Genome-Wide CRISPR-Cas9 Screen Identifies MicroRNAs That Regulate Myeloid Leukemia Cell Growth

Mammalian microRNA expression is dysregulated in human cancer. However, the functional relevance of many microRNAs in the context of tumor biology remains unclear. Using CRISPR-Cas9 technology, we performed a global loss-of-function screen to simultaneously test the functions of individual microRNAs and protein-coding genes during the growth of a myeloid leukemia cell line. This approach identified evolutionarily conserved human microRNAs that suppress or promote cell growth, revealing that microRNAs are extensively integrated into the molecular networks that control tumor cell physiology. miR-155 was identified as a top microRNA candidate promoting cellular fitness, which we confirmed with two distinct miR-155-targeting CRISPR-Cas9 lentiviral constructs. Further, we performed anti-correlation functional profiling to predict relevant microRNA-tumor suppressor gene or microRNA-oncogene interactions in these cells. This analysis identified miR-150 targeting of p53, a connection that was experimentally validated. Taken together, our study describes a powerful genetic approach by which the function of individual microRNAs can be assessed on a global level, and its use will rapidly advance our understanding of how microRNAs contribute to human disease.     

Genomic and phenotypic signatures of climate adaptation in an Anolis lizard

Integrated knowledge on phenotype, physiology, and genomic adaptations is required to understand the effects of climate on evolution. The functional genomic basis of organismal adaptation to changes in the abiotic environment, its phenotypic consequences, and its possible convergence across vertebrates are still understudied. In this study, we use a comparative approach to verify predicted gene functions for vertebrate thermal adaptation with observed functions underlying repeated genomic adaptations in response to elevation in the lizard Anolis cybotes. We establish a direct link between recurrently evolved phenotypes and functional genomics of altitude‐related climate adaptation in three highland and lowland populations in the Dominican Republic. We show that across vertebrates, genes contained in this interactome are expressed within the brain, the endocrine system, and during development. These results are relevant to elucidate the effect of global climate change across vertebrates and might aid in furthering insight into gene–environment relationships under disturbances to homeostasis.     

Tasks and perspectives of development of evolutionary physiology

The paper considers the main tasks facing evolutionary physiology as well as the methods that allow these tasks to be solved. It is shown that alongside with traditional, classical methods (phylogenetic, ontogenetic, experimental, clinical), there developed and got recognition in the present-day evolutionary physiology principally new methodological approaches important for solution of actual problems of this discipline. It is the method of mathematical modeling of functions, which makes it possible not only to study on the model, in temporary scales convenient for analysis, evolution of the given functions under the given actions, but also to model the evolutionary process itself in its different manifestations. It is the method of gene engineering that allows studying processes of formation of specific functional systems, their interaction and “mutual adjustment” at the organism level. It is lastly the method of tissue cultures allowing reconstruction under artificial conditions various systems of organism and study of regularities and mechanisms of morphogenesis and formation of functions. The paper discusses trends of development of evolutionary physiology for the last few years, considers perspective directions of studies, using specific examples, and shows possible ways for practical applications of fundamental investigations in the field of evolutionary physiology. Journal variant of the report published in the Collection of Papers “Tendency of Development of Physiological Sciences,” St. Petersburg, 2000.     

Global Genotype-Phenotype Correlations in Pseudomonas aeruginosa

Once the genome sequence of an organism is obtained, attention turns from identifying genes to understanding their function, their organization and control of metabolic pathways and networks that determine its physiology. Recent technical advances in acquiring genome-wide data have led to substantial progress in identifying gene functions. However, we still do not know the function of a large number of genes and, even when a gene product has been assigned to a functional class, we cannot normally predict its contribution to the phenotypic behaviour of the cell or organism - the phenome. In this study, we assessed bacterial growth parameters of 4030 non-redundant PA14 transposon mutants in the pathogenic bacterium Pseudomonas aeruginosa. The genome-wide simultaneous analysis of 119 distinct growth-related phenotypes uncovered a comprehensive phenome and provided evidence that most genotypes are not phenotypically isolated but rather define specific complex phenotypic clusters of genotypes. Since phenotypic overlap was demonstrated to reflect the relatedness of genotypes on a global scale, knowledge of an organism''s phenome might significantly contribute to the advancement of functional genomics.     

Finding New Order in Biological Functions from the Network Structure of Gene Annotations

The Gene Ontology (GO) provides biologists with a controlled terminology that describes how genes are associated with functions and how functional terms are related to one another. These term-term relationships encode how scientists conceive the organization of biological functions, and they take the form of a directed acyclic graph (DAG). Here, we propose that the network structure of gene-term annotations made using GO can be employed to establish an alternative approach for grouping functional terms that captures intrinsic functional relationships that are not evident in the hierarchical structure established in the GO DAG. Instead of relying on an externally defined organization for biological functions, our approach connects biological functions together if they are performed by the same genes, as indicated in a compendium of gene annotation data from numerous different sources. We show that grouping terms by this alternate scheme provides a new framework with which to describe and predict the functions of experimentally identified sets of genes.     

根据GO分类体系选择与实验条件相关的差异表达功能结点进行基因功能深化预测

利用有限个实验条件下的基因表达谱数据,只能对与实验条件相关的基因功能类进行有效预测,所以有必要限定可预测的基因功能类范围。据此,首先基于GeneOntology(GO)选择富集差异表达基因与实验条件相关的功能类。再通过支持向量机分类器,深化预测迄今只注释到实验条件相关功能类的父结点的基因是否属于该实验条件相关功能类。应用于一套酵母基因表达谱数据,结果显示,在剔除了高度不平衡的训练集合后,平均真阳性率(precision)与平均覆盖率(recall)都分别达到了71%与47%以上。     

On mathematical modeling of circadian rhythms, performance, and alertness

Mathematical models of neurobehavioral performance and alertness have both basic science and practical applications. These models can be especially useful in predicting the effect of different sleep-wake schedules on human neurobehavioral objective performance and subjective alertness under many conditions. Several relevant models currently exist in the literature. In principle, the development and refinement of any mathematical model should be based on an explicit modeling methodology, such as the Box modeling paradigm, that formally defines the model structure and calculates the set of parameters. While most mathematical models of neurobehavioral performance and alertness include homeostatic, circadian, and sleep inertia components and their interactions, there may be fundamental differences in the equations included in these models. In part, these may be due to differences in the assumptions of the underlying physiology. Because the choice of model equations can have a dramatic influence on the results, it is necessary to consider these differences in assumptions when examining the results from a model and when comparing results across models. This article presents principles of mathematical modeling and examples of how such procedures can be applied to the development and refinement of mathematical models of neurobehavioral performance and alertness. This article also presents several methods of testing and comparing these models, suggests different uses of the models, and discusses problems with current models.     

<Emphasis Type="Italic">PLP1</Emphasis> Gene Variation Modulates Leftward and Rightward Functional Hemispheric Asymmetries

Molecular neurobiological factors determining corpus callosum physiology and anatomy have been suggested to be one of the major factors determining functional hemispheric asymmetries. Recently, it was shown that allelic variations in two myelin-related genes, the proteolipid protein 1 gene PLP1 and the contactin 1 gene CNTN1, are associated with differences in interhemispheric integration. Here, we investigated whether three single nucleotide polymorphisms that were associated with interhemispheric integration via the corpus callosum in a previous study also are relevant for functional hemispheric asymmetries. To this end, we tested more than 900 healthy adults with the forced attention dichotic listening task, a paradigm to assess language lateralization and its modulation by cognitive control processes. Moreover, we used the line bisection task, a paradigm to assess functional hemispheric asymmetries in spatial attention. We found that a polymorphism in PLP1, but not CNTN1, was associated with performance differences in both tasks. Both functional hemispheric asymmetries and their modulation by cognitive control processes were affected. These findings suggest that both left and right hemisphere dominant cognitive functions can be modulated by allelic variation in genes affecting corpus callosum structure. Moreover, higher order cognitive processes may be relevant parameters when investigating the molecular basis of hemispheric asymmetries.     

Genome-wide transcriptional orchestration of circadian rhythms in Drosophila

Circadian rhythms govern the behavior, physiology, and metabolism of living organisms. Recent studies have revealed the role of several genes in the clock mechanism both in Drosophila and in mammals. To study how gene expression is globally regulated by the clock mechanism, we used a high density oligonucleotide probe array (GeneChip) to profile gene expression patterns in Drosophila under light-dark and constant dark conditions. We found 712 genes showing a daily fluctuation in mRNA levels under light-dark conditions, and among these the expression of 115 genes was still cycling in constant darkness, i.e. under free-running conditions. Unexpectedly the expression of a large number of genes cycled exclusively under constant darkness. We found that cycling in most of these genes was lost in the arrhythmic Clock (Clk) mutant under light-dark conditions. Expression of periodically regulated genes is coordinated locally on chromosomes where small clusters of genes are regulated jointly. Our findings reveal that many genes involved in diverse functions are under circadian control and reveal the complexity of circadian gene expression in Drosophila.     

Reverse-engineering of gene networks for regulating early blood development from single-cell measurements

Background

Recent advances in omics technologies have raised great opportunities to study large-scale regulatory networks inside the cell. In addition, single-cell experiments have measured the gene and protein activities in a large number of cells under the same experimental conditions. However, a significant challenge in computational biology and bioinformatics is how to derive quantitative information from the single-cell observations and how to develop sophisticated mathematical models to describe the dynamic properties of regulatory networks using the derived quantitative information.

Methods

This work designs an integrated approach to reverse-engineer gene networks for regulating early blood development based on singel-cell experimental observations. The wanderlust algorithm is initially used to develop the pseudo-trajectory for the activities of a number of genes. Since the gene expression data in the developed pseudo-trajectory show large fluctuations, we then use Gaussian process regression methods to smooth the gene express data in order to obtain pseudo-trajectories with much less fluctuations. The proposed integrated framework consists of both bioinformatics algorithms to reconstruct the regulatory network and mathematical models using differential equations to describe the dynamics of gene expression.

Results

The developed approach is applied to study the network regulating early blood cell development. A graphic model is constructed for a regulatory network with forty genes and a dynamic model using differential equations is developed for a network of nine genes. Numerical results suggests that the proposed model is able to match experimental data very well. We also examine the networks with more regulatory relations and numerical results show that more regulations may exist. We test the possibility of auto-regulation but numerical simulations do not support the positive auto-regulation. In addition, robustness is used as an importantly additional criterion to select candidate networks.

Conclusion

The research results in this work shows that the developed approach is an efficient and effective method to reverse-engineer gene networks using single-cell experimental observations.

     

A verification protocol for the probe sequences of Affymetrix genome arrays reveals high probe accuracy for studies in mouse,human and rat

Background  

The Affymetrix GeneChip technology uses multiple probes per gene to measure its expression level. Individual probe signals can vary widely, which hampers proper interpretation. This variation can be caused by probes that do not properly match their target gene or that match multiple genes. To determine the accuracy of Affymetrix arrays, we developed an extensive verification protocol, for mouse arrays incorporating the NCBI RefSeq, NCBI UniGene Unique, NIA Mouse Gene Index, and UCSC mouse genome databases.     

Functional grouping of osteoclast genes revealed through microarray analysis

We describe for the first time functional clusters of genes that are modulated during the differentiation of osteoclasts. Pathway analysis was applied to gene array data generated from affymetrix chips hybridized to RNA isolated from RAW264.7 cells exposed to RANK-ligand (RANK-L) for 5 days. This analysis revealed major functional gene clusters that were either up- or down-regulated during osteoclastogenesis. Some of the genes within the clusters have known functions, while others do not. We discuss herein the relevance of these functional gene clusters and their modulation to biological processes underlying the formation, function, and fate of osteoclasts.     

Use of transcriptomic data for extending a model of the AppA/PpsR system in <Emphasis Type="Italic">Rhodobacter sphaeroides</Emphasis>

Background

Photosynthetic (PS) gene expression in Rhodobacter sphaeroides is regulated in response to changes in light and redox conditions mainly by PrrB/A, FnrL and AppA/PpsR systems. The PrrB/A and FnrL systems activate the expression of them under anaerobic conditions while the AppA/PpsR system represses them under aerobic conditions. Recently, two mathematical models have been developed for the AppA/PpsR system and demonstrated how the interaction between AppA and PpsR could lead to a phenotype in which PS genes are repressed under semi-aerobic conditions. These models have also predicted that the transition from aerobic to anaerobic growth mode could occur via a bistable regime. However, they lack experimentally quantifiable inputs and outputs. Here, we extend one of them to include such quantities and combine all relevant micro-array data publically available for a PS gene of this bacterium and use that to parameterise the model. In addition, we hypothesise that the AppA/PpsR system alone might account for the observed trend of PS gene expression under semi-aerobic conditions.

Results

Our extended model of the AppA/PpsR system includes the biological input of atmospheric oxygen concentration and an output of photosynthetic gene expression. Following our hypothesis that the AppA/PpsR system alone is sufficient to describe the overall trend of PS gene expression we parameterise the model and suggest that the rate of AppA reduction in vivo should be faster than its oxidation. Also, we show that despite both the reduced and oxidised forms of PpsR binding to the PS gene promoters in vitro, binding of the oxidised form as a repressor alone is sufficient to reproduce the observed PS gene expression pattern. Finally, the combination of model parameters which fit the biological data well are broadly consistent with those which were previously determined to be required for the system to show (i) the repression of PS genes under semi-aerobic conditions, and (ii) bistability.

Conclusion

We found that despite at least three pathways being involved in the regulation of photosynthetic genes, the AppA/PpsR system alone is capable of accounting for the observed trends in photosynthetic gene expression seen at different oxygen levels.

     

The role of the retinoid receptor,RAR/RXR heterodimer,in liver physiology

Activated by retinoids, metabolites of vitamin A, the retinoic acid receptors (RARs) and the retinoid X receptors (RXRs) play important roles in a wide variety of biological processes, including embryo development, homeostasis, cell proliferation, differentiation and death. In this review, we summarized the functional roles of nuclear receptor RAR/RXR heterodimers in liver physiology. Specifically, RAR/RXR modulate the synthesis and metabolism of lipids and bile acids in hepatocytes, regulate cholesterol transport in macrophages, and repress fibrogenesis in hepatic stellate cells. We have also listed the specific genes that carry these functions and how RAR/RXR regulate their expression in liver cells, providing a mechanistic view of their roles in liver physiology. Meanwhile, we pointed out many questions regarding the detailed signaling of RAR/RXR in regulating the expression of liver genes, and hope future studies will address these issues.     

Multiple functional linear model for association analysis of RNA-seq with imaging

Emerging integrative analysis of genomic and anatomical imaging data which has not been well developed, provides invaluable information for the holistic discovery of the genomic structure of disease and has the potential to open a new avenue for discovering novel disease susceptibility genes which cannot be identified if they are analyzed separately. A key issue to the success of imaging and genomic data analysis is how to reduce their dimensions. Most previous methods for imaging information extraction and RNA-seq data reduction do not explore imaging spatial information and often ignore gene expression variation at the genomic positional level. To overcome these limitations, we extend functional principle component analysis from one dimension to two dimensions (2DFPCA) for representing imaging data and develop a multiple functional linear model (MFLM) in which functional principal scores of images are taken as multiple quantitative traits and RNA-seq profile across a gene is taken as a function predictor for assessing the association of gene expression with images. The developed method has been applied to image and RNA-seq data of ovarian cancer and kidney renal clear cell carcinoma (KIRC) studies. We identified 24 and 84 genes whose expressions were associated with imaging variations in ovarian cancer and KIRC studies, respectively. Our results showed that many significantly associated genes with images were not differentially expressed, but revealed their morphological and metabolic functions. The results also demonstrated that the peaks of the estimated regression coefficient function in the MFLM often allowed the discovery of splicing sites and multiple isoforms of gene expressions.     

"Guilt by association" is the exception rather than the rule in gene networks

Gene networks are commonly interpreted as encoding functional information in their connections. An extensively validated principle called guilt by association states that genes which are associated or interacting are more likely to share function. Guilt by association provides the central top-down principle for analyzing gene networks in functional terms or assessing their quality in encoding functional information. In this work, we show that functional information within gene networks is typically concentrated in only a very few interactions whose properties cannot be reliably related to the rest of the network. In effect, the apparent encoding of function within networks has been largely driven by outliers whose behaviour cannot even be generalized to individual genes, let alone to the network at large. While experimentalist-driven analysis of interactions may use prior expert knowledge to focus on the small fraction of critically important data, large-scale computational analyses have typically assumed that high-performance cross-validation in a network is due to a generalizable encoding of function. Because we find that gene function is not systemically encoded in networks, but dependent on specific and critical interactions, we conclude it is necessary to focus on the details of how networks encode function and what information computational analyses use to extract functional meaning. We explore a number of consequences of this and find that network structure itself provides clues as to which connections are critical and that systemic properties, such as scale-free-like behaviour, do not map onto the functional connectivity within networks.