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ACE2 plays a critical role in SARS-CoV-2 infection to cause COVID-19 and SARS-CoV-2 spike protein binds to ACE2 and probably functionally inhibits ACE2 to aggravate the underlying diseases of COVID-19. The important factors that affect the severity and fatality of COVID-19 include patients' underlying diseases and ages. Therefore, particular care to the patients with underlying diseases is needed during the treatment of COVID-19 patients.

Coronavirus possesses the largest RNA genome among all the RNA viruses. Its genome encodes about 29 proteins. Most of the viral proteins are non-structural proteins (NSP) except envelop (E), membrane (M), nucleocapsid (N) and Spike (S) proteins that constitute the viral nucleocapsid, envelop and surface. We have recently cloned all the 29 SARS-CoV-2 genes into vectors for their expressions in mammalian cells except NSP11 that has only 14 amino acids (aa). We are able to express all the 28 cloned SARS-CoV-2 genes in human cells to characterize their subcellular distributions. The proteins of SARS-CoV-2 are mostly cytoplasmic but some are both cytoplasmic and nuclear. Those punctate staining proteins were further investigated by immunofluorescent assay (IFA) using specific antibodies or by co-transfection with an organelle marker-expressing plasmid. As a result, we found that NSP15, ORF6, M and ORF7a are related to Golgi apparatus, and that ORF7b, ORF8 and ORF10 colocalize with endoplasmic reticulum (ER). Interestingly, ORF3a distributes in cell membrane, early endosome, endosome, late endosome and lysosome, which suggests that ORF3a might help the infected virus to usurp endosome and lysosome for viral use. Furthermore, we revealed that NSP13 colocalized with SC35, a protein standing for splicing compartments in the nucleus. Our studies for the first time visualized the subcellular locations of SARS-CoV-2 proteins and might provide novel insights into the viral proteins’ biological functions.

Although the genetic contribution is under debate, biological studies in multiple mouse models have suggested that the Disrupted-in-Schizophrenia-1 (DISC1) protein may contribute to susceptibility to psychiatric disorders. In the present study, we took the advantages of the Drosophila model to dissect the molecular pathways that can be affected by DISC1 in the context of pathology-related phenotypes. We found that three pathways that include the homologs of Drosophila Dys, Trio, and Shot were downregulated by introducing a C-terminal truncated mutant DISC1. Consistently, these three molecules were downregulated in the induced pluripotent stem cell-derived forebrain neurons from the subjects carrying a frameshift deletion in DISC1 C-terminus. Importantly, the three pathways were underscored in the pathophysiology of psychiatric disorders in bioinformatics analysis. Taken together, our findings are in line with the polygenic theory of psychiatric disorders.

Hereditary spastic paraplegias (HSPs) are a genetically heterogeneous collection of neurodegenerative disorders categorized by progressive lower-limb spasticity and frailty. The complex HSP forms are characterized by various neurological features including progressive spastic weakness, urinary sphincter dysfunction, extra pyramidal signs and intellectual disability (ID). The kinesin superfamily proteins (KIFs) are microtubule-dependent molecular motors involved in intracellular transport. Kinesins directionally transport membrane vesicles, protein complexes, and mRNAs along neurites, thus playing important roles in neuronal development and function. Recent genetic studies have identified kinesin mutations in patients with HSPs. In this study, we used the computational approaches to investigate the 40 missense mutations associated with HSP and ID in KIF1A and KIF5A. We performed homology modeling to construct the structures of kinesin–microtubule binding domain and kinesin–tubulin complex. We applied structure-based energy calculation methods to determine the effects of missense mutations on protein stability and protein–protein interaction. The results revealed that the most of disease-causing mutations could change the folding free energy of kinesin motor domain and the binding free energy of kinesin–tubulin complex. We found that E253K associated with ID in KIF1A decrease the protein stability of kinesin motor domains. We showed that the HSP mutations located in kinesin–tubulin complex interface, such as K253N and R280C in KIF5A, can destabilize the kinesin–tubulin complex. The computational analysis provides useful information for understanding the roles of kinesin mutations in the development of ID and HSPs.

The ability to predict if a given mutation is disease-causing or not has enormous potential to impact human health. Typically, these predictions are made by assessing the effects of mutation on macromolecular stability and amino acid conservation. Here we report a novel feature: the electrostatic component of the force acting between a kinesin motor domain and tubulin. We demonstrate that changes in the electrostatic component of the binding force are able to discriminate between disease-causing and non-disease-causing mutations found in human kinesin motor domains using the receiver operating characteristic (ROC). Because diseases may originate from multiple effects not related to kinesin-microtubule binding, the prediction rate of 0.843 area under the ROC plot due to the change in magnitude of the electrostatic force alone is remarkable. These results reflect the dependence of kinesin’s function on motility along the microtubule, which suggests a precise balance of microtubule binding forces is required.

Schizophrenia (SCZ), bipolar disorder (BD) and recurrent major depressive disorder (rMDD) are common psychiatric illnesses. All have been associated with lower cognitive ability, and show evidence of genetic overlap and substantial evidence of pleiotropy with cognitive function and neuroticism. Disrupted in schizophrenia 1 (DISC1) protein directly interacts with a large set of proteins (DISC1 Interactome) that are involved in brain development and signaling. Modulation of DISC1 expression alters the expression of a circumscribed set of genes (DISC1 Regulome) that are also implicated in brain biology and disorder. Here we report targeted sequencing of 59 DISC1 Interactome genes and 154 Regulome genes in 654 psychiatric patients and 889 cognitively-phenotyped control subjects, on whom we previously reported evidence for trait association from complete sequencing of the DISC1 locus. Burden analyses of rare and singleton variants predicted to be damaging were performed for psychiatric disorders, cognitive variables and personality traits. The DISC1 Interactome and Regulome showed differential association across the phenotypes tested. After family-wise error correction across all traits (FWERacross), an increased burden of singleton disruptive variants in the Regulome was associated with SCZ (FWERacross P=0.0339). The burden of singleton disruptive variants in the DISC1 Interactome was associated with low cognitive ability at age 11 (FWERacross P=0.0043). These results identify altered regulation of schizophrenia candidate genes by DISC1 and its core Interactome as an alternate pathway for schizophrenia risk, consistent with the emerging effects of rare copy number variants associated with intellectual disability.

Schizophrenia (SCZ), bipolar disorder (BD) and recurrent major depressive disorder (rMDD) are common psychiatric illnesses. All have been associated with lower cognitive ability, and show evidence of genetic overlap and substantial evidence of pleiotropy with cognitive function and neuroticism. Disrupted in schizophrenia 1 (DISC1) protein directly interacts with a large set of proteins (DISC1 Interactome) that are involved in brain development and signaling. Modulation of DISC1 expression alters the expression of a circumscribed set of genes (DISC1 Regulome) that are also implicated in brain biology and disorder. Here, we report targeted sequencing of 59 DISC1 Interactome genes and 154 Regulome genes in 654 psychiatric patients and 889 cognitively-phenotyped control subjects, on whom we previously reported evidence for trait association from complete sequencing of the DISC1 locus. Burden analyses of rare and singleton variants predicted to be damaging were performed for psychiatric disorders, cognitive variables and personality traits. The DISC1 Interactome and Regulome showed differential association across the phenotypes tested. After family-wise error correction across all traits (FWERacross), an increased burden of singleton disruptive variants in the Regulome was associated with SCZ (FWERacrossP=0.0339). The burden of singleton disruptive variants in the DISC1 Interactome was associated with low cognitive ability at age 11 (FWERacrossP=0.0043). These results suggest that variants in the DISC1 Interactome effect the risk of psychiatric illness through altered expression of schizophrenia-associated genes. The biological impact of rare variants highlighted here merit further study.

Background: Obsessive-compulsive disorder (OCD) is a debilitating neuropsychiatric condition estimated to afflict 1-3 % of the world population. Dozens of OCD candidate genes have been reported by an increased number of articles. Nevertheless, each patient/patient group may demonstrate unique etiologic characteristics that need personalized treatment. Method: We integrated a sparse representation based variable selection (SRVS) approach with an OCD-gene ResNet relation data analysis to select top genes for a specific group of 118 subjects, including 16 OCD cases and 102 healthy controls. The gene expression profile were acquired from the dorsolateral prefrontal cortex (DLPFC) of postmortem tissue of these subjects. A 77 OCD candidate genes were acquired from ResNet relation data analysis. Pathway enrichment analysis (PEA), sub-network enrichment analysis (SNEA) and gene-gene Interaction analysis (GGI) were conducted to study the functional profile of the top genes selected by SRVS, and compared with previous reported genetic markers. Results: A significantly high classification accuracy (CR) of 79.66 % was acquired (permutation p-value = 0.0046) using the top 9 genes selected by SRVS, including HOXB8, HTR2C, CRHR2, GRIK3, HGF, OXT, TPH2, DRD2 and ADRA1A. These genes were enriched within multiple pathways and sub-networks that were previously implicated with OCD. In contrast, using the same number of most frequently reported, a CR of only 65.5 % is achieved. Moreover, GGI results showed that these genes demonstrated a strong functional correlation with the frequently reported OCD genes. Conclusion: Our study suggests that SRVS is an effective method for data driven variable selection for OCD, and that the genes that were frequently reported to associate with OCD might not be the best biomarkers for a specific OCD patient/ patient group.

Schizophrenia (SCZ) is a serious psychiatric disorder that affects 1% of general population and places a heavy burden worldwide. The underlying genetic mechanism of SCZ remains unknown, but studies indicate that the disease is associated with a global gene expression disturbance across many genes. Next-generation sequencing, particularly of RNA sequencing (RNA-Seq), provides a powerful genome-scale technology to investigate the pathological processes of SCZ. RNA-Seq has been used to analyze the gene expressions and identify the novel splice isoforms and rare transcripts associated with SCZ. This paper provides an overview on the genetics of SCZ, the advantages of RNA-Seq for transcriptome analysis, the accomplishments of RNA-Seq in SCZ cohorts, and the applications of induced pluripotent stem cells and RNA-Seq in SCZ research.

field with online, open access to highly relevant scholarly articles by leading international researchers. In a field where the literature is ever-expanding, researchers increasingly need access to up-to-date, high quality scholarly articles on areas of specific contemporary interest. This supplement aims to address this by presenting high-quality articles that allow readers to distinguish the signal from the noise. The editor in chief hopes that through this effort, practitioners and researchers will be aided in finding answers to some of the most complex and pressing issues of our time. At the discretion of the guest editors other articles on other relevant topics within the scope of the supplement may be included. This supplement is intended to focus on RNA sequence analysis. Topics of interest include, but are not limited to, the following: Data preprocessing and mappingHybrid Computing § § Gene expression § § Novel genes and transcripts discovery and De novo § § transcript assembly Alternative splicing § § RNA editing § § Single nucleotide variation discovery § § Fusion gene detection § § Coexpression networks § § Pathway and master regulator analysis § §

Applying in silico simulations and in vitro experiments, the amino acid proline was proved to have a profound influence on Streptomyces griseus trypsinogen, and the hydrogen bond between H 57 and D 102 was found to be crucial for trypsin activity. By introducing an artificial propeptide, IVEF, the titer of trypsin was increased 6.71-fold.

Many studies have shown that missense mutations might play an important role in carcinogenesis. However, the extent to which cancer mutations might affect biomolecular interactions remains unclear. Here, we map glioblastoma missense mutations on the human protein interactome, model the structures of affected protein complexes and decipher the effect of mutations on protein-protein, protein-nucleic acid and protein-ion binding interfaces. Although some missense mutations over-stabilize protein complexes, we found that the overall effect of mutations is destabilizing, mostly affecting the electrostatic component of binding energy. We also showed that mutations on interfaces resulted in more drastic changes of amino acid physico-chemical properties than mutations occurring outside the interfaces. Analysis of glioblastoma mutations on interfaces allowed us to stratify cancer-related interactions, identify potential driver genes, and propose two dozen additional cancer biomarkers, including those specific to functions of the nervous system. Such an analysis also offered insight into the molecular mechanism of the phenotypic outcomes of mutations, including effects on complex stability, activity, binding and turnover rate. As a result of mutated protein and gene network analysis, we observed that interactions of proteins with mutations mapped on interfaces had higher bottleneck properties compared to interactions with mutations elsewhere on the protein or unaffected interactions. Such observations suggest that genes with mutations directly affecting protein binding properties are preferably located in central network positions and may influence critical nodes and edges in signal transduction networks.

The Snyder-Robinson syndrome is caused by missense mutations in the spermine sythase gene that encodes a protein (SMS) of 529 amino acids. Here we investigate, in silico, the molecular effect of three missense mutations, c.267G>A (p.G56S), c.496T>G (p.V132G), and c.550T>C (p.I150T) in SMS that were clinically identified to cause the disease. Single-point energy calculations, molecular dynamics simulations, and pKa calculations revealed the effects of these mutations on SMS's stability, flexibility, and interactions. It was predicted that the catalytic residue, Asp276, should be protonated prior binding the substrates. The pKa calculations indicated the p.I150T mutation causes pKa changes with respect to the wild-type SMS, which involve titratable residues interacting with the S-methyl-5'-thioadenosine (MTA) substrate. The p.I150T missense mutation was also found to decrease the stability of the C-terminal domain and to induce structural changes in the vicinity of the MTA binding site. The other two missense mutations, p.G56S and p.V132G, are away from active site and do not perturb its wild-type properties, but affect the stability of both the monomers and the dimer. Specifically, the p.G56S mutation is predicted to greatly reduce the affinity of monomers to form a dimer, and therefore should have a dramatic effect on SMS function because dimerization is essential for SMS activity.

Almost all (99.9%) nucleotide bases are exactly the same in all people, however, the remaining 0.1% account for about 1.4 million locations where single-base DNA differences/polymorphisms (SNPs) occur in humans. Some of these SNPs, called non-synonymous SNPs (nsSNPs), result in a change of the amino acid sequences of the corresponding proteins affecting protein functions and interactions. This review summarizes the plausible mechanisms that nsSNPs may affect the normal cellular function. It outlines the approaches that have been developed in the past to predict the effects caused by nsSNPs with special emphasis on the methods that use structural information. The review provides systematic information on the available resources for predicting the effects of nsSNPs and includes a comprehensive list of existing SNP databases and their features. While nsSNPs resulting in amino acid substitution in the core of a protein may affect protein stability irreversibly, the effect of an nsSNP resulting to a mutation at the surface of a protein or at the interface of protein-protein complexes, could, in principle be, subject of drug therapy. The importance of understanding the effects caused by nsSNP mutations at the protein-protein and protein-DNA interfaces is outlined.

Background Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification. Results In this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests (RFs) and Support Vector Machines (SVMs) were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues. Conclusions A machine learning approach has been developed for accurately identifying the candidate genes for tissue specific/selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression.