Phenopedia and Genopedia: disease-centered and gene-centered views of the evolving knowledge of human genetic associations

Multiscale mapping of transcriptomic signatures for cardiotoxic drugs

Drug-induced gene expression profiles can identify potential mechanisms of toxicity. We focus on obtaining signatures for cardiotoxicity of FDA-approved tyrosine kinase inhibitors (TKIs) in human induced-pluripotent-stem-cell-derived cardiomyocytes, using bulk transcriptomic profiles. We use singular value decomposition to identify drug-selective patterns across cell lines obtained from multiple healthy human subjects. Cellular pathways affected by cardiotoxic TKIs include energy metabolism, contractile, and extracellular matrix dynamics. Projecting these pathways to published single cell expression profiles indicates that TKI responses can be evoked in both cardiomyocytes and fibroblasts. Integration of transcriptomic outlier analysis with whole genomic sequencing of our six cell lines enables us to correctly reidentify a genomic variant causally linked to anthracycline-induced cardiotoxicity and predict genomic variants potentially associated with TKI-induced cardiotoxicity. We conclude that mRNA expression profiles when integrated with publicly available genomic, pathway, and single cell transcriptomic datasets, provide multiscale signatures for cardiotoxicity that could be used for drug development and patient stratification.

Unraveling the genetic and molecular landscape of sepsis and acute kidney injury: A comprehensive GWAS and machine learning approach

OBJECTIVES

This study aimed to explore the underlying mechanisms of sepsis and acute kidney injury (AKI), including sepsis-associated AKI (SA-AKI), a frequent complication in critically ill sepsis patients.

METHODS

GWAS data was analyzed for genetic association between AKI and sepsis. Then, we systematically applied three distinct machine learning algorithms (LASSO, SVM-RFE, RF) to rigorously identify and validate signature genes of SA-AKI, assessing their diagnostic and prognostic value through ROC curves and survival analysis. The study also examined the functional and immunological aspects of these genes, potential drug targets, and ceRNA networks. A mouse model of sepsis was created to test the reliability of these signature genes.

RESULTS

LDSC confirmed a positive genetic correlation between AKI and sepsis, although no significant shared loci were found. Bidirectional MR analysis indicated mutual increased risks of AKI and sepsis. Then, 311 key genes common to sepsis and AKI were identified, with 42 significantly linked to sepsis prognosis. Six genes, selected through LASSO, SVM-RFE, and RF algorithms, showed excellent predictive performance for sepsis, AKI, and SA-AKI. The models demonstrated near-perfect AUCs in both training and testing datasets, and a perfect AUC in a sepsis mouse model. Significant differences in immune cells, immune-related pathways, HLA, and checkpoint genes were found between high- and low-risk groups. The study identified 62 potential drug treatments for sepsis and AKI and constructed a ceRNA network.

CONCLUSIONS

The identified signature genes hold potential clinical applications, including prognostic evaluation and targeted therapeutic strategies for sepsis and AKI. However, further research is needed to confirm these findings.

Medium-Dose Formoterol Attenuated Abdominal Aortic Aneurysm Induced by EPO via β2AR/cAMP/SIRT1 Pathway

Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease but effective drugs for treatment of AAA are still lacking. Recently, erythropoietin (EPO) is reported to induce AAA formation in apolipoprotein-E knock out (ApoE-/-) mice but an effective antagonist is unknown. In this study, formoterol, a β2 adrenergic receptor (β2AR) agonist, is found to be a promising agent for inhibiting AAA. To test this hypothesis, ApoE-/- mice are treated with vehicle, EPO, and EPO plus low-, medium-, and high-dose formoterol, respectively. The incidence of AAA is 0, 55%, 35%,10%, and 55% in these 5 groups, respectively. Mechanistically, senescence of vascular smooth muscle cell (VSMC) is increased by EPO while decreased by medium-dose formoterol both in vivo and in vitro, manifested by the altered expression of senescence biomarkers including phosphorylation of H2AXserine139, senescence-associated β-galactosidase activity, and P21 protein level. In addition, expression of sirtuin 1 (SIRT1) in aorta is decreased in EPO-induced AAA but remarkably elevated by medium-dose formoterol. Knockdown of β2AR and blockage of cyclic adenosine monophosphate (cAMP) attenuate the inhibitory role of formoterol in EPO-induced VSMC senescence. In summary, medium-dose formoterol attenuates EPO-induced AAA via β2AR/cAMP/SIRT1 pathways, which provides a promising medication for the treatment of AAA.

Crym-positive striatal astrocytes gate perseverative behaviour

Astrocytes are heterogeneous glial cells of the central nervous system1-3. However, the physiological relevance of astrocyte diversity for neural circuits and behaviour remains unclear. Here we show that a specific population of astrocytes in the central striatum expresses μ-crystallin (encoded by Crym in mice and CRYM in humans) that is associated with several human diseases, including neuropsychiatric disorders4-7. In adult mice, reducing the levels of μ-crystallin in striatal astrocytes through CRISPR-Cas9-mediated knockout of Crym resulted in perseverative behaviours, increased fast synaptic excitation in medium spiny neurons and dysfunctional excitatory-inhibitory synaptic balance. Increased perseveration stemmed from the loss of astrocyte-gated control of neurotransmitter release from presynaptic terminals of orbitofrontal cortex-striatum projections. We found that perseveration could be remedied using presynaptic inhibitory chemogenetics8, and that this treatment also corrected the synaptic deficits. Together, our findings reveal converging molecular, synaptic, circuit and behavioural mechanisms by which a molecularly defined and allocated population of striatal astrocytes gates perseveration phenotypes that accompany neuropsychiatric disorders9-12. Our data show that Crym-positive striatal astrocytes have key biological functions within the central nervous system, and uncover astrocyte-neuron interaction mechanisms that could be targeted in treatments for perseveration.

Understanding a point mutation signature D54K in the caspase activation recruitment domain of NOD1 capitulating concerted immunity via atomistic simulation

Point mutation D54K in the human N-terminal caspase recruitment domain (CARD) of nucleotide-binding oligomerization domain -1 (NOD1) abrogates an imperative downstream interaction with receptor-interacting protein kinase (RIPK2) that entails combating bacterial infections and inflammatory dysfunction. Here, we addressed the molecular details concerning conformational changes and interaction patterns (monomeric-dimeric states) of D54K by signature-based molecular dynamics simulation. Initially, the sequence analysis prioritized D54K as a pathogenic mutation, among other variants, based on a sequence signature. Since the mutation is highly conserved, we derived the distant ortholog to predict the sequence and structural similarity between native and mutant. This analysis showed the utility of 33 communal core residues associated with structural-functional preservation and variations, concurrently served to infer the cryptic hotspots Cys39, Glu53, Asp54, Glu56, Ile57, Leu74, and Lys78 determining the inter helical fold forming homodimers for putative receptor interaction. Subsequently, the atomistic simulations with free energy (MM/PB(GB)SA) calculations predicted structural alteration that takes place in the N-terminal mutant CARD where coils changed to helices (45 α3- L4-α4-L6- α683) in contrast to native (45T2-L4-α4-L6-T483). Likewise, the C-terminal helices 93T1-α7105 connected to the loops distorted compared to native 93α6-L7105 may result in conformational misfolding that promotes functional regulation and activation. These structural perturbations of D54K possibly destabilize the flexible adaptation of critical homotypic NOD1CARD-CARDRIPK2 interactions (α4Asp42-Arg488α5 and α6Phe86-Lys471α4) is consistent with earlier experimental reports. Altogether, our findings unveil the conformational plasticity of mutation-dependent immunomodulatory response and may aid in functional validation exploring clinical investigation on CARD-regulated immunotherapies to prevent systemic infection and inflammation.Communicated by Ramaswamy H. Sarma.

Identification of Potentially Repurposable Drugs for Lewy Body Dementia Using a Network-Based Approach

The conventional method of one drug being used for one target has not yielded therapeutic solutions for Lewy body dementia (LBD), which is a leading progressive neurological disorder characterized by significant loss of neurons. The age-related disease is marked by memory loss, hallucinations, sleep disorder, mental health deterioration, palsy, and cognitive impairment, all of which have no known effective cure. The present study deploys a network medicine pipeline to repurpose drugs having considerable effect on the genes and proteins related to the diseases of interest. We utilized the novel SAveRUNNER algorithm to quantify the proximity of all drugs obtained from DrugBank with the disease associated gene dataset obtained from Phenopedia and targets in the human interactome. We found that most of the 154 FDA-approved drugs predicted by SAveRUNNER were used to treat nervous system disorders, but some off-label drugs like quinapril and selegiline were interestingly used to treat hypertension and Parkinson's disease (PD), respectively. Additionally, we performed gene set enrichment analysis using Connectivity Map (CMap) and pathway enrichment analysis using EnrichR to validate the efficacy of the drug candidates obtained from the pipeline approach. The investigation enabled us to identify the significant role of the synaptic vesicle pathway in our disease and accordingly finalize 8 suitable antidepressant drugs from the 154 drugs initially predicted by SAveRUNNER. These potential anti-LBD drugs are either selective or non-selective inhibitors of serotonin, dopamine, and norepinephrine transporters. The validated selective serotonin and norepinephrine inhibitors like milnacipran, protriptyline, and venlafaxine are predicted to manage LBD along with the affecting symptomatic issues.

Drug repurposing: databases and pipelines

The concept of drug repurposing is focused on the repositioning of drug molecules that have already undergone safety trials. There are different strategies for drug repurposing. Network-based strategy focuses on the evaluation of drug combinations in a molecular environment with multi-target hits and analysis of drug interactions. Implementation of any in silico strategy requires several databases and pipelines for executing the process of shortlisting appropriate drugs.

Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn's disease patients

High-throughput sequencing allowed the discovery of many disease variants, but nowadays it is becoming clear that the abundance of genomics data mostly just moved the bottleneck in Genetics and Precision Medicine from a data availability issue to a data interpretation issue. To solve this empasse it would be beneficial to apply the latest Deep Learning (DL) methods to the Genome Interpretation (GI) problem, similarly to what AlphaFold did for Structural Biology. Unfortunately DL requires large datasets to be viable, and aggregating genomics datasets poses several legal, ethical and infrastructural complications. Federated Learning (FL) is a Machine Learning (ML) paradigm designed to tackle these issues. It allows ML methods to be collaboratively trained and tested on collections of physically separate datasets, without requiring the actual centralization of sensitive data. FL could thus be key to enable DL applications to GI on sufficiently large genomics data. We propose FedCrohn, a FL GI Neural Network model for the exome-based Crohn's Disease risk prediction, providing a proof-of-concept that FL is a viable paradigm to build novel ML GI approaches. We benchmark it in several realistic scenarios, showing that FL can indeed provide performances similar to conventional ML on centralized data, and that collaborating in FL initiatives is likely beneficial for most of the medical centers participating in them.

Huperzine A injection ameliorates motor and cognitive abnormalities via regulating multiple pathways in a murine model of Parkinson's disease

As a common progressive neurodegenerative disorder, the satisfied therapies for Parkinson's disease (PD) are still unavailable. As a natural acetylcholinesterase inhibitor, the neuroprotective characteristic of Huperzine A (HupA) was supported by previous studies. However, questions remain on whether HupA injection (HAI, a main preparation of HupA) intervention conduces to PD treatment and if so, the potential molecular mechanisms. In this study, the efficacies of HAI treatment on PD-like pathological phenotypes were evaluated in a MPTP-induced PD murine model. The network pharmacology, transcriptome sequencing and experimental verification were integrated to comprehensively reveal the primary molecular mechanisms. Therapeutically, HAI intervention significantly improved the impaired locomotor behaviors as well as learning and memory abilities, and prevented the degeneration of dopaminergic neurons of PD mice. The network pharmacology analysis combined with experimental results showed that HAI treatment could effectively restore the disordered transcriptional levels of inflammatory factors and apoptosis related genes in the SNpc and striatum tissues of PD mice. Transcriptome sequencing results found that inflammation and oxidative phosphorylation served as significant functional mechanisms involved in HAI administration. The experimental verification indicated that HAI treatment effectively regulated the abnormal transcription levels of inflammation and oxidative phosphorylation related hub genes in the hippocampal samples of PD mice. In addition, molecular docking suggested strong affinity between HupA and the above core targets. Overall, this work displayed the reliable therapeutic effects of HAI on ameliorating the pathological symptoms of PD mice via modulating multiple pathways. The current findings were expected to provide a potential anti-PD agent.

The Potential Regulation of A-to-I RNA Editing on Genes in Parkinson's Disease

Parkinson's disease (PD) is characterized by dopaminergic neurodegeneration and an abnormal accumulation of α-synuclein aggregates. A number of genetic factors have been shown to increase the risk of PD. Exploring the underlying molecular mechanisms that mediate PD's transcriptomic diversity can help us understand neurodegenerative pathogenesis. In this study, we identified 9897 A-to-I RNA editing events associated with 6286 genes across 372 PD patients. Of them, 72 RNA editing events altered miRNA binding sites and this may directly affect miRNA regulations of their host genes. However, RNA editing effects on the miRNA regulation of genes are more complex. They can (1) abolish existing miRNA binding sites, which allows miRNAs to regulate other genes; (2) create new miRNA binding sites that may sequester miRNAs from regulating other genes; or (3) occur in the miRNA seed regions and change their targets. The first two processes are also referred to as miRNA competitive binding. In our study, we found 8 RNA editing events that may alter the expression of 1146 other genes via miRNA competition. We also found one RNA editing event that modified a miRNA seed region, which was predicted to disturb the regulation of four genes. Considering the PD-related functions of the affected genes, 25 A-to-I RNA editing biomarkers for PD are proposed, including the 3 editing events in the EIF2AK2, APOL6, and miR-4477b seed regions. These biomarkers may alter the miRNA regulation of 133 PD-related genes. All these analyses reveal the potential mechanisms and regulations of RNA editing in PD pathogenesis.

Astrocyte-neuron subproteomes and obsessive-compulsive disorder mechanisms

Astrocytes and neurons extensively interact in the brain. Identifying astrocyte and neuron proteomes is essential for elucidating the protein networks that dictate their respective contributions to physiology and disease. Here we used cell-specific and subcompartment-specific proximity-dependent biotinylation1 to study the proteomes of striatal astrocytes and neurons in vivo. We evaluated cytosolic and plasma membrane compartments for astrocytes and neurons to discover how these cells differ at the protein level in their signalling machinery. We also assessed subcellular compartments of astrocytes, including end feet and fine processes, to reveal their subproteomes and the molecular basis of essential astrocyte signalling and homeostatic functions. Notably, SAPAP3 (encoded by Dlgap3), which is associated with obsessive-compulsive disorder (OCD) and repetitive behaviours2-8, was detected at high levels in striatal astrocytes and was enriched within specific astrocyte subcompartments where it regulated actin cytoskeleton organization. Furthermore, genetic rescue experiments combined with behavioural analyses and molecular assessments in a mouse model of OCD4 lacking SAPAP3 revealed distinct contributions of astrocytic and neuronal SAPAP3 to repetitive and anxiety-related OCD-like phenotypes. Our data define how astrocytes and neurons differ at the protein level and in their major signalling pathways. Moreover, they reveal how astrocyte subproteomes vary between physiological subcompartments and how both astrocyte and neuronal SAPAP3 mechanisms contribute to OCD phenotypes in mice. Our data indicate that therapeutic strategies that target both astrocytes and neurons may be useful to explore in OCD and potentially other brain disorders.

Proline and glucose metabolic reprogramming supports vascular endothelial and medial biomass in pulmonary arterial hypertension

In pulmonary arterial hypertension (PAH), inflammation promotes a fibroproliferative pulmonary vasculopathy. Reductionist studies emphasizing single biochemical reactions suggest a shift toward glycolytic metabolism in PAH; however, key questions remain regarding the metabolic profile of specific cell types within PAH vascular lesions in vivo. We used RNA-Seq to profile the transcriptome of pulmonary artery endothelial cells (PAECs) freshly isolated from an inflammatory vascular injury model of PAH ex vivo, and these data were integrated with information from human gene ontology pathways. Network medicine was then used to map all aa and glucose pathways to the consolidated human interactome, which includes data on 233,957 physical protein-protein interactions. Glucose and proline pathways were significantly close to the human PAH disease module, suggesting that these pathways are functionally relevant to PAH pathobiology. To test this observation in vivo, we used multi-isotope imaging mass spectrometry to map and quantify utilization of glucose and proline in the PAH pulmonary vasculature at subcellular resolution. Our findings suggest that elevated glucose and proline avidity underlie increased biomass in PAECs and the media of fibrosed PAH pulmonary arterioles. Overall, these data show that anabolic utilization of glucose and proline are fundamental to the vascular pathology of PAH.

Analysis of the Genetic Relationship between Atherosclerosis and Non-Alcoholic Fatty Liver Disease through Biological Interaction Networks

Non-alcoholic fatty liver disease (NAFLD) seems to have some molecular links with atherosclerosis (ATH); however, the molecular pathways which connect both pathologies remain unexplored to date. The identification of common factors is of great interest to explore some therapeutic strategies to improve the outcomes for those affected patients. Differentially expressed genes (DEGs) for NAFLD and ATH were extracted from the GSE89632 and GSE100927 datasets, and common up- and downregulated DEGs were identified. Subsequently, a protein-protein interaction (PPI) network based on the common DEGs was performed. Functional modules were identified, and the hub genes were extracted. Then, a Gene Ontology (GO) and pathway analysis of common DEGs was performed. DEGs analysis in NAFLD and ATH showed 21 genes that were regulated similarly in both pathologies. The common DEGs with high centrality scores were ADAMTS1 and CEBPA which appeared to be down- and up-regulated in both disorders, respectively. For the analysis of functional modules, two modules were identified. The first one was oriented to post-translational protein modification, where ADAMTS1 and ADAMTS4 were identified, and the second one mainly related to the immune response, where CSF3 was identified. These factors could be key proteins with an important role in the NAFLD/ATH axis.

Uncovering common pathobiological processes between COVID-19 and pulmonary arterial hypertension by integrating Omics data

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which led to the current pandemic. Many factors, including age and comorbidities, influence the severity and mortality of COVID-19. SARS-CoV-2 infection can cause pulmonary vascular dysfunction. The COVID-19 case-fatality rate in patients with pulmonary arterial hypertension (PAH) is higher in comparison with the general population. In this study, we aimed to identify pathobiological processes common to COVID-19 and PAH by utilizing the human protein-protein interactome and whole-genome transcription data from peripheral blood mononuclear cells (PBMCs) and from lung tissue. We found that there are significantly more interactions between SARS-CoV-2 targets and PAH disease proteins than expected by chance, suggesting that the PAH disease module is in the neighborhood of SARS-CoV-2 targets in the human interactome. In addition, SARS-CoV-2 infection-induced changes in gene expression significantly overlap with PAH-induced gene expression changes in both tissues, indicating SARS-CoV-2 and PAH may share common transcriptional regulators. We identified many upregulated genes and downregulated genes common to COVID-19 and PAH. Interestingly, we observed different co-regulation patterns and dysfunctional signaling pathways in PBMCs versus lung tissue. Endophenotype enrichment analysis revealed that genes regulating fibrosis, inflammation, hypoxia, oxidative stress, immune response, and thromboembolism are significantly enriched in the COVID-19-PAH co-expression modules. We examined the network proximity of the targets of repositioned drugs for COVID-19 to the co-expression modules in PBMCs and lung tissue, and identified 42 drugs that can be potentially used for COVID-19 patients with PAH as a comorbidity. The uncovered common pathobiological pathways are crucial for discovering therapeutic targets and designing tailored treatments for COVID-19 patients who also have PAH.

Molecular basis of astrocyte diversity and morphology across the CNS in health and disease

Astrocytes, a type of glia, are abundant and morphologically complex cells. Here, we report astrocyte molecular profiles, diversity, and morphology across the mouse central nervous system (CNS). We identified shared and region-specific astrocytic genes and functions and explored the cellular origins of their regional diversity. We identified gene networks correlated with astrocyte morphology, several of which unexpectedly contained Alzheimer's disease (AD) risk genes. CRISPR/Cas9-mediated reduction of candidate genes reduced astrocyte morphological complexity and resulted in cognitive deficits. The same genes were down-regulated in human AD, in an AD mouse model that displayed reduced astrocyte morphology, and in other human brain disorders. We thus provide comprehensive molecular data on astrocyte diversity and mechanisms across the CNS and on the molecular basis of astrocyte morphology in health and disease.

A single-cell transcriptome atlas of glial diversity in the human hippocampus across the postnatal lifespan

The molecular diversity of glia in the human hippocampus and their temporal dynamics over the lifespan remain largely unknown. Here, we performed single-nucleus RNA sequencing to generate a transcriptome atlas of the human hippocampus across the postnatal lifespan. Detailed analyses of astrocytes, oligodendrocyte lineages, and microglia identified subpopulations with distinct molecular signatures and revealed their association with specific physiological functions, age-dependent changes in abundance, and disease relevance. We further characterized spatiotemporal heterogeneity of GFAP-enriched astrocyte subpopulations in the hippocampal formation using immunohistology. Leveraging glial subpopulation classifications as a reference map, we revealed the diversity of glia differentiated from human pluripotent stem cells and identified dysregulated genes and pathological processes in specific glial subpopulations in Alzheimer's disease (AD). Together, our study significantly extends our understanding of human glial diversity, population dynamics across the postnatal lifespan, and dysregulation in AD and provides a reference atlas for stem-cell-based glial differentiation.

Copy number variant analysis for syndromic congenital heart disease in the Chinese population

Background

Syndromic congenital heart disease (CHD) is among the most severe conditions in the pediatric population. Copy number variant (CNV) is an important cause of syndromic CHD, but few studies focused on CNVs related to these patients in China. The present study aimed to identify pathogenic CNVs associated with syndromic CHD in the Chinese population.

Methods

A total of 109 sporadic patients with syndromic CHD were applied chromosomal microarray analysis (CMA). Phenotype spectrum of pathogenic or likely pathogenic CNVs was analyzed. CHD-related genes were prioritized from genes within pathogenic or likely pathogenic CNVs by VarElect, OVA, AMELIE, and ToppGene.

Results

Using CMA, we identified 43 candidate CNVs in 37/109 patients. After filtering CNVs present in the general population, 29 pathogenic/likely pathogenic CNVs in 24 patients were identified. The diagnostic yield of CMA for pathogenic/likely pathogenic CNVs was 23.1% (24/104), excluding 5 cases with aneuploidies or gross chromosomal aberrations. The overlapping analysis of CHD-related gene lists from different prioritization tools highlighted 16 CHD candidate genes.

Conclusion

As the first study focused on CNVs in syndromic CHD from the Chinese population, this study reveals the importance of CMA in exploring the genetic etiology of syndromic CHD and expands our understanding of these complex diseases. The bioinformatic analysis of candidate genes suggests several CHD-related genes for further functional research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-022-00426-8.

Mapping brain gene coexpression in daytime transcriptomes unveils diurnal molecular networks and deciphers perturbation gene signatures

Brain tissue transcriptomes may be organized into gene coexpression networks, but their underlying biological drivers remain incompletely understood. Here, we undertook a large-scale transcriptomic study using 508 wild-type mouse striatal tissue samples dissected exclusively in the afternoons to define 38 highly reproducible gene coexpression modules. We found that 13 and 11 modules are enriched in cell-type and molecular complex markers, respectively. Importantly, 18 modules are highly enriched in daily rhythmically expressed genes that peak or trough with distinct temporal kinetics, revealing the underlying biology of striatal diurnal gene networks. Moreover, the diurnal coexpression networks are a dominant feature of daytime transcriptomes in the mouse cortex. We next employed the striatal coexpression modules to decipher the striatal transcriptomic signatures from Huntington's disease models and heterozygous null mice for 52 genes, uncovering novel functions for Prkcq and Kdm4b in oligodendrocyte differentiation and bipolar disorder-associated Trank1 in regulating anxiety-like behaviors and nocturnal locomotion.

MiR-93-5p promotes granulosa cell apoptosis and ferroptosis by the NF-kB signaling pathway in polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders in women of reproductive age. miR-93-5p has been reported to be elevated in granulosa cells of PCOS patients. However, the mechanism by which miR-93-5p drives granulosa cell (GC) progression remains unclear. Thus, this study focuses on the roles and mechanisms of miR-93-5p in the GCs of PCOS.

METHODS

KGN cells have similar ovarian physiological characteristics and are used to study the function and regulatory mechanism of GCs. In this study, KGN cells were transfected with si-NC, si-miR93-5p, oe-NC and oe-miR93-5p. A cell counting kit-8 assay, flow cytometry and western blotting were performed to observe the proliferation and apoptosis of KGN in different groups. Subsequently, the levels of reactive oxygen species, malondialdehyde, GPX4, SLC7A11 and Nrf2, which are indicators of ferroptosis, were measured by a dihydroethidium fluorescent dye probe, biochemical kit, western blotting and reverse transcription quantitative polymerase chain reaction. Ultimately, bioinformatic analysis and experimental methods were used to examine the interaction between miR-93-5p and the NF-κB signaling pathway.

RESULTS

miR-93-5p was upregulated in the GCs of PCOS patients. Overexpression of miR-93-5p promoted apoptosis and ferroptosis in KGN cells, while knockdown of miR-93-5p showed the reverse effect. Biological analysis and subsequent experiments demonstrated that miR-93-5p negatively regulates the NF- κB signaling pathway.

CONCLUSION

miR-93-5p promotes the apoptosis and ferroptosis in GC by regulating the NF-κB signaling pathway. Silencing of miR-93-5p protects against GC dysfunction. Our study identified miR-93-5p as a new molecular target for improving the function of GCs in PCOS patients.

Identification of Potential Repurposable Drugs in Alzheimer’s Disease Exploiting a Bioinformatics Analysis

Alzheimer’s disease (AD) is a neurologic disorder causing brain atrophy and the death of brain cells. It is a progressive condition marked by cognitive and behavioral impairment that significantly interferes with daily activities. AD symptoms develop gradually over many years and eventually become more severe, and no cure has been found yet to arrest this process. The present study is directed towards suggesting putative novel solutions and paradigms for fighting AD pathogenesis by exploiting new insights from network medicine and drug repurposing strategies. To identify new drug–AD associations, we exploited SAveRUNNER, a recently developed network-based algorithm for drug repurposing, which quantifies the vicinity of disease-associated genes to drug targets in the human interactome. We complemented the analysis with an in silico validation of the candidate compounds through a gene set enrichment analysis, aiming to determine if the modulation of the gene expression induced by the predicted drugs could be counteracted by the modulation elicited by the disease. We identified some interesting compounds belonging to the beta-blocker family, originally approved for treating hypertension, such as betaxolol, bisoprolol, and metoprolol, whose connection with a lower risk to develop Alzheimer’s disease has already been observed. Moreover, our algorithm predicted multi-kinase inhibitors such as regorafenib, whose beneficial effects were recently investigated for neuroinflammation and AD pathology, and mTOR inhibitors such as sirolimus, whose modulation has been associated with AD.

Large-Scale Targeted Sequencing Study of Ischemic Stroke in the Han Chinese Population

Background Ischemic stroke is likely caused by interactions of multiple genes and environmental determinants. However, large-scale sequencing studies to discern functional genetic variants and their interactions with clinical and lifestyle risk factors on ischemic stroke are limited. Methods and Results We sequenced functional regions of 740 previously identified genes associated with atherosclerotic disease among 999 ischemic stroke cases and 1001 controls of Chinese ancestry. Multiple logistic regression models were used to examine the associations between variants and ischemic stroke and test interactions between variants and clinical and lifestyle risk factors. Functional variants achieving suggestive significance were replicated in an independent sample of 4724 ischemic stroke cases and 5029 controls. Driven by variant main effects, each minor allele of the correlated rs174535, rs174545, and rs3834458 variants at MYRF-FADS1-FADS2 conferred an average 0.83-fold (95% CI, 0.78-0.88) decreased odds of stroke. Significant main effects of MTHFR rs1801133 missense variant were also observed, with each copy of the A allele associated with a 1.20-fold (95% CI, 1.13-1.27) higher odds of ischemic stroke. The functional ALDH2 rs671 variant was identified in interaction analyses with alcohol drinking (Meta-P=3.39×10-17). Each minor allele conferred a 0.54-fold (95% CI, 0.45-0.64) decreased odds of stroke among drinkers and a 0.89-fold (95% CI, 0.83-0.97) decreased odds among nondrinkers. Conclusions Significant associations at MYRF-FADS1-FADS2 indicate that genetically elevated polyunsaturated fatty acids may decrease ischemic stroke risk in East Asians. Significant associations at MTHFR and ALDH2 robustly confirm deleterious effects of genetically elevated homocysteine and alcohol intake, respectively, on ischemic stroke.

Circulating miRNAs associated with bone mineral density in healthy adult baboons

MicroRNAs (miRNAs) regulate gene expression post-transcriptionally and circulate in the blood, making them attractive biomarkers of disease state for tissues like bone that are challenging to interrogate directly. Here, we report on five miRNAs-miR-197-3p, miR-320a, miR-320b, miR-331-5p, and miR-423-5p-associated with bone mineral density (BMD) in 147 healthy adult baboons. These baboons ranged in age from 15 to 25 years (45-75 human equivalent years) and 65% were female with a broad range of BMD values including a minority of osteopenic animals. miRNAs were generated via RNA sequencing from buffy coats collected at necropsy and areal BMD (aBMD) measured postmortem via dual-energy X-ray absorptiometry (DXA) of the lumbar vertebrae. Differential expression analysis controlled for the underlying pedigree structure of these animals to account for genetic variation which may drive miRNA abundance and aBMD values. While many of these miRNAs have been associated with the risk of osteoporosis in humans, this finding is of interest because the cohort represents a model of normal aging and bone metabolism rather than a disease cohort. The replication of miRNA associations with osteoporosis or other bone metabolic disorders in animals with healthy aBMD suggests an overlap in normal variation and disease states. We suggest that these miRNAs are involved in the regulation of cellular proliferation, apoptosis, and protein composition in the extracellular matrix throughout life; and age-related dysregulation of these systems may lead to disease. These miRNAs may be early indicators of progression to disease in advance of clinically detectible osteoporosis.

Molecular landscapes of human hippocampal immature neurons across lifespan

Immature dentate granule cells (imGCs) arising from adult hippocampal neurogenesis contribute to plasticity and unique brain functions in rodents1,2 and are dysregulated in multiple human neurological disorders3-5. Little is known about the molecular characteristics of adult human hippocampal imGCs, and even their existence is under debate1,6-8. Here we performed single-nucleus RNA sequencing aided by a validated machine learning-based analytic approach to identify imGCs and quantify their abundance in the human hippocampus at different stages across the lifespan. We identified common molecular hallmarks of human imGCs across the lifespan and observed age-dependent transcriptional dynamics in human imGCs that suggest changes in cellular functionality, niche interactions and disease relevance, that differ from those in mice9. We also found a decreased number of imGCs with altered gene expression in Alzheimer's disease. Finally, we demonstrated the capacity for neurogenesis in the adult human hippocampus with the presence of rare dentate granule cell fate-specific proliferating neural progenitors and with cultured surgical specimens. Together, our findings suggest the presence of a substantial number of imGCs in the adult human hippocampus via low-frequency de novo generation and protracted maturation, and our study reveals their molecular properties across the lifespan and in Alzheimer's disease.

Modeling Kaempferol as a Potential Pharmacological Agent for COVID-19/PF Co-Occurrence Based on Bioinformatics and System Pharmacological Tools

Objective: People suffering from coronavirus disease 2019 (COVID-19) are prone to develop pulmonary fibrosis (PF), but there is currently no definitive treatment for COVID-19/PF co-occurrence. Kaempferol with promising antiviral and anti-fibrotic effects is expected to become a potential treatment for COVID-19 and PF comorbidities. Therefore, this study explored the targets and molecular mechanisms of kaempferol against COVID-19/PF co-occurrence by bioinformatics and network pharmacology.

Methods: Various open-source databases and Venn Diagram tool were applied to confirm the targets of kaempferol against COVID-19/PF co-occurrence. Protein-protein interaction (PPI), MCODE, key transcription factors, tissue-specific enrichment, molecular docking, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to clarify the influential molecular mechanisms of kaempferol against COVID-19 and PF comorbidities.

Results: 290 targets and 203 transcription factors of kaempferol against COVID-19/PF co-occurrence were captured. Epidermal growth factor receptor (EGFR), proto-oncogene tyrosine-protein kinase SRC (SRC), mitogen-activated protein kinase 3 (MAPK3), mitogen-activated protein kinase 1 (MAPK1), mitogen-activated protein kinase 8 (MAPK8), RAC-alpha serine/threonine-protein kinase (AKT1), transcription factor p65 (RELA) and phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform (PIK3CA) were identified as the most critical targets, and kaempferol showed effective binding activities with the above critical eight targets. Further, anti-COVID-19/PF co-occurrence effects of kaempferol were associated with the regulation of inflammation, oxidative stress, immunity, virus infection, cell growth process and metabolism. EGFR, interleukin 17 (IL-17), tumor necrosis factor (TNF), hypoxia inducible factor 1 (HIF-1), phosphoinositide 3-kinase/AKT serine/threonine kinase (PI3K/AKT) and Toll-like receptor signaling pathways were identified as the key anti-COVID-19/PF co-occurrence pathways.

Conclusion: Kaempferol is a candidate treatment for COVID-19/PF co-occurrence. The underlying mechanisms may be related to the regulation of critical targets (EGFR, SRC, MAPK3, MAPK1, MAPK8, AKT1, RELA, PIK3CA and so on) and EGFR, IL-17, TNF, HIF-1, PI3K/AKT and Toll-like receptor signaling pathways. This study contributes to guiding development of new drugs for COVID-19 and PF comorbidities.

The Potential Mechanisms of Cinobufotalin Treating Colon Adenocarcinoma by Network Pharmacology

Network pharmacology, as a novel way using bioinformatics to explore drug targets and interactions in cancer, broadens our understanding of drug action, thereby facilitating drug discovery. Here, we utilized network pharmacology to explore the role and mechanism by which cinobufotalin functions in colon adenocarcinoma (COAD). We found that cinobufotalin represses the growth and proliferation of colon cancer cells, and integrated public databases for targets reported to be associated with COAD, together with those predicted to be targets of cinobufotalin. Targets overlapped between COAD-associated proteins and cinobufotalin target proteins were used to filter candidate targets of cinobufotalin in COAD. The following proteins were thought to occupy a key position in COAD-cinobufotalin target networks: SRC, PIK3R1, MAPK1, PIK3CA, HSP90AA1, CTNNB1, GRB2, RHO1, PTPN11, and EGFR. The networks regulated by cinobufotalin were involved mainly in extracellular signal stimulation and transduction, including MAPK signaling pathway, PI3K-AKT signaling pathway, and JAK-STAT signaling pathway. Besides, transcriptome sequencing results also indicated that cinobufotalin inhibits the response of colon cancer cells to extracellular stimulation and promotes cell apoptosis. Molecular docking results showed that cinobufotalin matches in the pocket of the top candidate cinobufotalin target proteins (SRC, PIK3R1, MAPK1 and PIK3CA). These findings demonstrate cinobufotalin can be developed as potential anti-cancer therapeutics.

Repurposing Histaminergic Drugs in Multiple Sclerosis

Multiple sclerosis is an autoimmune disease with a strong neuroinflammatory component that contributes to severe demyelination, neurodegeneration and lesions formation in white and grey matter of the spinal cord and brain. Increasing attention is being paid to the signaling of the biogenic amine histamine in the context of several pathological conditions. In multiple sclerosis, histamine regulates the differentiation of oligodendrocyte precursors, reduces demyelination, and improves the remyelination process. However, the concomitant activation of histamine H1–H4 receptors can sustain either damaging or favorable effects, depending on the specifically activated receptor subtype/s, the timing of receptor engagement, and the central versus peripheral target district. Conventional drug development has failed so far to identify curative drugs for multiple sclerosis, thus causing a severe delay in therapeutic options available to patients. In this perspective, drug repurposing offers an exciting and complementary alternative for rapidly approving some medicines already approved for other indications. In the present work, we have adopted a new network-medicine-based algorithm for drug repurposing called SAveRUNNER, for quantifying the interplay between multiple sclerosis-associated genes and drug targets in the human interactome. We have identified new histamine drug-disease associations and predicted off-label novel use of the histaminergic drugs amodiaquine, rupatadine, and diphenhydramine among others, for multiple sclerosis. Our work suggests that selected histamine-related molecules might get to the root causes of multiple sclerosis and emerge as new potential therapeutic strategies for the disease.

A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

Consensus Gene Co-Expression Network Analysis Identifies Novel Genes Associated with Severity of Fibrotic Lung Disease

Idiopathic pulmonary fibrosis (IPF) is a severe fibrotic lung disease characterized by irreversible scarring of the lung parenchyma leading to dyspnea, progressive decline in lung function, and respiratory failure. We analyzed lung transcriptomic data from independent IPF cohorts using weighted gene co-expression network analysis (WGCNA) to identify gene modules based on their preservation status in these cohorts. The consensus gene modules were characterized by leveraging existing clinical and molecular data such as lung function, biological processes, pathways, and lung cell types. From a total of 32 consensus gene modules identified, two modules were found to be significantly correlated with the disease, lung function, and preserved in other IPF datasets. The upregulated gene module was enriched for extracellular matrix, collagen metabolic process, and BMP signaling while the downregulated module consisted of genes associated with tube morphogenesis, blood vessel development, and cell migration. Using a combination of connectivity-based and trait-based significance measures, we identified and prioritized 103 "hub" genes (including 25 secretory candidate biomarkers) by their similarity to known IPF genetic markers. Our validation studies demonstrate the dysregulated expression of CRABP2, a retinol-binding protein, in multiple lung cells of IPF, and its correlation with the decline in lung function.

Comprehensive network medicine-based drug repositioning via integration of therapeutic efficacy and side effects

Despite advances in modern medicine that led to improvements in cardiovascular outcomes, cardiovascular disease (CVD) remains the leading cause of mortality and morbidity globally. Thus, there is an urgent need for new approaches to improve CVD drug treatments. As the development time and cost of drug discovery to clinical application are excessive, alternate strategies for drug development are warranted. Among these are included computational approaches based on omics data for drug repositioning, which have attracted increasing attention. In this work, we developed an adjusted similarity measure implemented by the algorithm SAveRUNNER to reposition drugs for cardiovascular diseases while, at the same time, considering the side effects of drug candidates. We analyzed nine cardiovascular disorders and two side effects. We formulated both disease disorders and side effects as network modules in the human interactome, and considered those drug candidates that are proximal to disease modules but far from side-effects modules as ideal. Our method provides a list of drug candidates for cardiovascular diseases that are unlikely to produce common, adverse side-effects. This approach incorporating side effects is applicable to other diseases, as well.

Human Astrocytes Exhibit Tumor Microenvironment-, Age-, and Sex-Related Transcriptomic Signatures

Astrocytes are critical for the development and function of synapses. There are notable species differences between human astrocytes and commonly used animal models. Yet, it is unclear whether astrocytic genes involved in synaptic function are stable or exhibit dynamic changes associated with disease states and age in humans, which is a barrier in understanding human astrocyte biology and its potential involvement in neurologic diseases. To better understand the properties of human astrocytes, we acutely purified astrocytes from the cerebral cortices of over 40 humans across various ages, sexes, and disease states. We performed RNA sequencing to generate transcriptomic profiles of these astrocytes and identified genes associated with these biological variables. We found that human astrocytes in tumor-surrounding regions downregulate genes involved in synaptic function and sensing of signals in the microenvironment, suggesting involvement of peritumor astrocytes in tumor-associated neural circuit dysfunction. In aging, we also found downregulation of synaptic regulators and upregulation of markers of cytokine signaling, while in maturation we identified changes in ionic transport with implications for calcium signaling. In addition, we identified subtle sexual dimorphism in human cortical astrocytes, which has implications for observed sex differences across many neurologic disorders. Overall, genes involved in synaptic function exhibit dynamic changes in the peritumor microenvironment and aging. These data provide powerful new insights into human astrocyte biology in several biologically relevant states that will aid in generating novel testable hypotheses about homeostatic and reactive astrocytes in humans.SIGNIFICANCE STATEMENT Astrocytes are an abundant class of cells playing integral roles at synapses. Astrocyte dysfunction is implicated in a variety of human neurologic diseases. Yet our knowledge of astrocytes is largely based on mouse studies. Direct knowledge of human astrocyte biology remains limited. Here, we present transcriptomic profiles of human cortical astrocytes, and we identified molecular differences associated with age, sex, and disease state. We found that peritumor and aging astrocytes downregulate genes involved in astrocyte-synapse interactions. These data provide necessary insight into human astrocyte biology that will improve our understanding of human disease.

Analyzing Association between Expression Quantitative Trait and CNV for Breast Cancer Based on Gene Interaction Network Clustering and Group Sparse Learning

Aims:

The occurrence and development of tumor is accompanied by the change of pathogenic gene expression. Tumor cells avoid the damage of immune cells by regulating the expression of immune related genes.

Background:

Tracing the causes of gene expression variation is helpful to understand tumor evolution and metastasis.

Objective:

Current gene expression variation explanation methods are confronted with several main challenges: low explanation power, insufficient prediction accuracy, and lack of biological meaning.

Method:

In this study, we propose a novel method to analyze the mRNA expression variations of breast cancers risk genes. Firstly, we collected some high-confidence risk genes related to breast cancer and then designed a rank-based method to preprocess the breast cancers copy number variation (CNV) and mRNA data. Secondly, to elevate the biological meaning and narrow down the combinatorial space, we introduced a prior gene interaction network and applied a network clustering algorithm to generate high density subnetworks. Lastly, to describe the interlinked structure within and between subnetworks and target genes mRNA expression, we proposed a group sparse learning model to identify CNVs for pathogenic genes expression variations.

Result:

The performance of the proposed method is evaluated by both significantly improved predication accuracy and biological meaning of pathway enrichment analysis.

Conclusion:

The experimental results show that our method has practical significance

A network pharmacology approach to explore and validate the potential targets of ginsenoside on osteoporosis

Background: Osteoporosis (OP) is determined as a chronic systemic bone disorder to increase the susceptibility to fracture. Ginsenosides have been found the anti-osteoporotic activity of in vivo and in vitro. However, its mechanism remains unknown.Methods: The potential mechanism of ginsenosides in anti-osteoporotic activity was identified by using network phamacology analysis. The active compounds of ginsenosides and their targets associated to OP were retrieved from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, Drug Bank, Pharmmapper, and Cytoscape. The Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis target genes were performed in String, Phenopedia, DisGeNET database, and Metascape software. The protein to protein interaction were created by String database and Cytoscape software. The molecular docking was used to investigate the interactions between active coumpounds and potential targets by utilizing SwissDock tool, UCSF Chimera, and Pymol software. Results: A total of eight important active ingredients and 17 potential targets related to OP treatment were subjected to analyze. GO analysis showed the anti-osteoporosis targets of ginsenoside mainly play a role in the response to steroid hormone. KEGG enrichment analysis indicated that ginsenoside treats OP by osteoblast differentiation signal pathway. Lastly, the molecular docking outcomes indicated that ginsenoside rh2 had a good binding ability with four target proteins IL1B, TNF, IFNG, and NFKBIA. Conclusion: IL1B, TNF, IFNG, and NFKBIA are the most important targets and osteoblast differentiation is the most valuable signaling pathways in ginsenoside for the treatment of OP, which might be beneficial to elucidate the mechanism concerned to the action of ginsenoside and might supply a better understanding of its anti-OP effects.

Genetic Variants Associated With Resilience in Human and Animal Studies

Resilience is broadly defined as the ability to maintain or regain functioning in the face of adversity and is influenced by both environmental and genetic factors. The identification of specific genetic factors and their biological pathways underpinning resilient functioning can help in the identification of common key factors, but heterogeneities in the operationalisation of resilience have hampered advances. We conducted a systematic review of genetic variants associated with resilience to enable the identification of general resilience mechanisms. We adopted broad inclusion criteria for the definition of resilience to capture both human and animal model studies, which use a wide range of resilience definitions and measure very different outcomes. Analyzing 158 studies, we found 71 candidate genes associated with resilience. OPRM1 (Opioid receptor mu 1), NPY (neuropeptide Y), CACNA1C (calcium voltage-gated channel subunit alpha1 C), DCC (deleted in colorectal carcinoma), and FKBP5 (FKBP prolyl isomerase 5) had both animal and human variants associated with resilience, supporting the idea of shared biological pathways. Further, for OPRM1, OXTR (oxytocin receptor), CRHR1 (corticotropin-releasing hormone receptor 1), COMT (catechol-O-methyltransferase), BDNF (brain-derived neurotrophic factor), APOE (apolipoprotein E), and SLC6A4 (solute carrier family 6 member 4), the same allele was associated with resilience across divergent resilience definitions, which suggests these genes may therefore provide a starting point for further research examining commonality in resilience pathways.

Verification of immunology-related genetic associations in BPD supports ABCA3 and five other genes

BACKGROUND

Inflammatory processes are key drivers of bronchopulmonary dysplasia (BPD), a chronic lung disease in preterm infants. In a large sample, we verify previously reported associations of genetic variants of immunology-related genes with BPD.

METHODS

Preterm infants with a gestational age ≤32 weeks from PROGRESS and the German Neonatal Network (GNN) were included. Through a consensus case/control definition, 278 BPD cases and 670 controls were identified. We identified 49 immunity-related genes and 55 single-nucleotide polymorphisms (SNPs) previously associated with BPD through a comprehensive literature survey. Additionally, a quantitative genetic association analysis regarding oxygen supplements, mechanical ventilation, and continuous positive air pressure (CPAP) was performed.

RESULTS

Five candidate SNPs were nominally associated with BPD-related phenotypes with effect directions not conflicting the original studies: rs11265269-CRP, rs1427793-NUAK1, rs2229569-SELL, rs1883617-VNN2, and rs4148913-CHST3. Four of these genes are involved in cell adhesion. Extending our analysis to all well-imputed SNPs of all candidate genes, the strongest association was rs45538638-ABCA3 with CPAP (p = 4.9 × 10^-7, FDR = 0.004), an ABC transporter involved in surfactant formation.

CONCLUSIONS

Most of the previously reported associations could not be replicated. We found additional support for SNPs in CRP, NUAK1, SELL, VNN2, and ABCA3. Larger studies and meta-analyses are required to corroborate these findings.

IMPACT

Larger cohort for improved statistical power to detect genetic associations with bronchopulmonary dysplasia (BPD). Most of the previously reported genetic associations with BPD could not be replicated in this larger study. Among investigated immunological relevant candidate genes, additional support was found for variants in genes CRP, NUAK1, SELL, VNN2, and CHST3, four of them related to cell adhesion. rs45538638 is a novel candidate SNP in reported candidate gene ABC-transporter ABCA3. Results help to prioritize molecular candidate pathomechanisms in follow-up studies.

Network pharmacology reveals the potential mechanism of Baiying Qinghou decoction in treating laryngeal squamous cell carcinoma

Context: Baiying Qinghou as a traditional Chinese medicine decoction shows anticancer property on laryngeal squamous cell carcinoma. However, little is known about the precise mechanism of Baiying Qinghou detection against laryngeal squamous cell carcinoma.

Objective: This study was aimed to explore potential mechanism of therapeutic actions of Baiying Qinghou decoction on laryngeal squamous cell carcinoma.

Materials and Methods: The active chemical components of Baiying Qinghou decoction were predicted, followed by integrated analysis of network pharmacology and molecular docking approach. The network pharmacology approach included target protein prediction, protein-protein interaction network construction and functional enrichment analysis.

Results: Sitosterol and quercetin were predicted to be the overlapped active ingredients among three Chinese herbs of Baiying Qinghou decoction. The target proteins were closely associated with response to chemical, response to drug related biological process and cancer related pathways such as PI3K-Akt signaling, HIF-1 signaling and Estrogen signaling pathway. The target proteins of TP53, EGFR, PTGS2, NOS3 and IL1B as the key nodes in PPI network were cross-validated, among which EGFR, IL1B, NOS3 and TP53 were significantly correlated with the prognosis of patients with laryngeal squamous cell carcinoma. Finally, the binding modes of EGFR, IL1B, NOS3 and TP53 with quercetin were visualized.

Discussion and Conclusion: Quercetin of Baiying Qinghou decoction showed therapeutic effect against laryngeal squamous cell carcinoma by regulating TP53, EGFR, NOS3 and IL1B involved with drug resistance and PI3K-AKT signaling pathway. TP53, EGFR, NOS3 and IL1B may be the candidate targets for the treatment of laryngeal squamous cell carcinoma.

Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications

Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.

Drug Repurposing: A Network-based Approach to Amyotrophic Lateral Sclerosis

The continuous adherence to the conventional "one target, one drug" paradigm has failed so far to provide effective therapeutic solutions for heterogeneous and multifactorial diseases as amyotrophic lateral sclerosis (ALS), a rare progressive and chronic, debilitating neurological disease for which no cure is available. The present study is aimed at finding innovative solutions and paradigms for therapy in ALS pathogenesis, by exploiting new insights from Network Medicine and drug repurposing strategies. To identify new drug-ALS disease associations, we exploited SAveRUNNER, a recently developed network-based algorithm for drug repurposing, which quantifies the proximity of disease-associated genes to drug targets in the human interactome. We prioritized 403 SAveRUNNER-predicted drugs according to decreasing values of network similarity with ALS. Among catecholamine, dopamine, serotonin, histamine, and GABA receptor modulators, as well as angiotensin-converting enzymes, cyclooxygenase isozymes, and serotonin transporter inhibitors, we found some interesting no customary ALS drugs, including amoxapine, clomipramine, mianserin, and modafinil. Furthermore, we strengthened the SAveRUNNER predictions by a gene set enrichment analysis that confirmed modafinil as a drug with the highest score among the 121 identified drugs with a score > 0. Our results contribute to gathering further proofs of innovative solutions for therapy in ALS pathogenesis.

Interaction between early-life pet exposure and methylation pattern of ADAM33 on allergic rhinitis among children aged 3-6 years in China

Background

Recent research has pointed out the important roles of epigenetic modifications in the development and persistence of allergic rhinitis (AR), especially in relation to DNA methylation of disease-associated genes. We investigated whether AR susceptibility genes were epigenetically regulated, and whether methylation modulation of these genes in response to early-life environment could be a molecular mechanism underlying the risk for AR onset in a cohort of children aged 3–6 years in China.

Methods

Peripheral blood mononuclear cell (PBMC) samples were collected from 130 children patients, aged 3–6 years and diagnosed with AR; and 154 matched controls to detect promoter methylation in 25 AR susceptibility genes with the MethylTarget approach. Methylation levels were compared for each CpG site, each amplified region, and each gene. In addition, the relationship among DNA methylation, early-life environmental risk factors and AR onset were assessed.

Results

Maternal allergic history (P = 0.0390) and pet exposure (P = 0.0339) were significantly associated with increased AR risk. Differential methylation analyses were successfully performed for 507 CpG sites, 34 amplified regions and 17 genes and significant hypomethylation was observed in the promoter region of ADAM33 in AR patients [multiple test-corrected (FDR) P-value < 0.05]. Spearman correlation analysis revealed that the hypomethylation of ADAM33 was significantly associated with higher eosinophil counts (Spearman’s ρ: − 0.187, P-value = 0.037). According to the results of the multiple regression analysis, after adjusting for cofounders, the interaction of early-life pet exposure with methylation level of ADAM33 increased the risk for AR onset 1.423 times more in children (95% CI = 0.0290–4.109, P-value = 0.005).

Conclusion

This study provides evidence that early-life pet exposure and low methylation level of ADAM33 increase AR risk in children, and the interaction between pet exposure and methylation level of ADAM33 may play an important role in the development of AR.

Guidelines for biomarker discovery in endometrium: correcting for menstrual cycle bias reveals new genes associated with uterine disorders

Transcriptomic approaches are increasingly used in reproductive medicine to identify candidate endometrial biomarkers. However, it is known that endometrial progression in the molecular biology of the menstrual cycle is a main factor that could affect the discovery of disorder-related genes. Therefore, the aim of this study was to systematically review current practices for considering the menstrual cycle effect and to demonstrate its bias in the identification of potential biomarkers. From the 35 studies meeting the criteria, 31.43% did not register the menstrual cycle phase. We analysed the menstrual cycle effect in 11 papers (including 12 studies) from Gene Expression Omnibus: three evaluating endometriosis, two evaluating recurrent implantation failure, one evaluating recurrent pregnancy loss, one evaluating uterine fibroids and five control studies, which collected endometrial samples throughout menstrual cycle. An average of 44.2% more genes were identified after removing menstrual cycle bias using linear models. This effect was observed even if studies were balanced in the proportion of samples collected at different endometrial stages or only in the mid-secretory phase. Our bias correction method increased the statistical power by retrieving more candidate genes than per-phase independent analyses. Thanks to this practice, we discovered 544 novel candidate genes for eutopic endometriosis, 158 genes for ectopic ovarian endometriosis and 27 genes for recurrent implantation failure. In conclusion, we demonstrate that menstrual cycle progression masks molecular biomarkers, provides new guidelines to unmask them and proposes a new classification that distinguishes between biomarkers of disorder or/and menstrual cycle progression.

SAveRUNNER: an R-based tool for drug repurposing

BACKGROUND

Currently, no proven effective drugs for the novel coronavirus disease COVID-19 exist and despite widespread vaccination campaigns, we are far short from herd immunity. The number of people who are still vulnerable to the virus is too high to hamper new outbreaks, leading a compelling need to find new therapeutic options devoted to combat SARS-CoV-2 infection. Drug repurposing represents an effective drug discovery strategy from existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery.

RESULTS

We developed a network-based tool for drug repurposing provided as a freely available R-code, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), with the aim to offer a promising framework to efficiently detect putative novel indications for currently marketed drugs against diseases of interest. SAveRUNNER predicts drug-disease associations by quantifying the interplay between the drug targets and the disease-associated proteins in the human interactome through the computation of a novel network-based similarity measure, which prioritizes associations between drugs and diseases located in the same network neighborhoods.

CONCLUSIONS

The algorithm was successfully applied to predict off-label drugs to be repositioned against the new human coronavirus (2019-nCoV/SARS-CoV-2), and it achieved a high accuracy in the identification of well-known drug indications, thus revealing itself as a powerful tool to rapidly detect potential novel medical indications for various drugs that are worth of further investigation. SAveRUNNER source code is freely available at https://github.com/giuliafiscon/SAveRUNNER.git , along with a comprehensive user guide.

Genetics in the Host-Mycobacterium ulcerans interaction

Buruli ulcer is an emerging infectious disease associated with high morbidity and unpredictable outbreaks. It is caused by Mycobacterium ulcerans, a slow-growing pathogen evolutionarily shaped by the acquisition of a plasmid involved in the production of a potent macrolide-like cytotoxin and by genome rearrangements and downsizing. These events culminated in an uncommon infection pattern, whereby M. ulcerans is both able to induce the initiation of the inflammatory cascade and the cell death of its proponents, as well as to survive within the phagosome and in the extracellular milieu. In such extreme conditions, the host is sentenced to rely on a highly orchestrated genetic landscape to be able to control the infection. We here revisit the dynamics of M. ulcerans infection, drawing parallels from other mycobacterioses and integrating the most recent knowledge on its evolution and pathogenicity in its interaction with the host immune response.

An interpretable low-complexity machine learning framework for robust exome-based in-silico diagnosis of Crohn's disease patients

Whole exome sequencing (WES) data are allowing researchers to pinpoint the causes of many Mendelian disorders. In time, sequencing data will be crucial to solve the genome interpretation puzzle, which aims at uncovering the genotype-to-phenotype relationship, but for the moment many conceptual and technical problems need to be addressed. In particular, very few attempts at the in-silico diagnosis of oligo-to-polygenic disorders have been made so far, due to the complexity of the challenge, the relative scarcity of the data and issues such as batch effects and data heterogeneity, which are confounder factors for machine learning (ML) methods. Here, we propose a method for the exome-based in-silico diagnosis of Crohn’s disease (CD) patients which addresses many of the current methodological issues. First, we devise a rational ML-friendly feature representation for WES data based on the gene mutational burden concept, which is suitable for small sample sizes datasets. Second, we propose a Neural Network (NN) with parameter tying and heavy regularization, in order to limit its complexity and thus the risk of over-fitting. We trained and tested our NN on 3 CD case-controls datasets, comparing the performance with the participants of previous CAGI challenges. We show that, notwithstanding the limited NN complexity, it outperforms the previous approaches. Moreover, we interpret the NN predictions by analyzing the learned patterns at the variant and gene level and investigating the decision process leading to each prediction.

SAveRUNNER: A network-based algorithm for drug repurposing and its application to COVID-19

The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by quantifying the interplay between the drug targets and the disease-specific proteins in the human interactome via a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14 selected diseases with a consolidated knowledge about their disease-causing genes and that have been found to be related to COVID-19 for genetic similarity (i.e., SARS), comorbidity (e.g., cardiovascular diseases), or for their association to drugs tentatively repurposed to treat COVID-19 (e.g., malaria, HIV, rheumatoid arthritis). Focusing specifically on SARS subnetwork, we identified 282 repurposable drugs, including some the most rumored off-label drugs for COVID-19 treatments (e.g., chloroquine, hydroxychloroquine, tocilizumab, heparin), as well as a new combination therapy of 5 drugs (hydroxychloroquine, chloroquine, lopinavir, ritonavir, remdesivir), actually used in clinical practice. Furthermore, to maximize the efficiency of putative downstream validation experiments, we prioritized 24 potential anti-SARS-CoV repurposable drugs based on their network-based similarity values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies (e.g., anti-IFNγ, anti-TNFα, anti-IL12, anti-IL1β, anti-IL6), and thrombin inhibitors. Finally, our findings were in-silico validated by performing a gene set enrichment analysis, which confirmed that most of the network-predicted repurposable drugs may have a potential treatment effect against human coronavirus infections.

Sharing genetic variants with the NGS pipeline is essential for effective genomic data sharing and reproducibility in health information exchange

Genetic variants causing underlying pharmacogenetic and disease phenotypes have been used as the basis for clinical decision-making. However, due to the lack of standards for next-generation sequencing (NGS) pipelines, reproducing genetic variants among institutions is still difficult. The aim of this study is to show how many important variants for clinical decisions can be individually detected using different pipelines. Genetic variants were derived from 105 breast cancer patient target DNA sequences via three different variant-calling pipelines. HaplotypeCaller, Mutect2 tumor-only mode in the Genome Analysis ToolKit (GATK), and VarScan were used in variant calling from the sequence read data processed by the same NGS preprocessing tools using Variant Effect Predictor. GATK HaplotypeCaller, VarScan, and MuTect2 found 25,130, 16,972, and 4232 variants, comprising 1491, 1400, and 321 annotated variants with ClinVar significance, respectively. The average number of ClinVar significant variants in the patients was 769.43, 16.50% of the variants were detected by only one variant caller. Despite variants with significant impact on clinical decision-making, the detected variants are different for each algorithm. To utilize genetic variants in the clinical field, a strict standard for NGS pipelines is essential.

Frequency of Pathogenic Germline Variants in Cancer-Susceptibility Genes in Patients With Osteosarcoma

Importance

Osteosarcoma, the most common malignant bone tumor in children and adolescents, occurs in a high number of cancer predisposition syndromes that are defined by highly penetrant germline mutations. The germline genetic susceptibility to osteosarcoma outside of familial cancer syndromes remains unclear.

Objective

To investigate the germline genetic architecture of 1244 patients with osteosarcoma.

Design, Setting, and Participants

Whole-exome sequencing (n = 1104) or targeted sequencing (n = 140) of the DNA of 1244 patients with osteosarcoma from 10 participating international centers or studies was conducted from April 21, 2014, to September 1, 2017. The results were compared with the DNA of 1062 individuals without cancer assembled internally from 4 participating studies who underwent comparable whole-exome sequencing and 27 173 individuals of non-Finnish European ancestry who were identified through the Exome Aggregation Consortium (ExAC) database. In the analysis, 238 high-interest cancer-susceptibility genes were assessed followed by testing of the mutational burden across 736 additional candidate genes. Principal component analyses were used to identify 732 European patients with osteosarcoma and 994 European individuals without cancer, with outliers removed for patient-control group comparisons. Patients were subsequently compared with individuals in the ExAC group. All data were analyzed from June 1, 2017, to July 1, 2019.

Main Outcomes and Measures

The frequency of rare pathogenic or likely pathogenic genetic variants.

Results

Among 1244 patients with osteosarcoma (mean [SD] age at diagnosis, 16 [8.9] years [range, 2-80 years]; 684 patients [55.0%] were male), an analysis restricted to individuals with European ancestry indicated a significantly higher pathogenic or likely pathogenic variant burden in 238 high-interest cancer-susceptibility genes among patients with osteosarcoma compared with the control group (732 vs 994, respectively; P = 1.3 × 10-18). A pathogenic or likely pathogenic cancer-susceptibility gene variant was identified in 281 of 1004 patients with osteosarcoma (28.0%), of which nearly three-quarters had a variant that mapped to an autosomal-dominant gene or a known osteosarcoma-associated cancer predisposition syndrome gene. The frequency of a pathogenic or likely pathogenic cancer-susceptibility gene variant was 128 of 1062 individuals (12.1%) in the control group and 2527 of 27 173 individuals (9.3%) in the ExAC group. A higher than expected frequency of pathogenic or likely pathogenic variants was observed in genes not previously linked to osteosarcoma (eg, CDKN2A, MEN1, VHL, POT1, APC, MSH2, and ATRX) and in the Li-Fraumeni syndrome-associated gene, TP53.

Conclusions and Relevance

In this study, approximately one-fourth of patients with osteosarcoma unselected for family history had a highly penetrant germline mutation requiring additional follow-up analysis and possible genetic counseling with cascade testing.

[6S]-5-Methyltetrahydrofolic Acid and Folic Acid Pregnancy Diets Differentially Program Metabolic Phenotype and Hypothalamic Gene Expression of Wistar Rat Dams Post-Birth

[6S]-5-methyltetrahydrofolic acid (MTHF) is a proposed replacement for folic acid (FA) in diets and prenatal supplements. This study compared the effects of these two forms on maternal metabolism and hypothalamic gene expression. Pregnant Wistar rats received an AIN-93G diet with recommended FA (1X, 2 mg/kg, control), 5X-FA or equimolar levels of MTHF. During lactation they received the control diet and then a high fat diet for 19-weeks post-weaning. Body weight, adiposity, food intake, energy expenditure, plasma hormones, folate, and 1-carbon metabolites were measured. RNA-sequencing of the hypothalamus was conducted at parturition. Weight-loss from weaning to 1-week post-weaning was less in dams fed either form of the 5X vs. 1X folate diets, but final weight-gain was higher in 5X-MTHF vs. 5X-FA dams. Both doses of the MTHF diets led to 8% higher food intake and associated with lower plasma leptin at parturition, but higher leptin at 19-weeks and insulin resistance at 1-week post-weaning. RNA-sequencing revealed 279 differentially expressed genes in the hypothalamus in 5X-MTHF vs. 5X-FA dams. These findings indicate that MTHF and FA differ in their programing effects on maternal phenotype, and a potential adverse role of either form when given at the higher doses.

Context-Specific Striatal Astrocyte Molecular Responses Are Phenotypically Exploitable

Astrocytes tile the central nervous system and are widely implicated in brain diseases, but the molecular mechanisms by which astrocytes contribute to brain disorders remain incompletely explored. By performing astrocyte gene expression analyses following 14 experimental perturbations of relevance to the striatum, we discovered that striatal astrocytes mount context-specific molecular responses at the level of genes, pathways, and upstream regulators. Through data mining, we also identified astrocyte pathways in Huntington's disease (HD) that were reciprocally altered with respect to the activation of striatal astrocyte G protein-coupled receptor (GPCR) signaling. Furthermore, selective striatal astrocyte stimulation of the G_i-GPCR pathway in vivo corrected several HD-associated astrocytic, synaptic, and behavioral phenotypes, with accompanying improvement of HD-associated astrocyte signaling pathways, including those related to synaptogenesis and neuroimmune functions. Overall, our data show that astrocytes are malleable, using context-specific responses that can be dissected molecularly and used for phenotypic benefit in brain disorders.

Trophoblast Stem-Cell-Derived Exosomes Improve Doxorubicin-Induced Dilated Cardiomyopathy by Modulating the let-7i/YAP Pathway

Trophoblast stem cells (TSCs) have been confirmed to play a cardioprotective role in heart failure. However, whether TSC-derived exosomes (TSC-exos) can protect against cardiac injury remains unclear. In the present study, TSC-exos were isolated from the supernatant of TSCs using the ultracentrifugation method and characterized by transmission electron microscopy and western blotting. Utilizing the public Gene Expression Omnibus (GEO) database, we found that let-7i and Yes-associated protein 1 (YAP) could participate in the development of heart failure. In vitro, AC16 cardiomyocytes subjected to doxorubicin (DOX) were treated with TSC-exos or let-7i mimic. Flow cytometry showed that TSC-exos and let-7i both decreased cardiomyocyte apoptosis. In vivo, mice that were intraperitoneally injected into DOX received either PBS, TSC-exos, or AAV9-let7i^up for let-7i overexpression. Mice receiving TSC-exos and AAV9-let7i^up showed improved cardiac function and decreased inflammatory responses, accompanied by downregulated YAP signaling. Mechanistically, TSC-exos could transfer let-7i to cardiomyocytes and silence the YAP signaling pathway. In conclusion, TSC-exos could alleviate DOX-induced cardiac injury via the let-7i/YAP pathway, which sheds new light on the application of TSC-exos as a potential therapeutic tool for heart failure.

Identification of Germline Mutations in Melanoma Patients with Early Onset, Double Primary Tumors, or Family Cancer History by NGS Analysis of 217 Genes

Cutaneous melanoma is the deadliest skin malignity with a rising prevalence worldwide. Patients carrying germline mutations in melanoma-susceptibility genes face an increased risk of melanoma and other cancers. To assess the spectrum of germline variants, we analyzed 264 Czech melanoma patients indicated for testing due to early melanoma (at <25 years) or the presence of multiple primary melanoma/melanoma and other cancer in their personal and/or family history. All patients were analyzed by panel next-generation sequencing targeting 217 genes in four groups: high-to-moderate melanoma risk genes, low melanoma risk genes, cancer syndrome genes, and other genes with an uncertain melanoma risk. Population frequencies were assessed in 1479 population-matched controls. Selected POT1 and CHEK2 variants were characterized by functional assays. Mutations in clinically relevant genes were significantly more frequent in melanoma patients than in controls (31/264; 11.7% vs. 58/1479; 3.9%; p = 2.0 × 10⁻⁶). A total of 9 patients (3.4%) carried mutations in high-to-moderate melanoma risk genes (CDKN2A, POT1, ACD) and 22 (8.3%) patients in other cancer syndrome genes (NBN, BRCA1/2, CHEK2, ATM, WRN, RB1). Mutations in high-to-moderate melanoma risk genes (OR = 52.2; 95%CI 6.6–413.1; p = 3.2 × 10⁻⁷) and in other cancer syndrome genes (OR = 2.3; 95%CI 1.4–3.8; p = 0.003) were significantly associated with melanoma risk. We found an increased potential to carry these mutations (OR = 2.9; 95%CI 1.2–6.8) in patients with double primary melanoma, melanoma and other primary cancer, but not in patients with early age at onset. The analysis revealed affected genes in Czech melanoma patients and identified individuals who may benefit from genetic testing and future surveillance management of mutation carriers.