OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders

Exploring therapeutic targets and molecular mechanisms for treating diabetes mellitus-associated heart failure with Qishen Yiqi dropping pills: A network pharmacology and bioinformatics approach

Diabetes mellitus (DM) and heart failure frequently coexist, presenting significant public health challenges. QiShenYiQi Dropping Pills (QSDP) are widely employed in the treatment of diabetes mellitus concomitant with heart failure (DM-HF). Nevertheless, the precise mechanisms underlying their efficacy have yet to be elucidated. Active ingredients and likely targets of QSDP were retrieved from the TCMSP and UniProt databases. Genes associated with DM-HF were pinpointed through searches in the GeneCards, OMIM, DisGeNET, and TTD databases. Differential genes connected to DM-HF were sourced from the GEO database. Enrichment analyses via gene ontology and Kyoto Encyclopedia of Genes and Genomes pathways, as well as immune infiltration assessments, were conducted using R software. Further analysis involved employing molecular docking strategies to explore the interactions between the identified targets and active substances in QSDP that are pertinent to DM-HF treatment. This investigation effectively discerned 108 active compounds and 257 targets relevant to QSDP. A protein-protein interaction network was constructed, highlighting 6 central targets for DM-HF treatment via QSDP. Gene ontology enrichment analysis predominantly linked these targets with responses to hypoxia, metabolism of reactive oxygen species, and cytokine receptor interactions. Analysis of Kyoto Encyclopedia of Genes and Genomes pathways demonstrated that these targets mainly participate in pathways linked to diabetic complications, such as AGE-RAGE signaling, dyslipidemia, arteriosclerosis, the HIF-1 signaling pathway, and the tumor necrosis factor signaling pathway. Further, immune infiltration analysis implied that QSDP's mechanism in treating DM-HF might involve immune-mediated inflammation and crucial signaling pathways. Additionally, molecular docking studies showed that the active substances in QSDP have strong binding affinities with these identified targets. This research presents a new model for addressing DM-HF through the use of QSDP, providing novel insights into incorporating traditional Chinese medicine (TCM) principles in the clinical treatment of DM-HF. The implications of these findings are substantial for both clinical application and further scientific inquiry.

Experience of the first adult-focussed undiagnosed disease program in Australia (AHA-UDP): solving rare and puzzling genetic disorders is ageless

BACKGROUND

Significant recent efforts have facilitated increased access to clinical genetics assessment and genomic sequencing for children with rare diseases in many centres, but there remains a service gap for adults. The Austin Health Adult Undiagnosed Disease Program (AHA-UDP) was designed to complement existing UDP programs that focus on paediatric rare diseases and address an area of unmet diagnostic need for adults with undiagnosed rare conditions in Victoria, Australia. It was conducted at a large Victorian hospital to demonstrate the benefits of bringing genomic techniques currently used predominantly in a research setting into hospital clinical practice, and identify the benefits of enrolling adults with undiagnosed rare diseases into a UDP program. The main objectives were to identify the causal mutation for a variety of diseases of individuals and families enrolled, and to discover novel disease genes.

METHODS

Unsolved patients in whom standard genomic diagnostic techniques such as targeted gene panel, exome-wide next generation sequencing, and/or chromosomal microarray, had already been performed were recruited. Genome sequencing and enhanced genomic analysis from the research setting were applied to aid novel gene discovery.

RESULTS

In total, 16/50 (32%) families/cases were solved. One or more candidate variants of uncertain significance were detected in 18/50 (36%) families. No candidate variants were identified in 16/50 (32%) families. Two novel disease genes (TOP3B, PRKACB) and two novel genotype-phenotype correlations (NARS, and KMT2C genes) were identified. Three out of eight patients with suspected mosaic tuberous sclerosis complex had their diagnosis confirmed which provided reproductive options for two patients. The utility of confirming diagnoses for patients with mosaic conditions (using high read depth sequencing and ddPCR) was not specifically envisaged at the onset of the project, but the flexibility to offer recruitment and analyses on an as-needed basis proved to be a strength of the AHA-UDP.

CONCLUSION

AHA-UDP demonstrates the utility of a UDP approach applying genome sequencing approaches in diagnosing adults with rare diseases who have had uninformative conventional genetic analysis, informing clinical management, recurrence risk, and recommendations for relatives.

The Combination of Zhuli Decoction and N-butylphthalide Inhibits Cell Apoptosis Induced by CO Poisoning through the PI3K/AKT/GSK-3β Signaling Pathway

Carbon monoxide poisoning (COP) represents a significant global health burden, characterized by its morbidity and high mortality rates. The pathogenesis of COP-induced brain injury is complex, and effective treatment modalities are currently lacking. In this study, we employed network pharmacology to identify therapeutic targets and associated signaling pathways of Zhuli Decoction (ZLD) for COP. Subsequently, we conducted both in vitro and in vivo experiments to validate the therapeutic efficacy of ZLD in combination with N-butylphthalide (NBP) for acute COP-induced injury. Our network pharmacology analysis revealed that the primary components of ZLD exerted therapeutic effects through the modulation of multiple targets and pathways. The in vitro and in vivo experiments demonstrated that the combination of NBP and ZLD effectively inhibited apoptosis and up-regulated the activities of P-PI3K (Tyr458), P-AKT (Ser473), P-GSK-3β (Ser9), and Bcl-2, thus leading to the protection of neuronal cells and improvement in cognitive function in rats following COP, which was better than the effects observed with NBP or ZLD alone. The rescue experiment further showed that LY294002, a PI3K inhibitor, significantly attenuated the therapeutic efficacy of NBP + ZLD. The neuroprotection effects of NBP and ZLD against COP-induced brain injury are closely linked to the activation of the PI3K/AKT/GSK-3β signaling pathway.

In-silico functional analyses identify TMPRSS15-mediated intestinal absorption of lithium as a modulator of lithium response in bipolar disorder

BACKGROUND

The therapeutic response to lithium in patients with bipolar disorder is highly variable and has a polygenic basis. Genome-wide association studies investigating lithium response have identified several relevant loci, though the precise mechanisms driving these associations are poorly understood. We aimed to prioritise the most likely effector gene and determine the mechanisms underlying an intergenic lithium response locus on chromosome 21 identified by the International Consortium on Lithium Genetics (ConLi+Gen).

METHODS

We conducted in-silico functional analyses by integrating and synthesising information from several publicly available functional genetic datasets and databases including the Genotype-Tissue Expression (GTEx) project and HaploReg.

RESULTS

The findings from this study highlighted TMPRSS15 as the most likely effector gene at the ConLi+Gen lithium response locus. TMPRSS15 encodes enterokinase, a gastrointestinal enzyme responsible for converting trypsinogen into trypsin and thus aiding digestion. Convergent findings from gene-based lookups in human and mouse databases as well as co-expression network analyses of small intestinal RNA-seq data (GTEx) implicated TMPRSS15 in the regulation of intestinal nutrient absorption, including ions like sodium and potassium, which may extend to lithium.

LIMITATIONS

Although the findings from this study indicated that TMPRSS15 was the most likely effector gene at the ConLi+Gen lithium response locus, the evidence was circumstantial. Thus, the conclusions from this study need to be validated in appropriately designed wet-lab studies.

CONCLUSIONS

The findings from this study are consistent with a model whereby TMPRSS15 impacts the efficacy of lithium treatment in patients with bipolar disorder by modulating intestinal lithium absorption.

ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification

The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification.

Beyond the Human Genome Project: The Age of Complete Human Genome Sequences and Pangenome References

The Human Genome Project was an enormous accomplishment, providing a foundation for countless explorations into the genetics and genomics of the human species. Yet for many years, the human genome reference sequence remained incomplete and lacked representation of human genetic diversity. Recently, two major advances have emerged to address these shortcomings: complete gap-free human genome sequences, such as the one developed by the Telomere-to-Telomere Consortium, and high-quality pangenomes, such as the one developed by the Human Pangenome Reference Consortium. Facilitated by advances in long-read DNA sequencing and genome assembly algorithms, complete human genome sequences resolve regions that have been historically difficult to sequence, including centromeres, telomeres, and segmental duplications. In parallel, pangenomes capture the extensive genetic diversity across populations worldwide. Together, these advances usher in a new era of genomics research, enhancing the accuracy of genomic analysis, paving the path for precision medicine, and contributing to deeper insights into human biology.

Intragenic MFSD8 duplication and histopathological findings in a rabbit with neuronal ceroid lipofuscinosis

Neuronal ceroid lipofuscinoses (NCL) are among the most prevalent neurodegenerative disorders of early life in humans. Disease-causing variants have been described for 13 different NCL genes. In this study, a refined pathological characterization of a female rabbit with progressive neurological signs reminiscent of NCL was performed. Cytoplasmic pigment present in neurons was weakly positive with Sudan black B and autofluorescent. Immunohistology revealed astrogliosis, microgliosis and axonal degeneration. During the subsequent genetic investigation, the genome of the affected rabbit was sequenced and examined for private variants in NCL candidate genes. The analysis revealed a homozygous ~10.7 kb genomic duplication on chromosome 15 comprising parts of the MFSD8 gene, NC_013683.1:g.103,727,963_103,738,667dup. The duplication harbors two internal protein coding exons and is predicted to introduce a premature stop codon into the transcript, truncating ~50% of the wild-type MFSD8 open reading frame encoding the major facilitator superfamily domain containing protein 8, XP_002717309.2:p.(Glu235Leufs*23). Biallelic loss-of-function variants in MFSD8 have been described to cause NCL7 in human patients, dogs and a single cat. The available clinical and pathological data, together with current knowledge about MFSD8 variants and their functional impact in other species, point to the MFSD8 duplication as a likely causative defect for the observed phenotype in the affected rabbit.

Bayesian estimation of gene constraint from an evolutionary model with gene features

Measures of selective constraint on genes have been used for many applications, including clinical interpretation of rare coding variants, disease gene discovery and studies of genome evolution. However, widely used metrics are severely underpowered at detecting constraints for the shortest ~25% of genes, potentially causing important pathogenic mutations to be overlooked. Here we developed a framework combining a population genetics model with machine learning on gene features to enable accurate inference of an interpretable constraint metric, shet. Our estimates outperform existing metrics for prioritizing genes important for cell essentiality, human disease and other phenotypes, especially for short genes. Our estimates of selective constraint should have wide utility for characterizing genes relevant to human disease. Finally, our inference framework, GeneBayes, provides a flexible platform that can improve the estimation of many gene-level properties, such as rare variant burden or gene expression differences.

Structural variant calling and clinical interpretation in 6224 unsolved rare disease exomes

Structural variants (SVs), including large deletions, duplications, inversions, translocations, and more complex events have the potential to disrupt gene function resulting in rare disease. Nevertheless, current pipelines and clinical decision support systems for exome sequencing (ES) tend to focus on small alterations such as single nucleotide variants (SNVs) and insertions-deletions shorter than 50 base pairs (indels). Additionally, detection and interpretation of large copy-number variants (CNVs) are frequently performed. However, detection of other types of SVs in ES data is hampered by the difficulty of identifying breakpoints in off-target (intergenic or intronic) regions, which makes robust identification of SVs challenging. In this paper, we demonstrate the utility of SV calling in ES resulting in a diagnostic yield of 0.4% (23 out of 5825 probands) for a large cohort of unsolved patients collected by the Solve-RD consortium. Remarkably, 8 out of 23 pathogenic SV were not found by comprehensive read-depth-based CNV analysis, resulting in a 0.13% increased diagnostic value.

Exploring putative drug properties associated with TNF-alpha inhibition and identification of potential targets in cardiovascular disease using machine learning-assisted QSAR modeling and virtual reverse pharmacology approach

Cardiovascular disease is a chronic inflammatory disease with high mortality rates. TNF-alpha is pro-inflammatory and associated with the disease, but current medications have adverse effects. Therefore, efficient inhibitors are urgently needed as alternatives. This study represents a structural-activity relationship investigation of TNF-alpha, curated from the ChEMBL database. Exploratory data analysis was performed to visualize the physicochemical properties of different bioactivity groups. The extracted molecules were subjected to PubChem and SubStructure fingerprints, and a QSAR-based Random Forest (QSAR-RF) model was generated using the WEKA tool. The QSAR random Forest model was built based on the SubStructure fingerprint with a correlation coefficient of 0.992 and 0.716 as the respective tenfold cross-validation scores. The variance important plot (VIP) method was used to extract the important features for TNF-alpha inhibition. The Substructure-based QSAR-RF (SS-QSAR-RF) model was validated using molecules from PubChem and ZINC databases. The generated model also predicts the pIC50 value of the molecules selected from the docking study followed by molecular dynamic simulation with the time step of 100 ns. Through virtual reverse pharmacology, we determined the main drug targets from the top four hit compounds obtained via molecular docking study. Our analysis included an integrated bioinformatics approach to pinpoint crucial targets like EGRF, HSP900A1, STAT3, PSEN1, AKT1, and MDM2. Further, GO and KEGG pathways analysis identified relevant cardiovascular disease-related pathways for the hub gene involved. However, this study provides valuable insights, it is important to note that it lacks experimental application. Future research may benefit from conducting in-vitro and in-vivo studies.

Screening study on significant Chinese herb for anti-idiopathic pulmonary fibrosis by combining clinical experience prescriptions and molecular dynamics simulation technologies

Various techniques such as data mining, network pharmacology, molecular docking and molecular dynamics simulation were used in this study to screen and validate effective herbal medicines for the treatment of idiopathic pulmonary fibrosis (IPF) and to reveal their mechanisms of action at the molecular level. The use of this approach will provide new tools and ideas for future drug screening, especially for the application of herbal medicines in the treatment of complex diseases. Among them, the five identified core targets, including IL6, TP53, AKT1, VEGFA, and TNF, as well as a series of major active compounds, will be important references for future anti-IPF drug development. This information will accelerate the discovery and development of relevant drugs. Meanwhile, this study further confirmed the potential value of four Chinese herbal medicines, including Gancao, Danshen, Huangqin, and Sanqi, in the treatment of IPF. This will promote more clinical trials and practices to confirm and optimise the application of these herbs. Finally, this study is an important theoretical guide to enhance the advantages of Chinese herbal medicines in the prevention and treatment of major and difficult diseases, as well as to understand and utilise the potential efficacy of Chinese herbal medicines. This will further promote the scientific research and clinical application of herbal medicines and provide more possibilities for future disease treatmentCommunicated by Ramaswamy H. Sarma.

Identification of renal protective gut microbiome derived-metabolites in diabetic chronic kidney disease: An integrated approach using network pharmacology and molecular docking

Metabolites from the gut microbiota define molecules in the gut-kidney cross talks. However, the mechanistic pathway by which the kidneys actively sense gut metabolites and their impact on diabetic chronic kidney disease (DCKD) remains unclear. This study is an attempt to investigate the gut microbiome metabolites, their host targeting genes, and their mechanistic action against DCKD. Gut microbiome, metabolites, and host targets were extracted from the gutMgene database and metabolites from the PubChem database. DCKD targets were identified from DisGeNET, GeneCard, NCBI, and OMIM databases. Computational examination such as protein-protein interaction networks, enrichment pathway, identification of metabolites for potential targets using molecular docking, hubgene-microbes-metabolite-samplesource-substrate (HMMSS) network architecture were executed using Network analyst, ShinyGo, GeneMania, Cytoscape, Autodock tools. There were 574 microbial metabolites, 2861 DCKD targets, and 222 microbes targeting host genes. After screening, we obtained 27 final targets, which are used for computational examination. From enrichment analysis, we found NF-ΚB1, AKT1, EGFR, JUN, and RELA as the main regulators in the DCKD development through mitogen activated protein kinase (MAPK) pathway signalling. The (HMMSS) network analysis found F.prausnitzi, B.adolescentis, and B.distasonis probiotic bacteria that are found in the intestinal epithelium, colonic region, metabolize the substrates like tryptophan, other unknown substrates might have direct interaction with the NF-kB1 and epidermal growth factor receptor (EGFR) targets. On docking of these target proteins with 3- Indole propionic acid (IPA) showed high binding energy affinity of -5.9 kcal/mol and -7.4kcal/mol. From this study we identified, the 3 IPA produced by F. prausnitzi A2-165 was found to have renal sensing properties inhibiting MAPK/NF-KB1 inflammatory pathway and would be useful in treating CKD in diabetics.

Integrated network pharmacology and experimental verification to reveal the role of Shezhi Huangling Decoction against glioma by inactivating PI3K/Akt-HIF1A axis

Shezhi Huangling Decoction (SHD) has been proven clinically effective in regulating metabolic and immune homeostasis in the treatment of glioma. The investigation aimed to deconstruct the active constituents and mechanisms of SHD. Effects of SHD on malignant characteristics of HS683 and KNS89 cells have been investigated by CCK-8, clone formation, flow cytometry, and Transwell assays. A mouse xenograft model was established to assess the effect of SHD or SHD + temozolomide (TMZ) in vivo. A total of 461 constituents were found from SHD in UPLC/Q-TOF-MS/MS analysis. Functional enrichment analysis showed that pathway in cancer, proteoglycans in cancer, regulation of epithelial cell proliferation, inflammation/immune, gliogenesis, brain development, cell adhesion, and autophagy could participate in the treatment of SHD. Additionally, 9 hub genes (AKT1, TP53, CTNNB1, STAT3, EGFR, VEGFA, PIK3CA, ERBB2, and HIF1A) were identified as hub genes. Moreover, we found that SHD may greatly reduce the migration and accelerate apoptosis of HS683 and KNS89 cells. Additionally, SHD coordinates TMZ to restrict tumor growth were found in the mice. Our results suggest that the malignant behaviors of glioma cells are suppressed by SHD and the mechanism may be closing on the inhibition of the PI3K/Akt-HIF1A axis. SHD may serve as a synergistic therapeutic choice for TMZ to suppress glioblastoma growth.

A network-based trans-omics approach for predicting synergistic drug combinations

BACKGROUND

Combination therapy can offer greater efficacy on medical treatments. However, the discovery of synergistic drug combinations is challenging. We propose a novel computational method, SyndrumNET, to predict synergistic drug combinations by network propagation with trans-omics analyses.

METHODS

The prediction is based on the topological relationship, network-based proximity, and transcriptional correlation between diseases and drugs. SyndrumNET was applied to analyzing six diseases including asthma, diabetes, hypertension, colorectal cancer, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML).

RESULTS

Here we show that SyndrumNET outperforms the previous methods in terms of high accuracy. We perform in vitro cell survival assays to validate our prediction for CML. Of the top 17 predicted drug pairs, 14 drug pairs successfully exhibits synergistic anticancer effects. Our mode-of-action analysis also reveals that the drug synergy of the top predicted combination of capsaicin and mitoxantrone is due to the complementary regulation of 12 pathways, including the Rap1 signaling pathway.

CONCLUSIONS

The proposed method is expected to be useful for discovering synergistic drug combinations for various complex diseases.

Deciphering the Anticancer Arsenal of Piper longum: Network Pharmacology and Molecular Docking Unveil Phytochemical Targets Against Lung Cancer

Introduction: Lung cancer, characterized by uncontrolled cellular proliferation within the lung tissues, is the predominant cause of cancer-related fatalities worldwide. The traditional medicinal herb Piper longum has emerged as a significant contender in oncological research because of its documented anticancer attributes, suggesting its potential for novel therapeutic development. Methods: This study adopted network pharmacology and omics methodology to elucidate the anti-lung cancer potential of P. longum by identifying its bioactive constituents and their corresponding molecular targets. Results: Through a comprehensive literature review and the Integrated Medicinal Plant Phytochemistry and Therapeutics database (IMPPAT), we identified 33 bioactive molecules from P. longum. Subsequent analyses employing tools such as SwissTargetPrediction, SuperPred, and DIGEP-Pred facilitated the isolation of 676 potential targets, among which 72 intersected with 666 lung cancer-associated genetic markers identified through databases including the Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man (OMIM), and GeneCards. Further validation through protein-protein interaction (PPI) networks, gene ontology, pathway analyses, boxplots, and overall survival metrics underscored the therapeutic potential of compounds such as 7-epi-eudesm-4(15)-ene-1β, demethoxypiplartine, methyl 3,4,5-trimethoxycinnamate, 6-alpha-diol, and aristolodione. Notably, our findings reaffirm the relevance of lung cancer genes, such as CTNNB1, STAT3, HIF1A, HSP90AA1, and ERBB2, integral to various cellular processes and pivotal in cancer genesis and advancement. Molecular docking assessments revealed pronounced affinity between 6-alpha-diol and HIF1A, underscoring their potential as therapeutic agents for lung cancer. Conclusion: This study not only highlights the bioactive compounds of P. longum but also reinforces the molecular underpinnings of its anticancer mechanism, paving the way for future lung cancer therapeutics.

YinChen WuLing powder attenuates non-alcoholic steatohepatitis through the inhibition of the SHP2/PI3K/NLRP3 pathway

Background

YinChen WuLing Powder (YCWLP) has been recommended by consensus for the treatment of non-alcoholic steatohepatitis (NASH); nevertheless, its specific pharmacological mechanisms remain to be elucidated. This study aims to dissect the mechanisms underlying the therapeutic effects of YCWLP on NASH using a hybrid approach that encompasses network pharmacology, molecular docking, and in vitro experimental validation.

Methods

We compiled the chemical constituents of YCWLP from the Traditional Chinese Medicine System Pharmacological Database and Analysis Platform (TCMSP), while potential targets were predicted using the SwissTargetPrediction database. To identify NASH-related candidate targets, comprehensive retrieval was carried out using five authoritative databases. Protein-Protein Interaction (PPI) networks of direct targets of YCWLP in NASH treatment were then constructed using the String database, and functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, were conducted through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. Core targets were discerned using the Molecular Complex Detection (MCODE) and cytoHubba algorithms. Subsequently, molecular docking of key compounds to core targets was conducted using AutoDock software. Moreover, we established a free fatty acid-induced HepG2 cell model to simulate NASH in vitro, with YCWLP medicated serum intervention employed to corroborate the network pharmacology-derived hypotheses. Furthermore, a combination of enzyme-linked immunosorbent assay (ELISA), and Western blotting analyses was employed to investigate the lipid, hepatic enzyme, SHP2/PI3K/NLRP3 signaling pathway and associated cytokine levels.

Results

The network pharmacology analysis furnished a list of 54 compounds from YCWLP and 167 intersecting targets associated with NASH. Through analytic integration with multiple algorithms, PTPN11 (also known as SHP2) emerged as a core target of YCWLP in mitigating NASH. The in vitro experiments validated that 10% YCWLP medicated serum could remarkably attenuate levels of total cholesterol (TC, 1.25 vs. 3.32) and triglyceride (TG, 0.23 vs. 0.57) while ameliorating alanine aminotransferase (ALT, 7.79 vs. 14.78) and aspartate aminotransferase (AST, 4.64 vs. 8.68) leakage in NASH-afflicted cells. In addition, YCWLP significantly enhanced the phosphorylation of SHP2 (0.55 vs. 0.20) and downregulated the expression of molecules within the SHP2/PI3K/NLRP3 signaling axis, including p-PI3K (0.42 vs. 1.02), NLRP3 (0.47 vs. 0.93), along with downstream effectors-cleaved Caspase-1 (0.21 vs. 0.49), GSDMD-NT (0.24 vs. 0.71), mature interleukin-1β (IL-1β, 0.17 vs. 0.48), pro-IL-1β (0.49 vs. 0.89), mature interleukin-18 (IL-18, 0.15 vs. 0.36), and pro-IL-18 (0.48 vs. 0.95).

Conclusion

Our research reveals that YCWLP exerts therapeutic effects against NASH by inhibiting lipid accumulation and inflammation, which involves the attenuation of pyroptosis via the SHP2/PI3K/NLRP3 pathway.

Network pharmacology and experimental evidence: ERK/CREB/BDNF signaling pathway is involved in the antidepressive roles of Kaiyu Zhishen decoction

ETHNOPHARMACOLOGICAL RELEVANCE

Major Depressive Disorder (MDD) emerges as a complex psychosomatic condition, notable for its considerable suicidality and mortality rates. Increasing evidence suggests the efficacy of Chinese herbal medicine in mitigating depression symptoms and offsetting the adverse effects associated with conventional Western therapeutics. Notably, clinical trials have revealed the adjunctive antidepressant potential of Kaiyu Zhishen Decoction (KZD) alongside Western medication. However, the standalone antidepressant efficacy of KZD and its underlying mechanisms merit in-depth investigation.

AIM OF THE STUDY

This research aims to elucidate the impact of KZD on MDD and delineate its mechanistic pathways through integrated network pharmacological assessments and empirical in vitro and in vivo analyses.

MATERIALS AND METHODS

To ascertain the optimal antidepressant dosage and mechanism of KZD, a Chronic Unpredictable Mild Stress (CUMS)-induced depression model in mice was established to evaluate depressive behaviors. High-Performance Liquid Chromatography (HPLC) and network pharmacological approaches were employed to predict KZD's antidepressant mechanisms. Subsequently, hippocampal samples were subjected to 4D-DIA proteomic sequencing and validated through Western blot, immunofluorescence, Nissl staining, and pathway antagonist applications. Additionally, cortisol-stimulated PC12 cells were utilized to simulate neuronal damage, analyzing protein and mRNA levels of MAPK-related signals and cell proliferation markers.

RESULTS

The integration of network pharmacology and HPLC identified kaempferol and quercetin as KZD's principal active compounds for MDD treatment. Proteomic and network pharmacological KEGG pathway analyses indicated the MAPK signaling pathway as a critical regulatory mechanism for KZD's therapeutic effect on MDD. KZD was observed to mitigate CUMS-induced upregulation of p-ERK/ERK, CREB, and BDNF protein expressions in hippocampal cells by attenuating oxidative stress, thereby ameliorating neuronal damage and exerting antidepressant effects. The administration of PD98059 counteracted KZD's improvements in depression-like behaviors and downregulated p-ERK/ERK and BDNF protein expressions in the hippocampus.

CONCLUSIONS

This investigation corroborates KZD's pivotal, dose-dependent role in antidepressant activity. Both in vivo and in vitro experiments demonstrate KZD's capacity to modulate the ERK-CREB-BDNF signaling pathway by diminishing ROS expression induced by oxidative stress, enhancing neuronal repair, and thus, manifesting antidepressant properties. Accordingly, KZD represents a promising herbal candidate for further antidepressant research.

The active components of Erzhi wan and their anti-Alzheimer's disease mechanisms determined by an integrative approach of network pharmacology, bioinformatics, molecular docking, and molecular dynamics simulation

Erzhi Wan (EZW), a classic Traditional Chinese Medicine formula, has shown promise as a potential therapeutic option for Alzheimer's disease (AD), yet its mechanism remains elusive. Herein, we employed an integrative in-silico approach to investigate the active components and their mechanisms against AD. We screened four active components with blood-brain barrier permeabilities from TCMSP, along with 307 corresponding targets predicted by SwissTargetPrediction, PharmMapper, and TCMbank websites. Then, we retrieved 2260 AD-related targets from Genecards, OMIM, and NCBI databases. Furthermore, we constructed the protein-protein interaction (PPI) network of the intersected targets via the STRING database and performed the GO and KEGG enrichment analyses using the "clusterProfiler" R package. The results showed that the intersected targets were intimately related to the p53/PI3K/Akt signaling pathway, serotonergic synapse, and response to oxygen level. Subsequently, 25 core targets were found differentially expressed in brain regions by bioinformatics analyses of GEO datasets of clinical samples from the Alzdata database. The binding sites and stabilities between the active components and the core targets were investigated by the molecular docking approach using Autodock 4.2.6 software, followed by pocket detection and druggability assessment via the DoGSiteScorer server. The results showed that acacetin, β-sitosterol, and 3-O-acetyldammarenediol-II strongly interacted with the druggable pockets of AR, CASP8, POLB, and PREP. Eventually, the docking results were further cross-referenced with the literature research and validated by 100 ns of molecular dynamics simulations using GROMACS software. Binding free energies were calculated via MM/PBSA strategy combined with interaction entropy. The simulation results indicated stable bindings between four docking pairs including acacetin-AR, acacetin-CASP8, β-sitosterol-POLB, and 3-O-acetyldammarenediol-II-PREP. Overall, our study demonstrated a theoretical basis for how three active components of EZW confer efficacy against AD. It provides a promising reference for subsequent research regarding drug discoveries and clinical applications.

Explore the active ingredients and potential mechanism of action on Actinidia arguta leaves against T2DM by integration of serum pharmacochemistry and network pharmacology

BACKGROUND

Actinidia arguta leaves (AAL) are traditionally consumed as a vegetable and as tea in folk China and Korea. Previous studies have reported the anti-diabetic effect of AAL, but its bioactive components and mechanism of action are still unclear.

AIM OF THE STUDY

This study aims to identify the hypoglycemic active components of AAL by combining serum pharmacochemistry and network pharmacology and to elucidate its possible mechanism of action.

METHODS

Firstly, the effective components in mice serum samples were characterized by UPLC-Q/TOF-MSE. Furthermore, based on these active ingredients, network pharmacology analysis was performed to establish an "H-C-T-P-D" interaction network and reveal possible biological mechanisms. Finally, the affinity between serum AAL components and the main proteins in the important pathways above was investigated through molecular docking analysis.

RESULTS

Serum pharmacochemistry analysis showed that 69 compounds in the serum samples were identified, including 23 prototypes and 46 metabolites. The metabolic reactions mainly included deglycosylation, dehydration, hydrogenation, methylation, acetylation, glucuronidation, and sulfation. Network pharmacology analysis showed that the key components quercetin, pinoresinol diglucoside, and 5-O-trans-p-coumaroyl quinic acid butyl ester mainly acted on the core targets PTGS2, HRAS, RELA, PRKCA, and BCL2 targets and through the PI3K-Akt signaling pathway, endocrine resistance, and MAPK signaling pathway to exert a hypoglycemic effect. Likewise, molecular docking results showed that the three potential active ingredients had good binding effects on the five key targets.

CONCLUSION

This study provides a basis for elucidating the pharmacodynamic substance basis of AA against T2DM and further exploring the mechanism of action.

Network pharmacology and molecular dynamics study of the effect of the Astragalus-Coptis drug pair on diabetic kidney disease

BACKGROUND

Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease. The Astragalus-Coptis drug pair is frequently employed in the management of DKD. However, the precise molecular mechanism underlying its therapeutic effect remains elusive.

AIM

To investigate the synergistic effects of multiple active ingredients in the Astragalus-Coptis drug pair on DKD through multiple targets and pathways.

METHODS

The ingredients of the Astragalus-Coptis drug pair were collected and screened using the TCMSP database and the SwissADME platform. The targets were predicted using the SwissTargetPrediction database, while the DKD differential gene expression analysis was obtained from the Gene Expression Omnibus database. DKD targets were acquired from the GeneCards, Online Mendelian Inheritance in Man database, and DisGeNET databases, with common targets identified through the Venny platform. The protein-protein interaction network and the "disease-active ingredient-target" network of the common targets were constructed utilizing the STRING database and Cytoscape software, followed by the analysis of the interaction relationships and further screening of key targets and core active ingredients. Gene Ontology (GO) function and Kyoto Ency-clopedia of Genes and Genomes (KEGG) pathway enrichments were performed using the DAVID database. The tissue and organ distributions of key targets were evaluated. PyMOL and AutoDock software validate the molecular docking between the core ingredients and key targets. Finally, molecular dynamics (MD) simulations were conducted to simulate the optimal complex formed by interactions between core ingredients and key target proteins.

RESULTS

A total of 27 active ingredients and 512 potential targets of the Astragalus-Coptis drug pair were identified. There were 273 common targets between DKD and the Astragalus-Coptis drug pair. Through protein-protein interaction network topology analysis, we identified 9 core active ingredients and 10 key targets. GO and KEGG pathway enrichment analyses revealed that Astragalus-Coptis drug pair treatment for DKD involves various biological processes, including protein phosphorylation, negative regulation of apoptosis, inflammatory response, and endoplasmic reticulum unfolded protein response. These pathways are mainly associated with the advanced glycation end products (AGE)-receptor for AGE products signaling pathway in diabetic complications, as well as the Lipid and atherosclerosis. Molecular docking and MD simulations demonstrated high affinity and stability between the core active ingredients and key targets. Notably, the quercetin-AKT serine/threonine kinase 1 (AKT1) and quercetin-tumor necrosis factor (TNF) protein complexes exhibited exceptional stability.

CONCLUSION

This study demonstrated that DKD treatment with the Astragalus-Coptis drug pair involves multiple ingredients, targets, and signaling pathways. We propose a novel approach for investigating the molecular mechanism underlying the therapeutic effects of the Astragalus-Coptis drug pair on DKD. Furthermore, we suggest that quercetin is the most potent active ingredient and specifically targets AKT1 and TNF, providing a theoretical foundation for further exploration of pharmacologically active ingredients and elucidating their molecular mechanisms in DKD treatment.

Mechanisms underlying the therapeutic effects of Gang Huo Qing wen granules in the treatment of influenza based on network pharmacology, molecular docking and molecular dynamics

Influenza (Flu) is a severe health, medical, and economic problem, but no medication that has excellent outcomes and lowers the occurrence of these problems is now available. GanghuoQingwenGranules (GHQWG) is a common Chinese herbal formula for the treatment of influenza (flu). However, its methods of action remain unknown. We used network pharmacology, molecular docking, and molecular dynamics simulation techniques to investigate the pharmacological mechanism of GHQWG in flu. TCMSP and various types of literature were used to obtain active molecules and targets of GHQWG. Flu-related targets were found in the Online Mendelian Inheritance in Man (OMIM) database, the DisFeNET database, the Therapeutic Target Database (TTD), and the DrugBank database. To screen the key targets, a protein-protein interaction (PPI) network was constructed. DAVID was used to analyze GO and KEGG pathway enrichment. Target tissue and organ distribution was assessed. Molecular docking was used to evaluate interactions between possible targets and active molecules. For the ideal core protein-compound complexes obtained using molecular docking, a molecular dynamics simulation was performed. In total, 90 active molecules and 312 GHQWG targets were discovered. The PPI network's topology highlighted six key targets. GHQWG's effects are mediated via genes involved in inflammation, apoptosis, and oxidative stress, as well as the TNF and IL-17 signaling pathways, according to GO and KEGG pathway enrichment analysis. Molecular docking and molecular dynamics simulations demonstrated that the active compounds and tested targets had strong binding capabilities. This analysis accurately predicts the effective components, possible targets, and pathways involved in GHQWG flu treatment. We proposed a novel study strategy for future studies on the molecular processes of GHQWG in flu treatment. Furthermore, the possible active components provide a dependable source for flu drug screening.

An experimentally validated approach to automated biological evidence generation in drug discovery using knowledge graphs

Explaining predictions for drug repositioning with biological knowledge graphs is a challenging problem. Graph completion methods using symbolic reasoning predict drug treatments and associated rules to generate evidence representing the therapeutic basis of the drug. Yet the vast amounts of generated paths that are biologically irrelevant or not mechanistically meaningful within the context of disease biology can limit utility. We use a reinforcement learning based knowledge graph completion model combined with an automatic filtering approach that produces the most relevant rules and biological paths explaining the predicted drug's therapeutic connection to the disease. In this work we validate the approach against preclinical experimental data for Fragile X syndrome demonstrating strong correlation between automatically extracted paths and experimentally derived transcriptional changes of selected genes and pathways of drug predictions Sulindac and Ibudilast. Additionally, we show it reduces the number of generated paths in two case studies, 85% for Cystic fibrosis and 95% for Parkinson's disease.

Unveiling novel genetic variants in 370 challenging medically relevant genes using the long read sequencing data of 41 samples from 19 global populations

BACKGROUND

A large number of challenging medically relevant genes (CMRGs) are situated in complex or highly repetitive regions of the human genome, hindering comprehensive characterization of genetic variants using next-generation sequencing technologies. In this study, we employed long-read sequencing technology, extensively utilized in studying complex genomic regions, to characterize genetic alterations, including short variants (single nucleotide variants and short insertions and deletions) and copy number variations, in 370 CMRGs across 41 individuals from 19 global populations.

RESULTS

Our analysis revealed high levels of genetic variants in CMRGs, with 68.73% exhibiting copy number variations and 65.20% containing short variants that may disrupt protein function across individuals. Such variants can influence pharmacogenomics, genetic disease susceptibility, and other clinical outcomes. We observed significant differences in CMRG variation across populations, with individuals of African ancestry harboring the highest number of copy number variants and short variants compared to samples from other continents. Notably, 15.79% to 33.96% of short variants were exclusively detectable through long-read sequencing. While the T2T-CHM13 reference genome significantly improved the assembly of CMRG regions, thereby facilitating variant detection in these regions, some regions still lacked resolution.

CONCLUSION

Our results provide an important reference for future clinical and pharmacogenetic studies, highlighting the need for a comprehensive representation of global genetic diversity in the reference genome and improved variant calling techniques to fully resolve medically relevant genes.

Yixin-Fumai granules modulate autophagy through the PI3K/AKT/FOXO pathway and lead to amelioration of aging mice with sick sinus syndrome

OBJECTIVE

By employing network pharmacology alongside molecular docking techniques, we can delve into the intricate workings of Yixin-Fumai granules (YXFMs) and their impact on sick sinus syndrome (SSS) within wrinkles mice. Specifically, we aim to understand how YXFMs enhance autophagy through the PI3K/AKT/FOXO path.

METHODS

The active ingredients and medicinal uses of Ginseng, ligusticum wallichii, Ophiopogon, Schisandra, salvia, and astragalus were compiled using the BATMAN-TCM database. We also used Genecards, OMIM, and Disgenet files to identify the disease goals. A hierarchical diagram of "disease-drug-key targets" was generated using the Cytoscape programs. In addition, we established a target protein interaction (PPI) network using the STRING database. Then, the Cluster Profiler R package was used to conduct GO functional enrichment evaluation and KEGG pathway enrichment analyses of the targets. Based on the PPI system, we chose the top communicating targets and substances over molecular docking. In vivo studies were performed to validate these selections further. The mouse model was induced to study the damaged sinoatrial node (SAN) in mice with lower heart rates due to age-related changes. Electrocardiogram and Masson staining assessments were performed to obtain the results. The transmission electron microscope was used to assess the autophagy level of SAN cells. Western blot was employed to analyze the impact of YXFMs on protein expression in the PI3K/AKT/FOXO signaling process throughout SSS therapy in aging mice.

RESULTS

One hundred forty-two active ingredients, 1858 targets, 1226 disease targets, and 266 intersection targets were obtained. The key targets of the PPI network encompassed TP53, AKT1, CTNNB1, INS, and TNF, among others. According to GO functional analysis, the mechanism underlying YXFMs in SSS treatment may primarily be associated with the control of ion transport across membranes, cardiac contraction, regulation of blood circulation, and other biological processes. Based on the results of KEGG pathway enrichment analysis, it was determined that they were mainly enriched in multiple pathways of signaling such as the PI3K-Akt signaling route, MAPK signaling process, AGE-RAGE signaling path, FOXO signaling path, HIF-1 signaling process, and several other paths. Molecular docking demonstrated that five compounds had excellent binding to the key candidate target proteins AKT1 and INS. Through the in vivo studies, we noticed notable effects when administering YXFMs. These effects included the suppression of aging-induced SSS, a decrease in the R-R interval, a rise in heart rate, a reduction in fibrosis, a boost in the autophagy process level, and a spike in the levels of expression of key protein molecules in the PI3K/AKT/FOXO signaling path.

CONCLUSION

This research has made preliminary predictions about the potential of YXFMs in treating SSS. It suggests that YXFMs may have the ability to target key proteins and critical paths associated with the condition. Further testing has been conducted to discover new findings and evidence of ideas for tackling SSS triggered by aging.

Alzheimer's Disease Knowledge Graph Enhances Knowledge Discovery and Disease Prediction

Background

Alzheimer's disease (AD), a progressive neurodegenerative disorder, continues to increase in prevalence without any effective treatments to date. In this context, knowledge graphs (KGs) have emerged as a pivotal tool in biomedical research, offering new perspectives on drug repurposing and biomarker discovery by analyzing intricate network structures. Our study seeks to build an AD-specific knowledge graph, highlighting interactions among AD, genes, variants, chemicals, drugs, and other diseases. The goal is to shed light on existing treatments, potential targets, and diagnostic methods for AD, thereby aiding in drug repurposing and the identification of biomarkers.

Results

We annotated 800 PubMed abstracts and leveraged GPT-4 for text augmentation to enrich our training data for named entity recognition (NER) and relation classification. A comprehensive data mining model, integrating NER and relationship classification, was trained on the annotated corpus. This model was subsequently applied to extract relation triplets from unannotated abstracts. To enhance entity linking, we utilized a suite of reference biomedical databases and refine the linking accuracy through abbreviation resolution. As a result, we successfully identified 3,199,276 entity mentions and 633,733 triplets, elucidating connections between 5,000 unique entities. These connections were pivotal in constructing a comprehensive Alzheimer's Disease Knowledge Graph (ADKG). We also integrated the ADKG constructed after entity linking with other biomedical databases. The ADKG served as a training ground for Knowledge Graph Embedding models with the high-ranking predicted triplets supported by evidence, underscoring the utility of ADKG in generating testable scientific hypotheses. Further application of ADKG in predictive modeling using the UK Biobank data revealed models based on ADKG outperforming others, as evidenced by higher values in the areas under the receiver operating characteristic (ROC) curves.

Conclusion

The ADKG is a valuable resource for generating hypotheses and enhancing predictive models, highlighting its potential to advance AD's disease research and treatment strategies.

Network Pharmacology Reveals Curcuma aeruginosa Roxb. Regulates MAPK and HIF-1 Pathways to Treat Androgenetic Alopecia

Androgenetic alopecia (AGA) is the most prevalent hair loss disorder worldwide, driven by excessive sensitivity or response to androgen. Herbal extracts, such as Curcuma aeruginosa Roxb., have shown promise in AGA treatment due to their anti-androgenic activities and hair growth effects. However, the precise mechanism of action remains unclear. Hence, this study aims to elucidate the active compounds, putative targets, and underlying mechanisms of C. aeruginosa for the therapy of AGA using network pharmacology and molecular docking. This study identified 66 bioactive compounds from C. aeruginosa, targeting 59 proteins associated with AGA. Eight hub genes were identified from the protein-protein interaction network, namely, CASP3, AKT1, AR, IL6, PPARG, STAT3, HIF1A, and MAPK3. Topological analysis of components-targets network revealed trans-verbenol, myrtenal, carvone, alpha-atlantone, and isoaromandendrene epoxide as the core components with potential significance in AGA treatment. The molecular docking verified the binding affinity between the hub genes and core compounds. Moreover, the enrichment analyses showed that C. aeruginosa is involved in hormone response and participates in HIF-1 and MAPK pathways to treat AGA. Overall, this study contributes to understanding the potential anti-AGA mechanism of C. aeruginosa by highlighting its multi-component interactions with several targets involved in AGA pathogenesis.

Ethanolic extract of Arctium lappa leaves alleviates cerebral ischemia reperfusion-induced inflammatory injury via HDAC9-mediated NF-κB pathway

BACKGROUND

Ischemic stroke (IS) is a major cause of mortality and disability worldwide. Inflammatory response is crucial in the pathogenesis of tissue injury in cerebral infarction. Arctium lappa leaves are traditionally used to treat IS.

PURPOSES

To investigate the neuroprotective effects and molecular mechanisms of the ethanolic extract of A. lappa leaves (ALLEE) on cerebral ischemia-reperfusion (CIR).

METHODS

Middle cerebral artery obstruction reperfusion (MCAO/R) rats and an oxygen-glucose deprivation/reoxygenation (OGD/R) cell model were used to evaluate ALLEE pharmacodynamics. Various methods, including neurological function, 2,3,5-triphenyltetrazolium chloride, hematoxylin and eosin, and Nissl, enzyme-linked immunosorbent, and TdT-mediated dUTP nick-end labeling assays, were used to analyze the neuroprotective effects of ALLEE in vitro and in vivo. The major chemical components and potential target genes of ALLEE were screened using network pharmacology. Molecular docking, western blotting, and immunofluorescence analyses were performed to confirm the effectiveness of the targets in related pathways.

RESULTS

ALLEE exerted potent effects on the MCAO/R model by decreasing the neurological scores, infarct volumes, and pathological features (p < 0.01). Furthermore, network pharmacology results revealed that the treatment of IS with ALLEE involved the regulation of various inflammatory pathways, such as the tumor necrosis factor (TNF) and chemokine signaling pathways. ALLEE also played key roles in targeting key molecules, including nuclear factor (NF)-κBIA, NF-κB1, interleukin (IL)-6, TNF-α and IL1β, and regulating the histone deacetylase (HDAC)-9-mediated signaling pathway. In vivo and in vitro analyses revealed that ALLEE significantly regulated the NF-κB pathway, promoted the phosphorylation activation of NF-κB P65, IκB and IKK (p < 0.01 or p < 0.05), and decreased the expression levels of the inflammatory factors, IL-1β, IL-6 and TNF-α (p < 0.01). Moreover, ALLEE significantly decreased the expression of HDAC9 (p < 0.01) that is associated with inflammatory responses. However, HDAC9 overexpression partially reversed the neuroprotective effects of ALLEE and its suppressive effects on inflammation and phosphorylation of NF-κB (p < 0.01).

CONCLUSIONS

In conclusion, our results revealed that ALLEE ameliorates MCAO/R-induced experimental CIR by modulating inflammatory responses via the inhibition of HDAC9-mediated NF-κB pathway.

Molecular Heterogeneity of Osteopetrosis in India: Report of 17 Novel Variants

Osteopetrosis is a clinically and genetically heterogeneous group of inherited bone disorders that is caused by defects in osteoclast formation or function. Treatment options vary with the disease severity and an accurate molecular diagnosis helps in prognostication and treatment decisions. We investigated the genetic causes of osteopetrosis in 31 unrelated patients of Indian origin. Screening for the genetic variants was done by Sanger sequencing or next generation sequencing in 48 samples that included 31 samples from index patients, 16 from parents' and 1 chorionic villus sample. A total of 30 variants, including 29 unique variants, were identified in 26 of the 31 patients in the study. TCIRG1 was the most involved gene (n = 14) followed by TNFRSF11A (n = 4) and CLCN7 (n = 3). A total of 17 novel variants were identified. Prenatal diagnosis was done in one family and the foetus showed homozygous c.807 + 2T > G variant in TCIRG1. Molecular diagnosis of osteopetrosis aids in therapeutic decisions including the need for a stem cell transplantation and gives a possible option of performing prenatal diagnosis in affected families. Further studies would help in understanding the genetic etiology in patients where no variants were identified.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12288-023-01732-4.

The complex etiology of autism spectrum disorder due to missense mutations of CHD8

CHD8 is an ATP-dependent chromatin-remodeling factor encoded by the most frequently mutated gene in individuals with autism spectrum disorder (ASD). Although many studies have examined the consequences of CHD8 haploinsufficiency in cells and mice, few have focused on missense mutations, the most common type of CHD8 alteration in ASD patients. We here characterized CHD8 missense mutations in ASD patients according to six prediction scores and experimentally examined the effects of such mutations on the biochemical activities of CHD8, neural differentiation of embryonic stem cells, and mouse behavior. Only mutations with high prediction scores gave rise to ASD-like phenotypes in mice, suggesting that not all CHD8 missense mutations detected in ASD patients are directly responsible for the development of ASD. Furthermore, we found that mutations with high scores cause ASD by mechanisms either dependent on or independent of loss of chromatin-remodeling function. Our results thus provide insight into the molecular underpinnings of ASD pathogenesis caused by missense mutations of CHD8.

Toward structure-multiple activity relationships (SMARts) using computational approaches: A polypharmacological perspective

In the current era of biological big data, which are rapidly populating the biological chemical space, in silico polypharmacology drug design approaches help to decode structure-multiple activity relationships (SMARts). Current computational methods can predict or categorize multiple properties simultaneously, which aids the generation, identification, curation, prioritization, optimization, and repurposing of molecules. Computational methods have generated opportunities and challenges in medicinal chemistry, pharmacology, food chemistry, toxicology, bioinformatics, and chemoinformatics. It is anticipated that computer-guided SMARts could contribute to the full automatization of drug design and drug repurposing campaigns, facilitating the prediction of new biological targets, side and off-target effects, and drug-drug interactions.

The annotation of GBA1 has been concealed by its protein-coding pseudogene GBAP1

Mutations in GBA1 cause Gaucher disease and are the most important genetic risk factor for Parkinson's disease. However, analysis of transcription at this locus is complicated by its highly homologous pseudogene, GBAP1. We show that >50% of short RNA-sequencing reads mapping to GBA1 also map to GBAP1. Thus, we used long-read RNA sequencing in the human brain, which allowed us to accurately quantify expression from both GBA1 and GBAP1. We discovered significant differences in expression compared to short-read data and identify currently unannotated transcripts of both GBA1 and GBAP1. These included protein-coding transcripts from both genes that were translated in human brain, but without the known lysosomal function-yet accounting for almost a third of transcription. Analyzing brain-specific cell types using long-read and single-nucleus RNA sequencing revealed region-specific variations in transcript expression. Overall, these findings suggest nonlysosomal roles for GBA1 and GBAP1 with implications for our understanding of the role of GBA1 in health and disease.

A novel mutation in SORD gene associated with distal hereditary motor neuropathies

BACKGROUND

Distal hereditary motor neuropathy (dHMN) is a heterogeneous group of hereditary diseases caused by the gradual degeneration of the lower motor neuron. More than 30 genes associated with dHMN have been reported, while 70-80% of those with the condition are still unable to receive a genetic diagnosis.

METHODS

A 26-year-old man experiencing gradual weakness in his lower limbs was referred to our hospital, and data on clinical features, laboratory tests, and electrophysiological tests were collected. To identify the disease-causing mutation, we conducted whole exome sequencing (WES) and then validated it through Sanger sequencing for the proband and his parents. Silico analysis was performed to predict the pathogenesis of the identified mutations. A literature review of all reported mutations of the related gene for the disease was performed.

RESULTS

The patient presented with dHMN phenotype harboring a novel homozygous variant c.361G > C (p.Ala121Pro) in SORD, inherited from his parents, respectively. A121 is a highly conserved site and the mutation was categorized as "likely pathogenic" according to the criteria and guidelines of the American College of Medical Genetics and Genomics (ACMG). A total of 13 published articles including 101 patients reported 18 SORD variants. Almost all described cases have the homozygous deletion variant c.757delG (p.A253Qfs*27) or compound heterozygous state of a combination of c.757delG (p.A253Qfs*27) with another variant. The variant c.361G > C (p.Ala121Pro) detected in our patient was the second homozygous variant in SORD-associated hereditary neuropathy.

CONCLUSION

One novel homozygous variant c.361G > C (p.Ala121Pro) in SORD was identified in a Chinese patient with dHMN phenotype, which expands the mutation spectrum of SORD-associated hereditary neuropathy and underscores the significance of screening for SORD variants in patients with undiagnosed hereditary neuropathy patients.

Considerations for reporting variants in novel candidate genes identified during clinical genomic testing

Since the first novel gene discovery for a Mendelian condition was made via exome sequencing (ES), the rapid increase in the number of genes known to underlie Mendelian conditions coupled with the adoption of exome (and more recently, genome) sequencing by diagnostic testing labs has changed the landscape of genomic testing for rare disease. Specifically, many individuals suspected to have a Mendelian condition are now routinely offered clinical ES. This commonly results in a precise genetic diagnosis but frequently overlooks the identification of novel candidate genes. Such candidates are also less likely to be identified in the absence of large-scale gene discovery research programs. Accordingly, clinical laboratories have both the opportunity, and some might argue a responsibility, to contribute to novel gene discovery which should in turn increase the diagnostic yield for many conditions. However, clinical diagnostic laboratories must necessarily balance priorities for throughput, turnaround time, cost efficiency, clinician preferences, and regulatory constraints, and often do not have the infrastructure or resources to effectively participate in either clinical translational or basic genome science research efforts. For these and other reasons, many laboratories have historically refrained from broadly sharing potentially pathogenic variants in novel genes via networks like Matchmaker Exchange, much less reporting such results to ordering providers. Efforts to report such results are further complicated by a lack of guidelines for clinical reporting and interpretation of variants in novel candidate genes. Nevertheless, there are myriad benefits for many stakeholders, including patients/families, clinicians, researchers, if clinical laboratories systematically and routinely identify, share, and report novel candidate genes. To facilitate this change in practice, we developed criteria for triaging, sharing, and reporting novel candidate genes that are most likely to be promptly validated as underlying a Mendelian condition and translated to use in clinical settings.

Structure, regulation, and mechanisms of nonmuscle myosin-2

Members of the myosin superfamily of molecular motors are large mechanochemical ATPases that are implicated in an ever-expanding array of cellular functions. This review focuses on mammalian nonmuscle myosin-2 (NM2) paralogs, ubiquitous members of the myosin-2 family of filament-forming motors. Through the conversion of chemical energy into mechanical work, NM2 paralogs remodel and shape cells and tissues. This process is tightly controlled in time and space by numerous synergetic regulation mechanisms to meet cellular demands. We review how recent advances in structural biology together with elegant biophysical and cell biological approaches have contributed to our understanding of the shared and unique mechanisms of NM2 paralogs as they relate to their kinetics, regulation, assembly, and cellular function.

Myricitrin inhibited ferritinophagy-mediated ferroptosis in cisplatin-induced human renal tubular epithelial cell injury

Cisplatin-induced acute kidney injury (AKI) increases the patient mortality dramatically and results in an unfavorable prognosis. A strong correlation between AKI and ferroptosis, which is a notable type of programmed cell death, was found in recent studies. Myricitrin is a natural flavonoid compound with diverse pharmacological properties. To investigate the protective effect of myricitrin against cisplatin induced human tubular epithelium (HK-2) cell injury and the underlying anti-ferroptic mechanism by this study. Firstly, a pharmacology network analysis was proposed to explore the myricitrin's effect. HK-2 cells were employed for in vitro experiments. Ferroptosis was detected by cell viability, quantification of iron, malondialdehyde, glutathione, lipid peroxidation fluorescence, and glutathione peroxidase (GPX4) expression. Ferritinophagy was detected by related protein expression (NCOA4, FTH, LC3II/I, and SQSTM1). In our study, GO enrichment presented that myricitrin might be effective in eliminating ferroptosis. The phenomenon of ferroptosis regulated by ferritinophagy was observed in cisplatin-activated HK-2 cells. Meanwhile, pretreatment with myricitrin significantly rescued HK-2 cells from cell death, reduced iron overload and lipid peroxidation biomarkers, and improved GPX4 expression. In addition, myricitrin downregulated the expression of LC3II/LC3I and NCOA4 and elevated the expression of FTH and SQTM. Furthermore, myricitrin inhibited ROS production and preserved mitochondrial function with a lower percentage of green JC-1 monomers. However, the protection could be reserved by the inducer of ferritinophagy rapamycin. Mechanically, the Hub genes analysis reveals that AKT and NF-κB are indispensable mediators in the anti-ferroptic process. In conclusion, myricitrin ameliorates cisplatin induced HK-2 cells damage by attenuating ferritinophagy mediated ferroptosis.

Automated profiling of gene function during embryonic development

Systematic functional profiling of the gene set that directs embryonic development is an important challenge. To tackle this challenge, we used 4D imaging of C. elegans embryogenesis to capture the effects of 500 gene knockdowns and developed an automated approach to compare developmental phenotypes. The automated approach quantifies features-including germ layer cell numbers, tissue position, and tissue shape-to generate temporal curves whose parameterization yields numerical phenotypic signatures. In conjunction with a new similarity metric that operates across phenotypic space, these signatures enabled the generation of ranked lists of genes predicted to have similar functions, accessible in the PhenoBank web portal, for ∼25% of essential development genes. The approach identified new gene and pathway relationships in cell fate specification and morphogenesis and highlighted the utilization of specialized energy generation pathways during embryogenesis. Collectively, the effort establishes the foundation for comprehensive analysis of the gene set that builds a multicellular organism.

Exploring the Underlying Mechanisms of Qingxing Granules Treating H1N1 Influenza Based on Network Pharmacology and Experimental Validation

BACKGROUND

H1N1 is one of the major subtypes of influenza A virus (IAV) that causes seasonal influenza, posing a serious threat to human health. A traditional Chinese medicine combination called Qingxing granules (QX) is utilized clinically to treat epidemic influenza. However, its chemical components are complex, and the potential pharmacological mechanisms are still unknown.

METHODS

QX's effective components were gathered from the TCMSP database based on two criteria: drug-likeness (DL ≥ 0.18) and oral bioavailability (OB ≥ 30%). SwissADME was used to predict potential targets of effective components, and Cytoscape was used to create a "Herb-Component-Target" network for QX. In addition, targets associated with H1N1 were gathered from the databases GeneCards, OMIM, and GEO. Targets associated with autophagy were retrieved from the KEGG, HAMdb, and HADb databases. Intersection targets for QX, H1N1 influenza, and autophagy were identified using Venn diagrams. Afterward, key targets were screened using Cytoscape's protein-protein interaction networks built using the database STRING. Biological functions and signaling pathways of overlapping targets were observed through GO analysis and KEGG enrichment analysis. The main chemical components of QX were determined by high-performance liquid chromatography (HPLC), followed by molecular docking. Finally, the mechanism of QX in treating H1N1 was validated through animal experiments.

RESULTS

A total of 786 potential targets and 91 effective components of QX were identified. There were 5420 targets related to H1N1 and 821 autophagy-related targets. The intersection of all targets of QX, H1N1, and autophagy yielded 75 intersecting targets. Ultimately, 10 core targets were selected: BCL2, CASP3, NFKB1, MTOR, JUN, TNF, HSP90AA1, EGFR, HIF1A, and MAPK3. Identification of the main chemical components of QX by HPLC resulted in the separation of seven marker ingredients within 195 min, which are amygdalin, puerarin, baicalin, phillyrin, wogonoside, baicalein, and wogonin. Molecular docking results showed that BCL2, CASP3, NFKB1, and MTOR could bind well with the compounds. In animal studies, QX reduced the degenerative alterations in the lung tissue of H1N1-infected mice by upregulating the expression of p-mTOR/mTOR and p62 and downregulating the expression of LC3, which inhibited autophagy.

CONCLUSIONS

According to this study's network pharmacology analysis and experimental confirmation, QX may be able to treat H1N1 infection by regulating autophagy, lowering the expression of LC3, and increasing the expression of p62 and p-mTOR/mTOR.

Variants in mitochondrial disease genes are common causes of inherited peripheral neuropathies

BACKGROUND

Peripheral neuropathies in mitochondrial disease are caused by mutations in nuclear genes encoding mitochondrial proteins, or in the mitochondrial genome. Whole exome or genome sequencing enable parallel testing of nuclear and mtDNA genes, and it has significantly advanced the genetic diagnosis of inherited diseases. Despite this, approximately 40% of all Charcot-Marie-Tooth (CMT) cases remain undiagnosed.

METHODS

The genome-phenome analysis platform (GPAP) in RD-Connect was utilised to create a cohort of 2087 patients with at least one Human Phenotype Ontology (HPO) term suggestive of a peripheral neuropathy, from a total of 10,935 patients. These patients' genetic data were then analysed and searched for variants in known mitochondrial disease genes.

RESULTS

A total of 1,379 rare variants were identified, 44 of which were included in this study as either reported pathogenic or likely causative in 42 patients from 36 families. The most common genes found to be likely causative for an autosomal dominant neuropathy were GDAP1 and GARS1. We also detected heterozygous likely pathogenic variants in DNA2, MFN2, DNM2, PDHA1, SDHA, and UCHL1. Biallelic variants in SACS, SPG7, GDAP1, C12orf65, UCHL1, NDUFS6, ETFDH and DARS2 and variants in the mitochondrial DNA (mtDNA)-encoded MT-ATP6 and MT-TK were also causative for mitochondrial CMT. Only 50% of these variants were already reported as solved in GPAP.

CONCLUSION

Variants in mitochondrial disease genes are frequent in patients with inherited peripheral neuropathies. Due to the clinical overlap between mitochondrial disease and CMT, agnostic exome or genome sequencing have better diagnostic yields than targeted gene panels.

Network pharmacology and multitarget analysis of Nigella sativa in the management of diabetes and obesity: a computational study

Obesity and diabetes are commonly associated with one another and represent a significant global health issue, with a recent surge in disease incidence. Nigella sativa, also known as black cumin, is believed to possess several health benefits, including anti-diabetic, anticancer, antioxidant, antimicrobial, and anti-obesity properties. In this study, we aimed to identify the active compounds derived from N. sativa, which can potentially inhibit key protein targets and signaling pathways associated with diabesity treatment. We employed an exhaustive in silico search, which led to the identification of 22 potential compounds. Out of these, only five hits were found to be non-toxic, including Arabic and ascorbic acids, dihydrocodeine, catechin, and kaempferol. Our analysis revealed that these hits were associated with genes such as AKT1, IL6, SRC, and EGFR. Finally, we conducted molecular docking and molecular dynamics simulations, which identified kaempferol as the best binder for AKT1 in comparison to the reference molecule. Overall, our in silico integrated pipeline provides a useful approach to identify non-toxic phytocompounds as promising drug candidates to treat diabetes and obesity.Communicated by Ramaswamy H. Sarma.

Naturally occurring genetic diseases caused by de novo variants in domestic animals

With the advent of next-generation sequencing, an increasing number of cases of de novo variants in domestic animals have been reported in scientific literature primarily associated with clinically severe phenotypes. The emergence of new variants at each generation is a crucial aspect in understanding the pathology of early-onset diseases in animals and can provide valuable insights into similar diseases in humans. With the aim of collecting deleterious de novo variants in domestic animals, we searched the scientific literature and compiled reports on 42 de novo variants in 31 genes in domestic animals. No clear disease-associated phenotype has been established in humans for three of these genes (NUMB, ANKRD28 and KCNG1). For the remaining 28 genes, a strong similarity between animal and human phenotypes was recognized from available information in OMIM and OMIA, revealing the importance of comparative studies and supporting the use of domestic animals as natural models for human diseases, in line with the One Health approach.

From GWASs toward Mechanistic Understanding with Case Studies in Dermatogenetics

Many human skin diseases result from the complex interplay of genetic and environmental mechanisms that are largely unknown. GWASs have yielded insight into the genetic aspect of complex disease by highlighting regions of the genome or specific genetic variants associated with disease. Leveraging this information to identify causal genes and cell types will provide insight into fundamental biology, inform diagnostics, and aid drug discovery. However, the etiological mechanisms from genetic variant to disease are still unestablished in most cases. There now exists an unprecedented wealth of data and computational methods for variant interpretation in a functional context. It can be challenging to decide where to start owing to a lack of consensus on the best way to identify causal genetic mechanisms. This article highlights 3 key aspects of genetic variant interpretation: prioritizing causal genes, cell types, and pathways. We provide a practical overview of the main methods and datasets, giving examples from recent atopic dermatitis studies to provide a blueprint for variant interpretation. A collection of resources, including brief description and links to the packages and web tools, is provided for researchers looking to start in silico follow-up genetic analysis of associated genetic variants.

Network toxicology and molecular docking for the toxicity analysis of food contaminants: A case of Aflatoxin B1

The present study aims to promote network toxicology and molecular docking strategies for the efficient evaluation of the toxicity of food contaminants. With the example of liver injury induced by the food contaminant Aflatoxin B1(AFB1), this study effectively investigated the putative toxicity of food contaminants and the potentially molecular mechanisms. The study found that AFB1 regulates multiple signalling pathways by modulating core targets such as AKT1, BCL2, TNF, CASP3, SRC and EGFR. These pathways encompass Pathways in cancer, PI3K-Akt signalling pathway, Endocrine resistance, Lipid and atherosclerosis, Apoptosis and other pathways, subsequently impacting immunotoxicity, inflammatory responses, apoptosis, cytogenetic mutations, and ultimately leading to liver injury. We provide a theoretical basis for understanding the molecular mechanisms of AFB1 hepatotoxicity and for the prevention and treatment of cancers caused by the food contaminant AFB1. Furthermore, our network toxicology and molecular docking methods also provide an effective method for the rapid evaluation of the toxicity of food contaminants, which effectively solves the cost and ethical problems associated with the use of experimental animals.

A Combined Manual Annotation and Deep-Learning Natural Language Processing Study on Accurate Entity Extraction in Hereditary Disease Related Biomedical Literature

We report a combined manual annotation and deep-learning natural language processing study to make accurate entity extraction in hereditary disease related biomedical literature. A total of 400 full articles were manually annotated based on published guidelines by experienced genetic interpreters at Beijing Genomics Institute (BGI). The performance of our manual annotations was assessed by comparing our re-annotated results with those publicly available. The overall Jaccard index was calculated to be 0.866 for the four entity types-gene, variant, disease and species. Both a BERT-based large name entity recognition (NER) model and a DistilBERT-based simplified NER model were trained, validated and tested, respectively. Due to the limited manually annotated corpus, Such NER models were fine-tuned with two phases. The F1-scores of BERT-based NER for gene, variant, disease and species are 97.28%, 93.52%, 92.54% and 95.76%, respectively, while those of DistilBERT-based NER are 95.14%, 86.26%, 91.37% and 89.92%, respectively. Most importantly, the entity type of variant has been extracted by a large language model for the first time and a comparable F1-score with the state-of-the-art variant extraction model tmVar has been achieved.

Material basis and pharmacodynamic mechanism of YangshenDingzhi granules in the intervention of viral pneumonia: Based on serum pharmacochemistry and network pharmacology

BACKGROUND

YangshenDingzhi granules (YSDZ) are clinically effective in preventing and treating COVID-19. The present study elucidates the underlying mechanism of YSDZ intervention in viral pneumonia by employing serum pharmacochemistry and network pharmacology.

METHODS

The chemical constituents of YSDZ in the blood were examined using ultra-performance liquid chromatography-quadrupole/orbitrap high-resolution mass spectrometry (UPLC-Q-Exactive Orbitrap MS). Potential protein targets were obtained from the SwissTargetPrediction database, and the target genes associated with viral pneumonia were identified using GeneCards, DisGeNET, and Online Mendelian Inheritance in Man (OMIM) databases. The intersection of blood component-related targets and disease-related targets was determined using Venny 2.1. Protein-protein interaction networks were constructed using the STRING database. The Metascape database was employed to perform enrichment analyses of Gene Ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways for the targets, while the Cytoscape 3.9.1 software was utilized to construct drug-component-disease-target-pathway networks. Further, in vitro and in vivo experiments were performed to establish the therapeutic effectiveness of YSDZ against viral pneumonia.

RESULTS

Fifteen compounds and 124 targets linked to viral pneumonia were detected in serum. Among these, MAPK1, MAPK3, AKT1, EGFR, and TNF play significant roles. In vitro tests revealed that the medicated serum suppressed the replication of H1N1, RSV, and SARS-CoV-2 replicon. Further, in vivo testing analysis shows that YSDZ decreases the viral load in the lungs of mice infected with RSV and H1N1.

CONCLUSION

The chemical constituents of YSDZ in the blood may elicit therapeutic effects against viral pneumonia by targeting multiple proteins and pathways.

Scutellarin alleviates microglia-mediated neuroinflammation and apoptosis after ischemic stroke through the PI3K/AKT/GSK3β signaling pathway

Microglia are resident immune cells in the central nervous system that are rapidly activated to mediate neuroinflammation and apoptosis, thereby aggravating brain tissue damage after ischemic stroke (IS). Although scutellarin has a specific therapeutic effect on IS, the potential target mechanism of its treatment has not been fully elucidated. In this study, we explored the potential mechanism of scutellarin in treating IS using network pharmacology. Lipopolysaccharide (LPS) was used to induce an in vitro BV-2 microglial cell model, while middle cerebral artery occlusion (MCAO) was used to induce an in vivo animal model. Our findings indicated that scutellarin promoted the recovery of cerebral blood flow in MCAO rats at 3 days, significantly different from that in the MCAO group. Western blotting and immunofluorescence revealed that scutellarin treatment of BV-2 microglial cells resulted in a significant reduction in the protein expression levels and incidence of cells immunopositive for p-NF-κB, TNF-α, IL-1β, Bax, and C-caspase-3. In contrast, the expression levels of p-PI3K, p-AKT, p-GSK3β, and Bcl-2 were further increased, significantly different from those in the LPS group. The PI3K inhibitor LY294002 had similar effects to scutellarin by inhibiting neuroinflammation and apoptosis in activated microglia. The results of the PI3K/AKT/GSK3β signaling pathway and NF-κB pathway in vivo in MCAO models induced microglia at 3 days were consistent with those obtained from in vitro cells. These findings indicate that scutellarin plays a neuroprotective role by reducing microglial neuroinflammation and apoptosis mediated by the activated PI3K/AKT/GSK3β/NF-κB signaling pathway.

TCMSSD: A comprehensive database focused on syndrome standardization

BACKGROUD

Quantitative and standardized research on syndrome differentiation has always been at the forefront of modernizing Traditional Chinese Medicine (TCM) theory. However, the majority of existing databases primarily concentrate on the network pharmacology of herbal prescriptions, and there are limited databases specifically dedicated to TCM syndrome differentiation.

PURPOSE

In response to this gap, we have developed the Traditional Chinese Medical Syndrome Standardization Database (TCMSSD, http://tcmssd.ratcm.cn).

METHODS

TCMSSD is a comprehensive database that gathers data from various sources, including TCM literature such as TCM Syndrome Studies (Zhong Yi Zheng Hou Xue) and TCM Internal Medicine (Zhong Yi Nei Ke Xue) and various public databases such as TCMID and ETCM. In our study, we employ a deep learning approach to construct the knowledge graph and utilize the BM25 algorithm for syndrome prediction.

RESULTS

The TCMSSD integrates the essence of TCM with the modern medical system, providing a comprehensive collection of information related to TCM. It includes 624 syndromes, 133,518 prescriptions, 8,073 diseases (including 1,843 TCM-specific diseases), 8,259 Chinese herbal medicines, 43,413 ingredients, 17,602 targets, and 8,182 drugs. By analyzing input data and comparing it with the patterns and characteristics recorded in the database, the syndrome prediction tool generates predictions based on established correlations and patterns.

CONCLUSION

The TCMSSD fills the gap in existing databases by providing a comprehensive resource for quantitative and standardized research on TCM syndrome differentiation and laid the foundation for research on the biological basis of syndromes.

Potential immune-related therapeutic mechanisms of multiple traditional Chinese medicines on type 2 diabetic nephropathy based on bioinformatics, network pharmacology and molecular docking

BACKGROUND

The prevalence of type 2 diabetic nephropathy (T2DN) ranges from 20 % to 40 % among individuals with type 2 diabetes. Multiple immune pathways play a pivotal role in the pathogenesis of T2DN. This study aimed to investigate the immunomodulatory effects of active ingredients derived from 14 traditional Chinese medicines (TCMs) on T2DN.

METHODS

By removing batch effect on the GSE30528 and GSE96804 datasets, we employed a combination of weighted gene co-expression network analysis, least absolute shrinkage and selection operator analysis, protein-protein interaction network analysis, and the CIBERSORT algorithm to identify the active ingredients of TCMs as well as potential hub biomarkers associated with immune cells. Functional analysis was conducted using Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and gene set variation analysis (GSVA). Additionally, molecular docking was employed to evaluate interactions between active ingredients and potential immunotherapy targets.

RESULTS

A total of 638 differentially expressed genes (DEGs) were identified in this study, comprising 5 hub genes along with 4 potential biomarkers. Notably, CXCR1, CXCR2, and FOS exhibit significant associations with immune cells while displaying robust or favorable affinities towards the active ingredients kaempferol, quercetin, and luteolin. Furthermore, functional analysis unveiled intricate involvement of DEGs, hub genes and potential biomarkers in pathways closely linked to immunity and diabetes.

CONCLUSION

The potential hub biomarkers and immunotherapy targets associated with immune cells of T2DN comprise CXCR1, CXCR2, and FOS. Furthermore, kaempferol, quercetin, and luteolin demonstrate potential immunomodulatory effects in modulating T2DN through the regulation of CXCR1, CXCR2, and FOS expression.

Ancient eukaryotic protein interactions illuminate modern genetic traits and disorders

All eukaryotes share a common ancestor from roughly 1.5 - 1.8 billion years ago, a single-celled, swimming microbe known as LECA, the Last Eukaryotic Common Ancestor. Nearly half of the genes in modern eukaryotes were present in LECA, and many current genetic diseases and traits stem from these ancient molecular systems. To better understand these systems, we compared genes across modern organisms and identified a core set of 10,092 shared protein-coding gene families likely present in LECA, a quarter of which are uncharacterized. We then integrated >26,000 mass spectrometry proteomics analyses from 31 species to infer how these proteins interact in higher-order complexes. The resulting interactome describes the biochemical organization of LECA, revealing both known and new assemblies. We analyzed these ancient protein interactions to find new human gene-disease relationships for bone density and congenital birth defects, demonstrating the value of ancestral protein interactions for guiding functional genetics today.

Genome-first evaluation with exome sequence and clinical data uncovers underdiagnosed genetic disorders in a large healthcare system

Population-based genomic screening may help diagnose individuals with disease-risk variants. Here, we perform a genome-first evaluation for nine disorders in 29,039 participants with linked exome sequences and electronic health records (EHRs). We identify 614 individuals with 303 pathogenic/likely pathogenic or predicted loss-of-function (P/LP/LoF) variants, yielding 644 observations; 487 observations (76%) lack a corresponding clinical diagnosis in the EHR. Upon further investigation, 75 clinically undiagnosed observations (15%) have evidence of symptomatic untreated disease, including familial hypercholesterolemia (3 of 6 [50%] undiagnosed observations with disease evidence) and breast cancer (23 of 106 [22%]). These genetic findings enable targeted phenotyping that reveals new diagnoses in previously undiagnosed individuals. Disease yield is greater with variants in penetrant genes for which disease is observed in carriers in an independent cohort. The prevalence of P/LP/LoF variants exceeds that of clinical diagnoses, and some clinically undiagnosed carriers are discovered to have disease. These results highlight the potential of population-based genomic screening.

Genetic variants for head size share genes and pathways with cancer

The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.