McKusick's Online Mendelian Inheritance in Man (OMIM)

Preclinical side effect prediction through pathway engineering of protein interaction network models

Modeling tools aim to predict potential drug side effects, although they suffer from imperfect performance. Specifically, protein-protein interaction models predict drug effects from proteins surrounding drug targets, but they tend to overpredict drug phenotypes and require well-defined pathway phenotypes. In this study, we used PathFX, a protein-protein interaction tool, to predict side effects for active ingredient-side effect pairs extracted from drug labels. We observed limited performance and defined new pathway phenotypes using pathway engineering strategies. We defined new pathway phenotypes using a network-based and gene expression-based approach. Overall, we discovered a trade-off between sensitivity and specificity values and demonstrated a way to limit overprediction for side effects with sufficient true positive examples. We compared our predictions to animal models and demonstrated similar performance metrics, suggesting that protein-protein interaction models do not need perfect evaluation metrics to be useful. Pathway engineering, through the inclusion of true positive examples and omics measurements, emerges as a promising approach to enhance the utility of protein interaction network models for drug effect prediction.

Characterization of the Common Genetic Variation in the Spanish Population of Navarre

Large-scale genomic studies have significantly increased our knowledge of genetic variability across populations. Regional genetic profiling is essential for distinguishing common benign variants from disease-causing ones. To this end, we conducted a comprehensive characterization of exonic variants in the population of Navarre (Spain), utilizing whole genome sequencing data from 358 unrelated individuals of Spanish origin. Our analysis revealed 61,410 biallelic single nucleotide variants (SNV) within the Navarrese cohort, with 35% classified as common (MAF > 1%). By comparing allele frequency data from 1000 Genome Project (excluding the Iberian cohort of Spain, IBS), Genome Aggregation Database, and a Spanish cohort (including IBS individuals and data from Medical Genome Project), we identified 1069 SNVs common in Navarre but rare (MAF ≤ 1%) in all other populations. We further corroborated this observation with a second regional cohort of 239 unrelated exomes, which confirmed 676 of the 1069 SNVs as common in Navarre. In conclusion, this study highlights the importance of population-specific characterization of genetic variation to improve allele frequency filtering in sequencing data analysis to identify disease-causing variants.

Exploring the mechanism of action of the combination of cinnamon and motherwort in the treatment of benign prostatic hyperplasia: A network pharmacology study

Cinnamon and motherwort are traditional Chinese medicines and are often combined to treat benign prostatic hyperplasia; however, the specific therapeutic mechanisms involved remain unclear. Therefore, in this study, we applied a network pharmacology approach to investigate the potential mechanisms of action of the drug pair cinnamon and motherwort (PCM) for the treatment of benign prostatic hyperplasia. Relevant targets for the use of PCM to treat benign prostatic hyperplasia were obtained through databases. Protein-protein interactions were then identified by the STRING database and core targets were screened. Enrichment analysis was conducted through the Metascape platform. Finally, molecular docking experiments were carried out to evaluate the affinity between the target proteins and ligands of PCM. We identified 22 active ingredients in PCM, 315 corresponding targets and 130 effective targets of PCM for the treatment of benign prostatic hyperplasia. These targets were related to the PI3K-Akt, MAPK, FoxO, TNF, and IL-17 signaling pathways. Network pharmacology was used to identify the effective components and action targets of PCM. We also identified potential mechanisms of action for PCM in the treatment of benign prostatic hyperplasia. Our results provide a foundation for expanding the clinical application of PCM and provide new ideas and directions for further research on the mechanisms of action of PCM and its components for the treatment of benign prostatic hyperplasia.

scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

BACKGROUND

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

METHODS

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

RESULTS

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

CONCLUSIONS

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

Whole-exome sequencing identifies cancer-associated variants of the endo-lysosomal ion transport channels in the Saudi population

Background

Although national efforts are underway to document the genomic variability of the Saudi population relative to other populations, such variability remains largely unexplored. Genetic variability is known to impact the fate of cells and increase or decrease the risk of a variety of complex diseases including cancer forms. Therefore, the identification of variants associated with cancer susceptibility in Saudi population may protect individuals from cancer or aid in patient-tailored therapies. The endo-lysosomal ion transport genes responsible for cationic ion homeostasis within the cell. We screened 703 single-nucleotide polymorphisms (SNPs) of the endo-lysosomal ion transporter genes in the Saudi population and identified cancer-associated variants that have been reported in other populations.

Methods

Utilizing previously derived local data of Whole-Exome Sequencing (WES), we examined SNPs of TPCN1, TPCN2, P2RX4, TRPM7, TRPV4, TRPV4, and TRPV6 genes. The SNPs were identified for those genes by our in-house database. We predicted the pathogenicity of these variants using in silico tools CADD, Polyphen-2, SIFT, PrimateAI, and FATHMM-XF. Then, we validated our findings by exploring the genetics database (VarSome, dbSNP NCB, OMIM, ClinVar, Ensembl, and GWAS Catalog) to further link cancer risk.

Results

The WES database yielded 703 SNPs found in TPCN2, P2RX4, TRPM7, TRPV4, and TRPV6 genes in 1,144 subjects. The number of variants that were found to be common in our population was 150 SNPs. We identified 13 coding-region non-synonymous variants of the endo-lysosomal genes that were most common with a minor allele frequency (MAF) of ≥ 1 %. Twelve of these variants are rs2376558, rs3750965, rs61746574, rs35264875, rs3829241, rs72928978, rs25644, rs8042919, rs17881456, rs4987682, rs4987667, and rs4987657 that were classified as cancer-associated genes.

Conclusion

Our study highlighted cancer-associated SNPs in the endo-lysosomal genes among Saudi individuals. The allelic frequencies on polymorphic variants confer susceptibility to complex diseases that are comparable to other populations. There is currently insufficient clinical data supporting the link between these SNPs and cancer risk in the Saudi population. Our data argues for initiating future cohort studies in which individuals with the identified SNPs are monitored and assessed for their likelihood of developing malignancies and therapy outcomes.

Shared Proteins and Pathways of Cardiovascular and Cognitive Diseases: Relation to Vascular Cognitive Impairment

One of the primary goals of systems medicine is the detection of putative proteins and pathways involved in disease progression and pathological phenotypes. Vascular cognitive impairment (VCI) is a heterogeneous condition manifesting as cognitive impairment resulting from vascular factors. The precise mechanisms underlying this relationship remain unclear, which poses challenges for experimental research. Here, we applied computational approaches like systems biology to unveil and select relevant proteins and pathways related to VCI by studying the crosstalk between cardiovascular and cognitive diseases. In addition, we specifically included signals related to oxidative stress, a common etiologic factor tightly linked to aging, a major determinant of VCI. Our results show that pathways associated with oxidative stress are quite relevant, as most of the prioritized vascular cognitive genes and proteins were enriched in these pathways. Our analysis provided a short list of proteins that could be contributing to VCI: DOLK, TSC1, ATP1A1, MAPK14, YWHAZ, CREB3, HSPB1, PRDX6, and LMNA. Moreover, our experimental results suggest a high implication of glycative stress, generating oxidative processes and post-translational protein modifications through advanced glycation end-products (AGEs). We propose that these products interact with their specific receptors (RAGE) and Notch signaling to contribute to the etiology of VCI.

Neurofibromatosis type 1: Clinical characteristics and mutation spectrum in a North Indian cohort

Background

Neurofibromatosis type 1 (NF1) is a unique, highly penetrant neuro-cutaneous disorder with a wide range of manifestations. Though the clinical diagnosis of NF1 is straight forward, there can be other disorders which mimic NF1, especially its cutaneous features. Here we describe the clinical and mutation spectrum of a series of individuals whose primary diagnosis was NF1 or NF1 related disorders.

Methods

We have screened 29 unrelated individuals who fulfilled the clinical criteria of NF1. Whole exome sequencing (WES) was done in all individuals except one with suspected microdeletion syndrome with NF1 in whom Cytogenetic microarray (CMA) was done.

Results

Out of 29 suspected patients, 25 had germline pathogenic/likely pathogenic variants involving NF1 gene. Five novel and 20 known variants in coding and non-coding regions were identified, among them 7 variants were deletions (28%), 7 nonsense (28%), 3 splice-site (12%), 4 missense (16%), 2 duplications (8%) and 2 (8%) were contiguous deletions. In those where NF1 variants were not detected, 3 had neurofibromatosis type 2 (NF2) and 1 rare autosomal recessive form of Elher Danlos syndrome.

Conclusion

We hereby present the wide range of manifestations in different age groups and the mutation spectrum ranging from small scale variants to contiguous gene deletion syndromes involving NF1 gene. We highlight the usefulness of molecular testing and its importance in tumor surveillance and genetic counseling in this disorder.

A syndromic neurodevelopmental disorder caused by rare variants in PPFIA3

PPFIA3 encodes the protein-tyrosine phosphatase, receptor-type, F-polypeptide-interacting-protein-alpha-3 (PPFIA3), which is a member of the LAR-protein-tyrosine phosphatase-interacting-protein (liprin) family involved in synapse formation and function, synaptic vesicle transport, and presynaptic active zone assembly. The protein structure and function are evolutionarily well conserved, but human diseases related to PPFIA3 dysfunction are not yet reported in OMIM. Here, we report 20 individuals with rare PPFIA3 variants (19 heterozygous and 1 compound heterozygous) presenting with developmental delay, intellectual disability, hypotonia, dysmorphisms, microcephaly or macrocephaly, autistic features, and epilepsy with reduced penetrance. Seventeen unique PPFIA3 variants were detected in 18 families. To determine the pathogenicity of PPFIA3 variants in vivo, we generated transgenic fruit flies producing either human wild-type (WT) PPFIA3 or five missense variants using GAL4-UAS targeted gene expression systems. In the fly overexpression assays, we found that the PPFIA3 variants in the region encoding the N-terminal coiled-coil domain exhibited stronger phenotypes compared to those affecting the C-terminal region. In the loss-of-function fly assay, we show that the homozygous loss of fly Liprin-α leads to embryonic lethality. This lethality is partially rescued by the expression of human PPFIA3 WT, suggesting human PPFIA3 function is partially conserved in the fly. However, two of the tested variants failed to rescue the lethality at the larval stage and one variant failed to rescue lethality at the adult stage. Altogether, the human and fruit fly data reveal that the rare PPFIA3 variants are dominant-negative loss-of-function alleles that perturb multiple developmental processes and synapse formation.

INF2 and ROBO2 gene mutation in an Indian family with end stage renal failure and follow-up of renal transplantation

BACKGROUND

Accurate genetic diagnosis of end-stage renal disease patients with a family history of renal dysfunction is very essential. It not only helps in proper prognosis, but becomes crucial in designating donor for live related renal transplant. We here present a case of family with deleterious mutations in INF2 and ROBO2 and its importance of genetic testing before preparing for kidney transplantation.

CASE PRESENTATION

We report the case of a 29-year-female with end-stage renal disease and rapidly progressive renal failure. Mutational analysis revealed an Autosomal Dominant inheritance pattern and mutation in exon 4 of the INF2 gene (p. Thr215Ser) and exon 26 of the ROBO2 gene (p. Arg1371Cys). Her mother was diagnosed for CKD stage 4 with creatinine level of 4.3 mg/dL. Genetic variants (INF2 and ROBO2) identified in proband were tested in her sisters and mother. Her elder sister was positive for both heterozygous variants (INF2 and ROBO2). Her mother was positive for mutation in INF2 gene, and her donor elder sister did not showed mutation in INF2 gene and had mutation in ROBO2 gene without any clinical symptoms.

CONCLUSION

This case report emphasize that familial genetic screening has allowed us in allocating the donor selection in family where family member had history of genetic defect of Chronic Kidney Disease. Information of the causative renal disorder is extremely valuable for risk-assessment and planning of kidney transplantation.

Phenotype-Driven Molecular Genetic Test Recommendation for Diagnosing Pediatric Rare Disorders

Rare disease patients often endure prolonged diagnostic odysseys and may still remain undiagnosed for years. Selecting the appropriate genetic tests is crucial to lead to timely diagnosis. Phenotypic features offer great potential for aiding genomic diagnosis in rare disease cases. We see great promise in effective integration of phenotypic information into genetic test selection workflow. In this study, we present a phenotype-driven molecular genetic test recommendation (Phen2Test) for pediatric rare disease diagnosis. Phen2Test was constructed using frequency matrix of phecodes and demographic data from the EHR before ordering genetic tests, with the objective to streamline the selection of molecular genetic tests (whole-exome / whole-genome sequencing, or gene panels) for clinicians with minimum genetic training expertise. We developed and evaluated binary classifiers based on 1,005 individuals referred to genetic counselors for potential genetic evaluation. In the evaluation using the gold standard cohort, the model achieved strong performance with an AUROC of 0.82 and an AUPRC of 0.92. Furthermore, we tested the model on another silver standard cohort (n=6,458), achieving an overall AUROC of 0.72 and an AUPRC of 0.671. Phen2Test was adjusted to align with current clinical guidelines, showing superior performance with more recent data, demonstrating its potential for use within a learning healthcare system as a genomic medicine intervention that adapts to guideline updates. This study showcases the practical utility of phenotypic features in recommending molecular genetic tests with performance comparable to clinical geneticists. Phen2Test could assist clinicians with limited genetic training and knowledge to order appropriate genetic tests.

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

Background

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

Methods

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

Results

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

Conclusion

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

Genomic architecture of autism spectrum disorder in Qatar: The BARAKA-Qatar Study

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impaired social and communication skills, restricted interests, and repetitive behaviors. The prevalence of ASD among children in Qatar was recently estimated to be 1.1%, though the genetic architecture underlying ASD both in Qatar and the greater Middle East has been largely unexplored. Here, we describe the first genomic data release from the BARAKA-Qatar Study-a nationwide program building a broadly consented biorepository of individuals with ASD and their families available for sample and data sharing and multi-omics research.

METHODS

In this first release, we present a comprehensive analysis of whole-genome sequencing (WGS) data of the first 100 families (372 individuals), investigating the genetic architecture, including single-nucleotide variants (SNVs), copy number variants (CNVs), tandem repeat expansions (TREs), as well as mitochondrial DNA variants (mtDNA) segregating with ASD in local families.

RESULTS

Overall, we identify potentially pathogenic variants in known genes or regions in 27 out of 100 families (27%), of which 11 variants (40.7%) were classified as pathogenic or likely-pathogenic based on American College of Medical Genetics (ACMG) guidelines. Dominant variants, including de novo and inherited, contributed to 15 (55.6%) of these families, consisting of SNVs/indels (66.7%), CNVs (13.3%), TREs (13.3%), and mtDNA variants (6.7%). Moreover, homozygous variants were found in 7 families (25.9%), with a sixfold increase in homozygous burden in consanguineous versus non-consanguineous families (13.6% and 1.8%, respectively). Furthermore, 28 novel ASD candidate genes were identified in 20 families, 23 of which had recurrent hits in MSSNG and SSC cohorts.

CONCLUSIONS

This study illustrates the value of ASD studies in under-represented populations and the importance of WGS as a comprehensive tool for establishing a molecular diagnosis for families with ASD. Moreover, it uncovers a significant role for recessive variation in ASD architecture in consanguineous settings and provides a unique resource of Middle Eastern genomes for future research to the global ASD community.

Deep multiple-instance learning accurately predicts gene haploinsufficiency and deletion pathogenicity

Copy number losses (deletions) are a major contributor to the etiology of severe genetic disorders. Although haploinsufficient genes play a critical role in deletion pathogenicity, current methods for deletion pathogenicity prediction fail to integrate multiple lines of evidence for haploinsufficiency at the gene level, limiting their power to pinpoint deleterious deletions associated with genetic disorders. Here we introduce DosaCNV, a deep multiple-instance learning framework that, for the first time, models deletion pathogenicity jointly with gene haploinsufficiency. By integrating over 30 gene-level features potentially predictive of haploinsufficiency, DosaCNV shows unmatched performance in prioritizing pathogenic deletions associated with a broad spectrum of genetic disorders. Furthermore, DosaCNV outperforms existing methods in predicting gene haploinsufficiency even though it is not trained on known haploinsufficient genes. Finally, DosaCNV leverages a state-of-the-art technique to quantify the contributions of individual gene-level features to haploinsufficiency, allowing for human-understandable explanations of model predictions. Altogether, DosaCNV is a powerful computational tool for both fundamental and translational research.

Genomic and computational analysis of four novel variants of MPL gene in Congenital Amegakaryocytic Thrombocytopenia

Congenital amegakaryocytic thrombocytopenia (CAMT) is a rare, genetic, autosomal recessive disorder characterized by severe thrombocytopenia, due to inefficient bone marrow megakaryopoiesis eventually leading to aplasia. Majority of the cases are due to homozygous or compound heterozygous mutations in MPL gene encoding for thrombopoietin (THPO) receptor protein. CAMT can be diagnosed at early phase of life, with major complication of transfusion dependency and hematopoietic transplantation as only curative treatment. We have investigated the sequence variations in MPL gene of 7 bone marrow failure (BMF) subjects, who presented with clinically diverse phenotypes, through next generation sequencing (NGS). Plasma THPO levels were estimated using ELISA. Insilico sequence and structure-based analyses were performed to understand the structural and functional implications of mutations, identified through NGS. We studied 7 CAMT subjects suspected of BMF, who presented with severe thrombocytopenia followed by pancytopenia, bleeding manifestation and physical anomalies. The plasma THPO levels were significantly elevated (p<0.05) in all the cases. Molecular analysis by NGS identified 9 genomic mutations in MPL gene. These included 7 non-synonymous substitution, 1 nonsense substitution and 1 in-del mutations, of which 4 are novel mutations. Insilico analysis predicted damaging effects on THPO-R and its reduced affinity for THPO for all the identified mutations. CAMT is a rare disorder with diverse clinical phenotypes and diagnosis is challenging. The elevated plasma THPO levels should be considered for the primary diagnosis and prognosis of the disease. However, molecular analysis of MPL gene is important for the diagnosis and management of the disease through genetic counselling. Though the cytokines, THPO-R agonist are used for the treatment of CAMT, HSCT is the only curative therapy.

Precision medicine in rare diseases: What is next?

Molecular diagnostics is a cornerstone of modern precision medicine, broadly understood as tailoring an individual's treatment, follow-up, and care based on molecular data. In rare diseases (RDs), molecular diagnoses reveal valuable information about the cause of symptoms, disease progression, familial risk, and in certain cases, unlock access to targeted therapies. Due to decreasing DNA sequencing costs, genome sequencing (GS) is emerging as the primary method for precision diagnostics in RDs. Several ongoing European initiatives for precision medicine have chosen GS as their method of choice. Recent research supports the role for GS as first-line genetic investigation in individuals with suspected RD, due to its improved diagnostic yield compared to other methods. Moreover, GS can detect a broad range of genetic aberrations including those in noncoding regions, producing comprehensive data that can be periodically reanalyzed for years to come when further evidence emerges. Indeed, targeted drug development and repurposing of medicines can be accelerated as more individuals with RDs receive a molecular diagnosis. Multidisciplinary teams in which clinical specialists collaborate with geneticists, genomics education of professionals and the public, and dialogue with patient advocacy groups are essential elements for the integration of precision medicine into clinical practice worldwide. It is also paramount that large research projects share genetic data and leverage novel technologies to fully diagnose individuals with RDs. In conclusion, GS increases diagnostic yields and is a crucial step toward precision medicine for RDs. Its clinical implementation will enable better patient management, unlock targeted therapies, and guide the development of innovative treatments.

Curation and expansion of the Human Phenotype Ontology for systemic autoinflammatory diseases improves phenotype-driven disease-matching

Introduction

Accurate and standardized phenotypic descriptions are essential in diagnosing rare diseases and discovering new diseases, and the Human Phenotype Ontology (HPO) system was developed to provide a rich collection of hierarchical phenotypic descriptions. However, although the HPO terms for inborn errors of immunity have been improved and curated, it has not been investigated whether this curation improves the diagnosis of systemic autoinflammatory disease (SAID) patients. Here, we aimed to study if improved HPO annotation for SAIDs enhanced SAID identification and to demonstrate the potential of phenotype-driven genome diagnostics using curated HPO terms for SAIDs.

Methods

We collected HPO terms from 98 genetically confirmed SAID patients across eight different European SAID expertise centers and used the LIRICAL (Likelihood Ratio Interpretation of Clinical Abnormalities) computational algorithm to estimate the effect of HPO curation on the prioritization of the correct SAID for each patient.

Results

Our results show that the percentage of correct diagnoses increased from 66% to 86% and that the number of diagnoses with the highest ranking increased from 38 to 45. In a further pilot study, curation also improved HPO-based whole-exome sequencing (WES) analysis, diagnosing 10/12 patients before and 12/12 after curation. In addition, the average number of candidate diseases that needed to be interpreted decreased from 35 to 2.

Discussion

This study demonstrates that curation of HPO terms can increase identification of the correct diagnosis, emphasizing the high potential of HPO-based genome diagnostics for SAIDs.

Integrated network pharmacology analysis and in vitro validation revealed the underlying mechanism of Xiyanping injection in treating coronavirus disease 2019

BACKGROUND

Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide, leading to a pandemic. In China, Xiyanping injection (XYP) has been recommended as a drug for COVID-19 treatment in the Guideline on Diagnosis and Treatment of COVID-19 by the National Health Commission of the People Republic of China and National Administration of Traditional Chinese Medicine (Trial eighth Edition). However, the relevant mechanisms at the molecular-level need to be further elucidated.

METHODS

In this study, XYP related active ingredients, potential targets and COVID-19 related genes were searched in public databases. Protein-protein interaction network and module analyzes were used to screen for key targets. gene ontology and Kyoto encyclopedia of genes and genomes were performed to investigate the potentially relevant signaling pathways. Molecular docking was performed using Autodock Tools and Vina. For the validation of potential mechanism, PolyI:C was used to induce human lung epithelial cells for an inflammation model. Subsequently, CCK-8 assays, enzyme-linked immunosorbent assay, reverse transcription quantitative polymerase chain reaction and western blot were employed to determine the effect of XYP on the expression of key genes.

RESULTS

Seven effective active ingredients in XYP were searched for 123 targets in the relevant databases. Furthermore, 6446 COVID-19 disease targets were identified. Sodium 9-dehydro-17-hydro-andrographolide-19-yl sulfate was identified as the vital active compounds, and IL-6, TNF, IL-1β, CXCL8, STAT3, MAPK1, MAPK14, and MAPK8 were considered as the key targets. In addition, molecular docking revealed that the active compound and the targets showed good binding affinities. The enrichment analysis predicted that the XYP could regulate the IL-17, Toll-like receptor, PI3K-Akt and JAK-STAT signaling pathways. Consistently, further in vitro experiments demonstrated that XYP could slow down the cytokine storm in the lung tissue of COVID-19 patients by down-regulating IL-6, TNF-α, IL-1β, CXCL8, and p-STAT3.

CONCLUSION

Through effective network pharmacology analysis and molecular docking, this study suggests that XYP contains many effective compounds that may target COVID-19 related signaling pathways. Moreover, the in vitro experiment confirmed that XYP could inhibit the cytokine storm by regulating genes or proteins related to immune and inflammatory responses.

Pharmacokinetics of Monoclonal Antibody and Antibody Fragments in The Mouse Eye Following Intravitreal Administration

Mice are rarely used in pharmacokinetic (PK) studies of ocular therapeutics due to the small size of their eyes and challenges in drug administration, tissue collection, and analysis of drug concentrations. Therefore, ocular PK of protein therapeutics in mouse eye following intravitreal (IVT) administration is not known. Here, we have presented the first of its kind investigation, to study the PK of 4 different size non-binding protein therapeutics in mouse plasma, cornea/ICB, vitreous humor, retina, and posterior cup (including choroid) following IVT administration. Administered proteins include trastuzumab (150 kDa) and F(ab)2 (100 kDa), Fab, and scFv (27 kDa) fragments of trastuzumab. An imaging and injection apparatus suitable for performing small (50 nL) IVT injections in mice was developed, and techniques for enucleation of the eye and dissection of ocular tissues were developed. Furthermore, a sensitive enzyme-linked immunosorbent assay (ELISA) for detection of proteins in very small amounts of ocular tissues were developed. It was observed that elimination from the vitreous chamber was the primary driver of PK in the cornea/ICB, retina, posterior cup, and plasma. Trastuzumab displays first-order kinetics in the vitreous humor with a half-life of 18.8 h. F(ab)2, Fab, and ScFv show biphasic PK profiles with distribution phases becoming more rapid as molecular weight decreases, and terminal elimination becoming longer as molecular weight decreases, with terminal half-lives of 16.3, 20.6, and 48.9 h, respectively. The mean residence times of trastuzumab, F(ab)2, Fab, and scFv in the vitreous humor were 26.0, 12.2, 10.7, and 8.16 h, respectively. It was found that the mean residence time in vitreous humor doubles with an increase in molecular weight of ∼69 kDa. Interestingly, the PK of proteins measured in the un-injected eye suggest the presence of a pathway for drug transfer between the eyes, which needs to be further validated. Overall, the findings presented here pave the way for drug discovery and development studies of protein therapeutics for ophthalmic indications in mice.

Network pharmacology, molecular docking, and molecular dynamics simulation to elucidate the mechanism of anti-aging action of Tinospora cordifolia

Scientific research has demonstrated that Tinospora cordifolia acts as an anti-aging agent in several experimental models, generating global interest in its underlying molecular mechanisms of this activity. The aim of the study was to identify the possible phytochemical compounds of T. cordifolia that might combat age-related illness through integrating network pharmacology, molecular docking techniques, and molecular dynamics (MD) study to explore their potential mechanisms of action. To carry out this study, several databases were used, including PubChem, KNApSAcK family database, PubMed, SwissADME, Molsoft, SwissTargetPrediction, GeneCards, and OMIM database. For network development and GO enrichment analysis KEGG, ShinyGo 0.77, and the STRING database were used. For better analysis, the networks were also constructed using Cytoscape 3.9.1. The Cytoscape network analyzer tool was used for data analysis, and molecular docking was done via Vina-GPU-2.0. The best compounds and AKT1 were finally subjected to MD simulation for 100 ns. The CytoHubba plugin of Cytoscape identified ten key targets, commonly called hub genes, including AKT1, GAPDH, and TP53, and so on. GO and KEGG pathway enrichment analysis revealed the relevant biological processes, cellular components, and molecular functions involved in treating aging-related disorders. KEGG pathway analysis involved neuroactive ligand-receptor interactions, lipid and atherosclerosis, and cAMP signaling. The docking of 100 T. cordifolia compounds with AKT1 demonstrated good binding affinity, particularly for Amritoside, Sitagliptin, Berberine, and Piperine. Finally, the relative stability of four-hit phytochemicals was validated by MD simulation, which may be the most crucial compound for anti-aging activity. In conclusion, this study used network pharmacology, molecular docking, and MD simulation to identify the compounds in T. cordifolia and proposed a potential mechanism for anti-aging activity. These results suggest future directions for the prevention and treatment of age-related diseases.

Towards artificial intelligence-enabled extracellular vesicle precision drug delivery

Extracellular Vesicles (EVs), particularly exosomes, recently exploded into nanomedicine as an emerging drug delivery approach due to their superior biocompatibility, circulating stability, and bioavailability in vivo. However, EV heterogeneity makes molecular targeting precision a critical challenge. Deciphering key molecular drivers for controlling EV tissue targeting specificity is in great need. Artificial intelligence (AI) brings powerful prediction ability for guiding the rational design of engineered EVs in precision control for drug delivery. This review focuses on cutting-edge nano-delivery via integrating large-scale EV data with AI to develop AI-directed EV therapies and illuminate the clinical translation potential. We briefly review the current status of EVs in drug delivery, including the current frontier, limitations, and considerations to advance the field. Subsequently, we detail the future of AI in drug delivery and its impact on precision EV delivery. Our review discusses the current universal challenge of standardization and critical considerations when using AI combined with EVs for precision drug delivery. Finally, we will conclude this review with a perspective on future clinical translation led by a combined effort of AI and EV research.

Candidate SNP Markers Significantly Altering the Affinity of TATA-Binding Protein for the Promoters of Human Hub Genes for Atherogenesis, Atherosclerosis and Atheroprotection

Atherosclerosis is a systemic disease in which focal lesions in arteries promote the build-up of lipoproteins and cholesterol they are transporting. The development of atheroma (atherogenesis) narrows blood vessels, reduces the blood supply and leads to cardiovascular diseases. According to the World Health Organization (WHO), cardiovascular diseases are the leading cause of death, which has been especially boosted since the COVID-19 pandemic. There is a variety of contributors to atherosclerosis, including lifestyle factors and genetic predisposition. Antioxidant diets and recreational exercises act as atheroprotectors and can retard atherogenesis. The search for molecular markers of atherogenesis and atheroprotection for predictive, preventive and personalized medicine appears to be the most promising direction for the study of atherosclerosis. In this work, we have analyzed 1068 human genes associated with atherogenesis, atherosclerosis and atheroprotection. The hub genes regulating these processes have been found to be the most ancient. In silico analysis of all 5112 SNPs in their promoters has revealed 330 candidate SNP markers, which statistically significantly change the affinity of the TATA-binding protein (TBP) for these promoters. These molecular markers have made us confident that natural selection acts against underexpression of the hub genes for atherogenesis, atherosclerosis and atheroprotection. At the same time, upregulation of the one for atheroprotection promotes human health.

Genetic disruption of mammalian endoplasmic reticulum-associated protein degradation: Human phenotypes and animal and cellular disease models

Endoplasmic reticulum-associated protein degradation (ERAD) is a stringent quality control mechanism through which misfolded, unassembled and some native proteins are targeted for degradation to maintain appropriate cellular and organelle homeostasis. Several in vitro and in vivo ERAD-related studies have provided mechanistic insights into ERAD pathway activation and its consequent events; however, a majority of these have investigated the effect of ERAD substrates and their consequent diseases affecting the degradation process. In this review, we present all reported human single-gene disorders caused by genetic variation in genes that encode ERAD components rather than their substrates. Additionally, after extensive literature survey, we present various genetically manipulated higher cellular and mammalian animal models that lack specific components involved in various stages of the ERAD pathway.

Resources and tools for rare disease variant interpretation

Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

A patient-specific functional module and path identification technique from RNA-seq data

With the advancement of new technologies, a huge amount of high dimensional data is being generated which is opening new opportunities and challenges to the study of cancer and diseases. In particular, distinguishing the patient-specific key components and modules which drive tumorigenesis is necessary to analyze. A complex disease generally does not initiate from the dysregulation of a single component but it is the result of the dysfunction of a group of components and networks which differs from patient to patient. However, a patient-specific network is required to understand the disease and its molecular mechanism. We address this requirement by constructing a patient-specific network by sample-specific network theory with integrating cancer-specific differentially expressed genes and elite genes. By elucidating patient-specific networks, it can identify the regulatory modules, driver genes as well as personalized disease networks which can lead to personalized drug design. This method can provide insight into how genes are associating with each other and characterized the patient-specific disease subtypes. The results show that this method can be beneficial for the detection of patient-specific differential modules and interaction between genes. Extensive analysis using existing literature, gene enrichment and survival analysis for three cancer types STAD, PAAD and LUAD shows the effectiveness of this method over other existing methods. In addition, this method can be useful for personalized therapeutics and drug design. This methodology is implemented in the R language and is available at https://github.com/riasatazim/PatientSpecificRNANetwork.

Biochemical and mutational analyses of HEXA in a cohort of Egyptian patients with infantile Tay-Sachs disease. Expansion of the mutation spectrum

BACKGROUND

Tay-Sachs disease (TSD), an autosomal recessively inherited neurodegenerative lysosomal storage disease, reported worldwide with a high incidence among population of Eastern European and Ashkenazi Jewish descent. Mutations in the alpha subunit of HEXA that encodes for the β-hexosaminidase-A lead to deficient enzyme activity and TSD phenotype. This study is the first to highlight the HEXA sequence variations spectrum in a cohort of Egyptian patients with infantile TSD.

RESULTS

This study involved 13 Egyptian infant/children patients presented with the infantile form of TSD, ten of the 13 patients were born to consanguineous marriages. β-hexosaminidase-A enzyme activity was markedly reduced in the 13 patients with a mean activity of 3 µmol/L/h ± 1.56. Sanger sequencing of the HEXA' coding regions and splicing junctions enabled a detection rate of ~ 62% (8/13) in our patients revealing the molecular defects in eight patients; six homozygous-mutant children (five of them were the product of consanguineous marriages) and two patients showed their mutant alleles in heterozygous genotypes, while no disease-causing mutation was identified in the remaining patients. Regulatory intragenic mutations or del/dup may underlie the molecular defect in those patients showing no relevant pathogenic sequencing variants or in the two patients with a heterozygous genotype of the mutant allele. This research identified three novel, likely pathogenic variants in association with the TSD phenotype; two missense, c.920A > C (E307A) and c.952C > G (H318D) in exon 8, and a single base deletion c.484delG causing a frameshift E162Rfs*37 (p.Glu162ArgfsTer37) in exon 5. Three recurrent disease-causing missense mutations; c.1495C > T (R499C), c.1511G > A(R504H), and c.1510C > T(R504C) in exon 13 were identified in five of the eight patients. None of the variants was detected in 50 healthy Egyptians' DNA. Five variants, likely benign or of uncertain significance, S3T, I436V, E506E, and T2T, in exons 1, 11,13, & 1 were detected in our study.

CONCLUSIONS

For the proper diagnostics, genetic counseling, and primary prevention, our study stresses the important role of Next Generation Sequencing approaches in delineating the molecular defect in TSD-candidate patients that showed negative Sanger sequencing or a heterozygous mutant allele in their genetic testing results. Interestingly, the three recurrent TSD associated mutations were clustered on chromosome 13 and accounted for 38% of the HEXA mutations detected in this study. This suggested exon 13 as the first candidate for sequencing screening in Egyptian patients with infantile TSD. Larger studies involving our regional population are recommended, hence unique disease associated pathogenic variations could be identified.

Movement disorders in hereditary spastic paraplegia (HSP): a systematic review and individual participant data meta-analysis

BACKGROUND

Hereditary spastic paraplegia (HSP) is a rare genetic disorder associated with mutations in > 80 loci designated SPG (SPastic parapleGia). The phenotypic spectrum of HSP can extend to include other neurologic features, including movement disorders. Our aim was to investigate genotype-phenotype associations in HSP with a focus on movement disorders.

METHODS

We performed a systematic review and individual participant data (IPD)-level meta-analysis by retrieving publications from Medline/EMBASE/Web of Science on HSP with a SPG genotype. Studies were included only if individual-level information was accessible and at least one patient with a movement disorder was reported for that genotype. Out of 21,957 hits, 192 manuscripts with a total of 1413 HSP cases were eligible. Data were compared between two HSP groups: manifested with (HSP-MD, n = 767) or without (HSP-nMD, n = 646) a movement disorder.

RESULTS

The HSP-MD group had an older age of onset (20.5 ± 16.0 vs. 17.1 ± 14.2 yr, p < 0.001) and less frequent autosomal dominant inheritance (7.6% vs. 30.1%, p < 0.001) compared to HSP-nMD. SPG7 (31.2%) and SPG11 (23.8%) were the most frequent genotypes in the HSP-MD group. HSP-MD with SPG7 had higher frequency of later onset during adulthood (82.9% vs. 8.5%), ataxia (OR = 12.6), extraocular movement disturbances (OR = 3.4) and seizure (OR = 3.7) compared to HSP-MD with SPG11. Conversely, SPG11 mutations were more frequently associated with consanguinity (OR = 4.1), parkinsonism (OR = 7.8), dystonia (OR = 5.4), peripheral neuropathy (OR = 26.9), and cognitive dysfunction (OR = 34.5).

CONCLUSION

This systematic IPD-level meta-analysis provides the largest data on genotype-phenotype associations in HSP-MD. Several clinically relevant phenotypic differences were found between various genotypes, which can possibly facilitate diagnosis in resource-limited settings.

Genetics of migraine: where are we now?

Migraine is a complex brain disorder explained by the interaction of genetic and environmental factors. In monogenic migraines, including familial hemiplegic migraine and migraine with aura associated with hereditary small-vessel disorders, the identified genes code for proteins expressed in neurons, glial cells, or vessels, all of which increase susceptibility to cortical spreading depression. The study of monogenic migraines has shown that the neurovascular unit plays a prominent role in migraine. Genome-wide association studies have identified numerous susceptibility variants that each result in only a small increase in overall migraine risk. The more than 180 known variants belong to several complex networks of "pro-migraine" molecular abnormalities, which are mainly neuronal or vascular. Genetics has also highlighted the importance of shared genetic factors between migraine and its major co-morbidities, including depression and high blood pressure. Further studies are still needed to map all of the susceptibility loci for migraine and then to understand how these genomic variants lead to migraine cell phenotypes.

Hypothesis-free phenotype prediction within a genetics-first framework

Cohort-wide sequencing studies have revealed that the largest category of variants is those deemed 'rare', even for the subset located in coding regions (99% of known coding variants are seen in less than 1% of the population. Associative methods give some understanding how rare genetic variants influence disease and organism-level phenotypes. But here we show that additional discoveries can be made through a knowledge-based approach using protein domains and ontologies (function and phenotype) that considers all coding variants regardless of allele frequency. We describe an ab initio, genetics-first method making molecular knowledge-based interpretations for exome-wide non-synonymous variants for phenotypes at the organism and cellular level. By using this reverse approach, we identify plausible genetic causes for developmental disorders that have eluded other established methods and present molecular hypotheses for the causal genetics of 40 phenotypes generated from a direct-to-consumer genotype cohort. This system offers a chance to extract further discovery from genetic data after standard tools have been applied.

Genome-wide subcellular protein map for the flagellate parasite Trypanosoma brucei

Trypanosoma brucei is a model trypanosomatid, an important group of human, animal and plant unicellular parasites. Understanding their complex cell architecture and life cycle is challenging because, as with most eukaryotic microbes, ~50% of genome-encoded proteins have completely unknown functions. Here, using fluorescence microscopy and cell lines expressing endogenously tagged proteins, we mapped the subcellular localization of 89% of the T. brucei proteome, a resource we call TrypTag. We provide clues to function and define lineage-specific organelle adaptations for parasitism, mapping the ultraconserved cellular architecture of eukaryotes, including the first comprehensive 'cartographic' analysis of the eukaryotic flagellum, which is vital for morphogenesis and pathology. To demonstrate the power of this resource, we identify novel organelle subdomains and changes in molecular composition through the cell cycle. TrypTag is a transformative resource, important for hypothesis generation for both eukaryotic evolutionary molecular cell biology and fundamental parasite cell biology.

Novel homozygous leptin receptor mutation in an infant with monogenic obesity

Monogenic obesity can be caused by a mutation in one of the single genes involved in hunger and satiety. The most common mutations affect melanocortin 4 (MC4) followed by the leptin gene and its receptor. Leptin receptor (LEPR) gene mutation is an extremely rare endocrine disease characterized by early-onset obesity, hyperphagia in addition to pituitary hormone deficiency, and metabolic abnormalities. We report the case of a 12-month-old male infant born of a non-consanguineous marriage. He presented to us with rapid weight gain from 2 months of age along with hyperphagia. Biochemistry revealed a deranged lipid profile, elevated transaminases, and markedly raised serum leptin levels. On genetic analysis, a novel mutation was detected, which was a homozygous variation In exon 12 of the LEPR gene (chr1:g.65608901G>A) that resulted in the synonymous amino acid change of lysine at codon 584 proximal to donor splice site (p.Lys584). The in silico prediction of the variant was 'damaging' by MutationTaster2. The mutation was classified as a 'variant of uncertain significance' due to a lack of published literature and had to be correlated carefully with the clinical symptoms. It was recommended to do Sanger sequencing of the parents and other family members. However, due to financial constraints, the family could not afford the same. At the time of writing, funds were being arranged for procuring setmelanotide, which is a novel and effective therapy for monogenic obesity due to LepR mutation.

A census of actin-associated proteins in humans

Actin filaments help in maintaining the cell structure and coordinating cellular movements and cargo transport within the cell. Actin participates in the interaction with several proteins and also with itself to form the helical filamentous actin (F-actin). Actin-binding proteins (ABPs) and actin-associated proteins (AAPs) coordinate the actin filament assembly and processing, regulate the flux between globular G-actin and F-actin in the cell, and help maintain the cellular structure and integrity. We have used protein-protein interaction data available through multiple sources (STRING, BioGRID, mentha, and a few others), functional annotation, and classical actin-binding domains to identify actin-binding and actin-associated proteins in the human proteome. Here, we report 2482 AAPs and present an analysis of their structural and sequential domains, functions, evolutionary conservation, cellular localization, abundance, and tissue-specific expression patterns. This analysis provides a base for the characterization of proteins involved in actin dynamics and turnover in the cell.

A novel compound heterozygous BEST1 gene mutation in two siblings causing autosomal recessive bestrophinopathy

PURPOSE

To describe the clinical features, imaging characteristics, and genetic test results associated with a novel compound heterozygous mutation of the BEST1 gene in two siblings with autosomal recessive bestrophinopathy.

METHODS

Two siblings underwent a complete ophthalmic examination, including dilated fundus examination, fundus photography, fundus autofluorescence imaging, spectral-domain optical coherence tomography, fluorescein angiography, electroretinography, and electrooculography. A clinical diagnosis of autosomal recessive bestrophinopathy was established based on ocular examination and multimodal retinal imaging. Subsequently, clinical exome sequencing consisting of a panel of 6670 genes was carried out to confirm the diagnosis and assess genetic alterations in the protein-coding region of the genome of the patients. The identified mutations were tested in the two affected siblings and one of their parents.

RESULTS

Two siblings (a 17-year-old female and a 15-year-old male) presented with reduced visual acuity and bilaterally symmetrical subretinal deposits of hyperautofluorescent materials in the posterior pole, which showed staining in the late phase of fluorescein angiogram. Spectral-domain optical coherence tomography demonstrated hyperreflective subretinal deposits and subretinal fluid accumulation. Both patients shared two mutations in the protein-coding region of the BEST1 gene, c.103G > A, p.(Glu35Lys) and c.313C > A, p.(Arg105Ser) (a novel disease-causing mutation). Sanger sequencing confirmed that the unaffected mother of the proband was carrying p.(Glu35Lys) variant in a heterozygous state.

CONCLUSIONS

We have identified and described the phenotype of a novel disease-causing mutation NM_004183.4:c.313C > A, p.(Arg105Ser) in a heterozygous state along with a previously reported mutation NM_004183.4:c.103G > A, p.(Glu35Lys) of the BEST1 gene in two related patients with autosomal recessive bestrophinopathy.

Detection of somatic copy number deletion of the CDKN2A gene by quantitative multiplex PCR for clinical practice

Background

A feasible method to detect somatic copy number deletion (SCND) of genes is still absent to date.

Methods

Interstitial base-resolution deletion/fusion coordinates for CDKN2A were extracted from published articles and our whole genome sequencing (WGS) datasets. The copy number of the CDKN2A gene was measured with a quantitative multiplex PCR assay P16-Light and confirmed with whole genome sequencing (WGS).

Results

Estimated common deletion regions (CDRs) were observed in many tumor suppressor genes, such as ATM, CDKN2A, FAT1, miR31HG, PTEN, and RB1, in the SNP array-based COSMIC datasets. A 5.1 kb base-resolution CDR could be identified in &gt;90% of cancer samples with CDKN2A deletion by sequencing. The CDKN2A CDR covers exon-2, which is essential for P16INK4A and P14ARF synthesis. Using the true CDKN2A CDR as a PCR target, a quantitative multiplex PCR assay P16-Light was programmed to detect CDKN2A gene copy number. P16-Light was further confirmed with WGS as the gold standard among cancer tissue samples from 139 patients.

Conclusion

The 5.1 kb CDKN2A CDR was found in &gt;90% of cancers containing CDKN2A deletion. The CDKN2A CDR was used as a potential target for developing the P16-Light assay to detect CDKN2A SCND and amplification for routine clinical practices.

TLNPMD: Prediction of miRNA-Disease Associations Based on miRNA-Drug-Disease Three-Layer Heterogeneous Network

Many microRNAs (miRNAs) have been confirmed to be associated with the generation of human diseases. Capturing miRNA-disease associations (M-DAs) provides an effective way to understand the etiology of diseases. Many models for predicting M-DAs have been constructed; nevertheless, there are still several limitations, such as generally considering direct information between miRNAs and diseases, usually ignoring potential knowledge hidden in isolated miRNAs or diseases. To overcome these limitations, in this study a novel method for predicting M-DAs was developed named TLNPMD, highlights of which are the introduction of drug heuristic information and a bipartite network reconstruction strategy. Specifically, three bipartite networks, including drug-miRNA, drug-disease, and miRNA-disease, were reconstructed as weighted ones using such reconstruction strategy. Based on these weighted bipartite networks, as well as three corresponding similarity networks of drugs, miRNAs and diseases, the miRNA-drug-disease three-layer heterogeneous network was constructed. Then, this heterogeneous network was converted into three two-layer heterogeneous networks, for each of which the network path computational model was employed to predict association scores. Finally, both direct and indirect miRNA-disease paths were used to predict M-DAs. Comparative experiments of TLNPMD and other four models were performed and evaluated by five-fold and global leave-one-out cross validations, results of which show that TLNPMD has the highest AUC values among those of compared methods. In addition, case studies of two common diseases were carried out to validate the effectiveness of the TLNPMD. These experiments demonstrate that the TLNPMD may serve as a promising alternative to existing methods for predicting M-DAs.

Decoding multilevel relationships with the human tissue-cell-molecule network

Understanding the biological functions of molecules in specific human tissues or cell types is crucial for gaining insights into human physiology and disease. To address this issue, it is essential to systematically uncover associations among multilevel elements consisting of disease phenotypes, tissues, cell types and molecules, which could pose a challenge because of their heterogeneity and incompleteness. To address this challenge, we describe a new methodological framework, called Graph Local InfoMax (GLIM), based on a human multilevel network (HMLN) that we established by introducing multiple tissues and cell types on top of molecular networks. GLIM can systematically mine the potential relationships between multilevel elements by embedding the features of the HMLN through contrastive learning. Our simulation results demonstrated that GLIM consistently outperforms other state-of-the-art algorithms in disease gene prediction. Moreover, GLIM was also successfully used to infer cell markers and rewire intercellular and molecular interactions in the context of specific tissues or diseases. As a typical case, the tissue-cell-molecule network underlying gastritis and gastric cancer was first uncovered by GLIM, providing systematic insights into the mechanism underlying the occurrence and development of gastric cancer. Overall, our constructed methodological framework has the potential to systematically uncover complex disease mechanisms and mine high-quality relationships among phenotypical, tissue, cellular and molecular elements.

Wilms tumor with Mulibrey Nanism: A case report and review of literature

BACKGROUND

Mulibrey-Nanism (Muscle-liver-brain-eye Nanism = dwarfism; MUL) is a rare genetic syndrome. The underlying TRIM37 mutation predisposes these children to develop tumors frequently. In the largest published series of MUL, 8% patients were reported to develop Wilms tumor (WT). The published literature lacks data regarding the best treatment protocol and outcome of this cohort of children with WT and MUL. We report here a 2-year-old boy with WT and MUL and present a review of literature on WT in MUL.

CASE

Our patient had associated cardiac problems of atrial septal defect, atrial flutter and an episode of sudden cardiac arrest. We managed him successfully with chemotherapy, surgery and multi-speciality care. He is alive and in remission at follow-up of 6 months.

CONCLUSION

A total of 14 cases (including present case) of WT have been reported in MUL and treatment details were available for six cases. They were managed primarily with surgery, chemotherapy with/without radiotherapy, and all achieved remission. The outcome data is available only for two cases, one has been followed up till 15 years post treatment for WT and other is our patient.

SCN1A Variants as the Underlying Cause of Genetic Epilepsy with Febrile Seizures Plus in Two Multi-Generational Colombian Families

Genetic epilepsy with febrile seizures plus (GEFS+) is an autosomal dominant disorder with febrile or afebrile seizures that exhibits phenotypic variability. Only a few variants in SCN1A have been previously characterized for GEFS+, in Latin American populations where studies on the genetic and phenotypic spectrum of GEFS+ are scarce. We evaluated members in two multi-generational Colombian Paisa families whose affected members present with classic GEFS+. Exome and Sanger sequencing were used to detect the causal variants in these families. In each of these families, we identified variants in SCN1A causing GEFS+ with incomplete penetrance. In Family 047, we identified a heterozygous variant (c.3530C > G; p.(Pro1177Arg)) that segregates with GEFS+ in 15 affected individuals. In Family 167, we identified a previously unreported variant (c.725A > G; p.(Gln242Arg)) that segregates with the disease in a family with four affected members. Both variants are located in a cytoplasmic loop region in SCN1A and based on our findings the variants are classified as pathogenic and likely pathogenic, respectively. Our results expand the genotypic and phenotypic spectrum associated with SCN1A variants and will aid in improving molecular diagnostics and counseling in Latin American and other populations.

The druggable schizophrenia genome: from repurposing opportunities to unexplored drug targets

There have been no new drugs for the treatment of schizophrenia in several decades and treatment resistance represents a major unmet clinical need. The drugs that exist are based on serendipitous clinical observations rather than an evidence-based understanding of disease pathophysiology. In the present review, we address these bottlenecks by integrating common, rare, and expression-related schizophrenia risk genes with knowledge of the druggability of the human genome as a whole. We highlight novel drug repurposing opportunities, clinical trial candidates which are supported by genetic evidence, and unexplored therapeutic opportunities in the lesser-known regions of the schizophrenia genome. By identifying translational gaps and opportunities across the schizophrenia disease space, we discuss a framework for translating increasingly well-powered genetic association studies into personalized treatments for schizophrenia and initiating the vital task of characterizing clinically relevant drug targets in underexplored regions of the human genome.

Network Theoretical Approach to Explore Factors Affecting Signal Propagation and Stability in Dementia’s Protein-Protein Interaction Network

Dementia—a syndrome affecting human cognition—is a major public health concern given to its rising prevalence worldwide. Though multiple research studies have analyzed disorders such as Alzheimer’s disease and Frontotemporal dementia using a systems biology approach, a similar approach to dementia syndrome as a whole is required. In this study, we try to find the high-impact core regulating processes and factors involved in dementia’s protein–protein interaction network. We also explore various aspects related to its stability and signal propagation. Using gene interaction databases such as STRING and GeneMANIA, a principal dementia network (PDN) consisting of 881 genes and 59,085 interactions was achieved. It was assortative in nature with hierarchical, scale-free topology enriched in various gene ontology (GO) categories and KEGG pathways, such as negative and positive regulation of apoptotic processes, macroautophagy, aging, response to drug, protein binding, etc. Using a clustering algorithm (Louvain method of modularity maximization) iteratively, we found a number of communities at different levels of hierarchy in PDN consisting of 95 “motif-localized hubs”, out of which, 7 were present at deepest level and hence were key regulators (KRs) of PDN (HSP90AA1, HSP90AB1, EGFR, FYN, JUN, CELF2 and CTNNA3). In order to explore aspects of network’s resilience, a knockout (of motif-localized hubs) experiment was carried out. It changed the network’s topology from a hierarchal scale-free topology to scale-free, where independent clusters exhibited greater control. Additionally, network experiments on interaction of druggable genome and motif-localized hubs were carried out where UBC, EGFR, APP, CTNNB1, NTRK1, FN1, HSP90AA1, MDM2, VCP, CTNNA1 and GRB2 were identified as hubs in the resultant network (RN). We finally concluded that stability and resilience of PDN highly relies on motif-localized hubs (especially those present at deeper levels), making them important therapeutic intervention candidates. HSP90AA1, involved in heat shock response (and its master regulator, i.e., HSF1), and EGFR are most important genes in pathology of dementia apart from KRs, given their presence as KRs as well as hubs in RN.

Molecular Diagnostic Outcomes from 700 Cases: What Can We Learn from a Retrospective Analysis of Clinical Exome Sequencing?

Clinical exome sequencing (CES) aids in the diagnosis of rare genetic disorders. Herein, we report the molecular diagnostic yield and spectrum of genetic alterations contributing to disease in 700 pediatric cases analyzed at the Children's Hospital of Philadelphia. The overall diagnostic yield was 23%, with three cases having more than one molecular diagnosis and 2.6% having secondary/additional findings. A candidate gene finding was reported in another 8.4% of cases. The clinical indications with the highest diagnostic yield were neurodevelopmental disorders (including seizures), whereas immune- and oncology-related indications were negatively associated with molecular diagnosis. The rapid expansion of knowledge regarding the genome's role in human disease necessitates reanalysis of CES samples. To capture these new discoveries, a subset of cases (n = 240) underwent reanalysis, with an increase in diagnostic yield. We describe our experience reporting CES results in a pediatric setting, including reporting of secondary findings, reporting newly discovered genetic conditions, and revisiting negative test results. Finally, we highlight the challenges associated with implementing critical updates to the CES workflow. Although these updates are necessary, they demand an investment of time and resources from the laboratory. In summary, these data demonstrate the clinical utility of exome sequencing and reanalysis, while highlighting the critical considerations for continuous improvement of a CES test in a clinical laboratory.

Deep Mutational Scanning of Protein-Protein Interactions Between Partners Expressed from Their Endogenous Loci In Vivo

Deep mutational scanning (DMS) generates mutants of a protein of interest in a comprehensive manner. CRISPR-Cas9 technology enables large-scale genome editing with high efficiency. Using both DMS and CRISPR-Cas9 therefore allows us to investigate the effects of thousands of mutations inserted directly in the genome. Combined with protein-fragment complementation assay (PCA), which enables the quantitative measurement of protein-protein interactions (PPIs) in vivo, these methods allow for the systematic assessment of the effects of mutations on PPIs in living cells. Here, we describe a method leveraging DMS, CRISPR-Cas9, and PCA to study the effect of point mutations on PPIs mediated by protein domains in yeast.

Parental age and retinoblastoma-a retrospective study of demographic data and genetic analysis

OBJECTIVE

To determine the association between the parental age gap and the absolute parental age with the risk of retinoblastoma (RB) development in an offspring.

METHODS

RB individuals diagnosed between March 2013 and December 2019 in a single tertiary eye care centre were included. We recorded the demographic data, parental age and RB1 gene mutation status in the patient's tumour, blood and the parental blood. We categorised RB1 mutation inheritance as sporadic RB with somatic mutations (only present in tumour), heritable RB with de novo (present in patient's blood) and familial (present in patient and parents' blood) germline mutations. The statistical significance was confirmed by Fisher's exact/Chi-square test.

RESULTS

Out of 259 RB patients, 247 were included in our study. Heritable RB with de novo germline mutations was significantly less common (p value: 0.0387; 95% CI: 0.2676-0.9329) and sporadic RB with somatic mutations was more common (p value: 0.0545; 95% CI: 1.025-3.39), if the parental age gap was <10 years. There were increased odds of a heritable RB with de novo germline mutation with an increase in paternal age and this was more intensified when combined with parental age gap of more than ≥10 years. The heritable RB with de novo germline mutations significantly increased as maternal age progressed, only when it was adjusted to ≥10 years parental age gap (p value: 0.0262; 95% CI: 1.26-17.91).

CONCLUSIONS

An increased parental age gap and increased paternal age are independent risk factors for the development of heritable RB with de novo germline mutation.

Pharmacokinetics of Monoclonal Antibody and Antibody Fragments in the Mouse Eye Following Systemic Administration

The ocular pharmacokinetics (PK) of antibody-based therapies are infrequently studied in mice due to the technical difficulties in working with the small murine eye. This study is the first of its kind to quantitatively measure the PK of variously sized proteins in the plasma, cornea/ICB, vitreous humor, retina, and posterior cup (including choroid) of the mouse and to evaluate the relationship between molecular weight (MW) and antibody biodistribution coefficient (BC) to the eye. Proteins analyzed include trastuzumab (150 kDa), trastuzumab-vc-MMAE (T-vc-MMAE, 155 kDa), F(ab)₂ (100 kDa), Fab (50 kDa), and scFv (27 kDa). As expected, ocular PK mirrored the systemic PK as plasma was the driving force for ocular exposure. For trastuzumab, T-vc-MMAE, F(ab)₂, Fab, and scFv, respectively, the BCs in the cornea/ICB were 0.610%, 0.475%, 1.74%, 3.39%, and 13.7%; the BCs in the vitreous humor were 0.0198%, 0.0427%, 0.0934%, 0.234%, and 5.56%; the BCs for the retina were 0.539%, 0.230%, 0.704%, 2.44%, and 20.4%; the BCs for the posterior cup were 0.557%, 0.650%, 1.47%, 4.06%, and 13.9%. The relationship between BC and MW was best characterized by a log–log regression in which BC decreased as MW increased, with every doubling in MW leading to a decrease in BC by a factor of 3.44 × , 6.76 × , 4.74 × , and 3.43 × in cornea/ICB, vitreous humor, retina, and posterior cup, respectively. In analyzing the disposition of protein therapeutics to the eye, these findings enhance our understanding of the potential for ocular toxicity of systemically administered protein therapeutics and may aid in the discovery of systemically administered protein therapeutics for ocular disorders.

Identification of viral-mediated pathogenic mechanisms in neurodegenerative diseases using network-based approaches

During the course of a viral infection, virus-host protein-protein interactions (PPIs) play a critical role in allowing viruses to replicate and survive within the host. These interspecies molecular interactions can lead to viral-mediated perturbations of the human interactome causing the generation of various complex diseases. Evidences suggest that viral-mediated perturbations are a possible pathogenic etiology in several neurodegenerative diseases (NDs). These diseases are characterized by chronic progressive degeneration of neurons, and current therapeutic approaches provide only mild symptomatic relief; therefore, there is unmet need for the discovery of novel therapeutic interventions. In this paper, we initially review databases and tools that can be utilized to investigate viral-mediated perturbations in complex NDs using network-based analysis by examining the interaction between the ND-related PPI disease networks and the virus-host PPI network. Afterwards, we present our theoretical-driven integrative network-based bioinformatics approach that accounts for pathogen-genes-disease-related PPIs with the aim to identify viral-mediated pathogenic mechanisms focusing in multiple sclerosis (MS) disease. We identified seven high centrality nodes that can act as disease communicator nodes and exert systemic effects in the MS-enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways network. In addition, we identified 12 KEGG pathways, 5 Reactome pathways and 52 Gene Ontology Immune System Processes by which 80 viral proteins from eight viral species might exert viral-mediated pathogenic mechanisms in MS. Finally, our analysis highlighted the Th17 differentiation pathway, a disease communicator node and part of the 12 underlined KEGG pathways, as a key viral-mediated pathogenic mechanism and a possible therapeutic target for MS disease.

Learning from low-rank multimodal representations for predicting disease-drug associations

Background

Disease-drug associations provide essential information for drug discovery and disease treatment. Many disease-drug associations remain unobserved or unknown, and trials to confirm these associations are time-consuming and expensive. To better understand and explore these valuable associations, it would be useful to develop computational methods for predicting unobserved disease-drug associations. With the advent of various datasets describing diseases and drugs, it has become more feasible to build a model describing the potential correlation between disease and drugs.

Results

In this work, we propose a new prediction method, called LMFDA, which works in several stages. First, it studies the drug chemical structure, disease MeSH descriptors, disease-related phenotypic terms, and drug-drug interactions. On this basis, similarity networks of different sources are constructed to enrich the representation of drugs and diseases. Based on the fused disease similarity network and drug similarity network, LMFDA calculated the association score of each pair of diseases and drugs in the database. This method achieves good performance on Fdataset and Cdataset, AUROCs were 91.6% and 92.1% respectively, higher than many of the existing computational models.

Conclusions

The novelty of LMFDA lies in the introduction of multimodal fusion using low-rank tensors to fuse multiple similar networks and combine matrix complement technology to predict potential association. We have demonstrated that LMFDA can display excellent network integration ability for accurate disease-drug association inferring and achieve substantial improvement over the advanced approach. Overall, experimental results on two real-world networks dataset demonstrate that LMFDA able to delivers an excellent detecting performance. Results also suggest that perfecting similar networks with as much domain knowledge as possible is a promising direction for drug repositioning.

Reading ADP-ribosylation signaling using chemical biology and interaction proteomics

ADP-ribose (ADPr) readers are essential components of ADP-ribosylation signaling, which regulates genome maintenance and immunity. The identification and discrimination between monoADPr (MAR) and polyADPr (PAR) readers is difficult because of a lack of suitable affinity-enrichment reagents. We synthesized well-defined ADPr probes and used these for affinity purifications combined with relative and absolute quantitative mass spectrometry to generate proteome-wide MAR and PAR interactomes, including determination of apparent binding affinities. Among the main findings, MAR and PAR readers regulate various common and distinct processes, such as the DNA-damage response, cellular metabolism, RNA trafficking, and transcription. We monitored the dynamics of PAR interactions upon induction of oxidative DNA damage and uncovered the mechanistic connections between ubiquitin signaling and ADP-ribosylation. Taken together, chemical biology enables exploration of MAR and PAR readers using interaction proteomics. Furthermore, the generated MAR and PAR interaction maps significantly expand our current understanding of ADPr signaling.