A Review on the Role of SPRY4-IT1 in the Carcinogenesis

Sprouty RTK signaling antagonist 4-intronic transcript 1 (SPRY4-IT1) is a long non-coding RNA (lncRNA) encoded by a gene located on 5q31.3. This lncRNA has a possible role in the regulation of cell growth, proliferation, and apoptosis. Moreover, since SPRY4-IT1 controls levels of lipin 2, it is also involved in the biosynthesis of lipids. During the process of biogenesis, SPRY4-IT1 is produced as a primary transcript which is then cleaved to generate a mature transcript which is localized in the cytoplasm. SPRY4-IT1 has oncogenic roles in diverse tissues. A possible route of participation of SPRY4-IT1 in the carcinogenesis is through sequestering miRNAs such as miR-101-3p, miR‐6882‐3p and miR-22-3p. The sponging effect of SPRY4-IT1 on miR-101 has been verified in colorectal cancer, osteosarcoma, cervical cancer, bladder cancer, gastric cancer and cholangiocarcinoma. SPRY4-IT1 has functional interactions with HIF-1α, NF-κB/p65, AMPK, ZEB1, MAPK and PI3K/Akt signaling. We explain the role of SPRY4-IT1 in the carcinogenesis according to evidence obtained from cell lines, xenograft models and clinical studies.

NOG-Derived Peptides Can Restore Neuritogenesis on a CRASH Syndrome Cell Model

Homo- and heterophilic binding mediated by the immunoglobulin (Ig)-like repeats of cell adhesion molecules play a pivotal role in cell-cell and cell-extracellular matrix interactions. L1CAM is crucial to neuronal differentiation, in both mature and developing nervous systems, and several studies suggest that its functional interactions are mainly mediated by Ig2–Ig2 binding. X-linked mutations in the human L1CAM gene are summarized as L1 diseases, including the most diagnosed CRASH neurodevelopmental syndrome. In silico simulations provided a molecular rationale for CRASH phenotypes resulting from mutations I179S and R184Q in the homophilic binding region of Ig2. A synthetic peptide reproducing such region could both mimic the neuritogenic capacity of L1CAM and rescue neuritogenesis in a cellular model of the CRASH syndrome, where the full L1CAM ectodomain proved ineffective. Presented functional evidence opens the route to the use of L1CAM-derived peptides as biotechnological and therapeutic tools.

Ghrelin and Cannabinoid Functional Interactions Mediated by Ghrelin/CB1 Receptor Heteromers That Are Upregulated in the Striatum From Offspring of Mice Under a High-Fat Diet

There is evidence of ghrelinergic-cannabinoidergic interactions in the central nervous system (CNS) that may impact on the plasticity of reward circuits. The aim of this article was to look for molecular and/or functional interactions between cannabinoid CB1 and ghrelin GHS-R1a receptors. In a heterologous system and using the bioluminescence resonance energy transfer technique we show that human versions of cannabinoid CB1 and ghrelin GHS-R1a receptors may form macromolecular complexes. Such receptor heteromers have particular properties in terms of CB1/Gi-mediated signaling and in terms of GHS-R1a-Gq-mediated signaling. On the one hand, just co-expression of CB1R and GHS-R1a led to impairment of cannabinoid signaling. On the other hand, cannabinoids led to an increase in ghrelin-derived calcium mobilization that was stronger at low concentrations of the CB1 receptor agonist, arachidonyl-2’-chloroethylamide (ACEA). The expression of CB1-GHS-R1a receptor complexes in striatal neurons was confirmed by in situ proximity ligation imaging assays. Upregulation of CB1-GHS-R1a- receptor complexes was found in striatal neurons from siblings of pregnant female mice on a high-fat diet. Surprisingly, the expression was upregulated after treatment of neurons with ghrelin (200 nM) or with ACEA (100 nM). These results help to better understand the complexities underlying the functional interactions of neuromodulators in the reward areas of the brain.

RefSeq Functional Elements as experimentally assayed nongenic reference standards and functional interactions in human and mouse

Eukaryotic genomes contain many nongenic elements that function in gene regulation, chromosome organization, recombination, repair or replication, and mutation of those elements can affect genome function and cause disease. While numerous epigenomic studies provide high coverage of gene regulatory regions, those data are not usually exposed in traditional genome annotation, and can be difficult to access and interpret without field-specific expertise. The National Center for Biotechnology Information (NCBI) therefore provides RefSeq Functional Elements (RefSeqFEs), which represent experimentally validated human and mouse nongenic elements derived from the literature. The curated dataset is comprised of richly annotated sequence records, descriptive records in the NCBI Gene database, reference genome feature annotation, and activity-based interactions between nongenic regions, target genes and each other. The dataset provides succinct functional details and transparent experimental evidence, leverages data from multiple experimental sources, is readily accessible and adaptable, and utilizes a flexible data model. The data have multiple uses for basic functional discovery, bioinformatics studies, genetic variant interpretation, as known positive controls for epigenomic data evaluation, and as reference standards for functional interactions. Comparisons to other gene regulatory datasets show that the RefSeqFE dataset includes a wider range of feature types representing more areas of biology, but it is comparatively smaller and subject to data selection biases. RefSeqFEs thus provide an alternative and complementary resource for experimentally assayed functional elements, with future dataset growth expected.

Chromatography-Free Purification Strategies for Large Biological Macromolecular Complexes Involving Fractionated PEG Precipitation and Density Gradients

A complex interplay between several biological macromolecules maintains cellular homeostasis. Generally, the demanding chemical reactions which sustain life are not performed by individual macromolecules, but rather by several proteins that together form a macromolecular complex. Understanding the functional interactions amongst subunits of these macromolecular machines is fundamental to elucidate mechanisms by which they maintain homeostasis. As the faithful function of macromolecular complexes is essential for cell survival, their mis-function leads to the development of human diseases. Furthermore, detailed mechanistic interrogation of the function of macromolecular machines can be exploited to develop and optimize biotechnological processes. The purification of intact macromolecular complexes is an essential prerequisite for this; however, chromatographic purification schemes can induce the dissociation of subunits or the disintegration of the whole complex. Here, we discuss the development and application of chromatography-free purification strategies based on fractionated PEG precipitation and orthogonal density gradient centrifugation that overcomes existing limitations of established chromatographic purification protocols. The presented case studies illustrate the capabilities of these procedures for the purification of macromolecular complexes.

Convergent, functionally independent signaling by mu and delta opioid receptors in hippocampal parvalbumin interneurons

Functional interactions between G protein-coupled receptors are poised to enhance neuronal sensitivity to neuromodulators and therapeutic drugs. Mu and Delta opioid receptors (MORs and DORs) can interact when overexpressed in the same cells, but whether co-expression of endogenous MORs and DORs in neurons leads to functional interactions is unclear. Here, in mice, we show that both MORs and DORs inhibit parvalbumin-expressing basket cells (PV-BCs) in hippocampal CA1 through partially occlusive signaling pathways that terminate on somato-dendritic potassium channels and presynaptic calcium channels. Using photoactivatable opioid neuropeptides, we find that DORs dominate the response to enkephalin in terms of both ligand-sensitivity and kinetics, which may be due to relatively low expression levels of MOR. Opioid-activated potassium channels do not show heterologous desensitization, indicating that MORs and DORs signal independently. In a direct test for heteromeric functional interactions, the DOR antagonist TIPP-Psi does not alter the kinetics or potency of either the potassium channel or synaptic responses to photorelease of the MOR agonist DAMGO. Thus, aside from largely redundant and convergent signaling, MORs and DORs do not functionally interact in PV-BCs in a way that impacts somato-dendritic potassium currents or synaptic transmission. These findings imply that crosstalk between MORs and DORs, either in the form of physical interactions or synergistic intracellular signaling, is not a preordained outcome of co-expression in neurons.

Co-Evolution of Opioid and Adrenergic Ligands and Receptors: Shared, Complementary Modules Explain Evolution of Functional Interactions and Suggest Novel Engineering Possibilities

Cross-talk between opioid and adrenergic receptors is well-characterized and involves second messenger systems, the formation of receptor heterodimers, and the presence of extracellular allosteric binding regions for the complementary ligand; however, the evolutionary origins of these interactions have not been investigated. We propose that opioid and adrenergic ligands and receptors co-evolved from a common set of modular precursors so that they share binding functions. We demonstrate the plausibility of this hypothesis through a review of experimental evidence for molecularly complementary modules and report unexpected homologies between the two receptor types. Briefly, opioids form homodimers also bind adrenergic compounds; opioids bind to conserved extracellular regions of adrenergic receptors while adrenergic compounds bind to conserved extracellular regions of opioid receptors; opioid-like modules appear in both sets of receptors within key ligand-binding regions. Transmembrane regions associated with homodimerization of each class of receptors are also highly conserved across receptor types and implicated in heterodimerization. This conservation of multiple functional modules suggests opioid–adrenergic ligand and receptor co-evolution and provides mechanisms for explaining the evolution of their crosstalk. These modules also suggest the structure of a primordial receptor, providing clues for engineering receptor functions.

Co-evolution based machine-learning for predicting functional interactions between human genes

Over the next decade, more than a million eukaryotic species are expected to be fully sequenced. This has the potential to improve our understanding of genotype and phenotype crosstalk, gene function and interactions, and answer evolutionary questions. Here, we develop a machine-learning approach for utilizing phylogenetic profiles across 1154 eukaryotic species. This method integrates co-evolution across eukaryotic clades to predict functional interactions between human genes and the context for these interactions. We benchmark our approach showing a 14% performance increase (auROC) compared to previous methods. Using this approach, we predict functional annotations for less studied genes. We focus on DNA repair and verify that 9 of the top 50 predicted genes have been identified elsewhere, with others previously prioritized by high-throughput screens. Overall, our approach enables better annotation of function and functional interactions and facilitates the understanding of evolutionary processes underlying co-evolution. The manuscript is accompanied by a webserver available at: https://mlpp.cs.huji.ac.il.

A Riemannian approach to predicting brain function from the structural connectome

Ongoing brain function is largely determined by the underlying wiring of the brain, but the specific rules governing this relationship remain unknown. Emerging literature has suggested that functional interactions between brain regions emerge from the structural connections through mono- as well as polysynaptic mechanisms. Here, we propose a novel approach based on diffusion maps and Riemannian optimization to emulate this dynamic mechanism in the form of random walks on the structural connectome and predict functional interactions as a weighted combination of these random walks. Our proposed approach was evaluated in two different cohorts of healthy adults (Human Connectome Project, HCP; Microstructure-Informed Connectomics, MICs). Our approach outperformed existing approaches and showed that performance plateaus approximately around the third random walk. At macroscale, we found that the largest number of walks was required in nodes of the default mode and frontoparietal networks, underscoring an increasing relevance of polysynaptic communication mechanisms in transmodal cortical networks compared to primary and unimodal systems.