-seq data, we report here an investigation into differential variability and distribution in cancer. Analysis of eight paired tumour-normal datasets from The Cancer Genome Atlas confirms that differential variability and distribution analyses are able to identify cancer-related genes. We further demonstrate that differential variability identifies cancer-related genes that are missed by differential expression analysis, and that differential expression and differential variability identify functionally distinct sets of potentially cancer-related genes. These results suggest that differential variability analysis may provide insights into genetic aspects of cancer that would not be revealed by differential expression, and that differential distribution analysis may allow for more comprehensive identification of cancer-related genes than analyses based on changes in mean or variability alone.

Copper promoting oyster larval growth and settlement: Molecular insights from RNA-seq

As a cofactor of key enzymes, Cu is required in living organisms, although Cu levels in the natural environment are typically low. In this study, the promotion of growth and settlement on the larvae of oyster Crassostrea angulata was observed at an environmentally relevant concentration (10 μg/L Cu). Interestingly, Cu accumulation in the soft tissue of oyster larvae increased during the larval development and exhibited a sharp increase at the late pelagic stage. With the help of RNA-seq, we constructed a high-quality transcriptional database of the oyster C. angulata larvae (24,257 genes with an average length of 1594 bp) via de novo assembly, which provided the basic molecular changes during the larval development. Network analysis of five early developmental stages and differential expression under Cu exposure were integrated to examine the roles of Cu in oyster larvae. Our molecular analysis demonstrated that both ion channels and organic transporters contributed to Cu internalization from the external environment, including zinc transporters and amino acid transporters. The followed distribution of Cu across cells was achieved by ATP7A, the circulatory system, and the Cu transporters (CTRs). Cu exposure enhanced the ribosome and the calcium binding proteins with a higher rate of translation and shell formation, giving rise to faster growth of oyster larvae. Furthermore, Cu facilitated the settling process by upregulating the chitin binding genes and then promoting the formation of the proteinaceous matrix between larvae and substrate. Our study presents the molecular basis for Cu promotion (i.e., hormesis) effects on oyster larval growth and settlement.

Intrauterine Hypoxia and Epigenetic Programming in Lung Development and Disease

Clinically, intrauterine hypoxia is the foremost cause of perinatal morbidity and developmental plasticity in the fetus and newborn infant. Under hypoxia, deviations occur in the lung cell epigenome. Epigenetic mechanisms (e.g., DNA methylation, histone modification, and miRNA expression) control phenotypic programming and are associated with physiological responses and the risk of developmental disorders, such as bronchopulmonary dysplasia. This developmental disorder is the most frequent chronic pulmonary complication in preterm labor. The pathogenesis of this disease involves many factors, including aberrant oxygen conditions and mechanical ventilation-mediated lung injury, infection/inflammation, and epigenetic/genetic risk factors. This review is focused on various aspects related to intrauterine hypoxia and epigenetic programming in lung development and disease, summarizes our current knowledge of hypoxia-induced epigenetic programming and discusses potential therapeutic interventions for lung disease.

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.

Variable expression quantitative trait loci analysis of breast cancer risk variants

Genome wide association studies (GWAS) have identified more than 180 variants associated with breast cancer risk, however the underlying functional mechanisms and biological pathways which confer disease susceptibility remain largely unknown. As gene expression traits are under genetic regulation we hypothesise that differences in gene expression variability may identify causal breast cancer susceptibility genes. We performed variable expression quantitative trait loci (veQTL) analysis using tissue-specific expression data from the Genotype-Tissue Expression (GTEx) Common Fund Project. veQTL analysis identified 70 associations (p < 5 × 10^–8) consisting of 60 genes and 27 breast cancer risk variants, including 55 veQTL that were observed in breast tissue only. Pathway analysis of genes associated with breast-specific veQTL revealed an enrichment of four genes (CYP11B1, CYP17A1 HSD3B2 and STAR) involved in the C21-steroidal biosynthesis pathway that converts cholesterol to breast-related hormones (e.g. oestrogen). Each of these four genes were significantly more variable in individuals homozygous for rs11075995 (A/A) breast cancer risk allele located in the FTO gene, which encodes an RNA demethylase. The A/A allele was also found associated with reduced expression of FTO, suggesting an epi-transcriptomic mechanism may underlie the dysregulation of genes involved in hormonal biosynthesis leading to an increased risk of breast cancer. These findings provide evidence that genetic variants govern high levels of expression variance in breast tissue, thus building a more comprehensive insight into the underlying biology of breast cancer risk loci.

Network mapping of primary CD34+ cells by Ampliseq based whole transcriptome targeted resequencing identifies unexplored differentiation regulatory relationships

With the exception of a few master transcription factors, regulators of neutrophil maturation are poorly annotated in the intermediate phenotypes between the granulocyte-macrophage progenitor (GMP) and the mature neutrophil phenotype. Additional challenges in identifying gene expression regulators in differentiation pathways relate to challenges wherein starting cell populations are heterogeneous in lineage potential and development, are spread across various states of quiescence, as well as sample quality and input limitations. These factors contribute to data variability make it difficult to draw simple regulatory inferences. In response we have applied a multi-omics approach using primary blood progenitor cells primed for homogeneous proliferation and granulocyte differentiation states which combines whole transcriptome resequencing (Ampliseq RNA) supported by droplet digital PCR (ddPCR) validation and mass spectrometry-based proteomics in a hypothesis-generation study of neutrophil differentiation pathways. Primary CD34+ cells isolated from human cord blood were first precultured in non-lineage driving medium to achieve an active, proliferating phenotype from which a neutrophil primed progenitor was isolated and cultured in neutrophil lineage supportive medium. Samples were then taken at 24-hour intervals over 9 days and analysed by Ampliseq RNA and mass spectrometry. The Ampliseq dataset depth, breadth and quality allowed for several unexplored transcriptional regulators and ncRNAs to be identified using a combinatorial approach of hierarchical clustering, enriched transcription factor binding motifs, and network mapping. Network mapping in particular increased comprehension of neutrophil differentiation regulatory relationships by implicating ARNT, NHLH1, PLAG1, and 6 non-coding RNAs associated with PU.1 regulation as cell-engineering targets with the potential to increase total neutrophil culture output. Overall, this study develops and demonstrates an effective new hypothesis generation methodology for transcriptome profiling during differentiation, thereby enabling identification of novel gene targets for editing interventions.

Genome-wide detection of copy number variants in European autochthonous and commercial pig breeds by whole-genome sequencing of DNA pools identified breed-characterising copy number states

In this study, we identified copy number variants (CNVs) in 19 European autochthonous pig breeds and in two commercial breeds (Italian Large White and Italian Duroc) that represent important genetic resources for this species. The genome of 725 pigs was sequenced using a breed-specific DNA pooling approach (30-35 animals per pool) obtaining an average depth per pool of 42×. This approach maximised CNV discovery as well as the related copy number states characterising, on average, the analysed breeds. By mining more than 17.5 billion reads, we identified a total of 9592 CNVs (~683 CNVs per breed) and 3710 CNV regions (CNVRs; 1.15% of the reference pig genome), with an average of 77 CNVRs per breed that were considered as private. A few CNVRs were analysed in more detail, together with other information derived from sequencing data. For example, the CNVR encompassing the KIT gene was associated with coat colour phenotypes in the analysed breeds, confirming the role of the multiple copies in determining breed-specific coat colours. The CNVR covering the MSRB3 gene was associated with ear size in most breeds. The CNVRs affecting the ELOVL6 and ZNF622 genes were private features observed in the Lithuanian Indigenous Wattle and in the Turopolje pig breeds respectively. Overall, the genome variability unravelled here can explain part of the genetic diversity among breeds and might contribute to explain their origin, history and adaptation to a variety of production systems.

RNA sequencing revealed the abnormal transcriptional profile in cloned bovine embryos

Somatic cell nuclear transfer (SCNT) has potential applications in agriculture and biomedicine, but the efficiency of cloning is still low. In this study, the transcriptional profiles in cloned and fertilized embryos were measured and compared by RNA sequencing. The 2-cell embryos were detected to identify the earliest transcriptional differences between embryos derived through IVF and SCNT. As a result, 364 genes showed decreased expression in cloned 2-cell embryos and were enriched in "intracellular protein transport" and "ubiquitin mediated proteolysis". In blastocysts, 593 genes showed decreased expression in cloned blastocysts and were enriched in "RNA binding", "nucleotide binding", "embryo development", and "adherens junction". We identified 14 development related genes that were not activated in the cloned embryos. Then, 68 and 245 long non-coding RNAs were recognized abnormally expressed in cloned 2-cell embryos and cloned blastocysts, respectively. Furthermore, we found that incomplete RNA-editing occurred in cloned embryos and might be caused by decreased ADAR expression. In conclusion, our study revealed the abnormal transcripts and deficient RNA-editing sites in cloned embryos and provided new data for further mechanistic studies of somatic nuclear reprogramming.

TRIM13 inhibited cell proliferation and induced cell apoptosis by regulating NF-κB pathway in non-small-cell lung carcinoma cells

Tripartite Motif Containing 13 (TRIM13), a member of TRIM proteins, is deleted in multiple tumor types, especially in B-cell chronic lymphocytic leukemia and multiple myeloma. The present study explored the expression and potential role of TRIM13 in non-small-cell lung carcinoma (NSCLC). We found that TRIM13 mRNA and protein expression was reduced in NSCLC tissues and cell lines in comparison to paired non-cancerous tissues and a human normal bronchial epithelial cell line, respectively. Overexpression of TRIM13 in NCI-H1975 and SPC-A-1 cells hampered cell proliferation. Additionally, TRIM13 overexpression increased the levels of cleaved caspase-3. TRIM13-induced NSCLC cell apoptosis was attenuated by a caspase-3 inhibitor Ac-DEVD-CHO, suggesting that TRIM13 induced cell apoptosis partially through a caspase-3-dependent pathway. Moreover, it has been reported that TRIM13 can regulate nuclear factor kappaB (NF-κB) activity. Our data showed that TRIM13 overexpression inactivated NF-κB as indicated by the increased cytosolic NF-κB and decreased nuclear NF-κB. Exposure to an NF-κB inhibitor PDTC significantly blocked the impact of TRIM13 knockdown on cell proliferation and apoptosis, indicating the functions of TRIM13 in NSCLC cells were mediated by the NF-κB pathway. Finally, we demonstrated that TRIM13 overexpression suppressed tumor growth and induced cell apoptosis in vivo by using a xenograft mouse model. Collectively, our results indicate that TRIM13 behaves as a tumor suppressor in NSCLC through regulating NF-κB pathway. Our findings may offer a promising therapeutic target for NSCLC.

Identification of an early cell fate regulator by detecting dynamics in transcriptional heterogeneity and co-regulation during astrocyte differentiation

There are an increasing number of reports that characterize the temporal behavior of gene expression at the single-cell level during cell differentiation. Despite accumulation of data describing the heterogeneity of biological responses, the dynamics of gene expression heterogeneity and its regulation during the differentiation process have not been studied systematically. To understand transcriptional heterogeneity during astrocyte differentiation, we analyzed single-cell transcriptional data from cells representing the different stages of astrocyte differentiation. When we compared the transcriptional variability of co-expressed genes between the undifferentiated and differentiated states, we found that there was significant increase in transcriptional variability in the undifferentiated state. The genes showing large changes in both "variability" and "correlation" between neural stem cells (NSCs) and astrocytes were found to be functionally involved in astrocyte differentiation. We determined that these genes are potentially regulated by Ascl1, a previously known oscillatory gene in NSCs. Pharmacological blockade of Ntsr2, which is transcriptionally co-regulated with Ascl1, showed that Ntsr2 may play an important role in the differentiation from NSCs to astrocytes. This study shows the importance of characterizing transcriptional heterogeneity and rearrangement of the co-regulation network between different cell states. It also highlights the potential for identifying novel regulators of cell differentiation that will further increase our understanding of the molecular mechanisms underlying the differentiation process.

The Pediatric Cell Atlas: Defining the Growth Phase of Human Development at Single-Cell Resolution

Single-cell gene expression analyses of mammalian tissues have uncovered profound stage-specific molecular regulatory phenomena that have changed the understanding of unique cell types and signaling pathways critical for lineage determination, morphogenesis, and growth. We discuss here the case for a Pediatric Cell Atlas as part of the Human Cell Atlas consortium to provide single-cell profiles and spatial characterization of gene expression across human tissues and organs. Such data will complement adult and developmentally focused HCA projects to provide a rich cytogenomic framework for understanding not only pediatric health and disease but also environmental and genetic impacts across the human lifespan.

Towards Improving Embryo Prioritization: Parallel Next Generation Sequencing of DNA and RNA from a Single Trophectoderm Biopsy

Improved embryo prioritization is crucial in optimizing the results in assisted reproduction, especially in light of increasing utilization of elective single embryo transfers. Embryo prioritization is currently based on morphological criteria and in some cases incorporates preimplantation genetic testing for aneuploidy (PGT-A). Recent technological advances have enabled parallel genomic and transcriptomic assessment of a single cell. Adding transcriptomic analysis to PGT-A holds promise for better understanding early embryonic development and implantation, and for enhancing available embryo prioritization tools. Our aim was to develop a platform for parallel genomic and transcriptomic sequencing of a single trophectoderm (TE) biopsy, that could later be correlated with clinical outcomes. Twenty-five embryos donated for research were utilized; eight for initial development and optimization of our method, and seventeen to demonstrate clinical safety and reproducibility of this method. Our method achieved 100% concordance for ploidy status with that achieved by the classic PGT-A. All sequencing data exceeded quality control metrics. Transcriptomic sequencing data was sufficient for performing differential expression (DE) analysis. All biopsies expressed specific TE markers, further validating the accuracy of our method. Using PCA, samples clustered in euploid and aneuploid aggregates, highlighting the importance of controlling for ploidy in every transcriptomic assessment.

The rise of the distributions: why non-normality is important for understanding the transcriptome and beyond

The application of statistics has been instrumental in clarifying our understanding of the genome. While insights have been derived for almost all levels of genome function, most importantly, statistics has had the greatest impact on improving our knowledge of transcriptional regulation. But the drive to extract the most meaningful inferences from big data can often force us to overlook the fundamental role that statistics plays, and specifically, the basic assumptions that we make about big data. Normality is a statistical property that is often swept up into an assumption that we may or may not be consciously aware of making. This review highlights the inherent value of non-normal distributions to big data analysis by discussing use cases of non-normality that focus on gene expression data. Collectively, these examples help to motivate the premise of why at this stage, now more than ever, non-normality is important for learning about gene regulation, transcriptomics, and more.

Gene expression variation and parental allele inheritance in a Xiphophorus interspecies hybridization model

Understanding the genetic mechanisms underlying segregation of phenotypic variation through successive generations is important for understanding physiological changes and disease risk. Tracing the etiology of variation in gene expression enables identification of genetic interactions, and may uncover molecular mechanisms leading to the phenotypic expression of a trait, especially when utilizing model organisms that have well-defined genetic lineages. There are a plethora of studies that describe relationships between gene expression and genotype, however, the idea that global variations in gene expression are also controlled by genotype remains novel. Despite the identification of loci that control gene expression variation, the global understanding of how genome constitution affects trait variability is unknown. To study this question, we utilized Xiphophorus fish of different, but tractable genetic backgrounds (inbred, F₁ interspecies hybrids, and backcross hybrid progeny), and measured each individual’s gene expression concurrent with the degrees of inter-individual expression variation. We found, (a) F₁ interspecies hybrids exhibited less variability than inbred animals, indicting gene expression variation is not affected by the fraction of heterozygous loci within an individual genome, and (b), that mixing genotypes in backcross populations led to higher levels of gene expression variability, supporting the idea that expression variability is caused by heterogeneity of genotypes of cis or trans loci. In conclusion, heterogeneity of genotype, introduced by inheritance of different alleles, accounts for the largest effects on global phenotypical variability.

Phenotypical variability is a multi-factorial phenomenon. Although it has been shown that inheriting certain gene is associated with lower phenotypical variability, how genome complexity affect phenotypical variability is still unclear. To study this question, we used inbred Xiphophorus fish, backcross interspecies hybrids, and F₁ interspecies hybrids between select Xiphophorus species to model genetic composition with minimum, medium, and maximum heterozygosity respectively, and measured their global gene expression variability. We found gene expression variation is not affected by the percentage of heterozygous loci in individual genome, but instead related to heterogeneity of genotype at local or remote loci.

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.

Small RNA-based prediction of hybrid performance in maize

BACKGROUND

Small RNA (sRNA) sequences are known to have a broad impact on gene regulation by various mechanisms. Their performance for the prediction of hybrid traits has not yet been analyzed. Our objective was to analyze the relation of parental sRNA expression with the performance of their hybrids, to develop a sRNA-based prediction approach, and to compare it to more common SNP and mRNA transcript based predictions using a factorial mating scheme of a maize hybrid breeding program.

RESULTS

Correlation of genomic differences and messenger RNA (mRNA) or sRNA expression differences between parental lines with hybrid performance of their hybrids revealed that sRNAs showed an inverse relationship in contrast to the other two data types. We associated differences for SNPs, mRNA and sRNA expression between parental inbred lines with the performance of their hybrid combinations and developed two prediction approaches using distance measures based on associated markers. Cross-validations revealed parental differences in sRNA expression to be strong predictors for hybrid performance for grain yield in maize, comparable to genomic and mRNA data. The integration of both positively and negatively associated markers in the prediction approaches enhanced the prediction accurary. The associated sRNAs belong predominantly to the canonical size classes of 22- and 24-nt that show specific genomic mapping characteristics.

CONCLUSION

Expression profiles of sRNA are a promising alternative to SNPs or mRNA expression profiles for hybrid prediction, especially for plant species without reference genome or transcriptome information. The characteristics of the sRNAs we identified suggest that association studies based on breeding populations facilitate the identification of sRNAs involved in hybrid performance.

RNA helicase, DDX27 regulates skeletal muscle growth and regeneration by modulation of translational processes

Gene expression in a tissue-specific context depends on the combined efforts of epigenetic, transcriptional and post-transcriptional processes that lead to the production of specific proteins that are important determinants of cellular identity. Ribosomes are a central component of the protein biosynthesis machinery in cells; however, their regulatory roles in the translational control of gene expression in skeletal muscle remain to be defined. In a genetic screen to identify critical regulators of myogenesis, we identified a DEAD-Box RNA helicase, DDX27, that is required for skeletal muscle growth and regeneration. We demonstrate that DDX27 regulates ribosomal RNA (rRNA) maturation, and thereby the ribosome biogenesis and the translation of specific transcripts during myogenesis. These findings provide insight into the translational regulation of gene expression in myogenesis and suggest novel functions for ribosomes in regulating gene expression in skeletal muscles.

Varying Intolerance of Gene Pathways to Mutational Classes Explain Genetic Convergence across Neuropsychiatric Disorders

Genetic susceptibility to intellectual disability (ID), autism spectrum disorder (ASD), and schizophrenia (SCZ) often arises from mutations in the same genes, suggesting that they share common mechanisms. We studied genes with de novo mutations in the three disorders and genes implicated in SCZ by genome-wide association study (GWAS). Using biological annotations and brain gene expression, we show that mutation class explains enrichment patterns more than specific disorder. Genes with loss-of-function mutations and genes with missense mutations were associated with different pathways across disorders. Conversely, gene expression patterns were specific for each disorder. ID genes were preferentially expressed in the cortex; ASD genes were expressed in the fetal cortex, cerebellum, and striatum; and genes associated with SCZ were expressed in the adolescent cortex. Our study suggests that convergence across neuropsychiatric disorders stems from common pathways that are consistently vulnerable to genetic variations but that spatiotemporal activity of genes contributes to specific phenotypes.

Comparative analyses of industrial-scale human platelet lysate preparations

BACKGROUND

Efforts are underway to eliminate fetal bovine serum from mammalian cell cultures for clinical use. An emerging, viable replacement option for fetal bovine serum is human platelet lysate (PL) as either a plasma-based or serum-based product.

STUDY DESIGN AND METHODS

Nine industrial-scale, serum-based PL manufacturing runs (i.e., lots) were performed, consisting of an average ± standard deviation volume of 24.6 ± 2.2 liters of pooled, platelet-rich plasma units that were obtained from apheresis donors. Manufactured lots were compared by evaluating various biochemical and functional test results. Comprehensive cytokine profiles of PL lots and product stability tests were performed. Global gene expression profiles of mesenchymal stromal cells (MSCs) cultured with plasma-based or serum-based PL were compared to MSCs cultured with fetal bovine serum.

RESULTS

Electrolyte and protein levels were relatively consistent among all serum-based PL lots, with only slight variations in glucose and calcium levels. All nine lots were as good as or better than fetal bovine serum in expanding MSCs. Serum-based PL stored at -80°C remained stable over 2 years. Quantitative cytokine arrays showed similarities as well as dissimilarities in the proteins present in serum-based PL. Greater differences in MSC gene expression profiles were attributable to the starting cell source rather than with the use of either PL or fetal bovine serum as a culture supplement.

CONCLUSION

Using a large-scale, standardized method, lot-to-lot variations were noted for industrial-scale preparations of serum-based PL products. However, all lots performed as well as or better than fetal bovine serum in supporting MSC growth. Together, these data indicate that off-the-shelf PL is a feasible substitute for fetal bovine serum in MSC cultures.

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.

Changes in gene expression variability reveal a stable synthetic lethal interaction network in BRCA2-ovarian cancers

Synthetic lethal interactions (SLIs) are robust mechanisms that provide cells with the ability to remain viable despite having mutations in genes critical to the DNA damage response, a core cellular process. Studies in model organisms such as S. cerevisiae showed that thousands of genes important in maintaining DNA integrity cooperated in a SLI network. Two genes participate in a SLI when a mutation in one gene has no effect on the cell, but mutations in both interacting genes are lethal. Furthermore in C. elegans, a mutation in a critical gene that is important for development induced a change in expression variability in the synthetic lethal interactor. In cancer, targeting SLIs shows promise in selectively killing cancer cells. For example, targeting PARP1 is an effective treatment for BRCA1/2- breast and ovarian cancers. Although PARP1 is already identified as having a SLI with BRCA1/2-, computationally searching for other genes that cooperate in the SLI network could highlight genes that may have promise for being a cancer-specific drug target. Using RNA sequencing data for ovarian cancer patients with BRCA2 mutations and the R Bioconductor package pathVar, we showed that genes whose expression changes to an invariant, stable expression state are likely candidates for SLIs with BRCA2. Our results highlight the interactions between the genes with predicted SLIs and protein-coding genes that are functionally important in the DNA damage response. The method of analyzing expression variability to computationally identify genes with SLIs can be applied to query SLIs in other tumor types.

pathVar: a new method for pathway-based interpretation of gene expression variability

Identifying the pathways that control a cellular phenotype is the first step to building a mechanistic model. Recent examples in developmental biology, cancer genomics, and neurological disease have demonstrated how changes in the variability of gene expression can highlight important genes that are under different degrees of regulatory control. Simple statistical tests exist to identify differentially-variable genes; however, methods for investigating how changes in gene expression variability in the context of pathways and gene sets are under-explored. Here we present pathVar, a new method that provides functional interpretation of gene expression variability changes at the level of pathways and gene sets. pathVar is based on a multinomial exact test, or an asymptotic Chi-squared test as a more computationally-efficient alternative. The method can be used for gene expression studies from any technology platform in all biological settings either with a single phenotypic group, or two-group comparisons. To demonstrate its utility, we applied the method to a diverse set of diseases, species and samples. Results from pathVar are benchmarked against analyses based on average expression and two methods of GSEA, and demonstrate that analyses using both statistics are useful for understanding transcriptional regulation. We also provide recommendations for the choice of variability statistic that have been informed through analyses on simulations and real data. Based on the datasets selected, we show how pathVar can be used to gain insight into expression variability of single cell versus bulk samples, different stem cell populations, and cancer versus normal tissue comparisons.

Comprehensive chromosome screening and gene expression analysis from the same biopsy in human preimplantation embryos

STUDY QUESTION

Can simultaneous comprehensive chromosome screening (CCS) and gene expression analysis be performed on the same biopsy of preimplantation human embryos?

SUMMARY ANSWER

For the first time, CCS and reliable gene expression analysis have been performed on the same human preimplantation embryo biopsy.

WHAT IS KNOWN ALREADY

A single trophectoderm (TE) biopsy is routinely used for many IVF programs offering CCS for selection of only chromosomally normal embryos for transfer. Although the gene expression profiling of human preimplantation embryos has been described, to date no protocol allows for simultaneous CCS and gene expression profiling from a single TE biopsy.

STUDY DESIGN, SIZE AND DURATION

This is a proof of concept and validation study structured in two phases. In Phase 1, cell lines were subjected to a novel protocol for combined CCS and gene expression analysis so as to validate the accuracy and reliability of the proposed protocol. In Phase 2, 20 donated human blastocysts were biopsied and processed with the proposed protocol in order to obtain an accurate CCS result and characterize their gene expression profiles using the same starting material.

PARTICIPANTS/MATERIALS, SETTING AND METHOD

A novel protocol coupling quantitative real-time PCR-based CCS and gene expression analysis using RT-PCR was designed for this study. Phase 1: six-cell aliquots of well-characterized fibroblast cell lines (GM00323, 46,XY and GM04435, 48,XY,+16,+21) were subjected to the proposed protocol. CCS results were compared with the known karyotypes for consistency, and gene expression levels were compared with levels of purified RNA from same cell lines for validation of reliable gene expression profiling. Phase 2: four biopsies were performed on 20 frozen human blastocysts previously diagnosed as trisomy 21 (10 embryos) and monosomy 21 (10 embryos) by CCS. All samples were processed with the proposed protocol and re-evaluated for concordance with the original CCS result. Their gene expression profiles were characterized and differential gene expression among embryos and early embryonic cell lineages was also evaluated.

MAIN RESULTS AND THE ROLE OF CHANCE

CCS results from cell lines showed 100% consistency with their known karyotypes. ΔΔCt values of differential gene expression of four selected target genes from the cell lines GM4435 and GM0323 were comparable between six-cell aliquots and purified RNA (Collagen type I alpha-1 (COL1A1), P = 0.54; Fibroblast growth factor-5 (FGF5), P = 0.11; Laminin subunit beta-1 (LAMB1), P = 1.00 and Atlastin-1 (ATL1), P = 0.23). With respect to human blastocysts, 92% consistency was reported after comparing embryonic CCS results with previous diagnosis. A total of 30 genes from a human stem cell pluripotency panel were selected to evaluate gene expression in human embryos. Correlation coefficients of expression profiles from biopsies of the same embryo (r = 0.96 ± 0.03 (standard deviation), n = 45) were significantly higher than when biopsies from unrelated embryos were evaluated (r = 0.93 ± 0.03, n = 945) (P < 0.0001). Growth differentiation factor 3 (GDF3) was found to be significantly up-regulated in the inner cell mass (ICM), whereas Caudal type homebox protein-2 (CDX2), Laminin subunit alpha-1 (LAMA1) and DNA methyltransferase 3-beta (DNMT3B) showed down-regulation in ICM compared with TE. Trisomy 21 embryos showed significant up-regulation of markers of cell differentiation (Cadherin-5 (CDH5) and Laminin subunit gamma-1 (LAMC1)), whereas monosomy 21 blastocysts showed higher expression of genes reported to be expressed in undifferentiated cells (Gamma-Aminobutyric Acid Type-A Receptor Beta3 Subunit (GABRB3) and GDF3).

LARGE SCALE DATA

N/A

LIMITATIONS, REASONS FOR CAUTION

Gene expression profiles of chromosomally normal embryos were not assessed due to restrictive access to euploid embryos for research. Nonetheless, the profile of blastocysts with single aneuploidies was characterized and compared. Only 30 target genes were analyzed for gene expression in this study. Increasing the number of target genes will provide a more comprehensive transcriptomic signature and reveal potential pathways paramount for embryonic competence and correct development.

WIDER IMPLICATIONS OF THE FINDINGS

This is the first time that CCS and gene expression analysis have been performed on the same human preimplantation embryo biopsy. Further optimization of this protocol with other CCS platforms and inclusion of more target genes will provide innumerable research and clinical applications, such as discovery of biomarkers for embryonic reproductive potential and characterization of the transcriptomic signatures of embryos, potentially allowing for further embryo selection prior to embryo transfer and therefore improving outcomes.

STUDY FUNDING AND COMPETING INTERESTS

This study was funded by the Foundation for Embryonic Competence, Basking Ridge, NJ, USA. No conflicts of interests declared.

TRP channels in brown and white adipogenesis from human progenitors: new therapeutic targets and the caveats associated with the common antibiotic, streptomycin

Transient receptor potential (TRP) channels are polymodal cell sensors responding to diverse stimuli and widely implicated in the developmental programs of numerous tissues. The evidence for an involvement of TRP family members in adipogenesis, however, is scant. We present the first comprehensive expression profile of all known 27 human TRP genes in mesenchymal progenitors cells during white or brown adipogenesis. Using positive trilineage differentiation as an exclusion criterion, TRP polycystic (P)3, and TPR melastatin (M)8 were found to be uniquely adipospecific. Knockdown of TRPP3 repressed the expression of the brown fat signature genes uncoupling protein (UCP)-1 and peroxisome proliferator-activated receptor γ coactivator (PGC)-1α as well as attenuated forskolin-stimulated uncoupled respiration. However, indices of generalized adipogenesis, such as lipid droplet morphology and fatty acid binding protein (FAPB)-4 expression, were not affected, indicating a principal mitochondrial role of TRPP3. Conversely, activating TRPM8 with menthol up-regulated UCP-1 expression and augmented uncoupled respiration predominantly in white adipocytes (browning), whereas streptomycin antagonized TRPM8-mediated calcium entry, downregulated UCP-1 expression, and mitigated uncoupled respiration; menthol was less capable of augmenting uncoupled respiration (thermogenesis) in brown adipocytes. TRPP3 and TRPM8 hence appear to be involved in the priming of mitochondria to perform uncoupled respiration downstream of adenylate cyclase. Our results also underscore the developmental caveats of using antibiotics in adipogenic studies.-Goralczyk, A., van Vijven, M., Koch, M., Badowski, C., Yassin, M. S., Toh, S.-A., Shabbir, A., Franco-Obregón, A., Raghunath, M. TRP channels in brown and white adipogenesis from human progenitors: new therapeutic targets and the caveats associated with the common antibiotic, streptomycin.

Stably Expressed Genes Involved in Basic Cellular Functions

Stably Expressed Genes (SEGs) whose expression varies within a narrow range may be involved in core cellular processes necessary for basic functions. To identify such genes, we re-analyzed existing RNA-Seq gene expression profiles across 11 organs at 4 developmental stages (from immature to old age) in both sexes of F344 rats (n = 4/group; 320 samples). Expression changes (calculated as the maximum expression / minimum expression for each gene) of >19000 genes across organs, ages, and sexes ranged from 2.35 to >10⁹-fold, with a median of 165-fold. The expression of 278 SEGs was found to vary ≤4-fold and these genes were significantly involved in protein catabolism (proteasome and ubiquitination), RNA transport, protein processing, and the spliceosome. Such stability of expression was further validated in human samples where the expression variability of the homologous human SEGs was significantly lower than that of other genes in the human genome. It was also found that the homologous human SEGs were generally less subject to non-synonymous mutation than other genes, as would be expected of stably expressed genes. We also found that knockout of SEG homologs in mouse models was more likely to cause complete preweaning lethality than non-SEG homologs, corroborating the fundamental roles played by SEGs in biological development. Such stably expressed genes and pathways across life-stages suggest that tight control of these processes is important in basic cellular functions and that perturbation by endogenous (e.g., genetics) or exogenous agents (e.g., drugs, environmental factors) may cause serious adverse effects.

DynOmics to identify delays and co-expression patterns across time course experiments

Dynamic changes in biological systems can be captured by measuring molecular expression from different levels (e.g., genes and proteins) across time. Integration of such data aims to identify molecules that show similar expression changes over time; such molecules may be co-regulated and thus involved in similar biological processes. Combining data sources presents a systematic approach to study molecular behaviour. It can compensate for missing data in one source, and can reduce false positives when multiple sources highlight the same pathways. However, integrative approaches must accommodate the challenges inherent in ‘omics’ data, including high-dimensionality, noise, and timing differences in expression. As current methods for identification of co-expression cannot cope with this level of complexity, we developed a novel algorithm called DynOmics. DynOmics is based on the fast Fourier transform, from which the difference in expression initiation between trajectories can be estimated. This delay can then be used to realign the trajectories and identify those which show a high degree of correlation. Through extensive simulations, we demonstrate that DynOmics is efficient and accurate compared to existing approaches. We consider two case studies highlighting its application, identifying regulatory relationships across ‘omics’ data within an organism and for comparative gene expression analysis across organisms.

Vertical flow array chips reliably identify cell types from single-cell mRNA sequencing experiments

Single-cell mRNA sequencing offers an unbiased approach to dissecting cell types as functional units in multicellular tissues. However, highly reliable cell typing based on single-cell gene expression analysis remains challenging because of the lack of methods for efficient sample preparation for high-throughput sequencing and evaluating the statistical reliability of the acquired cell types. Here, we present a highly efficient nucleic reaction chip (a vertical flow array chip (VFAC)) that uses porous materials to reduce measurement noise and improve throughput without a substantial increase in reagent. We also present a probabilistic evaluation method for cell typing depending on the amount of measurement noise. Applying the VFACs to 2580 monocytes provides 1967 single-cell expressions for 47 genes, including low-expression genes such as transcription factors. The statistical method can distinguish two cell types with probabilistic quality values, with the measurement noise level being considered for the first time. This approach enables the identification of various sub-types of cells in tissues and provides a foundation for subsequent analyses.

Linking Telomere Regulation to Stem Cell Pluripotency

Embryonic stem cells (ESCs), somatic cell nuclear transfer ESCs, and induced pluripotent stem cells (iPSCs) represent the most studied group of PSCs. Unlimited self-renewal without incurring chromosomal instability and pluripotency are essential for the potential use of PSCs in regenerative therapy. Telomere length maintenance is critical for the unlimited self-renewal, pluripotency, and chromosomal stability of PSCs. While telomerase has a primary role in telomere maintenance, alternative lengthening of telomere pathways involving recombination and epigenetic modifications are also required for telomere length regulation, notably in mouse PSCs. Telomere rejuvenation is part of epigenetic reprogramming to pluripotency. Insights into telomere reprogramming and maintenance in PSCs may have implications for understanding of aging and tumorigenesis. Here, I discuss the link between telomere elongation and homeostasis to the acquisition and maintenance of stem cell pluripotency, and their regulatory mechanisms by epigenetic modifications.

MSP-HTPrimer: a high-throughput primer design tool to improve assay design for DNA methylation analysis in epigenetics

Background

Bisulfite (BS) conversion-based and methylation-sensitive restriction enzyme (MSRE)-based PCR methods have been the most commonly used techniques for locus-specific DNA methylation analysis. However, both methods have advantages and limitations. Thus, an integrated approach would be extremely useful to quantify the DNA methylation status successfully with great sensitivity and specificity. Designing specific and optimized primers for target regions is the most critical and challenging step in obtaining the adequate DNA methylation results using PCR-based methods. Currently, no integrated, optimized, and high-throughput methylation-specific primer design software methods are available for both BS- and MSRE-based methods. Therefore an integrated, powerful, and easy-to-use methylation-specific primer design pipeline with great accuracy and success rate will be very useful.

Results

We have developed a new web-based pipeline, called MSP-HTPrimer, to design primers pairs for MSP, BSP, pyrosequencing, COBRA, and MSRE assays on both genomic strands. First, our pipeline converts all target sequences into bisulfite-treated templates for both forward and reverse strand and designs all possible primer pairs, followed by filtering for single nucleotide polymorphisms (SNPs) and known repeat regions. Next, each primer pairs are annotated with the upstream and downstream RefSeq genes, CpG island, and cut sites (for COBRA and MSRE). Finally, MSP-HTPrimer selects specific primers from both strands based on custom and user-defined hierarchical selection criteria. MSP-HTPrimer produces a primer pair summary output table in TXT and HTML format for display and UCSC custom tracks for resulting primer pairs in GTF format.

Conclusions

MSP-HTPrimer is an integrated, web-based, and high-throughput pipeline and has no limitation on the number and size of target sequences and designs MSP, BSP, pyrosequencing, COBRA, and MSRE assays. It is the only pipeline, which automatically designs primers on both genomic strands to increase the success rate. It is a standalone web-based pipeline, which is fully configured within a virtual machine and thus can be readily used without any configuration. We have experimentally validated primer pairs designed by our pipeline and shown a very high success rate of primer pairs: out of 66 BSP primer pairs, 63 were successfully validated without any further optimization step and using the same qPCR conditions. The MSP-HTPrimer pipeline is freely available from http://sourceforge.net/p/msp-htprimer.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-016-0269-3) contains supplementary material, which is available to authorized users.

SLC9A9 Co-expression modules in autism-associated brain regions

SLC9A9 is a sodium hydrogen exchanger present in the recycling endosome and highly expressed in the brain. It is implicated in neuropsychiatric disorders, including autism spectrum disorders (ASDs). Little research concerning its gene expression patterns and biological pathways has been conducted. We sought to investigate its possible biological roles in autism-associated brain regions throughout development. We conducted a weighted gene co-expression network analysis on RNA-seq data downloaded from Brainspan. We compared prenatal and postnatal gene expression networks for three ASD-associated brain regions known to have high SLC9A9 gene expression. We also performed an ASD-associated single nucleotide polymorphism enrichment analysis and a cell signature enrichment analysis. The modules showed differences in gene constituents (membership), gene number, and connectivity throughout time. SLC9A9 was highly associated with immune system functions, metabolism, apoptosis, endocytosis, and signaling cascades. Gene list comparison with co-immunoprecipitation data was significant for multiple modules. We found a disproportionately high autism risk signal among genes constituting the prenatal hippocampal module. The modules were enriched with astrocyte and oligodendrocyte markers. SLC9A9 is potentially involved in the pathophysiology of ASDs. Our investigation confirmed proposed functions for SLC9A9, such as endocytosis and immune regulation, while also revealing potential roles in mTOR signaling and cell survival.. By providing a concise molecular map and interactions, evidence of cell type and implicated brain regions we hope this will guide future research on SLC9A9. Autism Res 2017, 10: 414-429. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Evaluating biomarkers to model cancer risk post cosmic ray exposure

Robust predictive models are essential to manage the risk of radiation-induced carcinogenesis. Chronic exposure to cosmic rays in the context of the complex deep space environment may place astronauts at high cancer risk. To estimate this risk, it is critical to understand how radiation-induced cellular stress impacts cell fate decisions and how this in turn alters the risk of carcinogenesis. Exposure to the heavy ion component of cosmic rays triggers a multitude of cellular changes, depending on the rate of exposure, the type of damage incurred and individual susceptibility. Heterogeneity in dose, dose rate, radiation quality, energy and particle flux contribute to the complexity of risk assessment. To unravel the impact of each of these factors, it is critical to identify sensitive biomarkers that can serve as inputs for robust modeling of individual risk of cancer or other long-term health consequences of exposure. Limitations in sensitivity of biomarkers to dose and dose rate, and the complexity of longitudinal monitoring, are some of the factors that increase uncertainties in the output from risk prediction models. Here, we critically evaluate candidate early and late biomarkers of radiation exposure and discuss their usefulness in predicting cell fate decisions. Some of the biomarkers we have reviewed include complex clustered DNA damage, persistent DNA repair foci, reactive oxygen species, chromosome aberrations and inflammation. Other biomarkers discussed, often assayed for at longer points post exposure, include mutations, chromosome aberrations, reactive oxygen species and telomere length changes. We discuss the relationship of biomarkers to different potential cell fates, including proliferation, apoptosis, senescence, and loss of stemness, which can propagate genomic instability and alter tissue composition and the underlying mRNA signatures that contribute to cell fate decisions. Our goal is to highlight factors that are important in choosing biomarkers and to evaluate the potential for biomarkers to inform models of post exposure cancer risk. Because cellular stress response pathways to space radiation and environmental carcinogens share common nodes, biomarker-driven risk models may be broadly applicable for estimating risks for other carcinogens.