A human functional protein interaction network and its application to cancer data analysis

Research into how carvacrol and metformin affect several human proteins in a hyperglycemic condition: A comparative study in silico and in vitro

Carvacrol (CV) is an organic compound found in the essential oils of many aromatic herbs. It is nearly unfeasible to analyze all the current human proteins for a query ligand using in vitro and in vivo methods. This study aimed to clarify whether CV possesses an anti-diabetic feature via Docking-based inverse docking and molecular dynamic (MD) simulation and in vitro characterization against a set of novel human protein targets. Herein, the best poses of CV docking simulations according to binding energy ranged from -7.9 to -3.5 (kcal/mol). After pathway analysis of the protein list through GeneMANIA and WebGestalt, eight interacting proteins (DPP4, FBP1, GCK, HSD11β1, INSR, PYGL, PPARA, and PPARG) with CV were determined, and these proteins exhibited stable structures during the MD process with CV. In vitro application, statistically significant results were achieved only in combined doses with CV or metformin. Considering all these findings, PPARG and INSR, among these target proteins of CV, are FDA-approved targets for treating diabetes. Therefore, CV may be on its way to becoming a promising therapeutic compound for treating Diabetes Mellitus (DM). Our outcomes expose formerly unexplored potential target human proteins, whose association with diabetic disorders might guide new potential treatments for DM.

The splicing factor CCAR1 regulates the Fanconi anemia/BRCA pathway

The twenty-three Fanconi anemia (FA) proteins cooperate in the FA/BRCA pathway to repair DNA interstrand cross-links (ICLs). The cell division cycle and apoptosis regulator 1 (CCAR1) protein is also a regulator of ICL repair, though its possible function in the FA/BRCA pathway remains unknown. Here, we demonstrate that CCAR1 plays a unique upstream role in the FA/BRCA pathway and is required for FANCA protein expression in human cells. Interestingly, CCAR1 co-immunoprecipitates with FANCA pre-mRNA and is required for FANCA mRNA processing. Loss of CCAR1 results in retention of a poison exon in the FANCA transcript, thereby leading to reduced FANCA protein expression. A unique domain of CCAR1, the EF hand domain, is required for interaction with the U2AF heterodimer of the spliceosome and for excision of the poison exon. Taken together, CCAR1 is a splicing modulator required for normal splicing of the FANCA mRNA and other mRNAs involved in various cellular pathways.

AI-based histopathology image analysis reveals a distinct subset of endometrial cancers

Endometrial cancer (EC) has four molecular subtypes with strong prognostic value and therapeutic implications. The most common subtype (NSMP; No Specific Molecular Profile) is assigned after exclusion of the defining features of the other three molecular subtypes and includes patients with heterogeneous clinical outcomes. In this study, we employ artificial intelligence (AI)-powered histopathology image analysis to differentiate between p53abn and NSMP EC subtypes and consequently identify a sub-group of NSMP EC patients that has markedly inferior progression-free and disease-specific survival (termed 'p53abn-like NSMP'), in a discovery cohort of 368 patients and two independent validation cohorts of 290 and 614 from other centers. Shallow whole genome sequencing reveals a higher burden of copy number abnormalities in the 'p53abn-like NSMP' group compared to NSMP, suggesting that this group is biologically distinct compared to other NSMP ECs. Our work demonstrates the power of AI to detect prognostically different and otherwise unrecognizable subsets of EC where conventional and standard molecular or pathologic criteria fall short, refining image-based tumor classification. This study's findings are applicable exclusively to females.

Peroxiredoxin 6 suppresses ferroptosis in lung endothelial cells

Peroxiredoxin 6 (Prdx6) repairs peroxidized membranes by reducing oxidized phospholipids, and by replacing oxidized sn-2 fatty acyl groups through hydrolysis/reacylation by its phospholipase A2 (aiPLA2) and lysophosphatidylcholine acyltransferase activities. Prdx6 is highly expressed in the lung, and intact lungs and cells null for Prdx6 or with single-point mutations that inactivate either Prdx6-peroxidase or aiPLA2 activity alone exhibit decreased viability, increased lipid peroxidation, and incomplete repair when exposed to paraquat, hyperoxia, or organic peroxides. Ferroptosis is form of cell death driven by the accumulation of phospholipid hydroperoxides. We studied the role of Prdx6 as a ferroptosis suppressor in the lung. We first compared the expression Prdx6 and glutathione peroxidase 4 (GPx4) and visualized Prdx6 and GPx4 within the lung. Lung Prdx6 mRNA levels were five times higher than GPx4 levels. Both Prdx6 and GPx4 localized to epithelial and endothelial cells. Prdx6 knockout or knockdown sensitized lung endothelial cells to erastin-induced ferroptosis. Cells with genetic inactivation of either aiPLA2 or Prdx6-peroxidase were more sensitive to ferroptosis than WT cells, but less sensitive than KO cells. We then conducted RNA-seq analyses in Prdx6-depleted cells to further explore how the loss of Prdx6 sensitizes lung endothelial cells to ferroptosis. Prdx6 KD upregulated transcriptional signatures associated with selenoamino acid metabolism and mitochondrial function. Accordingly, Prdx6 deficiency blunted mitochondrial function and increased GPx4 abundance whereas GPx4 KD had the opposite effect on Prdx6. Moreover, we detected Prdx6 and GPx4 interactions in intact cells, suggesting that both enzymes cooperate to suppress lipid peroxidation. Notably, Prdx6-depleted cells remained sensitive to erastin-induced ferroptosis despite the compensatory increase in GPx4. These results show that Prdx6 suppresses ferroptosis in lung endothelial cells and that both aiPLA2 and Prdx6-peroxidase contribute to this effect. These results also show that Prdx6 supports mitochondrial function and modulates several coordinated cytoprotective pathways in the pulmonary endothelium.

Network pharmacology, molecular docking, and dynamics analyses to predict the antiviral activity of ginger constituents against coronavirus infection

COVID-19 is a global pandemic that caused a dramatic loss of human life worldwide, leading to accelerated research for antiviral drug discovery. Herbal medicine is one of the most commonly used alternative medicine for the prevention and treatment of many conditions including respiratory system diseases. In this study, a computational pipeline was employed, including network pharmacology, molecular docking simulations, and molecular dynamics simulations, to analyze the common phytochemicals of ginger rhizomes and identify candidate constituents as viral inhibitors. Furthermore, experimental assays were performed to analyze the volatile and non-volatile compounds of ginger and to assess the antiviral activity of ginger oil and hydroalcoholic extract. Network pharmacology analysis showed that ginger compounds target human genes that are involved in related cellular processes to the viral infection. Docking analysis highlighted five pungent compounds and zingiberenol as potential inhibitors for the main protease (Mpro), spike receptor-binding domain (RBD), and human angiotensin-converting enzyme 2 (ACE2). Then, (6)-gingerdiacetate was selected for molecular dynamics (MD) simulations as it exhibited the best binding interactions and free energies over the three target proteins. Trajectories analysis of the three complexes showed that RBD and ACE2 complexes with the ligand preserved similar patterns of root mean square deviation (RMSD) and radius of gyration (Rg) values to their respective native structures. Finally, experimental validation of the ginger hydroalcoholic extract confirmed the existence of (6)-gingerdiacetate and revealed the strong antiviral activity of the hydroalcoholic extract with IC50 of 2.727 μ g / ml . Our study provides insights into the potential antiviral activity of (6)-gingerdiacetate that may enhance the host immune response and block RBD binding to ACE2, thereby, inhibiting SARS-CoV-2 infection.

Can Chinese herbal medicine offer feasible solutions for newly diagnosed esophageal cancer patients with malnutrition? a multi-institutional real-world study

Background

Esophageal cancer (EC) is a major cause of cancer-related mortality in Taiwan and globally. Patients with EC are highly prone to malnutrition, which adversely affects their prognosis. While Chinese herbal medicine (CHM) is commonly used alongside conventional anti-cancer treatments, its long-term impact on EC patients with malnutrition remains unclear.

Methods

This study utilized a multi-center cohort from the Chang Gung Research Database, focusing on the long-term outcomes of CHM in EC patients with malnutrition between 1 January 2001, and 31 December 2018. Patients were monitored for up to 5 years or until death. Overall survival (OS) rates were calculated using the Kaplan-Meier method. Overlap weighting and landmark analysis were employed to address confounding and immortal time biases. Additionally, the study analyzed prescription data using a CHM network to identify key CHMs for EC with malnutrition, and potential molecular pathways were investigated using the Reactome database.

Results

EC patients with malnutrition who used CHM had a higher 5-year OS compared with nonusers (22.5% vs. 9% without overlap weighting; 24.3% vs. 13.3% with overlap weighting; log-rank test: p = 0.006 and 0.016, respectively). The median OS of CHM users was significantly longer than that of nonusers (19.8 vs. 12.9 months, respectively). Hazard ratio (HR) analysis showed a 31% reduction in all-cause mortality risk for CHM users compared with nonusers (HR: 0.69, 95% confidence interval: 0.50-0.94, p = 0.019). We also examined 665 prescriptions involving 306 CHM, with Hedyotis diffusa Willd. exhibiting the highest frequency of use. A CHM network was created to determine the primary CHMs and their combinations. The identified CHMs were associated with the regulation of immune and metabolic pathways, particularly in areas related to immune modulation, anti-cancer cachexia, promotion of digestion, and anti-tumor activity.

Conclusion

The results of this study suggest a correlation between CHM use and improved clinical outcomes in EC patients with malnutrition. The analysis identified core CHMs and combinations of formulations that play a crucial role in immunomodulation and metabolic regulation. These findings lay the groundwork for more extensive research on the use of CHM for the management of malnutrition in patients with EC.

The landscape of cancer-rewired GPCR signaling axes

We explored the dysregulation of G-protein-coupled receptor (GPCR) ligand systems in cancer transcriptomics datasets to uncover new therapeutics opportunities in oncology. We derived an interaction network of receptors with ligands and their biosynthetic enzymes. Multiple GPCRs are differentially regulated together with their upstream partners across cancer subtypes and are associated to specific transcriptional programs and to patient survival patterns. The expression of both receptor-ligand (or enzymes) partners improved patient stratification, suggesting a synergistic role for the activation of GPCR networks in modulating cancer phenotypes. Remarkably, we identified many such axes across several cancer molecular subtypes, including many involving receptor-biosynthetic enzymes for neurotransmitters. We found that GPCRs from these actionable axes, including, e.g., muscarinic, adenosine, 5-hydroxytryptamine, and chemokine receptors, are the targets of multiple drugs displaying anti-growth effects in large-scale, cancer cell drug screens, which we further validated. We have made the results generated in this study freely available through a webapp (gpcrcanceraxes.bioinfolab.sns.it).

Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators

Cellular senescence underlies many aging-related pathologies, but its heterogeneity poses challenges for studying and targeting senescent cells. We present here a machine learning program senescent cell identification (SenCID), which accurately identifies senescent cells in both bulk and single-cell transcriptome. Trained on 602 samples from 52 senescence transcriptome datasets spanning 30 cell types, SenCID identifies six major senescence identities (SIDs). Different SIDs exhibit different senescence baselines, stemness, gene functions, and responses to senolytics. SenCID enables the reconstruction of senescent trajectories under normal aging, chronic diseases, and COVID-19. Additionally, when applied to single-cell Perturb-seq data, SenCID helps reveal a hierarchy of senescence modulators. Overall, SenCID is an essential tool for precise single-cell analysis of cellular senescence, enabling targeted interventions against senescent cells.

Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction

Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at: https://github.com/mxqmxqmxq/SCIS-CDG.

Hypoxia exposure blunts angiogenic signaling and upregulates the antioxidant system in endothelial cells derived from elephant seals

BACKGROUND

Elephant seals exhibit extreme hypoxemic tolerance derived from repetitive hypoxia/reoxygenation episodes they experience during diving bouts. Real-time assessment of the molecular changes underlying protection against hypoxic injury in seals remains restricted by their at-sea inaccessibility. Hence, we developed a proliferative arterial endothelial cell culture model from elephant seals and used RNA-seq, functional assays, and confocal microscopy to assess the molecular response to prolonged hypoxia.

RESULTS

Seal and human endothelial cells exposed to 1% O2 for up to 6 h respond differently to acute and prolonged hypoxia. Seal cells decouple stabilization of the hypoxia-sensitive transcriptional regulator HIF-1α from angiogenic signaling. Rapid upregulation of genes involved in glutathione (GSH) metabolism supports the maintenance of GSH pools, and intracellular succinate increases in seal but not human cells. High maximal and spare respiratory capacity in seal cells after hypoxia exposure occurs in concert with increasing mitochondrial branch length and independent from major changes in extracellular acidification rate, suggesting that seal cells recover oxidative metabolism without significant glycolytic dependency after hypoxia exposure.

CONCLUSIONS

We found that the glutathione antioxidant system is upregulated in seal endothelial cells during hypoxia, while this system remains static in comparable human cells. Furthermore, we found that in contrast to human cells, hypoxia exposure rapidly activates HIF-1 in seal cells, but this response is decoupled from the canonical angiogenesis pathway. These results highlight the unique mechanisms that confer extraordinary tolerance to limited oxygen availability in a champion diving mammal.

CRISPR-Cas9 screen of E3 ubiquitin ligases identifies TRAF2 and UHRF1 as regulators of HIV latency in primary human T cells

During HIV infection of CD4+ T cells, ubiquitin pathways are essential to viral replication and host innate immune response; however, the role of specific E3 ubiquitin ligases is not well understood. Proteomics analyses identified 116 single-subunit E3 ubiquitin ligases expressed in activated primary human CD4+ T cells. Using a CRISPR-based arrayed spreading infectivity assay, we systematically knocked out 116 E3s from activated primary CD4+ T cells and infected them with NL4-3 GFP reporter HIV-1. We found 10 E3s significantly positively or negatively affected HIV infection in activated primary CD4+ T cells, including UHRF1 (pro-viral) and TRAF2 (anti-viral). Furthermore, deletion of either TRAF2 or UHRF1 in three JLat models of latency spontaneously increased HIV transcription. To verify this effect, we developed a CRISPR-compatible resting primary human CD4+ T cell model of latency. Using this system, we found that deletion of TRAF2 or UHRF1 initiated latency reactivation and increased virus production from primary human resting CD4+ T cells, suggesting these two E3s represent promising targets for future HIV latency reversal strategies.

IMPORTANCE

HIV, the virus that causes AIDS, heavily relies on the machinery of human cells to infect and replicate. Our study focuses on the host cell's ubiquitination system which is crucial for numerous cellular processes. Many pathogens, including HIV, exploit this system to enhance their own replication and survival. E3 proteins are part of the ubiquitination pathway that are useful drug targets for host-directed therapies. We interrogated the 116 E3s found in human immune cells known as CD4+ T cells, since these are the target cells infected by HIV. Using CRISPR, a gene-editing tool, we individually removed each of these enzymes and observed the impact on HIV infection in human CD4+ T cells isolated from healthy donors. We discovered that 10 of the E3 enzymes had a significant effect on HIV infection. Two of them, TRAF2 and UHRF1, modulated HIV activity within the cells and triggered an increased release of HIV from previously dormant or "latent" cells in a new primary T cell assay. This finding could guide strategies to perturb hidden HIV reservoirs, a major hurdle to curing HIV. Our study offers insights into HIV-host interactions, identifies new factors that influence HIV infection in immune cells, and introduces a novel methodology for studying HIV infection and latency in human immune cells.

Transcriptomics- and Genomics-Guided Drug Repurposing for the Treatment of Vesicular Hand Eczema

Vesicular hand eczema (VHE), a clinical subtype of hand eczema (HE), showed limited responsiveness to alitretinoin, the only approved systemic treatment for severe chronic HE. This emphasizes the need for alternative treatment approaches. Therefore, our study aimed to identify drug repurposing opportunities for VHE using transcriptomics and genomics data. We constructed a gene network by combining 52 differentially expressed genes (DEGs) from a VHE transcriptomics study with 3 quantitative trait locus (QTL) genes associated with HE. Through network analysis, clustering, and functional enrichment analyses, we investigated the underlying biological mechanisms of this network. Next, we leveraged drug-gene interactions and retrieved pharmaco-transcriptomics data from the DrugBank database to identify drug repurposing opportunities for (V)HE. We developed a drug ranking system, primarily based on efficacy, safety, and practical and pricing factors, to select the most promising drug repurposing candidates. Our results revealed that the (V)HE network comprised 78 genes that yielded several biological pathways underlying the disease. The drug-gene interaction search together with pharmaco-transcriptomics lookups revealed 123 unique drug repurposing opportunities. Based on our drug ranking system, our study identified the most promising drug repurposing opportunities (e.g., vitamin D analogues, retinoids, and immunomodulating drugs) that might be effective in treating (V)HE.

Human protein-protein interaction networks: A topological comparison review

Protein-Protein Interaction Networks aim to model the interactome, providing a powerful tool for understanding the complex relationships governing cellular processes. These networks have numerous applications, including functional enrichment, discovering cancer driver genes, identifying drug targets, and more. Various databases make protein-protein networks available for many species, including Homo sapiens. This work topologically compares four Homo sapiens networks using a coarse-to-fine approach, comparing global characteristics, sub-network topology, specific nodes centrality, and interaction significance. Results show that the four human protein networks share many common protein-encoding genes and some global measures, but significantly differ in the interactions and neighbourhood. Small sub-networks from cancer pathways performed better than the whole networks, indicating an improved topological consistency in functional pathways. The centrality analysis shows that the same genes play different roles in different networks. We discuss how studies and analyses that rely on protein-protein networks for humans should consider their similarities and distinctions.

Croix B. Uncovering receptor-ligand interactions using a high-avidity CRISPR activation screening platform

The majority of clinically approved drugs target proteins that are secreted or cell surface bound. However, further advances in this area have been hindered by the challenging nature of receptor deorphanization, as there are still many secreted and cell-bound proteins with unknown binding partners. Here, we developed an advanced screening platform that combines CRISPR-CAS9 guide-mediated gene activation (CRISPRa) and high-avidity bead-based selection. The CRISPRa platform incorporates serial enrichment and flow cytometry-based monitoring, resulting in substantially improved screening sensitivity for well-known yet weak interactions of the checkpoint inhibitor family. Our approach has successfully revealed that siglec-4 exerts regulatory control over T cell activation through a low affinity trans-interaction with the costimulatory receptor 4-1BB. Our highly efficient screening platform holds great promise for identifying extracellular interactions of uncharacterized receptor-ligand partners, which is essential to develop next-generation therapeutics, including additional immune checkpoint inhibitors.

Privacy-Preserving Identification of Cancer Subtype-Specific Driver Genes Based on Multigenomics Data with Privatedriver

Identifying cancer subtype-specific driver genes from a large number of irrelevant passengers is crucial for targeted therapy in cancer treatment. Recently, the rapid accumulation of large-scale cancer genomics data from multiple institutions has presented remarkable opportunities for identification of cancer subtype-specific driver genes. However, the insufficient subtype samples, privacy issues, and heterogenous of aberration events pose great challenges in precisely identifying cancer subtype-specific driver genes. To address this, we introduce privatedriver, the first model for identifying subtype-specific driver genes that integrates genomics data from multiple institutions in a data privacy-preserving collaboration manner. The process of identifying subtype-specific cancer driver genes using privatedriver involves the following two steps: genomics data integration and collaborative training. In the integration process, the aberration events from multiple genomics data sources are combined for each institution using the forward and backward propagation method of NetICS. In the collaborative training process, each institution utilizes the federated learning framework to upload encrypted model parameters instead of raw data of all institutions to train a global model by using the non-negative matrix factorization algorithm. We applied privatedriver on head and neck squamous cell and colon cancer from The Cancer Genome Atlas website and evaluated it with two benchmarks using macro-Fscore. The comparison analysis demonstrates that privatedriver achieves comparable results to centralized learning models and outperforms most other nonprivacy preserving models, all while ensuring the confidentiality of patient information. We also demonstrate that, for varying predicted driver gene distributions in subtype, our model fully considers the heterogeneity of subtype and identifies subtype-specific driver genes corresponding to the given prognosis and therapeutic effect. The success of privatedriver reveals the feasibility and effectiveness of identifying cancer subtype-specific driver genes in a data protection manner, providing new insights for future privacy-preserving driver gene identification studies.

Leveraging synthetic lethality to uncover potential therapeutic target in gastric cancer

Since trastuzumab was approved in 2012 for the first-line treatment of gastric cancer (GC), no significant advancement in GC targeted therapies has occurred. Synthetic lethality refers to the concept that simultaneous dysfunction of a pair of genes results in a lethal effect on cells, while the loss of an individual gene does not cause this effect. Through exploiting synthetic lethality, novel targeted therapies can be developed for the individualized treatment of GC. In this study, we proposed a computational strategy named Gastric cancer Specific Synthetic Lethality inference (GSSL) to identify synthetic lethal interactions in GC. GSSL analysis was used to infer probable synthetic lethality in GC using four accessible clinical datasets. In addition, prediction results were confirmed by experiments. GSSL analysis identified a total of 34 candidate synthetic lethal pairs, which included 33 unique targets. Among the synthetic lethal gene pairs, TP53-CHEK1 was selected for further experimental validation. Both computational and experimental results indicated that inhibiting CHEK1 could be a potential therapeutic strategy for GC patients with TP53 mutation. Meanwhile, in vitro experimental validation of two novel synthetic lethal pairs TP53-AURKB and ARID1A-EP300 further proved the universality and reliability of GSSL. Collectively, GSSL has been shown to be a reliable and feasible method for comprehensive analysis of inferring synthetic lethal interactions of GC, which may offer novel insight into the precision medicine and individualized treatment of GC.

New Insights into Clinical Management for Sickle Cell Disease: Uncovering the Significant Pathways Affected by the Involvement of Sickle Cell Disease

One of the severe monogenic conditions with the highest prevalence in the globe is sickle cell disease. Although the significance of chronic anemia, hemolysis, and vasculopathy has been established, hemoglobin polymerization, which results in erythrocyte stiffness and Vaso-occlusion, is important to the pathophysiology of this disease. Clinical management is elementary, and there is scant reliable data for many treatments. The onset of cerebrovascular illness and cognitive impairment are two of the major issues associated with sickle cell disease in children, and it is only now that researchers are beginning to understand how blood transfusions and hydroxycarbamide can prevent these complications. When Vaso occlusion and inflammation occur repeatedly, the majority of organs are gradually damaged, including the brain, kidneys, lungs, bones, and cardiovascular system. This damage worsens with age. In our study, we focused on the specific pathways which are affected by the involvement of effected genes. Firstly, we retrieved the gene datasets from the publically available data source website DisGNET. Using literature-based genes, we identified 290 highly regulated genes that are directly associated with sickle cell disease. We subsequently performed a gene expression analysis and extracted a gene set using GEO2R analysis, which was then used to prune 290 differentially expressed genes (DEGs). After pruning we got 60 highly expressed genes. After identification of DEGs, we used these genes for pathway analysis. For the pathway analysis, we used Reactome software and we found that these DEGs are directly associated with 7 different pathways, which are alpha beta signaling pathways, 15 antiviral mechanism, Oligoadenylate synthetase (OAS) antiviral response, interleukin 1 signaling pathways, interleukin 4 and 13, interleukin 10 signaling pathway, and aspirin ADME pathway. After pathway analysis, we can exactly relate how sickle cell disease alters the gene expression and how these genes affect the different pathways. Additionally, we performed gene ontology of 60 genes and identified the gene biological process, cellular component, and molecular functions as we mentioned in our results. With the help of our study data, there is a chance for pre-identification of sickle cell disease person. Our gene result was used as a biomarker of sickle cell disease. In this paper, our result is the primary approach for sickle cell disease; with the help of this paper any researcher can get their primary data and use that for further research.

Advances in computational methods for identifying cancer driver genes

Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.

Medication-Related Osteonecrosis of the Jaw: A Systematic Review and a Bioinformatic Analysis

The objective was to evaluate the current evidence regarding the etiology of medication-related osteonecrosis of the jaw (MRONJ). This study systematically reviewed the literature by searching PubMed, Web of Science, and ProQuest databases for genes, proteins, and microRNAs associated with MRONJ from the earliest records through April 2023. Conference abstracts, letters, review articles, non-human studies, and non-English publications were excluded. Twelve studies meeting the inclusion criteria involving exposure of human oral mucosa, blood, serum, saliva, or adjacent bone or periodontium to anti-resorptive or anti-angiogenic agents were analyzed. The Cochrane Collaboration risk assessment tool was used to assess the quality of the studies. A total of 824 differentially expressed genes/proteins (DEGs) and 22 microRNAs were extracted for further bioinformatic analysis using Cytoscape, STRING, BiNGO, cytoHubba, MCODE, and ReactomeFI software packages and web-based platforms: DIANA mirPath, OmicsNet, and miRNet tools. The analysis yielded an interactome consisting of 17 hub genes and hsa-mir-16-1, hsa-mir-21, hsa-mir-23a, hsa-mir-145, hsa-mir-186, hsa-mir-221, and hsa-mir-424. A dominance of cytokine pathways was observed in both the cluster of hub DEGs and the interactome of hub genes with dysregulated miRNAs. In conclusion, a panel of genes, miRNAs, and related pathways were found, which is a step toward understanding the complexity of the disease.

High-Level Secretion of Pregnancy Zone Protein Is a Novel Biomarker of DNA Damage-Induced Senescence and Promotes Spontaneous Senescence

Identification of unique and specific biomarkers to better detect and quantify senescent cells remains challenging. By a global proteomic profiling of senescent human skin BJ fibroblasts induced by ionizing radiation (IR), the cellular level of pregnancy zone protein (PZP), a presumable pan-protease inhibitor never been linked to cellular senescence before, was found to be decreased by more than 10-fold, while the level of PZP in the conditioned medium was increased concomitantly. This observation was confirmed in a variety of senescent cells induced by IR or DNA-damaging drugs, indicating that high-level secretion of PZP is a novel senescence-associated secretory phenotype. RT-PCR examination verified that the transcription of the PZP gene is enhanced in various cells at senescence or upregulated following DNA damage treatment in a p53-independent manner. Moreover, pretreatment with late pregnancy serum containing a high level of PZP led to inhibition of doxorubicin-induced senescence in A549 cells, and depletion of PZP in the pregnancy serum could enhance such inhibition. Finally, the addition of immuno-precipitated PZP complexes into tissue culture attenuated the growth of A549 cells and promoted the spontaneous senescence. Therefore, we revealed that high-level secretion of PZP is a novel and unique feature associated with DNA damage-induced senescence, and secreted PZP is a positive regulator of cellular senescence, particularly during the late stage of gestation.

The landscape of cancer rewired GPCR signaling axes

We explored the dysregulation of GPCR ligand signaling systems in cancer transcriptomics datasets to uncover new therapeutics opportunities in oncology. We derived an interaction network of receptors with ligands and their biosynthetic enzymes, which revealed that multiple GPCRs are differentially regulated together with their upstream partners across cancer subtypes. We showed that biosynthetic pathway enrichment from enzyme expression recapitulated pathway activity signatures from metabolomics datasets, providing valuable surrogate information for GPCRs responding to organic ligands. We found that several GPCRs signaling components were significantly associated with patient survival in a cancer type-specific fashion. The expression of both receptor-ligand (or enzymes) partners improved patient stratification, suggesting a synergistic role for the activation of GPCR networks in modulating cancer phenotypes. Remarkably, we identified many such axes across several cancer molecular subtypes, including many pairs involving receptor-biosynthetic enzymes for neurotransmitters. We found that GPCRs from these actionable axes, including e.g., muscarinic, adenosine, 5-hydroxytryptamine and chemokine receptors, are the targets of multiple drugs displaying anti-growth effects in large-scale, cancer cell drug screens. We have made the results generated in this study freely available through a webapp (gpcrcanceraxes.bioinfolab.sns.it).

Classification of IDH wild-type glioblastoma tumorspheres into low- and high-invasion groups based on their transcriptional program

BACKGROUND

Glioblastoma (GBM), one of the most lethal tumors, exhibits a highly infiltrative phenotype. Here, we identified transcription factors (TFs) that collectively modulate invasion-related genes in GBM.

METHODS

The invasiveness of tumorspheres (TSs) were quantified using collagen-based 3D invasion assays. TF activities were quantified by enrichment analysis using GBM transcriptome, and confirmed by cell-magnified analysis of proteome imaging. Invasion-associated TFs were knocked down using siRNA or shRNA, and TSs were orthotopically implanted into mice.

RESULTS

After classifying 23 patient-derived GBM TSs into low- and high-invasion groups, we identified active TFs in each group-PCBP1 for low invasion, and STAT3 and SRF for high invasion. Knockdown of these TFs reversed the phenotype and invasion-associated-marker expression of GBM TSs. Notably, MRI revealed consistent patterns of invasiveness between TSs and the originating tumors, with an association between high invasiveness and poor prognosis. Compared to controls, mice implanted with STAT3- or SRF-downregulated GBM TSs showed reduced normal tissue infiltration and tumor growth, and prolonged survival, indicating a therapeutic response.

CONCLUSIONS

Our integrative transcriptome analysis revealed three invasion-associated TFs in GBM. Based on the relationship among the transcriptional program, invasive phenotype, and prognosis, we suggest these TFs as potential targets for GBM therapy.

Genome-wide DNA methylation sequencing identifies epigenetic perturbations in the upper airways under long-term exposure to moderate levels of ambient air pollution

While the link between exposure to high levels of ambient particulate matter (PM) and increased incidences of respiratory and cardiovascular diseases is widely recognized, recent epidemiological studies have shown that low PM concentrations are equally associated with adverse health effects. As DNA methylation is one of the main mechanisms by which cells regulate and stabilize gene expression, changes in the methylome could constitute early indicators of dysregulated signaling pathways. So far, little is known about PM-associated DNA methylation changes in the upper airways, the first point of contact between airborne pollutants and the human body. Here, we focused on cells of the upper respiratory tract and assessed their genome-wide DNA methylation pattern to explore exposure-associated early regulatory changes. Using a mobile epidemiological laboratory, nasal lavage samples were collected from a cohort of 60 adults that lived in districts with records of low (Simmerath) or moderate (Stuttgart) PM10 levels in Germany. PM10 concentrations were verified by particle measurements on the days of the sample collection and genome-wide DNA methylation was determined by enzymatic methyl sequencing at single-base resolution. We identified 231 differentially methylated regions (DMRs) between moderately and lowly PM10 exposed individuals. A high proportion of DMRs overlapped with regulatory elements, and DMR target genes were involved in pathways regulating cellular redox homeostasis and immune response. In addition, we found distinct changes in DNA methylation of the HOXA gene cluster whose methylation levels have previously been linked to air pollution exposure but also to carcinogenesis in several instances. The findings of this study suggest that regulatory changes in upper airway cells occur at PM10 levels below current European thresholds, some of which may be involved in the development of air pollution-related diseases.

Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder

Protein-protein interactions (PPIs) play a critical role in the proteomics study, and a variety of computational algorithms have been developed to predict PPIs. Though effective, their performance is constrained by high false-positive and false-negative rates observed in PPI data. To overcome this problem, a novel PPI prediction algorithm, namely PASNVGA, is proposed in this work by combining the sequence and network information of proteins via variational graph autoencoder. To do so, PASNVGA first applies different strategies to extract the features of proteins from their sequence and network information, and obtains a more compact form of these features using principal component analysis. In addition, PASNVGA designs a scoring function to measure the higher-order connectivity between proteins and so as to obtain a higher-order adjacency matrix. With all these features and adjacency matrices, PASNVGA trains a variational graph autoencoder model to further learn the integrated embeddings of proteins. The prediction task is then completed by using a simple feedforward neural network. Extensive experiments have been conducted on five PPI datasets collected from different species. Compared with several state-of-the-art algorithms, PASNVGA has been demonstrated as a promising PPI prediction algorithm.

A Comprehensive Analysis of the PI3K/AKT Pathway: Unveiling Key Proteins and Therapeutic Targets for Cancer Treatment

Background

Cancer development and progression involve a complex network of pathways among which certain pathways play a pivotal role in promoting tumor growth and survival. An important pathway in this context is the PI3K/AKT pathway, which regulates crucial cellular processes including proliferation, viability, and metabolic regulation. Dysregulation of this pathway has been strongly linked to the development of various types of cancers. Consequently, it is imperative to identify the key proteins within this pathway as potential targets for impeding cancer cell proliferation and survival.

Results

One of the key findings of this study was the identification of signaling proteins that dominate various forms of PI3K/Akt pathway. Furthermore, proteins play critical roles in cancer networks, acting as oncogenes that promote cancer development or as tumor suppressor genes that inhibit tumor growth. This study identified several genes, including KIT, ERBB2, PDGFRA, MET, FGFR2, and FGFR3, which are involved in various types of the PI3K/Akt pathways. Additionally, this study identified 55 proteins that are commonly found in various forms of PI3K/Akt, and these proteins play crucial roles in regulating various biological functions.

Conclusions

This study highlights the importance of identifying key proteins involved in the PI3K/AKT pathway. In this study, we identified several genes involved in different pathways that play essential roles in the activation, signaling, and regulation of the pathway. Understanding the proteins participating in the PI3K/AKT pathway is vital for the development of targeted therapies, not only for cancer but also for other related diseases. By elucidating their roles and functions, this study contributes to the advancement of knowledge in the field and paves the way for the development of effective treatments targeting this pathway.

Biology-inspired graph neural network encodes reactome and reveals biochemical reactions of disease

Functional heterogeneity of healthy human tissues complicates interpretation of molecular studies, impeding precision therapeutic target identification and treatment. Considering this, we generated a graph neural network with Reactome-based architecture and trained it using 9,115 samples from Genotype-Tissue Expression (GTEx). Our graph neural network (GNN) achieves adjusted Rand index (ARI) = 0.7909, while a Resnet18 control model achieves ARI = 0.7781, on 370 held-out healthy human tissue samples from The Cancer Genome Atlas (TCGA), despite the Resnet18 using over 600 times the parameters. Our GNN also succeeds in separating 83 healthy skin samples from 95 lesional psoriasis samples, revealing that upregulation of 26S- and NUB1-mediated degradation of NEDD8, UBD, and their conjugates is central to the largest perturbed reaction network component in psoriasis. We show that our results are not discoverable using traditional differential expression and hypergeometric pathway enrichment analyses yet are supported by separate human multi-omics and small-molecule mouse studies, suggesting future molecular disease studies may benefit from similar GNN analytical approaches.

Illuminate the Functions of Dark Proteins Using the Reactome-IDG Web Portal

Understudied or dark proteins have the potential to shed light on as-yet undiscovered molecular mechanisms that underlie phenotypes and suggest innovative therapeutic approaches for many diseases. The Reactome-IDG (Illuminating the Druggable Genome) project aims to place dark proteins in the context of manually curated, highly reliable pathways in Reactome, the most comprehensive, open-source biological pathway knowledgebase, facilitating the understanding functions and predicting therapeutic potentials of dark proteins. The Reactome-IDG web portal, deployed at https://idg.reactome.org, provides a simple, interactive web page for users to search pathways that may functionally interact with dark proteins, enabling the prediction of functions of dark proteins in the context of Reactome pathways. Enhanced visualization features implemented at the portal allow users to investigate the functional contexts for dark proteins based on tissue-specific gene or protein expression, drug-target interactions, or protein or gene pairwise relationships in the original Reactome's systems biology graph notation (SBGN) diagrams or the new simplified functional interaction (FI) network view of pathways. The protocols in this chapter describe step-by-step procedures to use the web portal to learn biological functions of dark proteins in the context of Reactome pathways. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Search for interacting pathways of a protein Support Protocol: Interacting pathway results for an annotated protein Alternate Protocol: Use individual pairwise relationships to predict interacting pathways of a protein Basic Protocol 2: Using the IDG pathway browser to study interacting pathways Basic Protocol 3: Overlaying tissue-specific expression data Basic Protocol 4: Overlaying protein/gene pairwise relationships in the pathway context Basic Protocol 5: Visualizing drug/target interactions.

A novel approach for the analysis of single-cell RNA sequencing identifies TMEM14B as a novel poor prognostic marker in hepatocellular carcinoma

A fundamental goal in cancer-associated genome sequencing is to identify the key genes. Protein-protein interactions (PPIs) play a crucially important role in this goal. Here, human reference interactome (HuRI) map was generated and 64,006 PPIs involving 9094 proteins were identified. Here, we developed a physical link and co-expression combinatory network construction (PLACE) method for genes of interest, which provides a rapid way to analyze genome sequencing datasets. Next, Kaplan‒Meier survival analysis, CCK8 assays, scratch wound assays and Transwell assays were applied to confirm the results. In this study, we selected single-cell sequencing data from patients with hepatocellular carcinoma (HCC) in GSE149614. The PLACE method constructs a protein connection network for genes of interest, and a large fraction (80%) of the genes (screened by the PLACE method) were associated with survival. Then, PLACE discovered that transmembrane protein 14B (TMEM14B) was the most significant prognostic key gene, and target genes of TMEM14B were predicted. The TMEM14B-target gene regulatory network was constructed by PLACE. We also detected that TMEM14B-knockdown inhibited proliferation and migration. The results demonstrate that we proposed a new effective method for identifying key genes. The PLACE method can be used widely and make outstanding contributions to the tumor research field.

Illuminating Dark Proteins using Reactome Pathways

Limited knowledge about a substantial portion of protein coding genes, known as "dark" proteins, hinders our understanding of their functions and potential therapeutic applications. To address this, we leveraged Reactome, the most comprehensive, open source, open-access pathway knowledgebase, to contextualize dark proteins within biological pathways. By integrating multiple resources and employing a random forest classifier trained on 106 protein/gene pairwise features, we predicted functional interactions between dark proteins and Reactome-annotated proteins. We then developed three scores to measure the interactions between dark proteins and Reactome pathways, utilizing enrichment analysis and fuzzy logic simulations. Correlation analysis of these scores with an independent single-cell RNA sequencing dataset provided supporting evidence for this approach. Furthermore, systematic natural language processing (NLP) analysis of over 22 million PubMed abstracts and manual checking of the literature associated with 20 randomly selected dark proteins reinforced the predicted interactions between proteins and pathways. To enhance the visualization and exploration of dark proteins within Reactome pathways, we developed the Reactome IDG portal, deployed at https://idg.reactome.org, a web application featuring tissue-specific protein and gene expression overlay, as well as drug interactions. Our integrated computational approach, together with the user-friendly web platform, offers a valuable resource for uncovering potential biological functions and therapeutic implications of dark proteins.

Identification of Probucol as a candidate for combination therapy with Metformin for Type 2 diabetes

Type 2 Diabetes (T2D) is often managed with metformin as the drug of choice. While it is effective overall, many patients progress to exhibit complications. Strategic drug combinations to tackle this problem would be useful. We constructed a genome-wide protein-protein interaction network capturing a global perspective of perturbations in diabetes by integrating T2D subjects' transcriptomic data. We computed a 'frequently perturbed subnetwork' in T2D that captures common perturbations across tissue types and mapped the possible effects of Metformin onto it. We then identified a set of remaining T2D perturbations and potential drug targets among them, related to oxidative stress and hypercholesterolemia. We then identified Probucol as the potential co-drug for adjunct therapy with Metformin and evaluated the efficacy of the combination in a rat model of diabetes. We find Metformin-Probucol at 5:0.5 mg/kg effective in restoring near-normal serum glucose, lipid, and cholesterol levels.

Extending inherited metabolic disorder diagnostics with biomarker interaction visualizations

BACKGROUND

Inherited Metabolic Disorders (IMDs) are rare diseases where one impaired protein leads to a cascade of changes in the adjacent chemical conversions. IMDs often present with non-specific symptoms, a lack of a clear genotype-phenotype correlation, and de novo mutations, complicating diagnosis. Furthermore, products of one metabolic conversion can be the substrate of another pathway obscuring biomarker identification and causing overlapping biomarkers for different disorders. Visualization of the connections between metabolic biomarkers and the enzymes involved might aid in the diagnostic process. The goal of this study was to provide a proof-of-concept framework for integrating knowledge of metabolic interactions with real-life patient data before scaling up this approach. This framework was tested on two groups of well-studied and related metabolic pathways (the urea cycle and pyrimidine de-novo synthesis). The lessons learned from our approach will help to scale up the framework and support the diagnosis of other less-understood IMDs.

METHODS

Our framework integrates literature and expert knowledge into machine-readable pathway models, including relevant urine biomarkers and their interactions. The clinical data of 16 previously diagnosed patients with various pyrimidine and urea cycle disorders were visualized on the top 3 relevant pathways. Two expert laboratory scientists evaluated the resulting visualizations to derive a diagnosis.

RESULTS

The proof-of-concept platform resulted in varying numbers of relevant biomarkers (five to 48), pathways, and pathway interactions for each patient. The two experts reached the same conclusions for all samples with our proposed framework as with the current metabolic diagnostic pipeline. For nine patient samples, the diagnosis was made without knowledge about clinical symptoms or sex. For the remaining seven cases, four interpretations pointed in the direction of a subset of disorders, while three cases were found to be undiagnosable with the available data. Diagnosing these patients would require additional testing besides biochemical analysis.

CONCLUSION

The presented framework shows how metabolic interaction knowledge can be integrated with clinical data in one visualization, which can be relevant for future analysis of difficult patient cases and untargeted metabolomics data. Several challenges were identified during the development of this framework, which should be resolved before this approach can be scaled up and implemented to support the diagnosis of other (less understood) IMDs. The framework could be extended with other OMICS data (e.g. genomics, transcriptomics), and phenotypic data, as well as linked to other knowledge captured as Linked Open Data.

Determining human-coronavirus protein-protein interaction using machine intelligence

The Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) virus spread the novel CoronaVirus −19 (nCoV-19) pandemic, resulting in millions of fatalities globally. Recent research demonstrated that the Protein-Protein Interaction (PPI) between SARS-CoV-2 and human proteins is accountable for viral pathogenesis. However, many of these PPIs are poorly understood and unexplored, necessitating a more in-depth investigation to find latent yet critical interactions. This article elucidates the host-viral PPI through Machine Learning (ML) lenses and validates the biological significance of the same using web-based tools. ML classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins, namely Amino Acid Composition, Pseudo Amino Acid Composition, Conjoint Triad, Dipeptide Composition, and Normalized Auto Correlation. A majority voting rule-based ensemble method composed of the Random Forest Model (RFM), AdaBoost, and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this work. The proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor ≥70%, validated by utilizing Gene Ontology (GO) and KEGG pathway enrichment analysis. Consequently, this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.

VIGET: A web portal for study of vaccine-induced host responses based on Reactome pathways and ImmPort data

Host responses to vaccines are complex but important to investigate. To facilitate the study, we have developed a tool called Vaccine Induced Gene Expression Analysis Tool (VIGET), with the aim to provide an interactive online tool for users to efficiently and robustly analyze the host immune response gene expression data collected in the ImmPort/GEO databases. VIGET allows users to select vaccines, choose ImmPort studies, set up analysis models by choosing confounding variables and two groups of samples having different vaccination times, and then perform differential expression analysis to select genes for pathway enrichment analysis and functional interaction network construction using the Reactome's web services. VIGET provides features for users to compare results from two analyses, facilitating comparative response analysis across different demographic groups. VIGET uses the Vaccine Ontology (VO) to classify various types of vaccines such as live or inactivated flu vaccines, yellow fever vaccines, etc. To showcase the utilities of VIGET, we conducted a longitudinal analysis of immune responses to yellow fever vaccines and found an intriguing complex activity response pattern of pathways in the immune system annotated in Reactome, demonstrating that VIGET is a valuable web portal that supports effective vaccine response studies using Reactome pathways and ImmPort data.

Docetaxel targets aggressive methylation profiles and serves as a radiosensitizer in high-risk meningiomas

BACKGROUND

Meningioma is the most common primary intracranial tumor in adults. A subset of these tumors recur and invade the brain, even after surgery and radiation, resulting in significant disability. There is currently no standard-of-care chemotherapy for meningiomas. As genomic DNA methylation profiling can prognostically stratify these lesions, we sought to determine whether any existing chemotherapies might be effective against meningiomas with high-risk methylation profiles.

METHODS

A previously published dataset of meningioma methylation profiles was used to screen for clinically significant CpG methylation events and associated cellular pathways. Based on these results, patient-derived meningioma cell lines were used to test candidate drugs in vitro and in vivo, including efficacy in conjunction with radiotherapy.

RESULTS

We identified 981 genes for which methylation of mapped CpG sites was related to progression-free survival in meningiomas. Associated molecular pathways were cross-referenced with FDA-approved cancer drugs, which nominated Docetaxel as a promising candidate for further preclinical analyses. Docetaxel arrested growth in 17 meningioma cell sources, representing all tumor grades, with a clinically favorable IC50 values ranging from 0.3 nM to 10.7 mM. The inhibitory effects of this medication scaled with tumor doubling time, with maximal benefit in fast-growing lesions. The combination of Docetaxel and radiation therapy increased markers of apoptosis and double-stranded DNA breaks, and extended the survival of mice engrafted with meningioma cells relative to either modality alone.

CONCLUSIONS

Global patterns of DNA methylation may be informative for the selection of chemotherapies against meningiomas, and existing drugs may enhance radiation sensitivity in high-risk cases.

Studying the connection between SF3B1 and four types of cancer by analyzing networks constructed based on published research

Splicing factor 3B subunit 1 (SF3B1) is the largest component of SF3b protein complex which is involved in the pre-mRNA splicing mechanism. Somatic mutations of SF3B1 were shown to be associated with aberrant splicing, producing abnormal transcripts that drive cancer development and/or prognosis. In this study, we focus on the relationship between SF3B1 and four types of cancer, namely myelodysplastic syndrome (MDS), acute myeloid leukemia (AML), and chronic lymphocytic leukemia (CLL) and breast cancer (BC). For this purpose, we identified from the Pubmed library only articles which mentioned SF3B1 in connection with the investigated types of cancer for the period 2007 to 2018 to reveal how the connection has developed over time. We left out all published articles which mentioned SF3B1 in other contexts. We retrieved the target articles and investigated the association between SF3B1 and the mentioned four types of cancer. For this we utilized some of the publicly available databases to retrieve gene/variant/disease information related to SF3B1. We used the outcome to derive and analyze a variety of complex networks that reflect the correlation between the considered diseases and variants associated with SF3B1. The results achieved based on the analyzed articles and reported in this article illustrated that SF3B1 is associated with hematologic malignancies, such as MDS, AML, and CLL more than BC. We found that different gene networks may be required for investigating the impact of mutant splicing factors on cancer development based on the target cancer type. Additionally, based on the literature analyzed in this study, we highlighted and summarized what other researchers have reported as the set of genes and cellular pathways that are affected by aberrant splicing in cancerous cells.

Identification of candidate genes associated with skin yellowness in yellow chickens

The yellow color of the skin is an important economic trait for yellow chickens. Low and non-uniform skin yellowness would reduce economic efficiency. However, the regulatory mechanism of chicken skin yellowness has not been fully elucidated. In this study, we evaluated the skin yellowness of 819 chickens by colorimeter and digital camera, which are from the same batch and the same age of 2 pure lines with significant differences in skin yellowness. A total of 982 candidate differential expressed genes (DEGs) were detected in duodenal tissue by RNA-seq analysis for high and low yellowness chickens. Among the DEGs, we chose fatty acid translocase (CD36) gene and identified a single nucleotide polymorphism (SNP) upstream of the CD36 gene that was significantly associated with skin yellowness at multiple parts of the chicken, and its different genotypes had significant effects on the promoter activity of the CD36 gene. These findings will help to further elucidate the molecular mechanism of chicken skin yellowness and is helpful for improving chicken skin yellowness.

Evidence of polygenic regulation of the physiological presence of neurofilament light chain in human serum

Introduction

The measurement of neurofilament light chain (NfL) in blood is a promising biomarker of neurological injury and disease. We investigated the genetic factors that underlie serum NfL levels (sNfL) of individuals without neurological conditions.

Methods

We performed a discovery genome-wide association study (GWAS) of sNfL in participants of the German BiDirect Study (N = 1,899). A secondary GWAS for meta-analysis was performed in a small Austrian cohort (N = 287). Results from the meta-analysis were investigated in relation with several clinical variables in BiDirect.

Results

Our discovery GWAS identified 12 genomic loci at the suggestive threshold ((p < 1 × 10^-5). After meta-analysis, 7 loci were suggestive of an association with sNfL. Genotype-specific differences in sNfL were observed for the lead variants of meta-analysis loci (rs34523114, rs114956339, rs529938, rs73198093, rs34372929, rs10982883, and rs1842909) in BiDirect participants. We identified potential associations in meta-analysis loci with markers of inflammation and renal function. At least 6 protein-coding genes (ACTG2, TPRKB, DMXL1, COL23A1, NAT1, and RIMS2) were suggested as genetic factors contributing to baseline sNfL levels.

Discussion

Our findings suggest that polygenic regulation of neuronal processes, inflammation, metabolism and clearance modulate the variability of NfL in the circulation. These could aid in the interpretation of sNfL measurements in a personalized manner.

Characterization of an RNA binding protein interactome reveals a context-specific post-transcriptional landscape of MYC-amplified medulloblastoma

Pediatric medulloblastoma (MB) is the most common solid malignant brain neoplasm, with Group 3 (G3) MB representing the most aggressive subgroup. MYC amplification is an independent poor prognostic factor in G3 MB, however, therapeutic targeting of the MYC pathway remains limited and alternative therapies for G3 MB are urgently needed. Here we show that the RNA-binding protein, Musashi-1 (MSI1) is an essential mediator of G3 MB in both MYC-overexpressing mouse models and patient-derived xenografts. MSI1 inhibition abrogates tumor initiation and significantly prolongs survival in both models. We identify binding targets of MSI1 in normal neural and G3 MB stem cells and then cross referenced these data with unbiased large-scale screens at the transcriptomic, translatomic and proteomic levels to systematically dissect its functional role. Comparative integrative multi-omic analyses of these large datasets reveal cancer-selective MSI1-bound targets sharing multiple MYC associated pathways, providing a valuable resource for context-specific therapeutic targeting of G3 MB.

In silico approaches uncovering the systematic function of N-phosphorylated proteins in human cells

Phosphorylation plays a key role in the regulation of protein function. In addition to the extensively studied O-phosphorylation of serine, threonine, and tyrosine, emerging evidence suggests that the non-canonical phosphorylation of histidine, lysine, and arginine termed N-phosphorylation, exists widely in eukaryotes. At present, the study of N-phosphorylation is still in its infancy, and its regulatory role and specific biological functions in mammalian cells are still unknown. Here, we report the in silico analysis of the systematic biological significance of N-phosphorylated proteins in human cells. The protein structural and functional domain enrichment analysis revealed that N-phosphorylated proteins are rich in RNA recognition motif, nucleotide-binding and alpha-beta plait domains. The most commonly enriched biological pathway is the metabolism of RNA. Besides, arginine phosphorylated (pArg) proteins are highly related to DNA repair, while histidine phosphorylated (pHis) proteins may play a role in the regulation of the cell cycle, and lysine phosphorylated (pLys) proteins are linked to cellular stress response, intracellular signal transduction, and intracellular transport, which are of great significance for maintaining cell homeostasis. Protein-protein interaction (PPI) network analysis revealed important hub proteins (i.e., SRSF1, HNRNPA1, HNRNPC, SRSF7, HNRNPH1, SRSF2, SRSF11, HNRNPD, SRRM2 and YBX1) which are closely related to neoplasms, nervous system diseases, and virus infection and have potential as therapeutic targets. Those proteins with clinical significance are worthy of attention, and the rational considerations of N-phosphorylation in occurrence and progression of diseases might be beneficial for further translational applications.

Processes in DNA damage response from a whole-cell multi-omics perspective

Technological advances have made it feasible to collect multi-condition multi-omic time courses of cellular response to perturbation, but the complexity of these datasets impedes discovery due to challenges in data management, analysis, visualization, and interpretation. Here, we report a whole-cell mechanistic analysis of HL-60 cellular response to bendamustine. We integrate both enrichment and network analysis to show the progression of DNA damage and programmed cell death over time in molecular, pathway, and process-level detail using an interactive analysis framework for multi-omics data. Our framework, Mechanism of Action Generator Involving Network analysis (MAGINE), automates network construction and enrichment analysis across multiple samples and platforms, which can be integrated into our annotated gene-set network to combine the strengths of networks and ontology-driven analysis. Taken together, our work demonstrates how multi-omics integration can be used to explore signaling processes at various resolutions and demonstrates multi-pathway involvement beyond the canonical bendamustine mechanism.

Systems biology reveals anatabine to be an NRF2 activator

Anatabine, an alkaloid present in plants of the Solanaceae family (including tobacco and eggplant), has been shown to ameliorate chronic inflammatory conditions in mouse models, such as Alzheimer’s disease, Hashimoto’s thyroiditis, multiple sclerosis, and intestinal inflammation. However, the mechanisms of action of anatabine remain unclear. To understand the impact of anatabine on cellular systems and identify the molecular pathways that are perturbed, we designed a study to examine the concentration-dependent effects of anatabine on various cell types by using a systems pharmacology approach. The resulting dataset, consisting of measurements of various omics data types at different time points, was analyzed by using multiple computational techniques. To identify concentration-dependent activated pathways, we performed linear modeling followed by gene set enrichment. To predict the functional partners of anatabine and the involved pathways, we harnessed the LINCS L1000 dataset’s wealth of information and implemented integer linear programming on directed graphs, respectively. Finally, we experimentally verified our key computational predictions. Using an appropriate luciferase reporter cell system, we were able to demonstrate that anatabine treatment results in NRF2 (nuclear factor-erythroid factor 2-related factor 2) translocation, and our systematic phosphoproteomic assays showed that anatabine treatment results in activation of MAPK signaling. While there are certain areas to be explored in deciphering the exact anti-inflammatory mechanisms of action of anatabine and other NRF2 activators, we believe that anatabine constitutes an interesting molecule for its therapeutic potential in NRF2-related diseases.

Proteogenomic analysis of lung adenocarcinoma reveals tumor heterogeneity, survival determinants, and therapeutically relevant pathways

We present a deep proteogenomic profiling study of 87 lung adenocarcinoma (LUAD) tumors from the United States, integrating whole-genome sequencing, transcriptome sequencing, proteomics and phosphoproteomics by mass spectrometry, and reverse-phase protein arrays. We identify three subtypes from somatic genome signature analysis, including a transition-high subtype enriched with never smokers, a transversion-high subtype enriched with current smokers, and a structurally altered subtype enriched with former smokers, TP53 alterations, and genome-wide structural alterations. We show that within-tumor correlations of RNA and protein expression associate with tumor purity and immune cell profiles. We detect and independently validate expression signatures of RNA and protein that predict patient survival. Additionally, among co-measured genes, we found that protein expression is more often associated with patient survival than RNA. Finally, integrative analysis characterizes three expression subtypes with divergent mutations, proteomic regulatory networks, and therapeutic vulnerabilities. This proteogenomic characterization provides a foundation for molecularly informed medicine in LUAD.

Network inference from perturbation time course data

Networks underlie much of biology from subcellular to ecological scales. Yet, understanding what experimental data are needed and how to use them for unambiguously identifying the structure of even small networks remains a broad challenge. Here, we integrate a dynamic least squares framework into established modular response analysis (DL-MRA), that specifies sufficient experimental perturbation time course data to robustly infer arbitrary two and three node networks. DL-MRA considers important network properties that current methods often struggle to capture: (i) edge sign and directionality; (ii) cycles with feedback or feedforward loops including self-regulation; (iii) dynamic network behavior; (iv) edges external to the network; and (v) robust performance with experimental noise. We evaluate the performance of and the extent to which the approach applies to cell state transition networks, intracellular signaling networks, and gene regulatory networks. Although signaling networks are often an application of network reconstruction methods, the results suggest that only under quite restricted conditions can they be robustly inferred. For gene regulatory networks, the results suggest that incomplete knockdown is often more informative than full knockout perturbation, which may change experimental strategies for gene regulatory network reconstruction. Overall, the results give a rational basis to experimental data requirements for network reconstruction and can be applied to any such problem where perturbation time course experiments are possible.

Omics Data and Data Representations for Deep Learning-Based Predictive Modeling

Medical discoveries mainly depend on the capability to process and analyze biological datasets, which inundate the scientific community and are still expanding as the cost of next-generation sequencing technologies is decreasing. Deep learning (DL) is a viable method to exploit this massive data stream since it has advanced quickly with there being successive innovations. However, an obstacle to scientific progress emerges: the difficulty of applying DL to biology, and this because both fields are evolving at a breakneck pace, thus making it hard for an individual to occupy the front lines of both of them. This paper aims to bridge the gap and help computer scientists bring their valuable expertise into the life sciences. This work provides an overview of the most common types of biological data and data representations that are used to train DL models, with additional information on the models themselves and the various tasks that are being tackled. This is the essential information a DL expert with no background in biology needs in order to participate in DL-based research projects in biomedicine, biotechnology, and drug discovery. Alternatively, this study could be also useful to researchers in biology to understand and utilize the power of DL to gain better insights into and extract important information from the omics data.

ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells

Prior knowledge of perturbation data can significantly assist in inferring the relationship between chemical perturbations and their specific transcriptional response. However, current databases mostly contain cancer cell lines, which are unsuitable for the aforementioned inference in non-cancer cells, such as cells related to non-cancer disease, immunology and aging. Here, we present ChemPert (https://chempert.uni.lu/), a database consisting of 82 270 transcriptional signatures in response to 2566 unique perturbagens (drugs, small molecules and protein ligands) across 167 non-cancer cell types, as well as the protein targets of 57 818 perturbagens. In addition, we develop a computational tool that leverages the non-cancer cell datasets, which enables more accurate predictions of perturbation responses and drugs in non-cancer cells compared to those based onto cancer databases. In particular, ChemPert correctly predicted drug effects for treating hepatitis and novel drugs for osteoarthritis. The ChemPert web interface is user-friendly and allows easy access of the entire datasets and the computational tool, providing valuable resources for both experimental researchers who wish to find datasets relevant to their research and computational researchers who need comprehensive non-cancer perturbation transcriptomics datasets for developing novel algorithms. Overall, ChemPert will facilitate future in silico compound screening for non-cancer cells.

Integrative genomic analysis facilitates precision strategies for glioblastoma treatment

Glioblastoma (GBM) is the most common form of malignant primary brain tumor with a dismal prognosis. Currently, the standard treatments for GBM rarely achieve satisfactory results, which means that current treatments are not individualized and precise enough. In this study, a multiomics-based GBM classification was established and three subclasses (GPA, GPB, and GPC) were identified, which have different molecular features both in bulk samples and at single-cell resolution. A robust GBM poor prognostic signature (GPS) score model was then developed using machine learning method, manifesting an excellent ability to predict the survival of GBM. NVP−BEZ235, GDC−0980, dasatinib and XL765 were ultimately identified to have subclass-specific efficacy targeting patients with a high risk of poor prognosis. Furthermore, the GBM classification and GPS score model could be considered as potential biomarkers for immunotherapy response. In summary, an integrative genomic analysis was conducted to advance individual-based therapies in GBM.

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A multiomics-based classification of GBM was established

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Single-cell transcriptomic profiling of GBM subclasses was revealed using Scissor

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A robust prognostic risk model was developed for GBM by machine learning method

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Prediction of potential agents based on molecular and prognostic risk stratification

SARS-CoV-2 infects adipose tissue in a fat depot- and viral lineage-dependent manner

Visceral adiposity is a risk factor for severe COVID-19, and a link between adipose tissue infection and disease progression has been proposed. Here we demonstrate that SARS-CoV-2 infects human adipose tissue and undergoes productive infection in fat cells. However, susceptibility to infection and the cellular response depends on the anatomical origin of the cells and the viral lineage. Visceral fat cells express more ACE2 and are more susceptible to SARS-CoV-2 infection than their subcutaneous counterparts. SARS-CoV-2 infection leads to inhibition of lipolysis in subcutaneous fat cells, while in visceral fat cells, it results in higher expression of pro-inflammatory cytokines. Viral load and cellular response are attenuated when visceral fat cells are infected with the SARS-CoV-2 gamma variant. A similar degree of cell death occurs 4-days after SARS-CoV-2 infection, regardless of the cell origin or viral lineage. Hence, SARS-CoV-2 infects human fat cells, replicating and altering cell function and viability in a depot- and viral lineage-dependent fashion.

Visceral adiposity is a risk factor for severe COVID-19, and infection of adipose tissue by SARS-CoV-2 has been reported. Here the authors confirm that human adipose tissue is a possible site for SARS-CoV-2 infection, but the degree of adipose tissue infection and the way adipocytes respond to the virus depend on the adipose tissue depot and the viral strain.

A message passing framework with multiple data integration for miRNA-disease association prediction

Micro RNA or miRNA is a highly conserved class of non-coding RNA that plays an important role in many diseases. Identifying miRNA-disease associations can pave the way for better clinical diagnosis and finding potential drug targets. We propose a biologically-motivated data-driven approach for the miRNA-disease association prediction, which overcomes the data scarcity problem by exploiting information from multiple data sources. The key idea is to enrich the existing miRNA/disease-protein-coding gene (PCG) associations via a message passing framework, followed by the use of disease ontology information for further feature filtering. The enriched and filtered PCG associations are then used to construct the inter-connected miRNA-PCG-disease network to train a structural deep network embedding (SDNE) model. Finally, the pre-trained embeddings and the biologically relevant features from the miRNA family and disease semantic similarity are concatenated to form the pair input representations to a Random Forest classifier whose task is to predict the miRNA-disease association probabilities. We present large-scale comparative experiments, ablation, and case studies to showcase our approach's superiority. Besides, we make the model prediction results for 1618 miRNAs and 3679 diseases, along with all related information, publicly available at  http://software.mpm.leibniz-ai-lab.de/ to foster assessments and future adoption.