The value of high quality protein-protein interaction networks for systems biology

Protein-protein interaction (PPI) networks contain a large amount of useful information for the functional characterization of proteins and promote the understanding of the complex molecular relationships that determine the phenotype of a cell. Recently, large human interaction maps have been generated with high throughput technologies such as the yeast two-hybrid system. However, they are static and incomplete and do not provide immediate clues about the cellular processes that convert genetic information into complex phenotypes. Refined multiple-aspect PPI screening and confirmation strategies will have to be put in place to increase the validity of interaction maps. Integration of interaction data with other qualitative and quantitative information (e.g. protein expression or localization data), will be required to construct networks of protein function that reflect dynamic processes in the cell. In this way, combined PPI networks can become valuable resources for a systems-level understanding of cellular processes and complex phenotypes.     

Long term effects of ART: What do animals tell us?

Early stages of mammalian embryonic development are now known to be very sensitive to their microenvironment, with long term effects on fetal, postnatal, and adult health, thus extending to these early stages the concept of Developmental Origin of Health and Disease (DoHaD). In this scientific context, and with 3% of births in developed countries, safety of Assisted Reproductive Techniques procedures becomes a matter of concern. Besides, embryo technologies in domestic mammals, using huge number of embryos, do not seem to evidence heavy impacts on adult phenotypes. This paper first discusses what can or cannot be concluded from farm animal data, then develops long term effects of ART procedures (ovarian stimulation, in vitro fertilization and embryo culture) evidenced in model species (mainly mouse model). Recent literature demonstrates both individual and cumulative effects of each ART procedure on fetal and postnatal phenotypes. In a second part, because they are sources for further perturbations, immediate effects of ART on early embryo phenotypes at the cellular and molecular levels are described in both farm animals and model species. Mechanistic hypotheses supporting these ART induced phenotypic alterations are subsequently considered. Finally, taking into account interspecies differences in the mechanisms likely to be involved, the relevance of results obtained in animal models for human ART are discussed.     

超突变细胞在进化工程中的开发与应用

通过进化工程技术改造微生物细胞的生理表型是生物技术和生物炼制领域的重要研究方向,但是现阶段的各种进化工程技术面临效率低或连续性差的问题。超突变细胞能够进行自发、连续的胞内诱变,将其应用于进化工程技术能够实现连续、高效的菌种改造。本文详细介绍了自然界中超突变细胞产生的遗传机制和相应的人工超突变细胞的构建策略,以及应用此类超突变系统在微生物细胞生理性能改造和蛋白质突变体文库高效构建上取得的进展。随着相关领域认识和技术的加强,未来人工超突变细胞的构建将继续向着不同维度上可控性、靶向性不断提高的方向发展,从而为细胞和蛋白的改造提供更强更优的进化动力。     

Image-based cell phenotyping with deep learning

A cell's phenotype is the culmination of several cellular processes through a complex network of molecular interactions that ultimately result in a unique morphological signature. Visual cell phenotyping is the characterization and quantification of these observable cellular traits in images. Recently, cellular phenotyping has undergone a massive overhaul in terms of scale, resolution, and throughput, which is attributable to advances across electronic, optical, and chemical technologies for imaging cells. Coupled with the rapid acceleration of deep learning–based computational tools, these advances have opened up new avenues for innovation across a wide variety of high-throughput cell biology applications. Here, we review applications wherein deep learning is powering the recognition, profiling, and prediction of visual phenotypes to answer important biological questions. As the complexity and scale of imaging assays increase, deep learning offers computational solutions to elucidate the details of previously unexplored cellular phenotypes.     

Evaluating different methods of microarray data normalization

Background  

With the development of DNA hybridization microarray technologies, nowadays it is possible to simultaneously assess the expression levels of thousands to tens of thousands of genes. Quantitative comparison of microarrays uncovers distinct patterns of gene expression, which define different cellular phenotypes or cellular responses to drugs. Due to technical biases, normalization of the intensity levels is a pre-requisite to performing further statistical analyses. Therefore, choosing a suitable approach for normalization can be critical, deserving judicious consideration.     

High-throughput screening assays for the identification of chemical probes

High-throughput screening (HTS) assays enable the testing of large numbers of chemical substances for activity in diverse areas of biology. The biological responses measured in HTS assays span isolated biochemical systems containing purified receptors or enzymes to signal transduction pathways and complex networks functioning in cellular environments. This Review addresses factors that need to be considered when implementing assays for HTS and is aimed particularly at investigators new to this field. We discuss assay design strategies, the major detection technologies and examples of HTS assays for common target classes, cellular pathways and simple cellular phenotypes. We conclude with special considerations for configuring sensitive, robust, informative and economically feasible HTS assays.     

Enabling inverse metabolic engineering through genomics

Inverse metabolic engineering (IME) is a powerful framework for engineering cellular phenotypes. Progress in this field has been limited by a lack of comprehensive methods for efficiently identifying the genetic basis of relevant phenotypes. Advances in genomics technologies, including DNA microarrays and gene sequencing, have dramatically improved our ability to relate changes in phenotype with associated changes in genotype. When applied in the context of IME, these tools should enable the integration of "evolutionary" and "direct" approaches to engineering cell physiology, which should improve our understanding of the complex interactions affecting the expression, evolution and engineering of traits in natural and industrial hosts.     

Dissecting the complex genetic basis of mate choice

The genetic analysis of mate choice is fraught with difficulties. Males produce complex signals and displays that can consist of a combination of acoustic, visual, chemical and behavioural phenotypes. Furthermore, female preferences for these male traits are notoriously difficult to quantify. During mate choice, genes not only affect the phenotypes of the individual they are in, but can influence the expression of traits in other individuals. How can genetic analyses be conducted to encompass this complexity? Tighter integration of classical quantitative genetic approaches with modern genomic technologies promises to advance our understanding of the complex genetic basis of mate choice.     

The genetic control of arista lateral morphogenesis in Drosophila

The sensory bristles and epidermal hairs of Drosophila have proven to be valuable model cell types for studying the role of the cytoskeleton in cellular morphogenesis. We have recently begun to use the arista laterals as a third model cell type. The laterals display a combination of bristle and hair characteristics and provide a system where we can compare the relative importance of specific genes and subcellular structures for the morphogenesis of different polarized cellular extensions. We have characterized the lateral phenotype of a collection of mutations selected because of their phenotypes in hairs and bristles. In many but not all ways the lateral phenotypes are similar to the hair and bristle phenotypes. We provide compelling genetic evidence for the importance of the actin cytoskeleton in lateral elongation, shaping and integrity. Our observations provide evidence that defects in actin bundling can destabilize laterals so that they split during growth. Temperature shift experiments suggest that a defect in lateral initiation can lead to subsequent splitting. These observations provide a link between multiple hair and lateral cells forming by both multiple initiation events and by the splitting of individual cellular extensions. We also found that mutations that lead to lateral splitting typically alter the stereotypic arrangement of actin filament bundles and microtubules in laterals.     

Application of reprogrammed patient cells to investigate the etiology of neurological and psychiatric disorders

Cellular reprogramming allows for the de novo generation of human neurons and glial cells from patients with neurological and psychiatric disorders.Crucially,this technology preserves the genome of the...     

Lipidomics: a new window to biomedical frontiers

Lipids are a highly diverse class of molecules with crucial roles in cellular energy storage, structure and signaling. Lipid homeostasis is fundamental to maintain health, and lipid defects are central to the pathogenesis of important and devastating diseases. Newly emerging advances have facilitated the development of so-called lipidomics technologies and offer an opportunity to elucidate the mechanisms leading to disease. Furthermore, these advances also provide the tools to unravel the complexity of the 'allostatic forces' that allow maintenance of normal cellular/tissue phenotypes through the application of bioenergetically inefficient adaptive mechanisms. An alternative strategy is to focus on tissues with limited allostatic capacity, such as the eye, that could be used as readouts of metabolic stress over time. Identification of these allostatic mechanisms and pathological 'scares' might provide a window to unknown pathogenic mechanisms, as well as facilitate identification of early biomarkers of disease.     

Strategies to dissect parasite proteomes

Proteomes are complex and dynamic entities that are still poorly understood, but the application of proteomic technologies has become invaluable in many areas of biology, including parasitology. These technologies can be exploited to identify proteins in both complex or relatively simple samples, that formerly could only be characterized by targeted approaches such as Western blotting. Quantitative proteomic approaches can reveal modulations in protein expression that accompany phenotypes of interest. Proteomic approaches have been exploited to understand some of the molecular basis for host:parasite interactions and to elucidate phenotypes such as virulence, antigenicity and drug resistance. Many of the same technologies can also be more easily applied to targeted sub-proteomes. Examples from several studies on pathogen proteomes and sub-proteomes, from bacteria to helminths, are presented to illustrate the potential and limitations of proteomic technologies.     

Methods and approaches for the comprehensive characterization and quantification of cellular proteomes using mass spectrometry.

Proteomics has been proposed as one of the key technologies in the postgenomic era. So far, however, the comprehensive analysis of cellular proteomes has been a challenge because of the dynamic nature and complexity of the multitude of proteins in cells and tissues. Various approaches have been established for the analyses of proteins in a cell at a given state, and mass spectrometry (MS) has proven to be an efficient and versatile tool. MS-based proteomics approaches have significantly improved beyond the initial identification of proteins to comprehensive characterization and quantification of proteomes and their posttranslational modifications (PTMs). Despite these advances, there is still ongoing development of new technologies to profile and analyze cellular proteomes more completely and efficiently. In this review, we focus on MS-based techniques, describe basic approaches for MS-based profiling of cellular proteomes and analysis methods to identify proteins in complex mixtures, and discuss the different approaches for quantitative proteome analysis. Finally, we briefly discuss novel developments for the analysis of PTMs. Altered levels of PTM, sometimes in the absence of protein expression changes, are often linked to cellular responses and disease states, and the comprehensive analysis of cellular proteome would not be complete without the identification and quantification of the extent of PTMs of proteins.     

Switching control of an essential gene for reprogramming of cellular phenotypes in Escherichia coli

Biological systems designs require various dynamic controllers capable of modulating cellular phenotypes to adapt to changing environments. Cellular phenotypes are simultaneously affected by combinations of multiple genes that are controlled by global regulators. However, it is difficult to intentionally control the expression of these global regulators dynamically because they are essential for cell survival and are involved in regulatory networks clustered in operons. Here, we designed a platform that allows dynamic modulation of the expression of an essential gene. Using this system, comprising of on/off switches that respond to an extracellular stimulus, we successfully demonstrated the switching control of the expression of fusA encoding elongation factor G (EF-G). An additional control module in this system that responds to changed external signals was shown to provide the capacity to "switch gears" and reprogram cellular phenotypes with desired timing.     

<Emphasis Type="Italic">In-silico</Emphasis> identification of phenotype-biased functional modules

Background

Phenotypes exhibited by microorganisms can be useful for several purposes, e.g., ethanol as an alternate fuel. Sometimes, the target phenotype maybe required in combination with other phenotypes, in order to be useful, for e.g., an industrial process may require that the organism survive in an anaerobic, alcohol rich environment and be able to feed on both hexose and pentose sugars to produce ethanol. This combination of traits may not be available in any existing organism or if they do exist, the mechanisms involved in the phenotype-expression may not be efficient enough to be useful. Thus, it may be required to genetically modify microorganisms. However, before any genetic modification can take place, it is important to identify the underlying cellular subsystems responsible for the expression of the target phenotype.

Results

In this paper, we develop a method to identify statistically significant and phenotypically-biased functional modules. The method can compare the organismal network information from hundreds of phenotype expressing and phenotype non-expressing organisms to identify cellular subsystems that are more prone to occur in phenotype-expressing organisms than in phenotype non-expressing organisms. We have provided literature evidence that the phenotype-biased modules identified for phenotypes such as hydrogen production (dark and light fermentation), respiration, gram-positive, gram-negative and motility, are indeed phenotype-related.

Conclusion

Thus we have proposed a methodology to identify phenotype-biased cellular subsystems. We have shown the effectiveness of our methodology by applying it to several target phenotypes. The code and all supplemental files can be downloaded from (http://freescience.org/cs/phenotype-biased-biclusters/).

     

Establishing gene function by mutagenesis in Arabidopsis thaliana

The nuclear genome of Arabidopsis thaliana was sequenced to near completion a few years ago, and ahead lies the challenge of understanding its meaning and discerning its potential. How many genes are there? What are they? What do they do? Computer algorithms combined with genome array technologies have proven efficient in addressing the first two questions as shown in a recent report ( Yamada et al., 2003 ). However, assessing the function of every gene in every cell will require years of careful analyses of the phenotypes caused by mutations in each gene. Current progress in generating large numbers of molecular markers and near‐saturation insertion mutant collections has immensely facilitated functional genomics studies in Arabidopsis. In this review, we focus on how gene function can be revealed through the analysis of mutants by either forward or reverse genetics. These mutants generally fall into two distinct classes. The first class typically includes point mutations or small deletions derived from chemical or fast neutron mutagenesis whereas the second class includes insertions of transferred‐DNA or transposon elements. We describe the current methods that are used to identify the gene corresponding to these mutations, which can then be used as a probe to further dissect its function.     

The RhoGEF TEM4 Regulates Endothelial Cell Migration by Suppressing Actomyosin Contractility

Persistent cellular migration requires efficient protrusion of the front of the cell, the leading edge where the actin cytoskeleton and cell-substrate adhesions undergo constant rearrangement. Rho family GTPases are essential regulators of the actin cytoskeleton and cell adhesion dynamics. Here, we examined the role of the RhoGEF TEM4, an activator of Rho family GTPases, in regulating cellular migration of endothelial cells. We found that TEM4 promotes the persistence of cellular migration by regulating the architecture of actin stress fibers and cell-substrate adhesions in protruding membranes. Furthermore, we determined that TEM4 regulates cellular migration by signaling to RhoC as suppression of RhoC expression recapitulated the loss-of-TEM4 phenotypes, and RhoC activation was impaired in TEM4-depleted cells. Finally, we showed that TEM4 and RhoC antagonize myosin II-dependent cellular contractility and the suppression of myosin II activity rescued the persistence of cellular migration of TEM4-depleted cells. Our data implicate TEM4 as an essential regulator of the actin cytoskeleton that ensures proper membrane protrusion at the leading edge of migrating cells and efficient cellular migration via suppression of actomyosin contractility.     

Digest: Frog spots show broken receptors might work harder*

Posso‐Terranova and Andrés (2017) used harlequin poison frogs to investigate the genetic basis of pigmentation evolution and variation. Using a candidate gene approach, they clearly delimited the origins and distribution of MC1R haplotypes and associated them with key pigmentation phenotypes on multiple levels. They demonstrated that MC1R‐related cellular phenotypes and associated protein truncations evolved at least twice to produce dark dorsal skin colors in different clades.