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Neuroinflammation caused by COVID-19 negatively impacts brain metabolism and function, while preexisting brain pathology may make individuals more vulnerable to the adverse consequences of COVID-19. We used summary statistics from genome-wide association studies (GWAS) to perform Mendelian randomization (MR) analyses, thus assessing potential associations between multiple sclerosis (MS) and three COVID-19 outcomes (SARS-CoV-2 infection, COVID-19 hospitalization, and critical COVID-19). Genome-wide risk genes were compared between the GWAS datasets on hospitalized COVID-19 and MS. Literature-based analysis was conducted to construct molecular pathways connecting MS and COVID-19. The MR analyses indicated that genetic liability to MS confers a causal effect on hospitalized COVID-19 (OR: 1.09, 95% CI: 1.03-1.16) but not on SARS-CoV-2 infection (1.03, 1.00-1.05). Genetic liability to hospitalized COVID-19 confers a causal effect on MS (1.15, 1.02-1.30). Hospitalized COVID-19 and MS share five risk genes within two loci, including TNFAIP8, HSD17B4, CDC37, PDE4A, and KEAP1. Pathway analysis identified a panel of immunity-related genes that may mediate the links between MS and COVID-19. Our study suggests that MS was associated with a 9% increased risk for COVID-19 hospitalization, while hospitalized COVID-19 was associated with a 15% increased risk for MS. Immunity-related pathways may underlie the link between MS on COVID-19. This article is protected by copyright. All rights reserved.

With the continued spread of COVID-19 globally, it is crucial to identify the potential risk or protective factors associated with COVID-19. Here, we performed genetic correlation analysis and Mendelian randomization analysis to examine genetic relationships between COVID-19 hospitalization and 405 health conditions and lifestyle factors in 456 422 participants from the UK Biobank. The genetic correlation analysis revealed 134 positive and 65 negative correlations, including those with intakes of a variety of dietary components. The MR analysis indicates that a set of body fat-related traits, maternal smoking around birth, basal metabolic rate, lymphocyte count, peripheral enthesopathies and allied syndromes, blood clots in the leg, and arthropathy are causal risk factors for severe COVID-19, while higher education attainment, physical activity, asthma, and never smoking status protect against the illness. Our findings have implications for risk stratification in patients with COVID-19 and the prevention of its severe outcomes.

Myeloperoxidase (MPO) is a heme peroxidase with microbicidal properties. MPO plays a role in the host’s innate immunity by producing reactive oxygen species inside the cell against foreign organisms. However, there is little functional evidence linking missense mutations to human diseases. We utilized in silico saturation mutagenesis to generate and analyze the effects of 10,811 potential missense mutations on MPO stability. Our results showed that ~71% of the potential missense mutations destabilize MPO, and ~8% stabilize the MPO protein. We showed that G402W, G402Y, G361W, G402F, and G655Y would have the highest destabilizing effect on MPO. Meanwhile, D264L, G501M, D264H, D264M, and G501L have the highest stabilization effect on the MPO protein. Our computational tool prediction showed the destabilizing effects in 13 out of 14 MPO missense mutations that cause diseases in humans. We also analyzed putative post-translational modification (PTM) sites on the MPO protein and mapped the PTM sites to disease-associated missense mutations for further analysis. Our analysis showed that R327H associated with frontotemporal dementia and R548W causing generalized pustular psoriasis are near these PTM sites. Our results will aid further research into MPO as a biomarker for human complex diseases and a candidate for drug target discovery.

The spike (S) glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the binding to the permissive cells. The receptor-binding domain (RBD) of SARS-CoV-2 S protein directly interacts with the human angiotensin-converting enzyme 2 (ACE2) on the host cell membrane. In this study, we used computational saturation mutagenesis approaches, including structure-based energy calculations and sequence-based pathogenicity predictions, to quantify the systemic effects of missense mutations on SARS-CoV-2 S protein structure and function. A total of 18 354 mutations in S protein were analyzed, and we discovered that most of these mutations could destabilize the entire S protein and its RBD. Specifically, residues G431 and S514 in SARS-CoV-2 RBD are important for S protein stability. We analyzed 384 experimentally verified S missense variations and revealed that the dominant pandemic form, D614G, can stabilize the entire S protein. Moreover, many mutations in N-linked glycosylation sites can increase the stability of the S protein. In addition, we investigated 3705 mutations in SARS-CoV-2 RBD and 11 324 mutations in human ACE2 and found that SARS-CoV-2 neighbor residues G496 and F497 and ACE2 residues D355 and Y41 are critical for the RBD–ACE2 interaction. The findings comprehensively provide potential target sites in the development of drugs and vaccines against COVID-19.

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.

COVID-19, caused by SARS-CoV-2, is a systemic illness due to its multiorgan effects in patients. The disease has a detrimental impact on respiratory and cardiovascular systems. One early symptom of infection is anosmia or lack of smell; this implicates the involvement of the olfactory bulb in COVID-19 disease and provides a route into the central nervous system. However, little is known about how SARS-CoV-2 affects neurological or psychological symptoms. SARS-CoV-2 exploits host receptors that converge on pathways that impact psychological symptoms. This systemic review discusses the ways involved by coronavirus infection and their impact on mental health disorders. We begin by briefly introducing the history of coronaviruses, followed by an overview of the essential proteins to viral entry. Then, we discuss the downstream effects of viral entry on host proteins. Finally, we review the literature on host factors that are known to play critical roles in neuropsychiatric symptoms and mental diseases and discuss how COVID-19 could impact mental health globally. Our review details the host factors and pathways involved in the cellular mechanisms, such as systemic inflammation, that play a significant role in the development of neuropsychological symptoms stemming from COVID-19 infection.

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.

Neurexin-1 (NRXN1) is a membrane protein essential in synapse formation and cell signaling as a cell-adhesion molecule and cell-surface receptor. NRXN1 and its binding partner neuroligin have been associated with deficits in cognition. Recent genetics research has linked NRXN1 missense mutations to increased risk for brain disorders, including schizophrenia (SCZ) and autism spectrum disorder (ASD). Investigation of the structure–function relationship in NRXN1 has proven difficult due to a lack of the experimental full-length membrane protein structure. AlphaFold, a deep learning-based predictor, succeeds in high-quality protein structure prediction and offers a solution for membrane protein model construction. In the study, we applied a computational saturation mutagenesis method to analyze the systemic effects of missense mutations on protein functions in a human NRXN1 structure predicted from AlphaFold and an experimental Bos taurus structure. The folding energy changes were calculated to estimate the effects of the 29,540 mutations of AlphaFold model on protein stability. The comparative study on the experimental and computationally predicted structures shows that these energy changes are highly correlated, demonstrating the reliability of the AlphaFold structure for the downstream bioinformatics analysis. The energy calculation revealed that some target mutations associated with SCZ and ASD could make the protein unstable. The study can provide helpful information for characterizing the disease-causing mutations and elucidating the molecular mechanisms by which the variations cause SCZ and ASD. This methodology could provide the bioinformatics protocol to investigate the effects of target mutations on multiple AlphaFold structures.

The spike (S) glycoprotein of SARS-CoV-2 is responsible for the binding to the permissive cells. The receptor-binding domain (RBD) of SARS-CoV-2 S protein directly interacts with the human angiotensin-converting enzyme 2 (ACE2) on the host cell membrane. In this study, we used computational saturation mutagenesis approaches, including structure-based energy calculations and sequence-based pathogenicity predictions, to quantify the systemic effects of missense mutations on SARS-CoV-2 S protein structure and function. A total of 18,354 mutations in S protein were analyzed and we discovered that most of these mutations could destabilize the entire S protein and its RBD. Specifically, residues G431 and S514 in SARS-CoV-2 RBD are important for S protein stability. We analyzed 384 experimentally verified S missense variations and revealed that the dominant pandemic form, D614G, can stabilize the entire S protein. Moreover, many mutations in N-linked glycosylation sites can increase the stability of the S protein. In addition, we investigated 3,705 mutations in SARS-CoV-2 RBD and 11,324 mutations in human ACE2 and found that SARS-CoV-2 neighbor residues G496 and F497 and ACE2 residues D355 and Y41 are critical for the RBD-ACE2 interaction. The findings comprehensively provide potential target sites in the development of drugs and vaccines against COVID-19.

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.

Lymphotoxin-α (LTα), located in the Major Histocompatibility Complex (MHC) class III region on chromosome 6, encodes a cytotoxic protein that mediates a variety of antiviral responses among other biological functions. Furthermore, several genotypes at this gene have been implicated in the onset of a number of complex diseases, including myocardial infarction, autoimmunity, and various types of cancer. However, little is known about levels of nucleotide variation and linkage disequilibrium (LD) in and near LTα, which could also influence phenotypic variance. To address this gap in knowledge, we examined sequence variation across ~ 10 kilobases (kbs), encompassing LTα and the upstream region, in 2039 individuals from the 1000 Genomes Project originating from 21 global populations.Here, we observed striking patterns of diversity, including an excess of intermediate-frequency alleles, the maintenance of multiple common haplotypes and a deep coalescence time for variation (dating 1.0 million years ago), in global populations. While these results are generally consistent with a model of balancing selection, we also uncovered a signature of positive selection in the form of long-range LD on chromosomes with derived alleles primarily in Eurasian populations. To reconcile these findings, which appear to support different models of selection, we argue that selective sweeps (particularly, soft sweeps) of multiple derived alleles in and/or near LTα occurred in non-Africans after their ancestors left Africa. Furthermore, these targets of selection were predicted to alter transcription factor binding site affinity and protein stability, suggesting they play a role in gene function. Additionally, our data also showed that a subset of these functional adaptive variants are present in archaic hominin genomes.Overall, this study identified candidate functional alleles in a biologically-relevant genomic region, and offers new insights into the evolutionary origins of these loci in modern human populations.

Additional file 1 : Figure S1. contains pairwise LD plots (spanning ~9.64 kbs) for African and non-African populations that were not included in Fig. 3 of the main manuscript. Figure S2. contains pairwise LD plots for loci located across a larger genomic region (spanning ~ 35.5 kbs) for each population. Figure S3. displays Manhattan plots of standardized |iHS| values in populations that were not included in Fig. 4 of the main manuscript. Figure S4. shows Manhattan plots of standardized |nSL| scores for all populations. Figure S5. presents the remaining EHH plots for populations that were not included in Fig. 4 of the main manuscript.

Schizophrenia is a highly heritable psychiatric disorder that affects 1% of the population. Genome-wide association studies have identified common variants in candidate genes associated with schizophrenia, but the genetics mechanisms of this disorder have not yet been elucidated. The discovery of rare genetic variants that contribute to schizophrenia symptoms promises to help explain the missing heritability of the disease. Next generation sequencing techniques are revolutionizing the field of psychiatric genetics. Various statistical approaches have been developed for rare variant association testing in case-control and family studies. Targeted resequencing, whole exome sequencing and whole genome sequencing combined with these computational tools are used for the discovery of rare genetic variations in schizophrenia. The findings provide useful information for characterizing the rare mutations and elucidating the genetic mechanisms by which the variants cause schizophrenia.

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.

Background Disrupted In Schizophrenia 1 (DISC1) gene is a well-researched target for developing psychiatric disorders including schizophrenia, bipolar disorder and major depression. DISC1 protein directly interacts with a large set of proteins, so-called DISC1 Interactome, that are involved in brain development and neuronal signaling. Recent exome sequencing projects showed that coding variants with low allele frequency have large effects on the risk for developing schizophrenia, which indicates that rare mutations account for much of the missing heritability in the development of schizophrenia. Methods In the presented study, we applied bioinformatics approaches to analyse the effects of rare amino acid substitutions in the DISC1 Interactome on protein structure and function from public databases. We applied sequence-based machine learning methods to predict the effects of the rare disease-causing mutations on protein function, protein stability and post translational modification. In addition, we carried out structure-based modelling methods to analyse the effects of rare mutations on protein stability and protein-protein interaction. Results We collected 6,254 non-synonymous coding mutations reported in the 1000 Genomes Project located in 104 DISC1 Interactome genes from dbNSFP. Of the 6,254 variants, 5,929 coding mutations (95%) are rare amino acid substitutions with a minor allele frequency less than 1%. We observed that the distributions of damaging mutations are found to be higher in rare mutations than those in common mutations in the DISC1 Interactome. Some amino acid substitutions may be involved in post translational modifications such as phosphorylation and sumoylation. We showed that the rare missense mutations could significantly affect the protein structures, which result in the changes on protein stability and protein-protein interaction. Discussion The bioinformatics analysis improves our understanding of the roles of rare amino acid substitutions in the DISC1 interactome genes in susceptibility to schizophrenia. The findings can provide useful information to estimate the functional effects of rare DISC1 Interactome mutations on protein function and structure, which offer the targets for developing more precise treatments and medications for schizophrenia patients.

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.

Recent technological advances in next-generation sequencing (NGS) provide unprecedented power to sequence personal genomes, characterize genomic landscapes, and detect a large number of sequence variants. The discovery of disease-causing variants in patients' genomes has dramatically changed our perspective on precision medicine. This chapter provides an overview of sequence variant detection and analysis in NGS study. We outline the general methods for identifying different types of sequence variants from NGS data. We summarize the common approaches for analyzing and visualizing casual variants associated with complex diseases on precision medicine informatics.

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.

Recent technological advances in next-generation sequencing (NGS) provide unprecedented power to sequence personal genomes, characterize genomic landscapes, and detect a large number of sequence variants. The discovery of disease-causing variants in patients’ genomes has dramatically changed our perspective on precision medicine. This chapter provides an overview of sequence variant detection and analysis in NGS study. We outline the general methods for identifying different types of sequence variants from NGS data. We summarize the common approaches for analyzing and visualizing casual variants associated with complex diseases on precision medicine informatics.

Bacterial leaf streak (BLS), caused by the pathogen Xanthomonas campestris pv. Oryzicola, is a major rice disease in tropical and subtropical regions of Asia. Rice proteins responsive to BLS are still not well characterized. We took a proteomics approach to identify the proteins that are up-regulated in rice leaves after infection. Approximately 1,500 protein spots were detected on each 2-D gel after silver-staining; those with increased protein levels were selected for MALDI-TOF-MS analysis. We identified 32 up-regulated proteins that might be involved in disease resistance signal transduction, pathogenesis, and regulation of cell metabolism. By using publicly available microarray data, we determined the mRNA transcripts of 23 proteins expressed in the leaves. Seven genes were analyzed by northern blots, which demonstrated that transcript levels were increased after bacterial infection. Our findings help elucidate the molecular mechanisms underlying BLS and provide a solid foundation for further research on the functions of relevant genes.

Protein sumoylation is a post-translational modification that plays an important role in a wide range of cellular processes. Small ubiquitin-related modifier (SUMO) can be covalently and reversibly conjugated to the sumoylation sites of target proteins, many of which are implicated in various human genetic disorders. The accurate prediction of protein sumoylation sites may help biomedical researchers to design their experiments and understand the molecular mechanism of protein sumoylation. In this study, a new machine learning approach has been developed for predicting sumoylation sites from protein sequence information. Random forests (RFs) and support vector machines (SVMs) were trained with the data collected from the literature. Domain-specific knowledge in terms of relevant biological features was used for input vector encoding. It was shown that RF classifier performance was affected by the sequence context of sumoylation sites, and 20 residues with the core motif ΨKXE in the middle appeared to provide enough context information for sumoylation site prediction. The RF classifiers were also found to outperform SVM models for predicting protein sumoylation sites from sequence features. The results suggest that the machine learning approach gives rise to more accurate prediction of protein sumoylation sites than the other existing methods. The accurate classifiers have been used to develop a new web server, called seeSUMO (http://bioinfo.ggc.org/seesumo/), for sequence-based prediction of protein sumoylation sites.