Machine learning identifies molecular regulators and therapeutics for targeting SARS-CoV2-induced cytokine release

DNAJ-PKAc fusion heightens PLK1 inhibitor sensitivity in fibrolamellar carcinoma

BACKGROUND

Fibrolamellar carcinoma (FLC), a rare and fatal liver cancer lacking effective drug therapy, is driven by the DNAJ-PKAc fusion oncoprotein. However, the underlying mechanism of DNAJ-PKAc's role in FLC tumour growth remains enigmatic.

OBJECTIVE

We sought to determine the protein kinase-mediated signalling networks that drive growth and proliferation in FLC.

DESIGN

We integrated a combination of newly established preclinical models of FLC and an unbiased polypharmacology-based approach to identify downstream kinases involved in DNAJ-PKAc-mediated FLC cell growth. We validated our findings in multiple patient-derived mouse models and patient tumours.

RESULTS

Functional screening, coupled with computational analysis, highlighted Polo-like kinase 1 (PLK1) as vital for FLC cell viability. Genetic and pharmacological PLK1 inhibition significantly reduced FLC cell growth, inducing apoptosis. Further studies showed DNAJ-PKAc's centrosomal presence and direct interaction with PLK1, revealing a novel mechanism that promotes PLK1 activation and mitotic progression. Clinical-grade PLK1 inhibitors effectively suppressed FLC tumour growth across multiple preclinical models, including patient-derived xenograft and an orthotopic model of FLC, suggesting promising therapeutic avenues.

CONCLUSION

Our findings underscore the role of DNAJ-PKAc in rewiring signalling networks and highlight valuable clinical implications for PLK1-targeted therapies for FLC.

A phase 2 study of frontline pembrolizumab in follicular lymphoma

Background

The tumor microenvironment (TME), including infiltrating T-cells, is thought to play a major role in the pathogenesis and prognosis of follicular lymphoma (FL) and may contribute to its widely varied disease course. We hypothesized that programmed death-1 inhibition may be most effective in untreated, immunocompetent FL patients. Thus, we developed a phase 2 study to evaluate the efficacy of pembrolizumab as the initial treatment for indolent B-cell lymphoma.

Methods

Adults with FL or marginal zone lymphoma and an indication for treatment were eligible. Patients received pembrolizumab 200 mg IV in 21-day cycles for up to 18 cycles, until progression or unacceptable toxicity. Early response assessment was obtained after cycle 3 with computed tomography (CT), and a fluorodeoxyglucose (FDG)-positron emission tomography-computed tomography (PET-CT) was obtained after cycle 6 to determine candidacy for continuation in the study. Immunosecretome profiling was performed at baseline and on cycle 2 day 1.

Results

Nine patients with FL were enrolled between February 2019 and April 2021, including eight (89%) with advanced stage, seven (78%) with intermediate/high Follicular Lymphoma International Prognostic Index, and six (67%) with high-tumor burden by Groupe d'Etude des Lymphomes Folliculaires. The best overall response rate by FDG PET-CT was 33% (three partial metabolic responses). Three patients (33%) had stable disease, and three (33%) had progressive disease (including one patient who only had a follow-up CT). By CT four (44%) experienced a reduction in target lesions, but all were less than partial responses. Grade 3 or higher immune-related adverse events (IRAEs) were seen in two (22%) patients, both with transaminitis and one of whom had concurrent hypophysitis. Another patient had grade 1 pneumonitis, requiring treatment with steroids. No associations between the immunosecretome profile and clinical outcomes could be detected.

Conclusion

Frontline pembrolizumab for FL is associated with limited responses and a clinically significant rate of IRAEs. Alternative strategies for targeting the TME in FL should be explored.

Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions

A highly critical event in a virus's life cycle is successfully entering a given host. This process begins when a viral glycoprotein interacts with a target cell receptor, which provides the molecular basis for target virus-host cell interactions for novel drug discovery. Over the years, extensive research has been carried out in the field of virus-host cell interaction, generating a massive number of genetic and molecular data sources. These datasets are an asset for predicting virus-host interactions at the molecular level using machine learning (ML), a subset of artificial intelligence (AI). In this direction, ML tools are now being applied to recognize patterns in these massive datasets to predict critical interactions between virus and host cells at the protein-protein and protein-sugar levels, as well as to perform transcriptional and translational analysis. On the other end, deep learning (DL) algorithms-a subfield of ML-can extract high-level features from very large datasets to recognize the hidden patterns within genomic sequences and images to develop models for rapid drug discovery predictions that address pathogenic viruses displaying heightened affinity for receptor docking and enhanced cell entry. ML and DL are pivotal forces, driving innovation with their ability to perform analysis of enormous datasets in a highly efficient, cost-effective, accurate, and high-throughput manner. This review focuses on the complexity of virus-host cell interactions at the molecular level in light of the current advances of ML and AI in viral pathogenesis to improve new treatments and prevention strategies.

Repurposing Anticancer Drugs Targeting the MAPK/ERK Signaling Pathway for the Treatment of Respiratory Virus Infections

Respiratory virus infections remain a significant challenge to human health and the social economy. The symptoms range from mild rhinitis and nasal congestion to severe lower respiratory tract dysfunction and even mortality. The efficacy of therapeutic drugs targeting respiratory viruses varies, depending upon infection time and the drug resistance engendered by a high frequency of viral genome mutations, necessitating the development of new strategies. The MAPK/ERK pathway that was well delineated in the 1980s represents a classical signaling cascade, essential for cell proliferation, survival, and differentiation. Since this pathway is constitutively activated in many cancers by oncogenes, several drugs inhibiting Raf/MEK/ERK have been developed and currently used in anticancer treatment. Two decades ago, it was reported that viruses such as HIV and influenza viruses could exploit the host cellular MAPK/ERK pathway for their replication. Thus, it would be feasible to repurpose this category of the pathway inhibitors for the treatment of respiratory viral infections. The advantage is that the host genes are not easy to mutate such that the drug resistance rarely occurs during short-period treatment of viruses. Therefore, in this review we will summarize the research progress on the role of the MAPK/ERK pathway in respiratory virus amplification and discuss the potential of the pathway inhibitors (MEK inhibitors) in the treatment of respiratory viral infections.

A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death

The clinical manifestations of SARS-CoV-2 infection vary widely among patients, from asymptomatic to life-threatening. Host genetics is one of the factors that contributes to this variability as previously reported by the COVID-19 Host Genetics Initiative (HGI), which identified sixteen loci associated with COVID-19 severity. Herein, we investigated the genetic determinants of COVID-19 mortality, by performing a case-only genome-wide survival analysis, 60 days after infection, of 3904 COVID-19 patients from the GEN-COVID and other European series (EGAS00001005304 study of the COVID-19 HGI). Using imputed genotype data, we carried out a survival analysis using the Cox model adjusted for age, age2, sex, series, time of infection, and the first ten principal components. We observed a genome-wide significant (P-value < 5.0 × 10-8) association of the rs117011822 variant, on chromosome 11, of rs7208524 on chromosome 17, approaching the genome-wide threshold (P-value = 5.19 × 10-8). A total of 113 variants were associated with survival at P-value < 1.0 × 10-5 and most of them regulated the expression of genes involved in immune response (e.g., CD300 and KLR genes), or in lung repair and function (e.g., FGF19 and CDH13). Overall, our results suggest that germline variants may modulate COVID-19 risk of death, possibly through the regulation of gene expression in immune response and lung function pathways.

Longitudinal plasma proteomics reveals biomarkers of alveolar-capillary barrier disruption in critically ill COVID-19 patients

The pathobiology of respiratory failure in COVID-19 consists of a complex interplay between viral cytopathic effects and a dysregulated host immune response. In critically ill patients, imatinib treatment demonstrated potential for reducing invasive ventilation duration and mortality. Here, we perform longitudinal profiling of 6385 plasma proteins in 318 hospitalised patients to investigate the biological processes involved in critical COVID-19, and assess the effects of imatinib treatment. Nine proteins measured at hospital admission accurately predict critical illness development. Next to dysregulation of inflammation, critical illness is characterised by pathways involving cellular adhesion, extracellular matrix turnover and tissue remodelling. Imatinib treatment attenuates protein perturbations associated with inflammation and extracellular matrix turnover. These proteomic alterations are contextualised using external pulmonary RNA-sequencing data of deceased COVID-19 patients and imatinib-treated Syrian hamsters. Together, we show that alveolar capillary barrier disruption in critical COVID-19 is reflected in the plasma proteome, and is attenuated with imatinib treatment. This study comprises a secondary analysis of both clinical data and plasma samples derived from a clinical trial that was registered with the EU Clinical Trials Register (EudraCT 2020-001236-10, https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-001236-10/NL ) and Netherlands Trial Register (NL8491, https://www.trialregister.nl/trial/8491 ).

Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases

Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in-silico drug interventions. Here, we introduce UNAGI, a deep generative neural network tailored to analyze time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modeling and discovery. When applied to a dataset from patients with Idiopathic Pulmonary Fibrosis (IPF), UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation via proteomics reveals the accuracy of UNAGI's cellular dynamics analyses, and the use of the Fibrotic Cocktail treated human Precision-cut Lung Slices confirms UNAGI's predictions that Nifedipine, an antihypertensive drug, may have antifibrotic effects on human tissues. UNAGI's versatility extends to other diseases, including a COVID dataset, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond IPF, amplifying its utility in the quest for therapeutic solutions across diverse pathological landscapes.

Circulating Inflammatory Factor Levels in the Early Phase of COVID-19 are Associated with the Progression of Respiratory Failure: A Single-Center Retrospective Study

Purpose

To evaluate the potential relationships between serum interleukin (IL)-2, IL-4, IL-6, IL-10, IL-17, interferon (IFN)-γ, and tumor necrosis factor (TNF)-α levels and occurrence of respiratory failure in patients with early-stage COVID-19 disease.

Patients and Methods

We analyzed clinical characteristics, laboratory parameters, and immunoinflammatory markers in 302 patients diagnosed with SARS-CoV-2 infection who required hospitalization at Changshu Hospital of Nantong University. IL-2, IL-4, IL-6, IL-10, IL-17, IFN-γ, and TNF-α levels in the peripheral blood of patients hospitalized five days after disease onset were measured using multiplex bead-based flow fluorescent immunoassay (MBFFI).

Results

Patients with respiratory failure had higher serum IL-4 [0 (0, 0.54) pg/mL], IL-6 [40.76 (12.33, 90.28) pg/mL], IL-10 [6.65 (4.12, 11.34) pg/mL], and IL-17 [9.48 (4.31, 12.13) pg/mL] levels than patients without respiratory failure (P=0.042, P<0.0001, P=0.012, and P=0.036, respectively). Serum IL-2, IFN-γ, and TNF-α levels were not significantly different between the two groups. The occurrence of respiratory failure was positively correlated with sex (R=0.122, P=0.034), lactic acid (R=0.193, P=0.007), white blood cell count (R=0.121, P=0.038), erythrocyte distribution width (R=0.131, P=0.024), thyrocalcitonin (R=0.280, P<0.0001), and D-dimer levels (R=0.214, P<0.0001) but negatively correlated with oxygen partial pressure (R=-0.208, P=0.004), oxygen saturation (R=-0.220, P=0.002), lymphocyte count (R=-0.129, P=0.026), and calcium (R=-0.152, P=0.042). Among the immunoinflammatory biomarkers, the occurrence of respiratory failure was positively correlated with IL-4 (R=-0.117, P=0.042), IL-6 (R=0.206, P<0.0001), IL-10 (R=0.145, P=0.012), and IL-17 (R=0.121, P=0.036) levels.

Conclusion

Serum levels of pro-inflammatory cytokines IL-6 and IL-17 and anti-inflammatory cytokines IL-4 and IL-10 were significantly elevated in patients with respiratory failure and weakly positively correlated with the occurrence of respiratory failure. Further studies are required to explore these key immune mechanisms to help clinicians better manage acute complications, long-term sequelae, and possible future COVID-19 variants and be flexible in managing future epidemics and similar public health threats.

Host kinase regulation of Plasmodium vivax dormant and replicating liver stages

Upon transmission to the liver, Plasmodium vivax parasites form replicating schizonts, which continue to initiate blood-stage infection, or dormant hypnozoites that reactivate weeks to months after initial infection. P. vivax phenotypes in the field vary significantly, including the ratio of schizonts to hypnozoites formed and the frequency and timing of relapse. Evidence suggests that both parasite genetics and environmental factors underly this heterogeneity. We previously demonstrated that data on the effect of a panel of kinase inhibitors with overlapping targets on Plasmodium liver stage infection, in combination with a computational approach called kinase regression (KiR), can be used to uncover novel host regulators of infection. Here, we applied KiR to evaluate the extent to which P. vivax liver-stage parasites are susceptible to changes in host kinase activity. We identified a role for a subset of host kinases in regulating schizont and hypnozoite infection and schizont size and characterized overlap as well as variability in host phosphosignaling dependencies between parasite forms and across multiple patient isolates. Striking, our data point to variability in host dependencies across P. vivax isolates, suggesting one possible origin of the heterogeneity observed across P. vivax in the field.

Polypharmacology-based kinome screen identifies new regulators of KSHV reactivation

Kaposi's sarcoma-associated herpesvirus (KSHV) causes several human diseases including Kaposi's sarcoma (KS), a leading cause of cancer in Africa and in patients with AIDS. KS tumor cells harbor KSHV predominantly in a latent form, while typically <5% contain lytic replicating virus. Because both latent and lytic stages likely contribute to cancer initiation and progression, continued dissection of host regulators of this biological switch will provide insights into fundamental pathways controlling the KSHV life cycle and related disease pathogenesis. Several cellular protein kinases have been reported to promote or restrict KSHV reactivation, but our knowledge of these signaling mediators and pathways is incomplete. We employed a polypharmacology-based kinome screen to identify specific kinases that regulate KSHV reactivation. Those identified by the screen and validated by knockdown experiments included several kinases that enhance lytic reactivation: ERBB2 (HER2 or neu), ERBB3 (HER3), ERBB4 (HER4), MKNK2 (MNK2), ITK, TEC, and DSTYK (RIPK5). Conversely, ERBB1 (EGFR1 or HER1), MKNK1 (MNK1) and FRK (PTK5) were found to promote the maintenance of latency. Mechanistic characterization of ERBB2 pro-lytic functions revealed a signaling connection between ERBB2 and the activation of CREB1, a transcription factor that drives KSHV lytic gene expression. These studies provided a proof-of-principle application of a polypharmacology-based kinome screen for the study of KSHV reactivation and enabled the discovery of both kinase inhibitors and specific kinases that regulate the KSHV latent-to-lytic replication switch.

Dose optimisation of ponatinib in chronic phase chronic myeloid leukemia

INTRODUCTION

Ponatinib exhibits a high inhibition potency on wild-type and most mutated forms of the BCR:ABL1 kinase, but also a significant cardiovascular toxicity. Improving the efficacy/safety ratio should allow patients to safely draw benefit from the drug.

AREAS COVERED

Based on pharmacological findings and international guidelines on chronic myeloid leukemia and cardiovascular risk management, as well as on the most recent data collected in real-life studies and in a randomized phase II trial, we propose a decision-tree of dose selection of the drug.

EXPERT OPINION

We distinguish (1) highly resistant patients according to poor previous response to second generation tyrosine kinase inhibitors (complete hematologic response or less) or to mutational status (T315I, E255V, alone or within compound mutations), requiring a starting daily dose of 45 mg, reduced to 15 or 30 mg according to the patient's profile, preferentially upon major molecular achievement (3-log reduction or MR3, BCR:ABL1 ≤ 0.1%IS); (2) less-resistant patients justifying an initial dose of 30 mg, reduced to 15 mg upon MR2 (BCR:ABL1 ≤ 1%IS) or preferentially MR3 in patients with a favorable safety profile; (3) intolerant patients to be treated by 15 mg.

Targeting Human Proteins for Antiviral Drug Discovery and Repurposing Efforts: A Focus on Protein Kinases

Despite the great technological and medical advances in fighting viral diseases, new therapies for most of them are still lacking, and existing antivirals suffer from major limitations regarding drug resistance and a limited spectrum of activity. In fact, most approved antivirals are directly acting antiviral (DAA) drugs, which interfere with viral proteins and confer great selectivity towards their viral targets but suffer from resistance and limited spectrum. Nowadays, host-targeted antivirals (HTAs) are on the rise, in the drug discovery and development pipelines, in academia and in the pharmaceutical industry. These drugs target host proteins involved in the virus life cycle and are considered promising alternatives to DAAs due to their broader spectrum and lower potential for resistance. Herein, we discuss an important class of HTAs that modulate signal transduction pathways by targeting host kinases. Kinases are considered key enzymes that control virus-host interactions. We also provide a synopsis of the antiviral drug discovery and development pipeline detailing antiviral kinase targets, drug types, therapeutic classes for repurposed drugs, and top developing organizations. Furthermore, we detail the drug design and repurposing considerations, as well as the limitations and challenges, for kinase-targeted antivirals, including the choice of the binding sites, physicochemical properties, and drug combinations.

Identification of FDA-approved drugs against SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) through computational virtual screening

The SARS-CoV-2 coronavirus is responsible for the COVID-19 outbreak, which overwhelmed millions of people worldwide; hence, there is an urgency to identify appropriate antiviral drugs. This study focuses on screening compounds that inhibit RNA-dependent RNA-polymerase (RdRp) essential for RNA synthesis required for replication of positive-strand RNA viruses. Computational screening against RdRp using Food and Drug Administration (FDA)-approved drugs identified ten prominent compounds with binding energies of more than - 10.00 kcal/mol, each a potential inhibitor of RdRp. These compounds' binding energy is comparable to known RdRp inhibitors remdesivir (IC50 = 10.09 μM, SI = 4.96) and molnupiravir (EC50 = 0.67 - 2.66 µM) and 0.32-2.03 µM). Remdesivir and molnupiravir have been tested in clinical trial and remain authorized for emergency use in the treatment of COVID-19. In docking simulations, selected compounds are bound to the substrate-binding pocket of RdRp and showed hydrophobic and hydrogen bond interaction. For molecular dynamics simulation, capmatinib, pralsetinib, ponatinib, and tedizolid phosphate were selected from the initial ten candidate compounds. MD simulation indicated that these compounds are stable at 50-ns MD simulation when bound to RdRp protein. The screen hit compounds, remdesivir, molnupiravir, and GS-441524, are bound in the substrate binding pocket with good binding-free energy. As a consequence, capmatinib, pralsetinib, ponatinib, and tedizolid phosphate are potential new inhibitors of RdRp protein with potential of limiting COVID-19 infection by blocking RNA synthesis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11224-022-02072-1.

The JAK1/2 Inhibitor Baricitinib Mitigates the Spike-Induced Inflammatory Response of Immune and Endothelial Cells In Vitro

The purpose of this study was to examine the effect of the JAK-STAT inhibitor baricitinib on the inflammatory response of human monocyte-derived macrophages (MDM) and endothelial cells upon exposure to the spike S1 protein from SARS-CoV-2. The effect of the drug has been evaluated on the release of cytokines and chemokines from spike-treated MDM, as well as on the activation of endothelial cells (HUVECs) after exposure to conditioned medium collected from spike-activated MDM. Results obtained indicate that, in MDM, baricitinib prevents the S1-dependent phosphorylation of STAT1 and STAT3, along with the induction of IP-10- and MCP-1 secretion; the release of IL-6 and TNFα is also reduced, while all other mediators tested (IL-1β, IL-8, RANTES, MIP-1α and MIP-1β) are not modified. Baricitinib is, instead, poorly effective on endothelial activation when HUVECs are exposed to supernatants from S1-activated macrophages; the induction of VCAM-1, indeed, is not affected by the drug, while that of ICAM-1 is only poorly inhibited. The drug, however, also exerts protective effects on the endothelium by limiting the expression of pro-inflammatory mediators, specifically IL-6, RANTES and IP-10. No effect of baricitinib has been observed on IL-8 synthesis and, consistently, on neutrophils chemiotaxis. Our in vitro findings reveal that the efficacy of baricitinib is limited, with effects mainly focused on the inhibition of the IL-6-mediated inflammatory loop.

Deep Neural Network Modeling Identifies Biomarkers of Response to Immune-checkpoint Therapy

Immunotherapy has shown significant promise as a treatment for cancer, such as lung cancer and melanoma. However, only 10%–30% of the patients respond to treatment with immune checkpoint blockers (ICBs), underscoring the need for biomarkers to predict response to immunotherapy. Here, we developed DeepGeneX, a computational framework that uses advanced deep neural network modeling and feature elimination to reduce single-cell RNA-seq data on ∼26,000 genes to six of the most important genes (CCR7, SELL, GZMB, WARS, GZMH, and LGALS1), that accurately predict response to immunotherapy. We also discovered that the high LGALS1 and WARS-expressing macrophage population represent a biomarker for ICB therapy nonresponders, suggesting that these macrophages may be a target for improving ICB response. Taken together, DeepGeneX enables biomarker discovery and provides an understanding of the molecular basis for the model’s predictions.

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Predicting biomarkers for immunotherapy response remains a challenge

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DeepGeneX combines neural networks with single-cell RNAseq to predict responders

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LGALS1 and WARS-expressing macrophages in nonresponders impact T cell activation

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DeepGeneX enables biomarker discovery and elucidates underlying molecular mechanism

Very low levels of remdesivir resistance in SARS-COV-2 genomes after 18 months of massive usage during the COVID19 pandemic: A GISAID exploratory analysis

Massive usage of antiviral compounds during a pandemic creates an ideal ground for emergence of resistant strains. Remdesivir, a broad-spectrum inhibitor of the viral RNA-dependent RNA polymerase (RdRp), was extensively prescribed under emergency use authorization during the first 18 months of the COVID19 pandemic, before randomized controlled trials showed poor efficacy in hospitalized patients. RdRp mutations conferring resistance to remdesivir are well known from in vitro studies, and the huge SARS-CoV-2 sequencing effort during the ongoing COVID19 pandemic represents an unprecedented opportunity to assess emergence and fitness of antiviral resistance in vivo. We mined the GISAID database to extrapolate the frequency of remdesivir escape mutations. Our analysis reveals very low levels of remdesivir resistance worldwide despite massive usage.

Identifying potential novel insights for COVID-19 pathogenesis and therapeutics using an integrated bioinformatics analysis of host transcriptome

The molecular mechanisms underlying the pathogenesis of COVID-19 have not been fully discovered. This study aims to decipher potentially hidden parts of the pathogenesis of COVID-19, potential novel drug targets, and identify potential drug candidates. Two gene expression profiles were analyzed, and overlapping differentially expressed genes (DEGs) were selected for which top enriched transcription factors and kinases were identified, and pathway analysis was performed. Protein-protein interaction (PPI) of DEGs was constructed, hub genes were identified, and module analysis was also performed. DGIdb database was used to identify drugs for the potential targets (hub genes and the most enriched transcription factors and kinases for DEGs). A drug-potential target network was constructed, and drugs were ranked according to the degree. L1000FDW was used to identify drugs that can reverse transcriptional profiles of COVID-19. We identified drugs currently in clinical trials, others predicted by different methods, and novel potential drug candidates Entrectinib, Omeprazole, and Exemestane for combating COVID-19. Besides the well-known pathogenic pathways, it was found that axon guidance is a potential pathogenic pathway. Sema7A, which may exacerbate hypercytokinemia, is considered a potential novel drug target. Another potential novel pathway is related to TINF2 overexpression, which may induce potential telomere dysfunction and damage DNA that may exacerbate lung fibrosis. This study identified new potential insights regarding COVID-19 pathogenesis and treatment, which might help us improve our understanding of the mechanisms of COVID-19.