Expression2Kinases: mRNA profiling linked to multiple upstream regulatory layers

Inferring upstream regulatory genes of FOXP3 in human regulatory T cells from time-series transcriptomic data

The discovery of upstream regulatory genes of a gene of interest still remains challenging. Here we applied a scalable computational method to unbiasedly predict candidate regulatory genes of critical transcription factors by searching the whole genome. We illustrated our approach with a case study on the master regulator FOXP3 of human primary regulatory T cells (Tregs). While target genes of FOXP3 have been identified, its upstream regulatory machinery still remains elusive. Our methodology selected five top-ranked candidates that were tested via proof-of-concept experiments. Following knockdown, three out of five candidates showed significant effects on the mRNA expression of FOXP3 across multiple donors. This provides insights into the regulatory mechanisms modulating FOXP3 transcriptional expression in Tregs. Overall, at the genome level this represents a high level of accuracy in predicting upstream regulatory genes of key genes of interest.

Sparsentan ameliorates glomerular hypercellularity and inflammatory-gene networks induced by IgA1-IgG immune complexes in a mouse model of IgA nephropathy

IgA nephropathy (IgAN) is characterized by glomerular deposition of immune complexes (ICs) consisting of IgA1 with O-glycans deficient in galactose (Gd-IgA1) and Gd-IgA1-specific IgG autoantibodies. These ICs induce kidney injury, and in the absence of disease-specific therapy, up to 40% of patients with IgAN progress to kidney failure. IgA1 with its clustered O-glycans is unique to humans, which hampered development of small-animal models of IgAN. Here, we used a model wherein engineered ICs (EICs) formed from human Gd-IgA1 and recombinant human IgG autoantibody are injected into nude mice to induce glomerular injury mimicking human IgAN. In this model, we assessed the protective effects of sparsentan, a single-molecule dual endothelin angiotensin receptor antagonist (DEARA) versus vehicle on EIC-induced glomerular proliferation and dysregulation of gene expression in the kidney. Oral administration of sparsentan (60 or 120 mg/kg daily) to mice intravenously injected with EIC attenuated the EIC-induced glomerular hypercellularity. Furthermore, analysis of changes in the whole kidney transcriptome revealed that key inflammatory and proliferative biological genes and pathways that are upregulated in this EIC model of IgAN were markedly reduced by sparsentan, including complement genes, integrin components, members of the mitogen-activated protein kinase family, and Fc receptor elements. Partial overlap between mouse and human differentially expressed genes in IgAN further supported the translational aspect of the immune and inflammatory components from our transcriptional findings. In conclusion, our data indicate that in the mouse model of IgAN, sparsentan targets immune and inflammatory processes leading to protection from mesangial hypercellularity.NEW & NOTEWORTHY The mechanisms by which deposited IgA1 immune complexes cause kidney injury during early phases of IgA nephropathy are poorly understood. We used an animal model we recently developed that involves IgA1-IgG immune complex injections and determined pathways related to the induced mesangioproliferative changes. Treatment with sparsentan, a dual inhibitor of endothelin type A and angiotensin II type 1 receptors, ameliorated the induced mesangioproliferative changes and the associated alterations in the expression of inflammatory genes and networks.

Regulatory Effect of Ficus carica Latex on Cell Cycle Progression in Human Papillomavirus-Positive Cervical Cancer Cell Lines: Insights from Gene Expression Analysis

Cervical cancer presents a significant global health concern with high-risk human papillomaviruses (HPVs) identified as the main cause of this cancer. Although current treatment methods for cervical cancer can eliminate lesions, preventing metastatic spread and minimizing tissue damage remain a major challenge. Therefore, the development of a safer and innovative therapeutic approach is of the utmost importance. Natural products like fig latex, derived from the Ficus carica tree, have demonstrated promising anti-cancer properties when tested on cervical cancer cell lines. However, the specific mechanisms by which fig latex exerts its effects are still unknown. In this study, we conducted RNA-Seq analysis to explore how fig latex may counteract carcinogenesis in HPV-positive cervical cancer cell lines, namely, CaSki (HPV type 16-positive) and HeLa (HPV type 18-positive). Our results from this investigation indicate that fig latex influences the expression of genes associated with the development and progression of cervical cancer, including pathways related to "Nonsense-Mediated Decay (NMD)", "Cell Cycle regulation", "Transcriptional Regulation by TP53", and "Apoptotic Process". This selective impact of fig latex on cancer-related pathways suggests a potential novel therapeutic approach for HPV-related cervical cancer.

Identification of Central Nervous System Oncologic Disease Biomarkers in EVs from Cerebrospinal Fluid (CSF) of Pediatric Patients: A Pilot Neuro-Proteomic Study

Cerebrospinal fluid (CSF) is a biochemical-clinical window into the brain. Unfortunately, its wide dynamic range, low protein concentration, and small sample quantity significantly limit the possibility of using it routinely. Extraventricular drainage (EVD) of CSF allows us to solve quantitative problems and to study the biological role of extracellular vesicles (EVs). In this study, we implemented bioinformatic analysis of our previous data of EVD of CSF and its EVs obtained from congenital hydrocephalus with the aim of identifying a comprehensive list of potential tumor and non-tumor biomarkers of central nervous system diseases. Among all proteins identified, those enriched in EVs are associated with synapses, synaptosomes, and nervous system diseases including gliomas, embryonal tumors, and epilepsy. Among these EV-enriched proteins, given the broad consensus present in the recent scientific literature, we validated syntaxin-binding protein 1 (STXBP1) as a marker of malignancy in EVD of CSF and its EVs from patients with pilocytic astrocytoma and medulloblastoma. Our results show that STXBP1 is negatively enriched in EVs compared to non-tumor diseases and its downregulation correlates with adverse outcomes. Further experiments are needed to validate this and other EV markers in the blood of pediatric patients for translational medicine applications.

COVID-19 of differing severity: from bulk to single-cell expression data analysis

Coronavirus disease 2019 (COVID-19) is raging worldwide and causes an immense disease burden. Despite this, the biomarkers and targeting drugs of COVID-19 of differing severity remain largely unknown. Based on the GSE164805 dataset, we identified modules most critical for mild COVID-19 (mCOVID-19) and severe COVID-19 (sCOVID-19) through WGCNA, respectively. We subsequently constructed a protein-protein interaction network, and detected 16 hub genes for mCOVID-19 and 10 hub genes for sCOVID-19, followed by the prediction of upstream transcription factors (TFs) and kinases. The enrichment analysis then showed downregulation of TNFA signaling via NFKB for mCOVID-19, as well as downregulation of MYC targets V1 for sCOVID-19. Infiltration degrees of many immune cells, such as macrophages, were also sharply different between mCOVID-19 and sCOVID-19 samples. Predicted protein targeting drugs with the highest scores nearly all belong to naturally derived or synthetic glucocorticoids. For the two single-cell RNA-seq datasets, we explored the expression distribution of hub genes for mCOVID-19/sCOVID-19 in each cell type. The expression levels of PRKCA, MCM5, TYMS, RBBP4, BCL6, FLOT1, KDM6B, and TLR2 were found to be cell-type-specific. Furthermore, the expression levels of 10 hub genes for mCOVID-19 were significantly upregulated in PBMCs between eight healthy controls and eight mCOVID-19 patients at our institution. Collectively, we detected critical modules, pathways, TFs, kinases, immune cells, targeting drugs, hub genes, and their expression distributions in different cell types that may involve the pathogenesis of COVID-19 of differing severity, which may propel earlier diagnosis and more effective treatment of this intractable disease in the future.

Neurite Outgrowth and Gene Expression Profile Correlate with Efficacy of Human Induced Pluripotent Stem Cell-Derived Dopamine Neuron Grafts

Transplantation of human induced pluripotent stem cell-derived dopaminergic (iPSC-DA) neurons is a promising therapeutic strategy for Parkinson's disease (PD). To assess optimal cell characteristics and reproducibility, we evaluated the efficacy of iPSC-DA neuron precursors from two individuals with sporadic PD by transplantation into a hemiparkinsonian rat model after differentiation for either 18 (d18) or 25 days (d25). We found similar graft size and dopamine (DA) neuron content in both groups, but only the d18 cells resulted in recovery of motor impairments. In contrast, we report that d25 grafts survived equally as well and produced grafts rich in tyrosine hydroxylase-positive neurons, but were incapable of alleviating any motor deficits. We identified the mechanism of action as the extent of neurite outgrowth into the host brain, with d18 grafts supporting significantly more neurite outgrowth than nonfunctional d25 grafts. RNAseq analysis of the cell preparation suggests that graft efficacy may be enhanced by repression of differentiation-associated genes by REST, defining the optimal predifferentiation state for transplantation. This study demonstrates for the first time that DA neuron grafts can survive well in vivo while completely lacking the capacity to induce recovery from motor dysfunction. In contrast to other recent studies, we demonstrate that neurite outgrowth is the key factor determining graft efficacy and our gene expression profiling revealed characteristics of the cells that may predict their efficacy. These data have implication for the generation of DA neuron grafts for clinical application.

Oridonin promotes endoplasmic reticulum stress via TP53-repressed TCF4 transactivation in colorectal cancer

BACKGROUND

The incidence of colorectal cancer and cancer death rate are increasing every year, and the affected population is becoming younger. Traditional Chinese medicine therapy has a unique effect in prolonging survival time and improving the prognosis of patients with colorectal cancer. Oridonin has been reported to have anti-cancer effects in a variety of tumors, but the exact mechanism remains to be investigated.

METHODS

Cell Counting Kit-8 assay (CCK8) and 5-Ethynyl-2'-deoxyuridine (EdU) staining assay, Tranwell, and Wound healing assays were performed to measure cell proliferation, invasion, and migration capacities, respectively. The protein and mRNA expression levels of various molecules were reflected by Western blot and Reverse Transcription quantitative Polymerase Chain Reaction (qRT-PCR). Transcription Factor 4 (TCF4) and its target genes were analyzed by Position Weight Matrices (PWMs) software and the Gene Expression Omnibus (GEO) database. Immunofluorescence (IF) was performed to visualize the expression and position of Endoplasmic Reticulum (ER) stress biomarkers. The morphology of the ER was demonstrated by the ER tracker-red. Reactive Oxygen Species (ROS) levels were measured using a flow cytometer (FCM) or fluorescent staining. Calcium ion (Ca²⁺) concentration was quantified by Fluo-3 AM staining. Athymic nude mice were modeled with subcutaneous xenografts.

RESULTS

Oridonin inhibited the proliferation, invasion, and migration of colorectal cancer, and this effect was weakened in a concentration-dependent manner by ER stress inhibitors. In addition, oridonin-induced colorectal tumor cells showed increased expression of ER stress biomarkers, loose morphology of ER, increased vesicles, and irregular shape. TCF4 was identified as a regulator of ER stress by PWMs software and GEO survival analysis. In vitro and in vivo experiments confirmed that TCF4 inhibited ER stress, reduced ROS production, and maintained Ca²⁺ homeostasis. In addition, oridonin also activated TP53 and inhibited TCF4 transactivation, further exacerbating the elevated ROS levels and calcium ion release in tumor cells and inhibiting tumorigenesis in colorectal cancer cells in vivo.

CONCLUSIONS

Oridonin upregulated TP53, inhibited TCF4 transactivation, and induced ER stress dysregulation in tumor cells, promoting colorectal cancer cell death. Therefore, TCF4 may be one of the important nodes for tumor cells to regulate ER stress and maintain protein synthesis homeostasis. And the inhibition of the TP53/TCF4 axis plays a key role in the anti-cancer effects of oridonin.

Benchmarking of protein interaction databases for integration with manually reconstructed signalling network models

Protein interaction databases are critical resources for network bioinformatics and integrating molecular experimental data. Interaction databases may also enable construction of predictive computational models of biological networks, although their fidelity for this purpose is not clear. Here, we benchmark protein interaction databases X2K, Reactome, Pathway Commons, Omnipath and Signor for their ability to recover manually curated edges from three logic-based network models of cardiac hypertrophy, mechano-signalling and fibrosis. Pathway Commons performed best at recovering interactions from manually reconstructed hypertrophy (137 of 193 interactions, 71%), mechano-signalling (85 of 125 interactions, 68%) and fibroblast networks (98 of 142 interactions, 69%). While protein interaction databases successfully recovered central, well-conserved pathways, they performed worse at recovering tissue-specific and transcriptional regulation. This highlights a knowledge gap where manual curation is critical. Finally, we tested the ability of Signor and Pathway Commons to identify new edges that improve model predictions, revealing important roles of protein kinase C autophosphorylation and Ca2+ /calmodulin-dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy. This study provides a platform for benchmarking protein interaction databases for their utility in network model construction, as well as providing new insights into cardiac hypertrophy signalling. KEY POINTS: Protein interaction databases are used to recover signalling interactions from previously developed network models. The five protein interaction databases benchmarked recovered well-conserved pathways, but did poorly at recovering tissue-specific pathways and transcriptional regulation, indicating the importance of manual curation. We identify new signalling interactions not previously used in the network models, including a role for Ca2+ /calmodulin-dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy.

The genomic landscape of ANCA-associated vasculitis: Distinct transcriptional signatures, molecular endotypes and comparison with systemic lupus erythematosus

Introduction

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAVs) present with a complex phenotype and are associated with high mortality and multi-organ involvement. We sought to define the transcriptional landscape and molecular endotypes of AAVs and compare it to systemic lupus erythematosus (SLE).

Methods

We performed whole blood mRNA sequencing from 30 patients with AAV (granulomatosis with polyangiitis/GPA and microscopic polyangiitis/MPA) combined with functional enrichment and network analysis for aberrant pathways. Key genes and pathways were validated in an independent cohort of 18 AAV patients. Co-expression network and hierarchical clustering analysis, identified molecular endotypes. Multi-level transcriptional overlap analysis to SLE was based on our published data from 142 patients.

Results

We report here that “Pan-vasculitis” signature contained 1,982 differentially expressed genes, enriched in leukocyte differentiation, cytokine signaling, type I and type II IFN signaling and aberrant B-T cell immunity. Active disease was characterized by signatures linked to cell cycle checkpoints and metabolism pathways, whereas ANCA-positive patients exhibited a humoral immunity transcriptional fingerprint. Differential expression analysis of GPA and MPA yielded an IFN-g pathway (in addition to a type I IFN) in the former and aberrant expression of genes related to autophagy and mRNA splicing in the latter. Unsupervised molecular taxonomy analysis revealed four endotypes with neutrophil degranulation, aberrant metabolism and B-cell responses as potential mechanistic drivers. Transcriptional perturbations and molecular heterogeneity were more pronounced in SLE. Molecular analysis and data-driven clustering of AAV uncovered distinct transcriptional pathways that could be exploited for targeted therapy.

Discussion

We conclude that transcriptomic analysis of AAV reveals distinct endotypes and molecular pathways that could be targeted for therapy. The AAV transcriptome is more homogenous and less fragmented compared to the SLE which may account for its superior rates of response to therapy.

Potential target identification for osteosarcoma treatment: Gene expression re-analysis and drug repurposing

Survival rate of osteosarcoma has remained plateaued for the past three decades. New treatment is needed to improve survival rate. Drug repurposing, a method to identify new indications of previous drugs, which saves time and cost compared to the de novo drug discovery. Data mining from gene expression profile was carried out and new potential targets were identified by using drug repurposing strategy. Selected data were newly categorized as pathophysiology and metastasis groups. Data were normalized and calculated the differential gene expression. Genes with log fold change ≥ 2 and adjusted p-value ≤ 0.05 were selected as primary candidate genes (PCGs). PCGs were further enriched to determine the secondary candidate genes (SCGs) by protein interaction analysis, upstream transcription factor and related-protein kinase identification. PCGs and SCGs were further matched with gene targeted of corresponding drugs from the Drug Repurposing Hub. A total of 778 targets were identified (360 from PCGs, and 418 from SCGs). This newly identified KLHL13 is a new candidate target based on its molecular function. KLHL13 was upregulated in clinical samples. We found 256 drugs from matching processes (50anti-cancerand206non-anticancerdrugs). Clinical trials of anti-cancer drugs from 5 targets (CDK4, BCL-2, JUN, SRC, PIK3CA) are being performed for osteosarcoma treatment. Niclosamide and synthetic PPARɣ ligands are candidates for repurposing due to the possibility based on their mechanism and pharmacology properties. Re-analysis of gene expression profile could identify new potential targets, confirm a current implication, and expand the chance of repurposing drugs for osteosarcoma treatment.

LYN kinase programs stromal fibroblasts to facilitate leukemic survival via regulation of c-JUN and THBS1

Microenvironmental bystander cells are essential for the progression of chronic lymphocytic leukemia (CLL). We have discovered previously that LYN kinase promotes the formation of a microenvironmental niche for CLL. Here we provide mechanistic evidence that LYN regulates the polarization of stromal fibroblasts to support leukemic progression. LYN is overexpressed in fibroblasts of lymph nodes of CLL patients. LYN-deficient stromal cells reduce CLL growth in vivo. LYN-deficient fibroblasts show markedly reduced leukemia feeding capacity in vitro. Multi-omics profiling reveals that LYN regulates the polarization of fibroblasts towards an inflammatory cancer-associated phenotype through modulation of cytokine secretion and extracellular matrix composition. Mechanistically, LYN deletion reduces inflammatory signaling including reduction of c-JUN expression, which in turn augments the expression of Thrombospondin-1, which binds to CD47 thereby impairing CLL viability. Together, our findings suggest that LYN is essential for rewiring fibroblasts towards a leukemia-supportive phenotype.

The Stiffness-Sensitive Transcriptome of Human Tendon Stromal Cells

Extracellular matrix stiffness is a major regulator of cellular states. Stiffness-sensing investigations are typically performed using cells that have acquired "mechanical memory" through prolonged conditioning in rigid environments, e.g., tissue culture plastic (TCP). This potentially masks the full extent of the matrix stiffness-driven mechanosensing programs. Here, a biomaterial composed of 2D mechanovariant silicone substrates with simplified and scalable surface biofunctionalization chemistry is developed to facilitate large-scale cell culture expansion processes. Using RNA sequencing, stiffness-mediated mechano-responses of human tendon-derived stromal cells are broadly mapped. Matrix elasticity (E.) approximating tendon microscale stiffness range (E. ≈ 35 kPa) distinctly favors transcriptional programs related to chromatin remodeling and Hippo signaling; whereas compliant stiffnesses (E. ≈ 2 kPa) are enriched in processes related to cell stemness, synapse assembly, and angiogenesis. While tendon stromal cells undergo dramatic phenotypic drift on conventional TCP, mechanovariant substrates abrogate this activation with tenogenic stiffnesses inducing a transcriptional program that strongly correlates with established tendon tissue-specific expression signature. Computational inference predicts that AKT1 and ERK1/2 are major stiffness-sensing signaling hubs. Together, these findings highlight how matrix biophysical cues may dictate the transcriptional identity of tendon cells, and how matrix mechano-reciprocity regulates diverse sets of previously underappreciated mechanosensitive processes in tendon fibroblasts.

Bioinformatic prediction of the molecular links between Alzheimer's disease and diabetes mellitus

Background

Alzheimer's disease (AD) and type 2 diabetes mellitus (DM2) are chronic degenerative diseases with complex molecular processes that are potentially interconnected. The aim of this work was to predict the potential molecular links between AD and DM2 from different sources of biological information.

Materials and Methods

In this work, data mining of nine databases (DisGeNET, Ensembl, OMIM, Protein Data Bank, The Human Protein Atlas, UniProt, Gene Expression Omnibus, Human Cell Atlas, and PubMed) was performed to identify gene and protein information that was shared in AD and DM2. Next, the information was mapped to human protein-protein interaction (PPI) networks based on experimental data using the STRING web platform. Then, gene ontology biological process (GOBP) and pathway analyses with EnrichR showed its specific and shared biological process and pathway deregulations. Finally, potential biomarkers and drug targets were predicted with the Metascape platform.

Results

A total of 1,551 genes shared in AD and DM2 were identified. The highest average degree of nodes within the PPI was for DM2 (average = 2.97), followed by AD (average degree = 2.35). GOBP for AD was related to specific transcriptional and translation genetic terms occurring in neurons cells. The GOBP and pathway information for the association AD-DM2 were linked mainly to bioenergetics and cytokine signaling. Within the AD-DM2 association, 10 hub proteins were identified, seven of which were predicted to be present in plasma and exhibit pharmacological interaction with monoclonal antibodies in use, anticancer drugs, and flavonoid derivatives.

Conclusion

Our data mining and analysis strategy showed that there are a plenty of biological information based on experiments that links AD and DM2, which could provide a rational guide to design further diagnosis and treatment for AD and DM2.

Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment

We identified resistance mechanisms to abiraterone acetate/prednisone (AA/P) in patients with metastatic castration-resistant prostate cancer (mCRPC) in the Prostate Cancer Medically Optimized Genome-Enhanced Therapy (PROMOTE) study.

We analyzed whole-exome sequencing (WES) and RNA-sequencing data from 83 patients with metastatic biopsies before (V1) and after 12 weeks of AA/P treatment (V2). Resistance was determined by time to treatment change (TTTC).

At V2, 18 and 11 of 58 patients had either short-term (median 3.6 months; range 1.4-4.5) or long-term (median 29 months; range 23.5-41.7) responses, respectively. Nonresponders had low expression of TGFBR3 and increased activation of the Wnt pathway, cell cycle, upregulation of AR variants, both pre- and posttreatment, with further deletion of AR inhibitor CDK11B posttreatment. Deletion of androgen processing genes, HSD17B11, CYP19A1 were observed in nonresponders posttreatment. Genes involved in cell cycle, DNA repair, Wnt-signaling, and Aurora kinase pathways were differentially expressed between the responder and non-responder at V2. Activation of Wnt signaling in nonresponder and deactivation of MYC or its target genes in responders was detected via SCN loss, somatic mutations, and transcriptomics. Upregulation of genes in the AURKA pathway are consistent with the activation of MYC regulated genes in nonresponders. Several genes in the AKT1 axis had increased mutation rate in nonresponders. We also found evidence of resistance via PDCD1 overexpression in responders.

IMPLICATIONS

Finally, we identified candidates drugs to reverse AA/P resistance: topoisomerase inhibitors and drugs targeting the cell cycle via the MYC/AURKA/AURKB/TOP2A and/or PI3K_AKT_MTOR pathways.

Signatures of Co-Deregulated Genes and Their Transcriptional Regulators in Lung Cancer

Despite the significant progress made towards comprehending the deregulated signatures in lung cancer, these vary from study to study. We reanalyzed 25 studies from the Gene Expression Omnibus (GEO) to detect and annotate co-deregulated signatures in lung cancer and in single-gene or single-drug perturbation experiments. We aimed to decipher the networks that these co-deregulated genes (co-DEGs) form along with their upstream regulators. Differential expression and upstream regulators were computed using Characteristic Direction and Systems Biology tools, including GEO2Enrichr and X2K. Co-deregulated gene expression profiles were further validated across different molecular and immune subtypes in lung adenocarcinoma (TCGA-LUAD) and lung adenocarcinoma (TCGA-LUSC) datasets, as well as using immunohistochemistry data from the Human Protein Atlas, before being subjected to subsequent GO and KEGG enrichment analysis. The functional alterations of the co-upregulated genes in lung cancer were mostly related to immune response regulating the cell surface signaling pathway, in contrast to the co-downregulated genes, which were related to S-nitrosylation. Networks of hub proteins across the co-DEGs consisted of overlapping TFs (SOX2, MYC, KAT2A) and kinases (MAPK14, CSNK2A1 and CDKs). Furthermore, using Connectivity Map we highlighted putative repurposing drugs, including valproic acid, betonicine and astemizole. Similarly, we analyzed the co-DEG signatures in single-gene and single-drug perturbation experiments in lung cancer cell lines. In summary, we identified critical co-DEGs in lung cancer providing an innovative framework for their potential use in developing personalized therapeutic strategies.

Anlotinib exerts potent antileukemic activities in Ph chromosome negative and positive B-cell acute lymphoblastic leukemia via perturbation of PI3K/AKT/mTOR pathway

OBJECTIVES

Despite advances in the development of novel targeted therapies, the need for B-ALL alternative treatments has not been met. Anlotinib could blunt the proangiogenic activity of VEGFR, PDGFR, and FGFR, and has shown strong antitumor activities across multiple tumors. However, anlotinib cytotoxicity against B-ALL has not ever been evaluated, thus prompting us to initiate this study.

METHODS

Expression2Kinases program was used to identify potential treatment targets. Cell viability and apoptosis were determined by CCK-8 and Annexin V/PI staining kit, respectively. qRT-PCR and Western blotting were utilized to investigate the molecular mechanisms. In vivo antileukemia activity of Anlotinib was evaluated in a Ph⁺ B-ALL patient-Derived Xenograft (PDX) model.

RESULTS

Compared with treatment-naive B-ALL cases, RR B-ALL patients had higher activities in the VEGF/VEGFR signaling and the PI3K/AKT/mTOR pathway. Exposure of Ph^- and Ph⁺ B-ALL cells to anlotinib resulted in significant cell viability reduction, apoptosis enhancement, and cell cycle arrest at G2/M phase. Importantly, anlotinib treatment led to remarkably decreased leukemia burdens and extended the survival period in a Ph⁺ B-ALL PDX model. Blockade of the role of the proangiogenic mediators, comprising VEGFR2, PDGFR-beta, and FGFR3, played a critical role in the cytotoxicity of anlotinib against Ph^- and Ph⁺ B-ALL. Moreover, anlotinib dampened the activity of PI3K/AKT/mTOR pathway that resides in the convergence of the three mentioned proangiogenic signals.

CONCLUSION

This work provides impressive preclinical evidence of anlotinib against Ph^- and Ph⁺ B-ALL and raises a rationale for future clinical evaluation of this drug in the management of Ph^- and Ph⁺ B-ALL.

Cross-species transcriptome analysis for early detection and specific therapeutic targeting of human lupus nephritis

Objectives

Patients with lupus nephritis (LN) are in urgent need for early diagnosis and therapeutic interventions targeting aberrant molecular pathways enriched in affected kidneys.

Methods

We used mRNA-sequencing in effector (spleen) and target (kidneys, brain) tissues from lupus and control mice at sequential time points, and in the blood from 367 individuals (261 systemic lupus erythematosus (SLE) patients and 106 healthy individuals). Comparative cross-tissue and cross-species analyses were performed. The human dataset was split into training and validation sets and machine learning was applied to build LN predictive models.

Results

In murine SLE, we defined a kidney-specific molecular signature, as well as a molecular signature that underlies transition from preclinical to overt disease and encompasses pathways linked to metabolism, innate immune system and neutrophil degranulation. The murine kidney transcriptome partially mirrors the blood transcriptome of patients with LN with 11 key transcription factors regulating the cross-species active LN molecular signature. Integrated protein-to-protein interaction and drug prediction analyses identified the kinases TRRAP, AKT2, CDK16 and SCYL1 as putative targets of these factors and capable of reversing the LN signature. Using murine kidney-specific genes as disease predictors and machine-learning training of the human RNA-sequencing dataset, we developed and validated a peripheral blood-based algorithm that discriminates LN patients from normal individuals (based on 18 genes) and non-LN SLE patients (based on 20 genes) with excellent sensitivity and specificity (area under the curve range from 0.80 to 0.99).

Conclusions

Machine-learning analysis of a large whole blood RNA-sequencing dataset of SLE patients using human orthologs of mouse kidney-specific genes can be used for early, non-invasive diagnosis and therapeutic targeting of LN. The kidney-specific gene predictors may facilitate prevention and early intervention trials.

Co-inhibition of ATM and ROCK synergistically improves cell proliferation in replicative senescence by activating FOXM1 and E2F1

The multifaceted nature of senescent cell cycle arrest necessitates the targeting of multiple factors arresting or promoting the cell cycle. We report that co-inhibition of ATM and ROCK by KU-60019 and Y-27632, respectively, synergistically increases the proliferation of human diploid fibroblasts undergoing replicative senescence through activation of the transcription factors E2F1 and FOXM1. Time-course transcriptome analysis identified FOXM1 and E2F1 as crucial factors promoting proliferation. Co-inhibition of the kinases ATM and ROCK first promotes the G2/M transition via FOXM1 activation, leading to accumulation of cells undergoing the G1/S transition via E2F1 activation. The combination of both inhibitors increased this effect more significantly than either inhibitor alone, suggesting synergism. Our results demonstrate a FOXM1- and E2F1-mediated molecular pathway enhancing cell cycle progression in cells with proliferative potential under replicative senescence conditions, and treatment with the inhibitors can be tested for senomorphic effect in vivo.

Inhibitors to ATM and ROCK kinases increases the proliferation via transcriptional changes in human diploid fibroblasts undergoing replicative senescence in a synergistic manner

Multi-omics analyses provide novel biological insights to distinguish lobular ductal types of invasive breast cancers

BACKGROUND

Invasive lobular carcinoma (ILC) treatment is similar to invasive ductal carcinoma (IDC; now invasive carcinoma-no special type, IBC-NST), based on its intrinsic subtype. However, further investigation is required for an integrative understanding of differentially perturbed molecular patterns and pathways in these histotypes.

METHODS

A dataset of 780 IDC and 201 ILC samples from the TCGA-BRCA project for cross-platform multi-omics was analyzed. We leveraged a consensus approach integrating different bioinformatic algorithms to analyze mutations, CNAs, mRNA, miRNA abundance, methylation, and protein abundance to understand the complex crosstalks that distinguish ILC and IDC samples. A histotype-matched comparison was performed. We performed Cox survival analyses for prognosis based on our identified 53 histotype-specific and four discordant genes.

RESULTS

Approximately 90% of ILC cases were of the luminal subtype. Somatic mutations in CDH1 were higher in ILC than in IDC (FDR-adjusted p < 0.01). Fifty-three significant oncogenic or tumor-suppressive DEGs were identified in a single histotype. PPAR signaling and lipolysis regulation in adipocytes were significantly enriched in ILC tumors. CDH1 protein had the highest differential abundance (AUC: 0.85). Moreover, BTG2, GSTA2, GPR37L1, and PGBD5 amplification was associated with poorer OS in ILC compared with no alteration. RIMS2, NACA4P, MYC, ZFPM2, and POU5F1B amplification showed a lower overall survival in patients with IDC. miR-195 showed an IDC-specific downregulation, causing overexpression of CCNE1. Integrative multi-omics supervised analysis identified 296 differentially expressed genes that successfully distinguished IDC and ILC histotypes.

CONCLUSIONS

Our findings identify novel molecular candidates that potentially drive and modify the disease differentially among these histotypes.

Identification of key regulators associated with colon cancer prognosis and pathogenesis

Colon cancer (CC) is the fourth deadliest cancer in the world. New insights into prognostication might be helpful to define the optimal adjuvant treatments for patients in routine clinical practice. Here, a microarray dataset with 30 primary tumors and 30 normal samples was analyzed using GEO2R to find differentially expressed genes (DEGs). Then, DAVID, KEGG, ChEA and X2K were used to analyze DEGs-related Gene Ontology, pathways, transcription factors (TFs) and kinases, respectively. Protein-protein interaction (PPI) networks were constructed using the STRING database and Cytoscape. The modules and hub genes of DEGs was determined through MCODE and CytoHubba plugins, and the expression of hub genes was verified using GEPIA. To find microRNAs and metabolites associated with DEGs, miRTarBase and HMDB were used, respectively. It was found that 233 and 373 genes were upregulated and downregulated in CC, respectively. GO analysis showed that the upregulated DEGs were mainly involved in mitotic nuclear division and cell division. Top 10 hub genes were identified, including AURKB, CDK1, DLGAP5, AURKA, CCNB2, CCNB1, BUB1B, CCNA2, KIF20A and BUB1. Whereas, FOMX1, E2F7, E2F1, E2F4 and AR were identified as top 5 TFs in CC. Moreover, CDK1, CDC2, MAPK14, ATM and CK2ALPHA was identified as top 5 kinases in CC. miRNAs analysis showed that Hsa-miR-215-5p hsa-miR-193b-3p, hsa-miR-192-5p and hsa-miR-16-5p could target the largest number of CC genes. Taken together, CC-related genes, especially the hub genes, TFs, and metabolites might be used as novel biomarkers for CC, as well as for diagnosis and guiding therapeutic strategies for CC.

Cross-Platform in-silico Analyses Exploring Tumor Immune Microenvironment with Prognostic Value in Triple-Negative Breast Cancer

INTRODUCTION

Only a proportion of triple-negative breast cancer (TNBC) is immunotherapy-responsive. We hypothesized that the tumor microenvironment (TME) influences the outcomes of TNBC and investigated the relevant signaling pathways.

MATERIALS AND METHODS

Immune score (IS) and stromal score (SS) were calculated using the ESTIMATE and correlated with the overall survival (OS) in TNBC. RNA-seq data from 115 TNBC samples and 112 normal adjacent tissues were retrieved. Validations in the methylation levels in 10 TNBC and five non-TNBC cell lines were obtained. Cox model overall survival (OS) validated the derived transcription factor (TF) genes in cBioPortal breast cancer patients.

RESULTS

SS-low predicts a higher OS compared with SS-high patients (P = 0.0081 IS-high/SS-low patients had better OS (P = 0.045) than IS-low/SS-high patients. More macrophages were polarized to the M2 state in patients with IS-low/SS-high patients (P < 0.001). Moreover, CIBERSORTx showed more CD8+ cytotoxic T-cells in IS-high/SS-low patients (p = 0.0286) and more resting NK cells in the IS-low/SS-high TME (P = 0.0108). KEGG pathway analysis revealed that overexpressed genes were enriched in the IL-17 and cytokine-cytokine receptor interaction pathways. The lncRNA DRAIC, a tumor suppressor, was consistently deactivated in the 10 TNBC cell lines. On the cBioPortal platform, we validated that 13% of ER-negative, HER2-unamplified BC harbored IL17RA deep deletion and 25% harbored TRAF3IP2 amplification. On cBioPortal datasets, the nine altered TF genes derived from the X2K analysis showed significantly worse relapse-free survival in 2377 patients and OS in 4819 invasive BC patients than in the unaltered cohort.

CONCLUSION

Of note, the results of this integrated in silico study can only be generalized to approximately 17% of patients with TNBC, in which infiltrating stromal cells and immune cells play a determinant prognostic role.

Identification of Novel Kinase-Transcription Factor-mRNA-miRNA Regulatory Network in Nasopharyngeal Carcinoma by Bioinformatics Analysis

Purpose

Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumors of the head and neck. This study aimed to investigate the crucial genes and regulatory networks involved in the carcinogenesis of NPC using a bioinformatics approach.

Methods

Five mRNA and two miRNA expression datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and miRNAs (DEMs) between NPC and normal samples were analyzed using R software. The WebGestalt tool was used for functional enrichment analysis, and protein-protein interaction (PPI) network analysis of DEGs was performed using STRING database. Transcription factors (TFs) were predicted using TRRUST and Transcriptional Regulatory Element Database (TRED). Kinases were identified using X2Kgui. The miRNAs of DEGs were predicted using miRWalk database. A kinase-TF-mRNA-miRNA integrated network was constructed, and hub nodes were selected. The hub genes were validated using NPC datasets from the GEO and Oncomine databases. Finally, candidate small-molecule agents were predicted using CMap.

Results

A total of 122 DEGs and 44 DEMs were identified. DEGs were associated with the immune response, leukocyte activation, endoplasmic reticulum stress in GO analysis, and the NF-κB signaling pathway in KEGG analysis. Four significant modules were identified using PPI network analysis. Subsequently, 26 TFs, 73 kinases, and 2499 miRNAs were predicted. The predicted miRNAs were cross-referenced with DEMs, and seven overlapping miRNAs were selected. In the kinase-TF-mRNA-miRNA integrated network, eight genes (PTGS2, FN1, MMP1, PLAU, MMP3, CD19, BMP2, and PIGR) were identified as hub genes. Hub genes were validated with consistent results, indicating the reliability of our findings. Finally, six candidate small-molecule agents (phenoxybenzamine, luteolin, thioguanosine, reserpine, blebbistatin, and camptothecin) were predicted.

Conclusion

We identified DEGs and an NPC regulatory network involving kinases, TFs, mRNAs, and miRNAs, which might provide promising insight into the pathogenesis, treatment, and prognosis of NPC.

Multidimensional Single-Nuclei RNA-Seq Reconstruction of Adipose Tissue Reveals Adipocyte Plasticity Underlying Thermogenic Response

Adipose tissue has been classified based on its morphology and function as white, brown, or beige/brite. It plays an essential role as a regulator of systemic metabolism through paracrine and endocrine signals. Recently, multiple adipocyte subtypes have been revealed using RNA sequencing technology, going beyond simply defined morphology but also by their cellular origin, adaptation to metabolic stress, and plasticity. Here, we performed an in-depth analysis of publicly available single-nuclei RNAseq from adipose tissue and utilized a workflow template to characterize adipocyte plasticity, heterogeneity, and secretome profiles. The reanalyzed dataset led to the identification of different subtypes of adipocytes including three subpopulations of thermogenic adipocytes, and provided a characterization of distinct transcriptional profiles along the adipocyte trajectory under thermogenic challenges. This study provides a useful resource for further investigations regarding mechanisms related to adipocyte plasticity and trans-differentiation.

Follicular dendritic cells restrict interleukin-4 availability in germinal centers and foster memory B cell generation

B cells within germinal centers (GCs) enter cycles of antibody affinity maturation or exit the GC as memory cells or plasma cells. Here, we examined the contribution of interleukin (IL)-4 on B cell fate decisions in the GC. Single-cell RNA-sequencing identified a subset of light zone GC B cells expressing high IL-4 receptor-a (IL4Ra) and CD23 and lacking a Myc-associated signature. These cells could differentiate into pre-memory cells. B cell-specific deletion of IL4Ra or STAT6 favored the pre-memory cell trajectory, and provision of exogenous IL-4 in a wild-type context reduced pre-memory cell frequencies. IL-4 acted during antigen-specific interactions but also influenced bystander cells. Deletion of IL4Ra from follicular dendritic cells (FDCs) increased the availability of IL-4 in the GC, impaired the selection of affinity-matured B cells, and reduced memory cell generation. We propose that GC FDCs establish a niche that limits bystander IL-4 activity, focusing IL-4 action on B cells undergoing selection and enhancing memory cell differentiation.

Tumor Necrosis Factor-Alpha Exacerbates Viral Entry in SARS-CoV2-Infected iPSC-Derived Cardiomyocytes

The coronavirus disease 2019 (COVID-19) pandemic with high infectivity and mortality has caused severe social and economic impacts worldwide. Growing reports of COVID-19 patients with multi-organ damage indicated that severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) may also disturb the cardiovascular system. Herein, we used human induced pluripotent stem cell (iPSC)-derived cardiomyocytes (iCMs) as the in vitro platform to examine the consequence of SARS-CoV2 infection on iCMs. Differentiated iCMs expressed the primary SARS-CoV2 receptor angiotensin-converting enzyme-II (ACE2) and the transmembrane protease serine type 2 (TMPRSS2) receptor suggesting the susceptibility of iCMs to SARS-CoV2. Following the infection of iCMs with SARS-CoV2, the viral nucleocapsid (N) protein was detected in the host cells, demonstrating the successful infection. Bioinformatics analysis revealed that the SARS-CoV2 infection upregulates several inflammation-related genes, including the proinflammatory cytokine tumor necrosis factor-α (TNF-α). The pretreatment of iCMs with TNF-α for 24 h, significantly increased the expression of ACE2 and TMPRSS2, SASR-CoV2 entry receptors. The TNF-α pretreatment enhanced the entry of GFP-expressing SARS-CoV2 pseudovirus into iCMs, and the neutralization of TNF-α ameliorated the TNF-α-enhanced viral entry. Collectively, SARS-CoV2 elevated TNF-α expression, which in turn enhanced the SARS-CoV2 viral entry. Our findings suggest that, TNF-α may participate in the cytokine storm and aggravate the myocardial damage in COVID-19 patients.

Transcriptomic analysis of elderly women with low muscle mass: association with immune system pathway

Despite the well-established association of gene expression deregulation with low muscle mass (LMM), the associated biological mechanisms remain unclear. Transcriptomic studies are capable to identify key mediators in complex diseases. We aimed to identify relevant mediators and biological mechanisms associated with age-related LMM. LMM-associated genes were detected by logistic regression using microarray data of 20 elderly women with LMM and 20 age and race-matched controls extracted from our SPAH Study (GSE152073). We performed weighted gene co-expression analysis (WGCNA) that correlated the identified gene modules with laboratorial characteristics. Gene enrichment analysis was performed and an LMM predictive model was constructed using Support Vector Machine (SVM). Overall, 821 discriminating transcripts clusters were identified (|beta coefficient| >1; p-value <0.01). From this list, 45 predictors of LMM were detected by SVM and validated with 0.7 of accuracy. Our results revealed that the well-described association of inflammation, immunity and metabolic alterations is also relevant at transcriptomic level. WGCNA highlighted a correlation of genes modules involved in immunity pathways with vitamin D level (R = 0.63, p = 0.004) and the Agatston score (R = 0.51, p = 0.02). Our study generated a predicted regulatory network and revealed significant metabolic pathways related to aging processes, showing key mediators that warrant further investigation.

Inhibition of HIPK2 Alleviates Thoracic Aortic Disease in Mice With Progressively Severe Marfan Syndrome

Objective

Despite considerable research, the goal of finding nonsurgical remedies against thoracic aortic aneurysm and acute aortic dissection remains elusive. We sought to identify a novel aortic PK (protein kinase) that can be pharmacologically targeted to mitigate aneurysmal disease in a well-established mouse model of early-onset progressively severe Marfan syndrome (MFS).

Approach and Results

Computational analyses of transcriptomic data derived from the ascending aorta of MFS mice predicted a probable association between thoracic aortic aneurysm and acute aortic dissection development and the multifunctional, stress-activated HIPK2 (homeodomain-interacting protein kinase 2). Consistent with this prediction, Hipk2 gene inactivation significantly extended the survival of MFS mice by slowing aneurysm growth and delaying transmural rupture. HIPK2 also ranked among the top predicted PKs in computational analyses of DEGs (differentially expressed genes) in the dilated aorta of 3 MFS patients, which strengthened the clinical relevance of the experimental finding. Additional in silico analyses of the human and mouse data sets identified the TGF (transforming growth factor)-β/Smad3 signaling pathway as a potential target of HIPK2 in the MFS aorta. Chronic treatment of MFS mice with an allosteric inhibitor of HIPK2-mediated stimulation of Smad3 signaling validated this prediction by mitigating thoracic aortic aneurysm and acute aortic dissection pathology and partially improving aortic material stiffness.

Conclusions

HIPK2 is a previously unrecognized determinant of aneurysmal disease and an attractive new target for antithoracic aortic aneurysm and acute aortic dissection multidrug therapy.

Single-cell-resolved differentiation of human induced pluripotent stem cells into pancreatic duct-like organoids on a microwell chip

Creating in vitro models of diseases of the pancreatic ductal compartment requires a comprehensive understanding of the developmental trajectories of pancreas-specific cell types. Here we report the single-cell characterization of the differentiation of pancreatic duct-like organoids (PDLOs) from human induced pluripotent stem cells (hiPSCs) on a microwell chip that facilitates the uniform aggregation and chemical induction of hiPSC-derived pancreatic progenitors. Using time-resolved single-cell transcriptional profiling and immunofluorescence imaging of the forming PDLOs, we identified differentiation routes from pancreatic progenitors through ductal intermediates to two types of mature duct-like cells and a few non-ductal cell types. PDLO subpopulations expressed either mucins or the cystic fibrosis transmembrane conductance regulator, and resembled human adult duct cells. We also used the chip to uncover ductal markers relevant to pancreatic carcinogenesis, and to establish PDLO co-cultures with stellate cells, which allowed for the study of epithelial-mesenchymal signalling. The PDLO microsystem could be used to establish patient-specific pancreatic duct models.

A Convergent Functional Genomics Analysis to Identify Biological Regulators Mediating Effects of Creatine Supplementation

Creatine (Cr) and phosphocreatine (PCr) are physiologically essential molecules for life, given they serve as rapid and localized support of energy- and mechanical-dependent processes. This evolutionary advantage is based on the action of creatine kinase (CK) isozymes that connect places of ATP synthesis with sites of ATP consumption (the CK/PCr system). Supplementation with creatine monohydrate (CrM) can enhance this system, resulting in well-known ergogenic effects and potential health or therapeutic benefits. In spite of our vast knowledge about these molecules, no integrative analysis of molecular mechanisms under a systems biology approach has been performed to date; thus, we aimed to perform for the first time a convergent functional genomics analysis to identify biological regulators mediating the effects of Cr supplementation in health and disease. A total of 35 differentially expressed genes were analyzed. We identified top-ranked pathways and biological processes mediating the effects of Cr supplementation. The impact of CrM on miRNAs merits more research. We also cautiously suggest two dose-response functional pathways (kinase- and ubiquitin-driven) for the regulation of the Cr uptake. Our functional enrichment analysis, the knowledge-based pathway reconstruction, and the identification of hub nodes provide meaningful information for future studies. This work contributes to a better understanding of the well-reported benefits of Cr in sports and its potential in health and disease conditions, although further clinical research is needed to validate the proposed mechanisms.

KEA3: improved kinase enrichment analysis via data integration

Phosphoproteomics and proteomics experiments capture a global snapshot of the cellular signaling network, but these methods do not directly measure kinase state. Kinase Enrichment Analysis 3 (KEA3) is a webserver application that infers overrepresentation of upstream kinases whose putative substrates are in a user-inputted list of proteins. KEA3 can be applied to analyze data from phosphoproteomics and proteomics studies to predict the upstream kinases responsible for observed differential phosphorylations. The KEA3 background database contains measured and predicted kinase-substrate interactions (KSI), kinase-protein interactions (KPI), and interactions supported by co-expression and co-occurrence data. To benchmark the performance of KEA3, we examined whether KEA3 can predict the perturbed kinase from single-kinase perturbation followed by gene expression experiments, and phosphoproteomics data collected from kinase-targeting small molecules. We show that integrating KSIs and KPIs across data sources to produce a composite ranking improves the recovery of the expected kinase. The KEA3 webserver is available at https://maayanlab.cloud/kea3.

Perturbation of HSP Network in MCF-7 Breast Cancer Cell Line Triggers Inducible HSP70 Expression and Leads to Tumor Suppression

BACKGROUND

Heat shock protein 70 (HSP70) is constitutively expressed in normal cells but aberrantly expressed in several types of tumor cells, helping their survival in extreme conditions. Thus, specific inhibition of HSP70 in tumor cells is a promising strategy in the treatment of cancer. HSP70 has a variety of isoforms in the cellular organelles and form different functions by coordinating and cooperating with cochaperones. Cancer cells overexpress HSPs during cell growth and proliferation and HSP network provides resistance against apoptosis. The present study aimed to evaluate quantitative changes in HSPs- and cancerassociated gene expressions and their interactions in the presence of 2-phenylethyenesulfonamide (PES) in MCF-7 cells.

METHODS

Antiproliferative activity of PES was evaluated using the XTT assay. Inducible HSP70 (HSP70i) levels in the PES-treated cells were determined using the ELISA kit. PCR Array was performed to assess the HSPs- and cancer-pathway focused gene expression profiling. Gene network analysis was performed using the X2K, yEd (V.3.18.1) programs, and web-based gene list enrichment analysis tool Enrichr.

RESULTS

The results demonstrated that PES exposure increased the amount of both HSP70i gene and protein expression surprisingly. However, the expression of HSP70 isoforms as well as other co-chaperones, and 17 cancer-associated genes decreased remarkably as expected. Additionally, interaction network analysis revealed a different mechanism; PES induction of HSP70i employs a cell cycle negative regulator, RB1, which is a tumor suppressor gene.

CONCLUSION

PES treatment inhibited MCF-7 cell proliferation and changed several HSPs- and cancer-related gene expressions along with their interactions through a unique mechanism although it causes an interesting increase at HSP70i gene and protein expressions. RB1 gene expression may play an important role in this effect as revealed by the interaction network analysis.

Inhibition of ERK 1/2 kinases prevents tendon matrix breakdown

Tendon extracellular matrix (ECM) mechanical unloading results in tissue degradation and breakdown, with niche-dependent cellular stress directing proteolytic degradation of tendon. Here, we show that the extracellular-signal regulated kinase (ERK) pathway is central in tendon degradation of load-deprived tissue explants. We show that ERK 1/2 are highly phosphorylated in mechanically unloaded tendon fascicles in a vascular niche-dependent manner. Pharmacological inhibition of ERK 1/2 abolishes the induction of ECM catabolic gene expression (MMPs) and fully prevents loss of mechanical properties. Moreover, ERK 1/2 inhibition in unloaded tendon fascicles suppresses features of pathological tissue remodeling such as collagen type 3 matrix switch and the induction of the pro-fibrotic cytokine interleukin 11. This work demonstrates ERK signaling as a central checkpoint to trigger tendon matrix degradation and remodeling using load-deprived tissue explants.

Gene Expression Profiling Reveals the Shared and Distinct Transcriptional Signatures in Human Lung Epithelial Cells Infected With SARS-CoV-2, MERS-CoV, or SARS-CoV: Potential Implications in Cardiovascular Complications of COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative virus for the current global pandemic known as coronavirus disease 2019 (COVID-19). SARS-CoV-2 belongs to the family of single-stranded RNA viruses known as coronaviruses, including the MERS-CoV and SARS-CoV that cause Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS), respectively. These coronaviruses are associated in the way that they cause mild to severe upper respiratory tract illness. This study has used an unbiased analysis of publicly available gene expression datasets from Gene Expression Omnibus to understand the shared and unique transcriptional signatures of human lung epithelial cells infected with SARS-CoV-2 relative to MERS-CoV or SARS-CoV. A major goal was to discover unique cellular responses to SARS-CoV-2 among these three coronaviruses. Analyzing differentially expressed genes (DEGs) shared by the three datasets led to a set of 17 genes, suggesting the lower expression of genes related to acute inflammatory response (TNF, IL32, IL1A, CXCL1, and CXCL3) in SARS-CoV-2. This subdued transcriptional response to SARS-CoV-2 may cause prolonged viral replication, leading to severe lung damage. Downstream analysis of unique DEGs of SARS-CoV-2 infection revealed changes in genes related to apoptosis (NRP1, FOXO1, TP53INP1, CSF2, and NLRP1), coagulation (F3, PROS1, ITGB3, and TFPI2), and vascular function (VAV3, TYMP, TCF4, and NR2F2), which may contribute to more systemic cardiovascular complications of COVID-19 than MERS and SARS. The study has uncovered a novel set of transcriptomic signatures unique to SARS-CoV-2 infection and shared by three coronaviruses, which may guide the initial efforts in the development of prognostic or therapeutic tools for COVID-19.

Causal Relationship and Shared Genetic Loci between Psoriasis and Type 2 Diabetes through Trans-Disease Meta-Analysis

Psoriasis and type 2 diabetes (T2D) are complex conditions with significant impacts on health. Patients with psoriasis have a higher risk of T2D (∼1.5 OR) and vice versa, controlling for body mass index; yet, there has been a limited study comparing their genetic architecture. We hypothesized that there are shared genetic components between psoriasis and T2D. Trans-disease meta-analysis was applied to 8,016,731 well-imputed genetic markers from large-scale meta-analyses of psoriasis (11,024 cases and 16,336 controls) and T2D (74,124 cases and 824,006 controls), adjusted for body mass index. We confirmed our findings in a hospital-based study (42,112 patients) and tested for causal relationships with multivariable Mendelian randomization. Mendelian randomization identified a causal relationship between psoriasis and T2D (P = 1.6 × 10^‒4, OR = 1.01) and highlighted the impact of body mass index. Trans-disease meta-analysis further revealed four genome-wide significant loci (P < 5 × 10^‒8) with evidence of colocalization and shared directions of effect between psoriasis and T2D not present in body mass index. The proteins coded by genes in these loci (ACTR2, ERLIN1, TRMT112, and BECN1) are connected through NF-κB signaling. Our results provide insight into the immunological components that connect immune-mediated skin conditions and metabolic diseases, independent of confounding factors.

TREAP: A New Topological Approach to Drug Target Inference

Over 50% of drugs fail in stage 3 clinical trials, many because of a poor understanding of the drug's mechanisms of action (MoA). A better comprehension of drug MoA will significantly improve research and development (R&D). Current proposed algorithms, such as ProTINA and DeMAND, can be overly complex. Additionally, they are unable to predict whether the drug-induced gene expression or the topology of the networks used to model gene regulation primarily impacts accurate drug target inference. In this work, we evaluate how network and gene expression data affect ProTINA's accuracy. We find that network topology predominantly determines the accuracy of ProTINA's predictions. We further show that the size of an interaction network and/or selecting cell-specific networks has a limited effect on accuracy. We then demonstrate that a specific network topology measure, betweenness, can be used to improve drug target prediction. Based on these results, we create a new algorithm, TREAP, that combines betweenness values and adjusted p-values for target inference. TREAP offers an alternative approach to drug target inference and is advantageous because it is not computationally demanding, provides easy-to-interpret results, and is often more accurate at predicting drug targets than current state-of-the-art approaches.

Molecular homology between canine spontaneous oral squamous cell carcinomas and human head-and-neck squamous cell carcinomas reveals disease drivers and therapeutic vulnerabilities

Spontaneously occurring canine oral squamous cell carcinomas (COSCC) are viewed as a useful model for human head and neck squamous cell carcinomas (HNSCC). To date however, the molecular basis of COSCC remains poorly understood. To identify changes pertinent to cancer cells in COSCC, we specifically analyzed tumor cells and matched normal epithelium from clinical formalin-fixed paraffin-embedded specimens using laser-capture-microdissection coupled with RNA-sequencing (RNAseq). Our results identify strong contributions of epithelial-to-mesenchymal transition (EMT), classical tumor-promoting (such as E2F, KRAS, MYC, mTORC1, and TGFB1 signaling) and immune-related pathways in the tumor epithelium of COSCC. Comparative analyses of COSCC with 43 paired tumor/normal HNSCC from The Cancer Genome Atlas revealed a high homology in transcriptional reprogramming, and identified processes associated with cell cycle progression, immune processes, and loss of cellular differentiation as likely central drivers of the disease. Similar to HNSCC, our analyses suggested a ZEB2-driven partial EMT in COSCC and identified selective upregulation of KRT14 and KRT17 in COSCC. Beyond homology in transcriptional signatures, we also found therapeutic vulnerabilities strongly conserved between the species: these included increased expression of PD-L1 and CTLA-4, coinciding with EMT and revealing the potential for immune checkpoint therapies, and overexpression of CDK4/6 that sensitized COSCC to treatment with palbociclib. In summary, our data significantly extend the current knowledge of molecular aberrations in COSCC and underline the potential of spontaneous COSCC as a model for HNSCC to interrogate therapeutic vulnerabilities and support translation of novel therapies from bench to bedside.

Design, Screening, and Testing of Non-Rational Peptide Libraries with Antimicrobial Activity: In Silico and Experimental Approaches

One of the challenges of modern biotechnology is to find new routes to mitigate the resistance to conventional antibiotics. Antimicrobial peptides (AMPs) are an alternative type of biomolecules, naturally present in a wide variety of organisms, with the capacity to overcome the current microorganism resistance threat. Here, we reviewed our recent efforts to develop a new library of non-rationally produced AMPs that relies on bacterial genome inherent diversity and compared it with rationally designed libraries. Our approach is based on a four-stage workflow process that incorporates the interplay of recent developments in four major emerging technologies: artificial intelligence, molecular dynamics, surface-display in microorganisms, and microfluidics. Implementing this framework is challenging because to obtain reliable results, the in silico algorithms to search for candidate AMPs need to overcome issues of the state-of-the-art approaches that limit the possibilities for multi-space data distribution analyses in extremely large databases. We expect to tackle this challenge by using a recently developed classification algorithm based on deep learning models that rely on convolutional layers and gated recurrent units. This will be complemented by carefully tailored molecular dynamics simulations to elucidate specific interactions with lipid bilayers. Candidate AMPs will be recombinantly-expressed on the surface of microorganisms for further screening via different droplet-based microfluidic-based strategies to identify AMPs with the desired lytic abilities. We believe that the proposed approach opens opportunities for searching and screening bioactive peptides for other applications.

miR155 regulation of behavior, neuropathology, and cortical transcriptomics in Alzheimer's disease

MicroRNAs are recognized as important regulators of many facets of physiological brain function while also being implicated in the pathogenesis of several neurological disorders. Dysregulation of miR155 is widely reported across a variety of neurodegenerative conditions, including Alzheimer's disease (AD), Parkinson's disease, amyotrophic lateral sclerosis, and traumatic brain injury. In previous work, we observed that experimentally validated miR155 gene targets were consistently enriched among genes identified as differentially expressed across multiple brain tissue and disease contexts. In particular, we found that human herpesvirus-6A (HHV-6A) suppressed miR155, recapitulating reports of miR155 inhibition by HHV-6A in infected T-cells, thyrocytes, and natural killer cells. In earlier studies, we also reported the effects of constitutive deletion of miR155 on accelerating the accumulation of Aβ deposits in 4-month-old APP/PSEN1 mice. Herein, we complete the cumulative characterization of transcriptomic, electrophysiological, neuropathological, and learning behavior profiles from 4-, 8- and 10-month-old WT and APP/PSEN1 mice in the absence or presence of miR155. We also integrated human post-mortem brain RNA-sequences from four independent AD consortium studies, together comprising 928 samples collected from six brain regions. We report that gene expression perturbations associated with miR155 deletion in mouse cortex are in aggregate observed to be concordant with AD-associated changes across these independent human late-onset AD (LOAD) data sets, supporting the relevance of our findings to human disease. LOAD has recently been formulated as the clinicopathological manifestation of a multiplex of genetic underpinnings and pathophysiological mechanisms. Our accumulated data are consistent with such a formulation, indicating that miR155 may be uniquely positioned at the intersection of at least four components of this LOAD "multiplex": (1) innate immune response pathways; (2) viral response gene networks; (3) synaptic pathology; and (4) proamyloidogenic pathways involving the amyloid β peptide (Aβ).

Systems level profiling of arginine starvation reveals MYC and ERK adaptive metabolic reprogramming

Arginine auxotrophy due to the silencing of argininosuccinate synthetase 1 (ASS1) occurs in many carcinomas and in the majority of sarcomas. Arginine deiminase (ADI-PEG20) therapy exploits this metabolic vulnerability by depleting extracellular arginine, causing arginine starvation. ASS1-negative cells develop resistance to ADI-PEG20 through a metabolic adaptation that includes re-expressing ASS1. As arginine-based multiagent therapies are being developed, further characterization of the changes induced by arginine starvation is needed. In order to develop a systems-level understanding of these changes, activity-based proteomic profiling (ABPP) and phosphoproteomic profiling were performed before and after ADI-PEG20 treatment in ADI-PEG20-sensitive and resistant sarcoma cells. When integrated with metabolomic profiling, this multi-omic analysis reveals that cellular response to arginine starvation is mediated by adaptive ERK signaling and activation of the Myc–Max transcriptional network. Concomitantly, these data elucidate proteomic changes that facilitate oxaloacetate production by enhancing glutamine and pyruvate anaplerosis and altering lipid metabolism to recycle citrate for oxidative glutaminolysis. Based on the complexity of metabolic and cellular signaling interactions, these multi-omic approaches could provide valuable tools for evaluating response to metabolically targeted therapies.

Gene Regulatory Network Analysis of Perivascular Adipose Tissue of Abdominal Aortic Aneurysm Identifies Master Regulators of Key Pathogenetic Pathways

The lack of medical therapy to treat abdominal aortic aneurysm (AAA) stems from our inadequate understanding of the mechanisms underlying AAA pathogenesis. To date, the only available treatment option relies on surgical intervention, which aims to prevent AAA rupture. Identifying specific regulators of pivotal pathogenetic mechanisms would allow the development of novel treatments. With this work, we sought to identify regulatory factors associated with co-expressed genes characterizing the diseased perivascular adipose tissue (PVAT) of AAA patients, which is crucially involved in AAA pathogenesis. We applied a reverse engineering approach to identify cis-regulatory elements of diseased PVAT genes, the associated transcription factors, and upstream regulators. Finally, by analyzing the topological properties of the reconstructed regulatory disease network, we prioritized putative targets for AAA interference treatment options. Overall, we identified NFKB1, SPIB, and TBP as the most relevant transcription factors, as well as MAPK1 and GSKB3 protein kinases and RXRA nuclear receptor as key upstream regulators. We showed that these factors could regulate different co-expressed gene subsets in AAA PVAT, specifically associated with both innate and antigen-driven immune response pathways. Inhibition of these factors may represent a novel option for the development of efficient immunomodulatory strategies to treat AAA.

Signatures of co-deregulated genes and their transcriptional regulators in colorectal cancer

The deregulated genes in colorectal cancer (CRC) vary significantly across different studies. Thus, a systems biology approach is needed to identify the co-deregulated genes (co-DEGs), explore their molecular networks, and spot the major hub proteins within these networks. We reanalyzed 19 GEO gene expression profiles to identify and annotate CRC versus normal signatures, single-gene perturbation, and single-drug perturbation signatures. We identified the co-DEGs across different studies, their upstream regulating kinases and transcription factors (TFs). Connectivity Map was used to identify likely repurposing drugs against CRC within each group. The functional changes of the co-upregulated genes in the first category were mainly associated with negative regulation of transforming growth factor β production and glomerular epithelial cell differentiation; whereas the co-downregulated genes were enriched in cotranslational protein targeting to the membrane. We identified 17 hub proteins across the co-upregulated genes and 18 hub proteins across the co-downregulated genes, composed of well-known TFs (MYC, TCF3, PML) and kinases (CSNK2A1, CDK1/4, MAPK14), and validated most of them using GEPIA2 and HPA, but also through two signature gene lists composed of the co-up and co-downregulated genes. We further identified a list of repurposing drugs that can potentially target the co-DEGs in CRC, including camptothecin, neostigmine bromide, emetine, remoxipride, cephaeline, thioridazine, and omeprazole. Similar analyses were performed in the co-DEG signatures in single-gene or drug perturbation experiments in CRC. MYC, PML, CDKs, CSNK2A1, and MAPKs were common hub proteins among all studies. Overall, we identified the critical genes in CRC and we propose repurposing drugs that could be used against them.

An In Vitro Model of Mast Cell Recruitment and Activation by Breast Cancer Cells Supports Anti-Tumoral Responses

Breast cancer (BrC) affects millions of women yearly. Mast cells (MCs) are common components of breast tumors with documented agonistic and antagonistic roles in tumor progression. Understanding the participation of MCs in BrC may lead to new therapies to control tumor growth. In this study, we looked into mechanistic models of MC responses triggered by BrC cells (BrCC), assessing both early degranulation and late transcriptional activities. We used aggressive and non-aggressive BrCC to model the progressive staging of the disease over HMC1 and LAD-2 human MC lines. We found that both MC lines were chemoattracted by all BrCC, but their activation was preferentially induced by aggressive lines, finding differences in their active transcriptional programs, both at basal level and after stimulation. Among those genes with altered expression were down-regulated SPP1, PDCD1, IL17A and TGFB1 and up-regulated KITLG and IFNG. A low expression of SPP1 and a high expression of KITLG and IFNG were associated with increased overall survival of BrC patients from public databases. The set of altered genes is more often associated with tumor stromas enriched with anti-tumoral signals, suggesting that MCs may participate in tumor control.

Network analysis, sequence and structure dynamics of key proteins of coronavirus and human host, and molecular docking of selected phytochemicals of nine medicinal plants

The novel coronavirus of 2019 (nCoV-19) has become a pandemic, affecting over 205 nations with over 7,410,000 confirmed cases which has resulted to over 418,000 deaths worldwide. This study aimed to identify potential therapeutic compounds and phytochemicals of medicinal plants that have potential to modulate the expression network of genes that are involve in SARS-CoV-2 pathology in human host and to understand the dynamics key proteins involved in the virus-host interactions. The method used include gene network analysis, molecular docking, and sequence and structure dynamics simulations. The results identified DNA-dependent protein kinase (DNA-PK) and Protein kinase CK2 as key players in SARS-CoV-2 lifecycle. Among the predicted drugs compounds, clemizole, monorden, spironolactone and tanespimycin showed high binding energies; among the studied repurposing compounds, remdesivir, simeprevir and valinomycin showed high binding energies; among the predicted acidic compounds, acetylursolic acid and hardwickiic acid gave high binding energies; while among the studied anthraquinones and glycosides compounds, ellagitannin and friedelanone showed high binding energies against 3-Chymotrypsin-like protease (3CL^pro), Papain-like protease (PL^pro), helicase (nsp13), RNA-dependent RNA polymerase (nsp12), 2'-O-ribose methyltransferase (nsp16) of SARS-CoV-2 and DNA-PK and CK2alpha in human. The order of affinity for CoV proteins is 5Y3E > 6NUS > 6JYT > 2XYR > 3VB6. Finally, medicinal plants with phytochemicals such as caffeine, ellagic acid, quercetin and their derivatives could possibly remediate COVID-19.Communicated by Ramaswamy H. Sarma.

Bioinformatics toolbox for exploring protein phosphorylation network

A clear systematic delineation of the interactions between phosphorylation sites on substrates and their effector kinases plays a fundamental role in revealing cellular activities, understanding signaling modulation mechanisms and proposing novel hypotheses. The emergence of bioinformatics tools contributes to studying phosphorylation network. Some of them feature the visualization of network, enabling more effective trace of the underlying biological problems in a clear and succinct way. In this review, we aimed to provide a toolbox for exploring phosphorylation network. We first systematically surveyed 19 tools that are available for exploring phosphorylation networks, and subsequently comparatively analyzed and summarized these tools to guide tool selection in terms of functionality, data sources, performance, network visualization and implementation, and finally briefly discussed the application cases of these tools. In different scenarios, the conclusion on the suitability of a tool for a specific user may vary. Nevertheless, easily accessible bioinformatics tools are proved to facilitate biological findings. Hopefully, this work might also assist non-specialists, students, as well as computational scientists who aim at developing novel tools in the field of phosphorylation modification.

Sporadic activation of an oxidative stress-dependent NRF2-p53 signaling network in breast epithelial spheroids and premalignancies

Breast and mammary epithelial cells experience different local environments during tissue development and tumorigenesis. Microenvironmental heterogeneity gives rise to distinct cell regulatory states whose identity and importance are just beginning to be appreciated. Cellular states diversify when clonal three-dimensional (3D) spheroids are cultured in basement membrane, and one such state is associated with stress tolerance and poor response to anticancer therapeutics. Here, we found that this state was jointly coordinated by the NRF2 and p53 pathways, which were costabilized by spontaneous oxidative stress within 3D cultures. Inhibition of NRF2 or p53 individually disrupted some of the transcripts defining the regulatory state but did not yield a notable phenotype in nontransformed breast epithelial cells. In contrast, combined perturbation prevented 3D growth in an oxidative stress-dependent manner. By integrating systems models of NRF2 and p53 signaling in a single oxidative stress network, we recapitulated these observations and made predictions about oxidative stress profiles during 3D growth. NRF2 and p53 signaling were similarly coordinated in normal breast epithelial tissue and hormone-negative ductal carcinoma in situ lesions but were uncoupled in triple-negative breast cancer (TNBC), a subtype in which p53 is usually mutated. Using the integrated model, we correlated the extent of this uncoupling in TNBC cell lines with the importance of NRF2 in the 3D growth of these cell lines and their predicted handling of oxidative stress. Our results point to an oxidative stress tolerance network that is important for single cells during glandular development and the early stages of breast cancer.

Janus effect of glucocorticoids on differentiation of muscle fibro/adipogenic progenitors

Muscle resident fibro-adipogenic progenitors (FAPs), support muscle regeneration by releasing cytokines that stimulate the differentiation of myogenic stem cells. However, in non-physiological contexts (myopathies, atrophy, aging) FAPs cause fibrotic and fat infiltrations that impair muscle function. We set out to perform a fluorescence microscopy-based screening to identify compounds that perturb the differentiation trajectories of these multipotent stem cells. From a primary screen of 1,120 FDA/EMA approved drugs, we identified 34 compounds as potential inhibitors of adipogenic differentiation of FAPs isolated from the murine model (mdx) of Duchenne muscular dystrophy (DMD). The hit list from this screen was surprisingly enriched with compounds from the glucocorticoid (GCs) chemical class, drugs that are known to promote adipogenesis in vitro and in vivo. To shed light on these data, three GCs identified in our screening efforts were characterized by different approaches. We found that like dexamethasone, budesonide inhibits adipogenesis induced by insulin in sub-confluent FAPs. However, both drugs have a pro-adipogenic impact when the adipogenic mix contains factors that increase the concentration of cAMP. Gene expression analysis demonstrated that treatment with glucocorticoids induces the transcription of Gilz/Tsc22d3, an inhibitor of the adipogenic master regulator PPARγ, only in anti-adipogenic conditions. Additionally, alongside their anti-adipogenic effect, GCs are shown to promote terminal differentiation of satellite cells. Both the anti-adipogenic and pro-myogenic effects are mediated by the glucocorticoid receptor and are not observed in the presence of receptor inhibitors. Steroid administration currently represents the standard treatment for DMD patients, the rationale being based on their anti-inflammatory effects. The findings presented here offer new insights on additional glucocorticoid effects on muscle stem cells that may affect muscle homeostasis and physiology.

An integrative method to predict signalling perturbations for cellular transitions

Induction of specific cellular transitions is of clinical importance, as it allows to revert disease cellular phenotype, or induce cellular reprogramming and differentiation for regenerative medicine. Signalling is a convenient way to accomplish such transitions without transfer of genetic material. Here we present the first general computational method that systematically predicts signalling molecules, whose perturbations induce desired cellular transitions. This probabilistic method integrates gene regulatory networks (GRNs) with manually-curated signalling pathways obtained from MetaCore from Clarivate Analytics, to model how signalling cues are received and processed in the GRN. The method was applied to 219 cellular transition examples, including cell type transitions, and overall correctly predicted experimentally validated signalling molecules, consistently outperforming other well-established approaches, such as differential gene expression and pathway enrichment analyses. Further, we validated our method predictions in the case of rat cirrhotic liver, and identified the activation of angiopoietins receptor Tie2 as a potential target for reverting the disease phenotype. Experimental results indicated that this perturbation induced desired changes in the gene expression of key TFs involved in fibrosis and angiogenesis. Importantly, this method only requires gene expression data of the initial and desired cell states, and therefore is suited for the discovery of signalling interventions for disease treatments and cellular therapies.

Integrated use of bioinformatic resources reveals that co-targeting of histone deacetylases, IKBK and SRC inhibits epithelial-mesenchymal transition in cancer

With the advent of high-throughput technologies leading to big data generation, increasing number of gene signatures are being published to predict various features of diseases such as prognosis and patient survival. However, to use these signatures for identifying therapeutic targets, use of additional bioinformatic tools is indispensible part of research. Here, we have generated a pipeline comprised of nearly 15 bioinformatic tools and enrichment statistical methods to propose and validate a drug combination strategy from already approved drugs and present our approach using published pan-cancer epithelial-mesenchymal transition (EMT) signatures as a case study. We observed that histone deacetylases were critical targets to tune expression of multiple epithelial versus mesenchymal genes. Moreover, SRC and IKBK were the principal intracellular kinases regulating multiple signaling pathways. To confirm the anti-EMT efficacy of the proposed target combination in silico, we validated expression of targets in mesenchymal versus epithelial subtypes of ovarian cancer. Additionally, we inhibited the pinpointed proteins in vitro using an invasive lung cancer cell line. We found that whereas low-dose mono-therapy failed to limit cell dispersion from collagen spheroids in a microfluidic device as a metric of EMT, the combination fully inhibited dissociation and invasion of cancer cells toward cocultured endothelial cells. Given the approval status and safety profiles of the suggested drugs, the proposed combination set can be considered in clinical trials.

From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL

While gene expression profiling is commonly used to gain an overview of cellular processes, the identification of upstream processes that drive expression changes remains a challenge. To address this issue, we introduce CARNIVAL, a causal network contextualization tool which derives network architectures from gene expression footprints. CARNIVAL (CAusal Reasoning pipeline for Network identification using Integer VALue programming) integrates different sources of prior knowledge including signed and directed protein-protein interactions, transcription factor targets, and pathway signatures. The use of prior knowledge in CARNIVAL enables capturing a broad set of upstream cellular processes and regulators, leading to a higher accuracy when benchmarked against related tools. Implementation as an integer linear programming (ILP) problem guarantees efficient computation. As a case study, we applied CARNIVAL to contextualize signaling networks from gene expression data in IgA nephropathy (IgAN), a condition that can lead to chronic kidney disease. CARNIVAL identified specific signaling pathways and associated mediators dysregulated in IgAN including Wnt and TGF-β, which we subsequently validated experimentally. These results demonstrated how CARNIVAL generates hypotheses on potential upstream alterations that propagate through signaling networks, providing insights into diseases.