DOMMINO 2.0: integrating structurally resolved protein-, RNA-, and DNA-mediated macromolecular interactions

Single-cell long-read sequencing-based mapping reveals specialized splicing patterns in developing and adult mouse and human brain

RNA isoforms influence cell identity and function. However, a comprehensive brain isoform map was lacking. We analyze single-cell RNA isoforms across brain regions, cell subtypes, developmental time points and species. For 72% of genes, full-length isoform expression varies along one or more axes. Splicing, transcription start and polyadenylation sites vary strongly between cell types, influence protein architecture and associate with disease-linked variation. Additionally, neurotransmitter transport and synapse turnover genes harbor cell-type variability across anatomical regions. Regulation of cell-type-specific splicing is pronounced in the postnatal day 21-to-postnatal day 28 adolescent transition. Developmental isoform regulation is stronger than regional regulation for the same cell type. Cell-type-specific isoform regulation in mice is mostly maintained in the human hippocampus, allowing extrapolation to the human brain. Conversely, the human brain harbors additional cell-type specificity, suggesting gain-of-function isoforms. Together, this detailed single-cell atlas of full-length isoform regulation across development, anatomical regions and species reveals an unappreciated degree of isoform variability across multiple axes.

Single-cell long-read mRNA isoform regulation is pervasive across mammalian brain regions, cell types, and development

RNA isoforms influence cell identity and function. Until recently, technological limitations prevented a genome-wide appraisal of isoform influence on cell identity in various parts of the brain. Using enhanced long-read single-cell isoform sequencing, we comprehensively analyze RNA isoforms in multiple mouse brain regions, cell subtypes, and developmental timepoints from postnatal day 14 (P14) to adult (P56). For 75% of genes, full-length isoform expression varies along one or more axes of phenotypic origin, underscoring the pervasiveness of isoform regulation across multiple scales. As expected, splicing varies strongly between cell types. However, certain gene classes including neurotransmitter release and reuptake as well as synapse turnover, harbor significant variability in the same cell type across anatomical regions, suggesting differences in network activity may influence cell-type identity. Glial brain-region specificity in isoform expression includes strong poly(A)-site regulation, whereas neurons have stronger TSS regulation. Furthermore, developmental patterns of cell-type specific splicing are especially pronounced in the murine adolescent transition from P21 to P28. The same cell type traced across development shows more isoform variability than across adult anatomical regions, indicating a coordinated modulation of functional programs dictating neural development. As most cell-type specific exons in P56 mouse hippocampus behave similarly in newly generated data from human hippocampi, these principles may be extrapolated to human brain. However, human brains have evolved additional cell-type specificity in splicing, suggesting gain-of-function isoforms. Taken together, we present a detailed single-cell atlas of full-length brain isoform regulation across development and anatomical regions, providing a previously unappreciated degree of isoform variability across multiple scales of the brain.

Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning

Alternative splicing introduces an additional layer of protein diversity and complexity in regulating cellular functions that can be specific to the tissue and cell type, physiological state of a cell, or disease phenotype. Recent high-throughput experimental studies have illuminated the functional role of splicing events through rewiring protein-protein interactions; however, the extent to which the macromolecular interactions are affected by alternative splicing has yet to be fully understood. In silico methods provide a fast and cheap alternative to interrogating functional characteristics of thousands of alternatively spliced isoforms. Here, we develop an accurate feature-based machine learning approach that predicts whether a protein-protein interaction carried out by a reference isoform is perturbed by an alternatively spliced isoform. Our method, called the alternatively spliced interactions prediction (ALT-IN) tool, is compared with the state-of-the-art PPI prediction tools and shows superior performance, achieving 0.92 in precision and recall values.

DISPOT: a simple knowledge-based protein domain interaction statistical potential

MOTIVATION

The complexity of protein-protein interactions (PPIs) is further compounded by the fact that an average protein consists of two or more domains, structurally and evolutionary independent subunits. Experimental studies have demonstrated that an interaction between a pair of proteins is not carried out by all domains constituting each protein, but rather by a select subset. However, determining which domains from each protein mediate the corresponding PPI is a challenging task.

RESULTS

Here, we present domain interaction statistical potential (DISPOT), a simple knowledge-based statistical potential that estimates the propensity of an interaction between a pair of protein domains, given their structural classification of protein (SCOP) family annotations. The statistical potential is derived based on the analysis of >352 000 structurally resolved PPIs obtained from DOMMINO, a comprehensive database of structurally resolved macromolecular interactions.

AVAILABILITY AND IMPLEMENTATION

DISPOT is implemented in Python 2.7 and packaged as an open-source tool. DISPOT is implemented in two modes, basic and auto-extraction. The source code for both modes is available on GitHub: https://github.com/korkinlab/dispot and standalone docker images on DockerHub: https://hub.docker.com/r/korkinlab/dispot. The web server is freely available at http://dispot.korkinlab.org/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ER-resident sensor PERK is essential for mitochondrial thermogenesis in brown adipose tissue

Mitochondria play a central role in the function of brown adipocytes (BAs). Although mitochondrial biogenesis, which is indispensable for thermogenesis, is regulated by coordination between nuclear DNA transcription and mitochondrial DNA transcription, the molecular mechanisms of mitochondrial development during BA differentiation are largely unknown. Here, we show the importance of the ER-resident sensor PKR-like ER kinase (PERK) in the mitochondrial thermogenesis of brown adipose tissue. During BA differentiation, PERK is physiologically phosphorylated independently of the ER stress. This PERK phosphorylation induces transcriptional activation by GA-binding protein transcription factor α subunit (GABPα), which is required for mitochondrial inner membrane protein biogenesis, and this novel role of PERK is involved in maintaining the body temperatures of mice during cold exposure. Our findings demonstrate that mitochondrial development regulated by the PERK-GABPα axis is indispensable for thermogenesis in brown adipose tissue.

Activation of HIF-1α by δ-Opioid Receptors Induces COX-2 Expression in Breast Cancer Cells and Leads to Paracrine Activation of Vascular Endothelial Cells

Opioids promote tumor angiogenesis in mammary malignancies, but the underlying signaling mechanism is largely unknown. The current study investigated the hypothesis that stimulation of δ-opioid receptors (DOR) in breast cancer (BCa) cells activates the hypoxia-inducible factor 1α (HIF-1α), which triggers synthesis and release of diverse angiogenic factors. Immunoblotting revealed that incubation of human MCF-7 and T47D breast cancer cells with the DOR agonist d-Ala2,d-Leu5-enkephalin (DADLE) resulted in a transient accumulation and thus activation of HIF-1α DADLE-induced HIF-1α activation preceded PI3K/Akt stimulation and was blocked by the DOR antagonist naltrindole and naloxone, pertussis toxin, different phosphoinositide 3-kinase (PI3K) inhibitors, and the Akt inhibitor Akti-1/2. Whereas DADLE exposure had no effect on the expression and secretion of vascular endothelial growth factor (VEGF) in BCa cells, an increased abundance of cyclooxygenase-2 (COX-2) and release of prostaglandin E2 (PGE2) was detected. DADLE-induced COX-2 expression was also observed in three-dimensional cultured MCF-7 cells and impaired by PI3K/Akt inhibitors and the HIF-1α inhibitor echinomycin. Supernatant from DADLE-treated MCF-7 cells triggered sprouting of endothelial (END) cells, which was blocked when MCF-7 cells were pretreated with echinomycin or the COX-2 inhibitor celecoxib. Also no sprouting was observed when END cells were exposed to the PGE2 receptor antagonist PF-04418948. The findings together indicate that DOR stimulation in BCa cells leads to PI3K/Akt-dependent HIF-1α activation and COX-2 expression, which trigger END cell sprouting by paracrine activation of PGE2 receptors. These findings provide a potential mechanism of opioid-driven tumor angiogenesis and thus therapeutic targets to combat the tumor-angiogenic opioid effect. SIGNIFICANCE STATEMENT: Opioids are indispensable analgesics for treating cancer-related pain. However, opioids were found to promote tumor growth and metastasis, which questions the use of these potent pain-relieving drugs in cancer patients. Enhanced tumor vascularization after opioid treatment implies that tumor progression results from angiogenic opioid effects. Thus, understanding the signaling mechanism of opioid-driven tumor angiogenesis helps to identify therapeutic targets to combat these undesired tumor effects. The present study reveals that stimulation of δ-opioid receptors in breast cancer cells leads to an activation of HIF-1α and expression of COX-2 via PI3K/Akt stimulation, which results in a paracrine activation of vascular endothelial cells by prostaglandin E2 receptors.

DNAproDB: an interactive tool for structural analysis of DNA-protein complexes

Many biological processes are mediated by complex interactions between DNA and proteins. Transcription factors, various polymerases, nucleases and histones recognize and bind DNA with different levels of binding specificity. To understand the physical mechanisms that allow proteins to recognize DNA and achieve their biological functions, it is important to analyze structures of DNA–protein complexes in detail. DNAproDB is a web-based interactive tool designed to help researchers study these complexes. DNAproDB provides an automated structure-processing pipeline that extracts structural features from DNA–protein complexes. The extracted features are organized in structured data files, which are easily parsed with any programming language or viewed in a browser. We processed a large number of DNA–protein complexes retrieved from the Protein Data Bank and created the DNAproDB database to store this data. Users can search the database by combining features of the DNA, protein or DNA–protein interactions at the interface. Additionally, users can upload their own structures for processing privately and securely. DNAproDB provides several interactive and customizable tools for creating visualizations of the DNA–protein interface at different levels of abstraction that can be exported as high quality figures. All functionality is documented and freely accessible at http://dnaprodb.usc.edu.

Structural database resources for biological macromolecules

This Briefing reviews the widely used, currently active, up-to-date databases derived from the worldwide Protein Data Bank (PDB) to facilitate browsing, finding and exploring its entries. These databases contain visualization and analysis tools tailored to specific kinds of molecules and interactions, often including also complex metrics precomputed by experts or external programs, and connections to sequence and functional annotation databases. Importantly, updates of most of these databases involves steps of curation and error checks based on specific expertise about the subject molecules or interactions, and removal of sequence redundancy, both leading to better data sets for mining studies compared with the full list of raw PDB entries. The article presents the databases in groups such as those aimed to facilitate browsing through PDB entries, their molecules and their general information, those built to link protein structure with sequence and dynamics, those specific for transmembrane proteins, nucleic acids, interactions of biomacromolecules with each other and with small molecules or metal ions, and those concerning specific structural features or specific protein families. A few webservers directly connected to active databases, and a few databases that have been discontinued but would be important to have back, are also briefly commented on. Along the Briefing, sample cases where these databases have been used to aid structural studies or advance our knowledge about biological macromolecules are referenced. A few specific examples are also given where using these databases is easier and more informative than using raw PDB data.

PixelDB: Protein-peptide complexes annotated with structural conservation of the peptide binding mode

PixelDB, the Peptide Exosite Location Database, compiles 1966 non-redundant, high-resolution structures of protein-peptide complexes filtered to minimize the impact of crystal packing on peptide conformation. The database is organized to facilitate study of structurally conserved versus non-conserved elements of protein-peptide engagement. PixelDB clusters complexes based on the structural similarity of the peptide-binding protein, and by comparing complexes within a cluster highlights examples of domains that engage peptides using more than one binding mode. PixelDB also identifies conserved peptide core structural motifs characteristic of each binding mode. Peptide regions that flank core motifs often make non-structurally conserved interactions with the protein surface in regions we call exosites. Many examples establish that exosite contacts can be important for enhancing protein binding and interaction specificity. PixelDB provides a resource for computational and structural biologists to study, model, and predict core-motif and exosite-contacting peptide interactions. PixelDB is available to the community without restriction in a convenient flat-file format with accompanying visualization tools.

Systematic Mutagenesis of Serine Hydroxymethyltransferase Reveals an Essential Role in Nematode Resistance

Rhg4 is a major genetic locus that contributes to soybean cyst nematode (SCN) resistance in the Peking-type resistance of soybean (Glycine max), which also requires the rhg1 gene. By map-based cloning and functional genomic approaches, we previously showed that the Rhg4 gene encodes a predicted cytosolic serine hydroxymethyltransferase (GmSHMT08); however, the novel gain of function of GmSHMT08 in SCN resistance remains to be characterized. Using a forward genetic screen, we identified an allelic series of GmSHMT08 mutants that shed new light on the mechanistic aspects of GmSHMT08-mediated resistance. The new mutants provide compelling genetic evidence that Peking-type rhg1 resistance in cv Forrest is fully dependent on the GmSHMT08 gene and demonstrates that this resistance is mechanistically different from the PI 88788-type of resistance that only requires rhg1 We also demonstrated that rhg1-a from cv Forrest, although required, does not exert selection pressure on the nematode to shift from HG type 7, which further validates the bigenic nature of this resistance. Mapping of the identified mutations onto the SHMT structural model uncovered key residues for structural stability, ligand binding, enzyme activity, and protein interactions, suggesting that GmSHMT08 has additional functions aside from its main enzymatic role in SCN resistance. Lastly, we demonstrate the functionality of the GmSHMT08 SCN resistance gene in a transgenic soybean plant.