SPLIFF: A Single-Cell Method to Map Protein-Protein Interactions in Time and Space

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

Use of red, far-red, and near-infrared light in imaging of yeasts and filamentous fungi

Abstract

While phototoxicity can be a useful therapeutic modality not only for eliminating malignant cells but also in treating fungal infections, mycologists aiming to observe morphological changes or molecular events in fungi, especially when long observation periods or high light fluxes are warranted, encounter problems owed to altered regulatory pathways or even cell death caused by various photosensing mechanisms. Consequently, the ever expanding repertoire of visible fluorescent protein toolboxes and high-resolution microscopy methods designed to investigate fungi in vitro and in vivo need to comply with an additional requirement: to decrease the unwanted side effects of illumination. In addition to optimizing exposure, an obvious solution is red-shifted illumination, which, however, does not come without compromises. This review summarizes the interactions of fungi with light and the various molecular biology and technology approaches developed for exploring their functions on the molecular, cellular, and in vivo microscopic levels, and outlines the progress towards reducing phototoxicity through applying far-red and near-infrared light.

Key points

• Fungal biological processes alter upon illumination, also under the microscope

• Red shifted fluorescent protein toolboxes decrease interference by illumination

• Innovations like two-photon, lightsheet, and near IR microscopy reduce phototoxicity

Functional module detection through integration of single-cell RNA sequencing data with protein-protein interaction networks

Background

Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states.

Results

In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With scPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein–protein interaction networks significantly enriched which represent biological pathways. In these pathways, scPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis.

Conclusions

The introduced scPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues.

Supplementary Information

The online version contains supplementary material available at (doi:10.1186/s12864-020-07144-2).

A time-resolved interaction analysis of Bem1 reconstructs the flow of Cdc42 during polar growth

Cdc42 organizes cellular polarity and directs the formation of cellular structures in many organisms. By locating Cdc24, the source of active Cdc42, to the growing front of the yeast cell, the scaffold protein Bem1, is instrumental in shaping the cellular gradient of Cdc42. This gradient instructs bud formation, bud growth, or cytokinesis through the actions of a diverse set of effector proteins. To address how Bem1 participates in these transformations, we systematically tracked its protein interactions during one cell cycle to define the ensemble of Bem1 interaction states for each cell cycle stage. Mutants of Bem1 that interact with only a discrete subset of the interaction partners allowed to assign specific functions to different interaction states and identified the determinants for their cellular distributions. The analysis characterizes Bem1 as a cell cycle-specific shuttle that distributes active Cdc42 from its source to its effectors. It further suggests that Bem1 might convert the PAKs Cla4 and Ste20 into their active conformations.

Cdc24 interacts with septins to create a positive feedback loop during bud site assembly in yeast

Yeast cells select the position of their new bud at the beginning of each cell cycle. The recruitment of septins to this prospective bud site is one of the critical events in a complex assembly pathway that culminates in the outgrowth of a new daughter cell. During recruitment, septin rods follow the high concentration of Cdc42GTP that is generated by the focused localization of the Cdc42 guanine-nucleotide-exchange factor Cdc24. We show that, shortly before budding, Cdc24 not only activates Cdc42 but also transiently interacts with Cdc11, the septin subunit that caps both ends of the septin rods. Mutations in Cdc24 that reduce affinity to Cdc11 impair septin recruitment and decrease the stability of the polarity patch. The interaction between septins and Cdc24 thus reinforces bud assembly at sites where septin structures are formed. Once the septins polymerize to form the septin ring, Cdc24 is found at the cortex of the bud and directs further outgrowth from this position.

Architecture, remodeling, and functions of the septin cytoskeleton

The septin family of proteins has fascinated cell biologists for decades due to the elaborate architecture they adopt in different eukaryotic cells. Whether they exist as rings, collars, or gauzes in different cell types and at different times in the cell cycle illustrates a complex series of regulation in structure. While the organization of different septin structures at the cortex of different cell types during the cell cycle has been described to various degrees, the exact structure and regulation at the filament level are still largely unknown. Recent advances in fluorescent and electron microscopy, as well as work in septin biochemistry, have allowed new insights into the aspects of septin architecture, remodeling, and function in many cell types. This mini-review highlights many of the recent findings with an emphasis on the budding yeast model.

sAPPβ and sAPPα increase structural complexity and E/I input ratio in primary hippocampal neurons and alter Ca2+ homeostasis and CREB1-signaling

One major pathophysiological hallmark of Alzheimer's disease (AD) is senile plaques composed of amyloid β (Aβ). In the amyloidogenic pathway, cleavage of the amyloid precursor protein (APP) is shifted towards Aβ production and soluble APPβ (sAPPβ) levels. Aβ is known to impair synaptic function; however, much less is known about the physiological functions of sAPPβ. The neurotrophic properties of sAPPα, derived from the non-amyloidogenic pathway of APP cleavage, are well-established, whereas only a few, conflicting studies on sAPPβ exist. The intracellular pathways of sAPPβ are largely unknown. Since sAPPβ is generated alongside Aβ by β-secretase (BACE1) cleavage, we tested the hypothesis that sAPPβ effects differ from sAPPα effects as a neurotrophic factor. We therefore performed a head-to-head comparison of both mammalian recombinant peptides in developing primary hippocampal neurons (PHN). We found that sAPPα significantly increases axon length (p = 0.0002) and that both sAPPα and sAPPβ increase neurite number (p < 0.0001) of PHN at 7 days in culture (DIV7) but not at DIV4. Moreover, both sAPPα- and sAPPβ-treated neurons showed a higher neuritic complexity in Sholl analysis. The number of glutamatergic synapses (p < 0.0001), as well as layer thickness of postsynaptic densities (PSDs), were significantly increased, and GABAergic synapses decreased upon sAPP overexpression in PHN. Furthermore, we showed that sAPPα enhances ERK and CREB1 phosphorylation upon glutamate stimulation at DIV7, but not DIV4 or DIV14. These neurotrophic effects are further associated with increased glutamate sensitivity and CREB1-signaling. Finally, we found that sAPPα levels are significantly reduced in brain homogenates of AD patients compared to control subjects. Taken together, our data indicate critical stage-dependent roles of sAPPs in the developing glutamatergic system in vitro, which might help to understand deleterious consequences of altered APP shedding in AD patients, beyond Aβ pathophysiology.

Tissue-, sex-, and age-specific DNA methylation of rat glucocorticoid receptor gene promoter and insulin-like growth factor 2 imprinting control region

Tissue-, sex-, and age-specific epigenetic modifications such as DNA methylation are largely unknown. Changes in DNA methylation of the glucocorticoid receptor gene (NR3C1) and imprinting control region (ICR) of IGF2 and H19 genes during the lifespan are particularly interesting since these genes are susceptible to epigenetic modifications by prenatal stress or malnutrition. They are important regulators of development and aging. Methylation changes of NR3C1 affect glucocorticoid receptor expression, which is associated with stress sensitivity and stress-related diseases predominantly occurring during aging. Methylation changes of IGF2/H19 affect growth trajectory and nutrient use with risk of metabolic syndrome. Using a locus-specific approach, we characterized DNA methylation patterns of different Nr3c1 promoters and Igf2/H19 ICR in seven tissues of rats at 3, 9, and 24 mo of age. We found a complex pattern of locus-, tissue-, sex-, and age-specific DNA methylation. Tissue-specific methylation was most prominent at the shores of the Nr3c1 CpG island (CGI). Sex-specific differences in methylation peaked at 9 mo. During aging, Nr3c1 predominantly displayed hypomethylation mainly in females and at shores, whereas hypermethylation occurred within the CGI. Igf2/H19 ICR exhibited age-related hypomethylation occurring mainly in males. Methylation patterns of Nr3c1 in the skin correlated with those in the cortex, hippocampus, and hypothalamus. Skin may serve as proxy for methylation changes in central parts of the hypothalamic-pituitary-adrenal axis and hence for vulnerability to stress- and age-associated diseases. Thus, we provide in-depth insight into the complex DNA methylation changes of rat Nr3c1 and Igf2/H19 during aging that are tissue and sex specific.

The cell polarity proteins Boi1p and Boi2p stimulate vesicle fusion at the plasma membrane of yeast cells

Eukaryotic cells can direct secretion to defined regions of their plasma membrane. These regions are distinguished by an elaborate architecture of proteins and lipids that are specialized to capture and fuse post-Golgi vesicles. Here we show that the proteins Boi1p and Boi2p are important elements of this area of active exocytosis at the tip of growing yeast cells. Cells lacking Boi1p and Boi2p accumulate secretory vesicles in their bud. The essential PH domains of Boi1p and Boi2p interact with Sec1p, a protein required for SNARE complex formation and vesicle fusion. Sec1p loses its tip localization in cells depleted of Boi1p and Boi2p but can partially compensate for their loss upon overexpression. The capacity to simultaneously bind phospholipids, Sec1p, multiple subunits of the exocyst, Cdc42p, and the module for generating active Cdc42p identify Boi1p and Boi2p as essential mediators between exocytosis and polar growth.

Detection of protein-protein interactions at the septin collar in Saccharomyces cerevisiae using a tripartite split-GFP system

A tripartite split-GFP system faithfully reports the order of the subunits in septin hetero-octamers (and thus can serve as a “molecular ruler”), conversely yields little or no false signal even with very highly expressed cytosolic proteins, and detects authentic interactions of other cellular proteins that are bona fide septin-binding proteins.

Various methods can provide a readout of the physical interaction between two biomolecules. A recently described tripartite split-GFP system has the potential to report by direct visualization via a fluorescence signal the intimate association of minimally tagged proteins expressed at their endogenous level in their native cellular milieu and can capture transient or weak interactions. Here we document the utility of this tripartite split-GFP system to assess in living cells protein–protein interactions in a dynamic cytoskeletal structure—the septin collar at the yeast bud neck. We show, first, that for septin–septin interactions, this method yields a robust signal whose strength reflects the known spacing between the subunits in septin filaments and thus serves as a “molecular ruler.” Second, the method yields little or no spurious signal even with highly abundant cytosolic proteins readily accessible to the bud neck (including molecular chaperone Hsp82 and glycolytic enzyme Pgk1). Third, using two proteins (Bni5 and Hsl1) that have been shown by other means to bind directly to septins at the bud neck in vivo, we validate that the tripartite split-GFP method yields the same conclusions and further insights about specificity. Finally, we demonstrate the capacity of this approach to uncover additional new information by examining whether three other proteins reported to localize to the bud neck (Nis1, Bud4, and Hof1) are able to interact physically with any of the subunits in the septin collar and, if so, with which ones.

Identification of Cell Cycle Dependent Interaction Partners of the Septins by Quantitative Mass Spectrometry

The septins are a conserved family of GTP-binding proteins that, in the baker's yeast, assemble into a highly ordered array of filaments at the mother bud neck. These filaments undergo significant structural rearrangements during the cell cycle. We aimed at identifying key components that are involved in or regulate the transitions of the septins. By combining cell synchronization and quantitative affinity-purification mass-spectrometry, we performed a screen for specific interaction partners of the septins at three distinct stages of the cell cycle. A total of 83 interaction partners of the septins were assigned. Surprisingly, we detected DNA-interacting/nuclear proteins and proteins involved in ribosome biogenesis and protein synthesis predominantly present in alpha-factor arrested that do not display an assembled septin structure. Furthermore, two distinct sets of regulatory proteins that are specific for cells at S-phase with a stable septin collar or at mitosis with split septin rings were identified.

Complementary methods like SPLIFF and immunoprecipitation allowed us to more exactly define the spatial and temporal characteristics of selected hits of the AP-MS screen.