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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 325 records · Page 18Linked to original sources

bioRxiv2019

Pan-transcriptome-based Candidate Therapeutic Discovery for Idiopathic Pulmonary Fibrosis

BACKGROUNDAlthough the advent of two FDA-approved therapies for idiopathic pulmonary fibrosis (IPF) has energized the field, their effects are largely suppressive than pulmonary fibrosis remission- or reversion-inducing. Hence, the pursuit for newer IPF therapeutics continues. Recent studies show that joint analysis of systems biology level information with drug-disease connectivity are effective in discovery of biologically relevant candidate therapeutics. METHODSPublicly available gene expression signatures from IPF patients are used to query large scale perturbagen signature library to discover compounds that can potentially reverse dysregulated gene expression in IPF. Two methods are used to calculate IPF-compound connectivity: gene expression-based connectivity and feature-based connectivity. Identified compounds are further prioritized based on their shared mechanism(s) of action. RESULTSWe identified 77 compounds as potential candidate therapeutics for IPF. Of these 39 compounds are either FDA-approved for other diseases or are currently in phase 2/3 clinical trials suggesting their repurposing potential for IPF. Among these compounds are multiple receptor kinase inhibitors (e.g., nintedanib, currently approved for IPF, and sunitinib), aurora kinase inhibitor (barasertib), EGFR inhibitors (erlotinib, gefitinib), calcium channel blocker (verapamil), phosphodiesterase inhibitors (roflumilast, sildenafil), PPAR agonists (pioglitazone), HDAC inhibitors (entinostat), and opioid receptor antagonists (nalbuphine). As a proof-of-concept, we performed in vitro validations with verapamil using lung fibroblasts from IPF and show its potential benefits in pulmonary fibrosis. CONCLUSIONSSince about half of the candidates discovered in this study are either FDA-approved or are currently in clinical trials for other diseases, rapid translation of these compounds as potential IPF therapeutics is feasible. Further, the generalizable, integrative connectivity analysis framework in this study can be readily adapted in early phase drug discovery for other common and rare diseases with transcriptomic profiles.

genomics↗

bioRxiv2023

Development of a new bioinspired peptide with fibroblast relaxation proprieties for cosmetics applications

The cosmetic field is driven by the development of new inputs and raw materials that are used to the development innovative products for consumers. Among the different sub-areas in cosmetology, skin care is the biggest area, with almost fifty percent of the market, and probably the most innovative of them. Thus, there is a complex value chain organized to develop and produce innovative inputs to supply the demands of the market. Within this context, different basic scientific areas, such as biochemistry and biotechnology, have been a source of inspiration for new active ingredients. In the present article, we will present the pre-clinical and clinical trials involved in the development of a new innovative biomimetic peptide derived from natural wasp venoms content. This new peptide sequence, here named WASP-PEP was designed based on natural peptide templates with some strategic amino acid modifications to increase the molecules effectiveness. As the main results, we observed that the WASP-PEP is a biocompatible and dermatology tolerate compound, and the peptide can promote some relaxation effects in dermal fibroblasts. This relaxation effect is compatible with a kind of regulated and cumulative botox-like effect. Notablyr, this relaxation effect was also observed in the clinical trial, when volunteer subjects reported that their facial skin was flatter after the use of the WASP-PEP product for one month. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/525652v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@7b475dorg.highwire.dtl.DTLVardef@18a4fd8org.highwire.dtl.DTLVardef@17ff8f4org.highwire.dtl.DTLVardef@b1717b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

FlowSpot Enables Decentralized Phenotypic and Functional Cellular Immune Profiling from Dried Blood Spots

Expanding access to cellular immune analysis is essential for decentralized clinical care, clinical trials, and population-based research. However, current flow cytometry workflows require rapid processing of fresh blood, proximity to a centralized laboratory, and cold chain logistics. Although dried blood spots (DBS) have transformed decentralized molecular diagnostics, no comparable approach has enabled robust flow cytometric analysis of immune cells. Here, we present FlowSpot, a novel platform that enables recovery of leukocytes from DBS and preserves their immunophenotypic characteristics, allowing downstream flow cytometric analysis following ambient-temperature storage and shipment. FlowSpot recovers intact leukocytes while preserving immune cell subset frequencies with strong concordance to fresh whole blood. We demonstrate its clinical utility by enabling remote CD4 T cell immunophenotyping in people living with HIV, showing high agreement with routine clinical measurements across a broad range of CD4 T cell frequencies. Beyond cellular phenotyping, FlowSpot extends immune monitoring to functional profiling by enabling detection of intracellular cytokine responses, including IFN{gamma}, IL-2, and TNF production by CD4 and CD8 T cells following ex vivo PMA/ionomycin stimulation. By overcoming a longstanding barrier to leukocyte recovery from DBS, FlowSpot extends flow cytometry beyond specialized laboratories, expanding access to cellular immune analysis for clinical care, decentralized clinical trials, and population-scale immunology.

immunology↗

bioRxiv2023

A Compound that Inhibits Glycolysis in Prostate Cancer Controls Growth of Advanced Prostate Cancer

PurposeMetastatic castration-resistant prostate cancer remains incurable regardless of recent therapeutic advances. Prostate cancer tumors display highly glycolytic phenotypes as the cancer progresses. Non-specific inhibitors of glycolysis have not been utilized successfully for chemotherapy, because of their penchant to cause systemic toxicity. This study reports the preclinical activity, safety, and pharmacokinetics of a novel small molecule preclinical candidate, BKIDC-1553, with antiglycolytic activity. Experimental designWe tested a large battery of prostate cancer cell lines for inhibition of cell proliferation, in vitro. Cell cycle, metabolic and enzymatic assays were used to demonstrate their mechanism of action. A human PDX model implanted in mice and a human organoid were studied for sensitivity to our BKIDC preclinical candidate. A battery of pharmacokinetic experiments, absorption, distribution, metabolism, and excretion experiments, and in vitro and in vivo toxicology experiments were carried out to assess readiness for clinical trials. ResultsWe demonstrate a new class of small molecule inhibitors where antiglycolytic activity in prostate cancer cell lines is mediated through inhibition of hexokinase 2. These compounds display selective growth inhibition across multiple prostate cancer models. We describe a lead BKIDC-1553 that demonstrates promising activity in a preclinical xenograft model of advanced prostate cancer, equivalent to that of enzalutamide. BKIDC-1553 demonstrates safety and pharmacologic properties consistent with a compound that can be taken into human studies with expectations of a good safety margin and predicted dosing for efficacy. ConclusionThis work supports testing BKIDC-1553 and its derivatives in clinical trials for patients with advanced prostate cancer.

cancer biology↗

bioRxiv2015

Using Cell line and Patient samples to improve Drug Response Prediction

BackgroundRecent advances in high-throughput technologies have facilitated the profiling of large panels of cancer cell lines with responses measured for thousands of drugs. The computational challenge is now to realize the potential of these data in predicting patients responses to these drugs in the clinic.\n\nMethodsWe address this issue by examining the spectrum of prediction models of patient response: models predicting directly from cell lines, those predicting directly from patients, and those trained on cell lines and patients at the same time. We tested 21 classification models on four drugs, that are bortezomib, erlotinib, docetaxel and epirubicin, for which clinical trial data were available.\n\nResultsOur integrative models consistently outperform cell line-based predictors, indicating that there are limitations to the predictive potential of in vitro data alone. Furthermore, these integrative models achieve better predictive accuracy and require substantially fewer patients than would be the case if only patient data were available.\n\nConclusionsThe integration of in vitro and ex vivo genomic data results in more accurate predictors using only a fraction of the patient information, which can help optimize the development of personalized predictors of therapy response. Altogether our results support the relevance of preclinical data for therapy prediction in clinical trials, enabling more efficient and cost-effective trial design.

Bioinformatics↗

bioRxiv2020

Sofosbuvir Terminated RNA is More Resistant to SARS-CoV-2 Proofreader than RNA Terminated by Remdesivir

SARS-CoV-2 is responsible for COVID-19, resulting in the largest pandemic in over a hundred years. After examining the molecular structures and activities of hepatitis C viral inhibitors and comparing hepatitis C virus and coronavirus replication, we previously postulated that the FDA-approved hepatitis C drug EPCLUSA (Sofosbuvir/Velpatasvir) might inhibit SARS-CoV-2.1 We subsequently demonstrated that Sofosbuvir triphosphate is incorporated by the relatively low fidelity SARS-CoV and SARS-CoV-2 RNA-dependent RNA polymerases (RdRps), serving as an immediate polymerase reaction terminator, but not by a host-like high fidelity DNA polymerase.2,3 Other investigators have since demonstrated the ability of Sofosbuvir to inhibit SARS-CoV-2 replication in lung and brain cells;4,5 additionally, COVID-19 clinical trials with EPCLUSA6 and with Sofosbuvir plus Daclatasvir7 have been initiated in several countries. SARS-CoV-2 has an exonuclease-based proofreader to maintain the viral genome integrity.8 Any effective antiviral targeting the SARS-CoV-2 RdRp must display a certain level of resistance to this proofreading activity. We report here that Sofosbuvir terminated RNA resists removal by the exonuclease to a substantially higher extent than RNA terminated by Remdesivir, another drug being used as a COVID-19 therapeutic. These results offer a molecular basis supporting the current use of Sofosbuvir in combination with other drugs in COVID-19 clinical trials.

pharmacology and toxicology↗

bioRxiv2022

An agent-based modeling approach for lung fibrosis in response to COVID-19

The severity of the COVID-19 pandemic has created an emerging need to investigate the long-term effects of infection on patients. Many individuals are at risk of suffering pulmonary fibrosis due to the pathogenesis of lung injury and impairment in the healing mechanism. Fibroblasts are the central mediators of extracellular matrix (ECM) deposition during tissue regeneration, regulated by anti-inflammatory cytokines including transforming growth factor beta (TGF-{beta}). The TGF-{beta}-dependent accumulation of fibroblasts at the damaged site and excess fibrillar collagen deposition lead to fibrosis. We developed an open-source, multiscale tissue simulator to investigate the role of TGF-{beta} sources in the progression of lung fibrosis after SARS-CoV-2 exposure, intracellular viral replication, infection of epithelial cells, and host immune response. Using the model, we predicted the dynamics of fibroblasts, TGF-{beta}, and collagen deposition for 15 days post-infection in virtual lung tissue. Our results showed variation in collagen area fractions between 2% and 40% depending on the spatial behavior of the sources (stationary or mobile), the rate of activation of TGF-{beta}, and the duration of TGF-{beta} sources. We identified M2 macrophages as primary contributors to higher collagen area fraction. Our simulation results also predicted fibrotic outcomes even with lower collagen area fraction when spatially-localized latent TGF-{beta} sources were active for longer times. We validated our model by comparing simulated dynamics for TGF-{beta}, collagen area fraction, and macrophage cell population with independent experimental data from mouse models. Our results showed that partial removal of TGF-{beta} sources changed the fibrotic patterns; in the presence of persistent TGF-{beta} sources, partial removal of TGF-{beta} from the ECM significantly increased collagen area fraction due to maintenance of chemotactic gradients driving fibroblast movement. The computational findings are consistent with independent experimental and clinical observations of collagen area fractions and cell population dynamics not used in developing the model. These critical insights into the activity of TGF-{beta} sources may find applications in the current clinical trials targeting TGF-{beta} for the resolution of lung fibrosis. Author summaryCOVID-19 survivors are at risk of lung fibrosis as a long-term effect. Lung fibrosis is the excess deposition of tissue materials in the lung that hinder gas exchange and can collapse the whole organ. We identified TGF-{beta} as a critical regulator of fibrosis. We built a model to investigate the mechanisms of TGF-{beta} sources in the process of fibrosis. Our results showed spatial behavior of sources (stationary or mobile) and their activity (activation rate of TGF-{beta}, longer activation of sources) could lead to lung fibrosis. Current clinical trials for fibrosis that target TGF-{beta} need to consider TGF-{beta} sources spatial properties and activity to develop better treatment strategies.

bioengineering↗

bioRxiv2025

RAPID: Reliable and efficient Automatic generation of submission rePortIng checklists with large language moDels

ImportanceMedical reporting guidelines are significant in improving the transparency, quality, and integrity of medical research, particularly in randomized clinical trials; adherence to these guidelines supports research interpretability and has direct implications for downstream applications, such as patient treatment. However, with over 600 distinct reporting guidelines, manual assessments are often time-consuming and labor-intensive. ObjectiveTo evaluate an automated reporting checklist generation tool using large language models and retrieval augmentation generation technology, called RAPID. Design, Setting, and ParticipantsThis study used large language models to design a retrieval augmentation generation architecture and collected published journal articles as training and validation sets to optimize prompts within the framework and comprehensively evaluate the performance of the framework. Medical reporting experiments were collected from 50 randomized controlled trials without the intervention of AI tools and 41 randomized controlled trials with the intervention of AI tools. Main Outcomes and MeasuresFor effective evaluation of the performance of this tool, a classification accuracy metric (Reported/Not Reported) defined as the number of correct judgments divided by all judgments and a content consistent score metric defined as the number of contents retrieved by the tool that are the same as those retrieved by researchers divided by the total number of judgments were calculated. ResultsThe RAPID tool uses the widely used Word document and Portable Document Format as an input file. Fifty published journal articles without the intervention of AI tools and 41 published journal articles with the intervention of AI tools were collected as CONSORT and CONSORT-AI datasets. All of the CONSORT reporting items (37) were included in the tool. RAPID achieved a high average accuracy rate of 92.11% and a content consistency score of 81.14% on the CONSORT dataset. Of the CONSORT-AI reporting items, 11 items related to the intervention of AI tools were included in the tool. RAPID achieved an average accuracy of 83.81% with a content consistency score of 72.51% on the CONSORT-AI dataset. For these two reporting guidelines, a training set of 5 articles was selected from each dataset to refine the prompts used in the tools for CONSORT and CONSORT-AI reporting checklist. The validation set of the remaining articles was used to assess the performance of the RAPID. The RAPID tool used the Word document and Portable Document Format of the articles as input files. A RAPID graphical user interface was built using JavaScript and Vue. Conclusions and RelevanceThe RAPID tool is designed to assist in the reporting of various types of trials. RAPID has strong scalability, which can be easily adapted to different medical reporting guidelines without transfer learning on a large dataset. RAPID may effectively save time and improve working efficiency for different user groups, for example, 1) simplifying the submission process and improving report quality by verifying manuscript completeness for medical authors; 2) facilitating evaluation of report quality for medical researchers; 3) expediting manuscript distribution for medical editors; and 4) identifying reporting deficiencies and providing deeper insights for review comments for reviewers. Key PointsO_ST_ABSQuestionC_ST_ABSCan large language model tools automatically generate medical reporting checklists for manuscripts of different types of clinic trials? FindingsAn automated reporting checklist generation tool using large language models and retrieval augmentation generation technology, RAPID, was developed. RAPID was fully evaluated on all items across two separate datasets related to the Consolidated Standards of Reporting Trials (CONSORT) and CONSORT-AI reporting guidelines. In the first dataset corresponding to CONSORT, RAPID achieved an average accuracy of 92.11%, while in the second dataset associated with CONSORT - AI, it reached an average accuracy of 83.81%. Additionally, RAPID is highly scalable. It can be easily and smoothly adapted to different medical reporting guidelines without transfer learning on a large dataset. MeaningRAPID can effectively save time and improve working efficiency for different user groups such as medical authors, researchers, editors, and reviewers.

bioinformatics↗

bioRxiv2023

G2C4 targeting antisense oligonucleotides potently mitigate TDP-43 dysfunction in C9orf72 ALS/FTD human neurons

The G4C2 repeat expansion in the C9orf72 gene is the most common genetic cause of Amyotrophic Lateral Sclerosis and Frontotemporal Dementia. Many studies suggest that dipeptide repeat proteins produced from this repeat are toxic, yet, the contribution of repeat RNA toxicity is under investigated and even less is known regarding the pathogenicity of antisense repeat RNA. Recently, two clinical trials targeting G4C2 (sense) repeat RNA via antisense oligonucleotide failed despite a robust decrease in sense encoded dipeptide repeat proteins demonstrating target engagement. Here, in this brief report, we show that G2C4 antisense, but not G4C2 sense, repeat RNA is sufficient to induce TDP-43 dysfunction in induced pluripotent stem cell (iPSC) derived neurons (iPSNs). Unexpectedly, only G2C4, but not G4C2 sense strand targeting, ASOs mitigate deficits in TDP-43 function in authentic C9orf72 ALS/FTD patient iPSNs. Collectively, our data suggest that the G2C4 antisense repeat RNA may be an important therapeutic target and provide insights into a possible explanation for the recent G4C2 ASO clinical trial failure.

neuroscience↗

bioRxiv2025

PRAME Epitopes are T-Cell Immunovulnerabilities in BRD4::NUTM1 Initiated NUT Carcinoma

BackgroundNUT carcinoma is a rare but highly lethal solid tumor without an effective standard of care. NUT carcinoma is caused by bromodomain-containing NUTM1 fusion oncogenes, most commonly BRD4::NUTM1. BRD4::NUTM1 recruits p300 to acetylate H3K27 forming expansive stretches of hyperacetylated chromatin called "megadomains" with the overexpression of corresponding oncogenes, including MYC. We hypothesized that transcriptional dysregulation caused by BRD4::NUTM1 would lead to the generation of cancer-specific antigens that could be therapeutically actionable. MethodsWe integrated genomics, computational antigen prediction software, targeted immunopeptidomics using single- and double-labeled peptide standards, and gain/loss-of-function genetic experiments on a panel of cell lines (N=5), a patient derived xenograft, a tissue microarray (N=77), and patient samples from the Tempus AI Sequencing Database harboring evidence of NUTM1 fusions (N=165). We created an PRAME425 T-cell receptor x SP34 CD3 bispecific molecule modeled after brenetafusp, an PRAME425 T-cell receptor bispecific T-cell engager, as well as PRAME425 TCR T-cells based on anzutresgene autoleucel and we applied these products to NUT carcinoma cells in vitro. ResultsWe identified PRAME as the most commonly expressed cancer/testis antigen in patient samples harboring the three canonical NUT carcinoma fusions (BRD4::NUTM1, BRD3::NUTM1, and NSD3::NUTM1). Additionally, 56% (43/77) of NUT carcinoma tissue microarray samples stained positive for PRAME. BRD4::NUTM1 expression in HEK 293T cells enhanced PRAME levels and BRD4::NUTM1 knockout in NUT carcinoma cells reduced PRAME levels. Immunopeptidomics detected more PRAME-derived HLA ligands (N=9) than all other cancer/testis antigens combined (N=5). Targeted mass spectrometry detected the HLA-A*02:01/SLLQHLIGL (PRAME425) epitope in 100% (4/4) of HLA-A*02+, PRAME+ NUT carcinoma samples at higher levels (>0.01 fM) than HLA-A*02:01/RLDQLLRHV (PRAME312) or HLA-A*02:01/YLHARLREL (PRAME462). The PRAME425 T-cell receptor x SP34 CD3 bispecific molecule and PRAME425 TCR T-cells each exhibited potent, T-cell mediated cytotoxicity against PRAME+ NUT carcinoma cells. ConclusionsPRAME is highly and frequently expressed in NUT carcinoma and the most common oncoprotein causing NUT carcinoma, BRD4::NUTM1, contributes to these high PRAME levels. PRAME epitopes presented by HLA Class I are a previously unrecognized therapeutic vulnerability for NUT carcinoma that warrant clinical trials testing PRAME targeted immunotherapies in this neglected patient population. What is already known on this topicNUT carcinoma is a devastating malignancy that is recalcitrant to cytotoxic chemotherapy, T-cell checkpoint blockade, and targeted therapies in the form of bromodomain inhibitors. What this study addsNUT carcinoma tumors are high in the cancer/testis gene PRAME. The oncogene most commonly causing NUT carcinoma, BRD4::NUTM1, contributes to these high levels. NUT carcinoma cells present PRAME epitopes on HLA Class I molecules and are susceptible to PRAME-directed, T-cell mediated cytotoxicity. How this study might affect research, practice or policyOur results argue for phase I/II clinical trials testing PRAME immunotherapies like brenetafusp or anzutresgene autoleucel in PRAME+ NUT carcinoma patients. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=149 SRC="FIGDIR/small/642090v3_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@495654org.highwire.dtl.DTLVardef@c2b975org.highwire.dtl.DTLVardef@1de507org.highwire.dtl.DTLVardef@a76d33_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2026

A human developmental and adult brain atlas benchmarks dopaminergic stem cell models and cell therapy candidates

Parkinsons disease (PD) is characterized by the progressive loss of midbrain dopaminergic (mDA) neurons1. Stem cell-derived mDA neurons hold promise for disease modelling2,3 and are currently in clinical trials for cell replacement therapy4-6. However, systematic benchmarking has been limited by the lack of a unified high-resolution reference and methods that quantify incomplete or mixed lineage specification in vitro7. We establish a single-cell and spatial atlas of the human developing diencephalon-midbrain-hindbrain axis resolving 93 cell subtypes, including 39 lacking prior single-cell characterization and 4 entirely novel populations. Using this atlas as a reference, we integrate 19 hPSC-derived mDA datasets, both published2,8-25 and unpublished, to build the Human Dopaminergic Neural Atlas (HDNA) spanning 2D, 3D, and graft models, including those used in clinical trials. To classify cells and quantify lineage fidelity, we develop CapybaraBrain, a marker-driven non-negative decomposition framework that assigns each cell continuous identity scores across all 93 developmental programs, enabling systematic discrimination of discrete, transitioning, and cross-lineage hybrid states26. We uncover a pervasive landscape of off-target populations reflecting relaxed transcriptional boundaries in vitro, including a previously unrecognized TH-PITX2 midbrain neuronal population, and we validate atlas-predicted latent lineage plasticity through inducible genetic fate mapping in mouse models. We further define maturation-associated transcriptional programs by harmonizing adult mDA subtype atlases, revealing that dopaminergic identity and maturation are partially decoupled across protocols. Finally, projecting PD patient-derived tri-cultures onto the HDNA uncovers genotype- and cell-type-specific transcriptional dysregulation. Together, these integrated atlases and computational framework establish a unified standard for benchmarking differentiation fidelity, exposing off-target states, and guiding next-generation PD models and cell therapies.

Developmental Biology↗

bioRxiv2020

Distinct Predictive Patterns of Antidepressant Response to Sertraline, Placebo and Bupropion using Pre-Treatment Reward Processing as examined through Functional MRI and Deep Learning: Key Results from the EMBARC Study

ImportanceThe lack of antidepressant-specific biomarkers to inform treatment selection is a key obstacle in the treatment of Major Depressive Disorder. Quantitative measurements of reward processing neural activity with task-based functional magnetic resonance imaging (fMRI) may allow prediction of individual outcomes for specific antidepressants. ObjectiveTo build and validate predictors of individual outcomes for sertraline, bupropion, and placebo using reward processing task-based fMRI, clinical assessments, and deep learning. DesignThis is a secondary analysis of data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a placebo-controlled, double-blind randomized clinical trial. The study ran from July 2011 to December 2015 and this analysis was performed between December 2018 and July 2019. SettingThe EMBARC study was conducted at 4 academic medical centers. ParticipantsA random sample of 296 un-medicated participants meeting DSIM-IV criteria for depression was enrolled. InterventionSubjects were randomized to sertraline or placebo during phase 1. In phase 2, non-responders to placebo were treated with sertraline and non-responders to sertraline were treated with bupropion. Each phase lasted for 8 weeks. Main outcomes and measuresThe primary outcome was the change in the 17-item Hamilton Rating Scale for Depression ({Delta}HAMD) after 8 weeks of treatment. Task-related brain activation measures, computed from pre-treatment reward processing task fMRI, and clinical measurements were used to train deep learning predictive models for each treatment group. ResultsThe current analysis includes 222 participants with complete imaging, clinical, and treatment outcome data (146 female, mean age 16.4 {+/-} 13.4 years). The predictive model for sertraline, trained on 106 participants, achieved an R2 of 35% (95% CI 20-50%, p < 10-3) in predicting {Delta}HAMD and a number-needed-to-treat (NNT) of 4.31 in predicting remission. The placebo model, trained on 116 participants, achieved an R2 of 23% (95% confidence interval of 11-37%, p < 10-3) and an NNT of 2.78. The bupropion model achieved an R2 of 37% (95% CI 12-61%, p < 10-3) and an NNT of 2.35. Reward processing activity in regions such as the medial frontal cortex, insula, and thalamus were important predictors of sertraline outcome, while the anterior cingulate cortex, striatum, insula, and thalamus were important regions for predicting bupropion outcome. These are consistent with previously reported findings, while regions not previously implicated such as the temporal pole and paracentral lobule were also found to be predictive. Conclusions and relevanceThese findings demonstrate the utility of reward processing measurements and deep learning in predicting individual antidepressant outcomes with high accuracy. They also present potential composite biomarkers for these treatments based on neuroimaging and clinical features. Trial registrationClinicalTrials.gov identifier: NCT01407094 KEY POINTSO_ST_ABSQuestionC_ST_ABSCan pre-treatment fMRI measurements of reward processing, in combination with multi-dimensional clinical assessments, be used to form personalized predictions of antidepressant outcomes? FindingsDeep learning predictive models trained on pre-treatment reward processing task-based neuroimaging and clinical data from a randomized clinical trial were able to explain up to 37% of the variance in individual treatment outcomes and predict remission with NNT of 2-4. Specific clinical variables and brain regions with reward processing activity important for the model predictions were identified, which may form composite biomarkers of antidepressant response. MeaningQuantitative measures of reward processing, as identified through deep learning, may move us closer to a precision medicine approach that enables clinicians to select the appropriate antidepressant for each patient with greater certainty.

bioinformatics↗

bioRxiv2021

Homo-harringtonine (HHT) - A highly effective drug against coronaviruses and the potential for large-scale clinical applications

In the search for treatment schemes of COVID-19, we start by examining the general weakness of coronaviruses and then identify approved drugs attacking that weakness. The approach, if successful, should identify drugs with a specific mechanism that is at least as effective as the best drugs proposed and are ready for clinical trials. All coronaviruses translate their non-structural proteins ([~]16) in concatenation, resulting in a very large super-protein. Homo-harringtonine (HHT), which has been approved for the treatment of leukemia, blocks protein elongation very effectively. Hence, HHT can repress the replication of many coronaviruses at the nano-molar concentration. In two mouse models, HHT clears SARS-CoV-2 in 3 days, especially by nasal dripping of 40 ug per day. We also use dogs to confirm the safety of HHT delivered by nebulization. The nebulization scheme could be ready for large-scale applications at the onset of the next epidemics. For the current COVID-19, a clinical trial has been approved by the Ditan hospital of Beijing but could not be implemented for want of patients. The protocol is available to qualified medical facilities.

microbiology↗

bioRxiv2021

TK216 targets microtubules in Ewing sarcoma cells

Ewing sarcoma (EWS) is a pediatric malignancy driven by the EWSR1-FLI1 fusion protein formed by the chromosomal translocation t(11;22). The small molecule TK216 was developed as a first-in-class direct EWSR1-FLI1 inhibitor and is in phase II clinical trials in combination with vincristine for EWS patients. However, TK216 exhibits anti-cancer activity against cancer cell lines and xenografts that do not express EWSR1-FLI1, and the mechanism underlying cytotoxicity remains unresolved. We apply a forward genetics screening platform utilizing engineered hypermutation in EWS cell lines and identify recurrent mutations in TUBA1B, encoding -tubulin, that prove sufficient to drive resistance to TK216. Using reconstituted microtubule (MT) polymerization in vitro and cell-based chemical probe competition assays, we demonstrate that TK216 acts as an MT destabilizing agent. This work defines the mechanism of cytotoxicity of TK216, explains the synergy observed with vincristine, and calls for a reexamination of ongoing clinical trials with TK216.

cancer biology↗

bioRxiv2022

Context-dependent triggering of STING-interferon signaling by CD11b agonists supports anti-tumor immunity in mouse models and human cancer patients

Chronic activation of inflammatory pathways and suppressed interferon signaling are hallmarks of myeloid cells in immunosuppressive tumors that drive poor responsiveness to conventional and immune therapies. Previous studies have identified agonistic activation of the CD11b integrin as a potential strategy to enhance anti-tumor immunity. However, the mechanisms by which CD11b-agonism reprogram tumor immunity are poorly understood, and this may impair patient selection and identification of effective treatment combinations. Herein we used a combination of in vitro systems, animal models, and samples from first in human clinical trials of the CD11b-agonist GB1275 to identify the mechanism of action of this approach and identify combinations for further testing. We found that CD11b agonism altered tumor-associated macrophage (TAM) phenotypes by simultaneously repressing NF{kappa}B/IL1-signaling and activating interferon (IFN) gene expression. Repression of NF{kappa}B/IL-1 signaling was due to rapid degradation of p65 protein by the proteosome and was not context dependent. In contrast, CD11b agonism triggered mitochondrial dysfunction to stimulate STING-induced, STAT1-mediated interferon signaling. The magnitude of CD11b agonist induction of STING/IFN signaling was dependent on the tumor microenvironment and was significantly amplified by cytotoxic therapies. Using tissues from phase I clinical trials, we demonstrated that GB1275 treatment activated STING and STAT1 signaling in TAMs in human tumors. Together, these mechanisms allowed macrophages to augment anti-tumor T cell immunity. These studies identified potential mechanism-based therapeutic strategies for CD11b agonist use and identified potential patient populations more likely to benefit from them. Statement of significanceCD11b agonists are a novel approach to reprogram myeloid cells in solid tumors. We show that GB1275, a CD11b-agonist, amplified STING/IFN signaling in TAMs to support anti-tumor immunity and this signaling is amplified further by cytotoxic therapy. These studies support new treatment strategies for advanced solid tumors with myeloid immunosuppression.

cancer biology↗

bioRxiv2022

A drug's most potent target is not necessarily the source of its anti-cancer activity

The small-molecule drug ralimetinib was developed as an inhibitor of the kinase p38, and it has advanced to phase 2 clinical trials in oncology. Here, we apply a multi-modal approach to demonstrate that ralimetinibs anti-cancer activity occurs due to its ability to inhibit EGFR, rather than p38. We find that cancer cell lines driven by EGFR mutations exhibit the greatest sensitivity to ralimetinib treatment, and ralimetinib phenocopies established EGFR inhibitors in pharmacogenomic profiling experiments. We further demonstrate that ralimetinib inhibits EGFR kinase activity in vitro and in cellulo, albeit at >30-fold higher concentrations than it inhibits p38. Finally, while deletion of the gene encoding p38 has no effect on ralimetinib sensitivity, expression of the EGFR-T790M gatekeeper mutation confers resistance to ralimetinib treatment. These findings suggest that future clinical trials involving ralimetinib could incorporate EGFR mutation status as a biomarker to identify sensitive patients. Moreover, our results demonstrate that a compounds anti-cancer effects should not necessarily be attributed to the protein that it inhibits most strongly, and instead, comprehensive cellular and genetic profiling is required to understand a drugs mechanism-of-action.

cancer biology↗

bioRxiv2023

BrainProt(™) 3.0: Understanding Human Brain Diseases using comprehensively curated & Integrated OMICS datasets

BrainProt 3.0 is an integrative and simplified omics-based knowledge base of the human brain and its associated diseases. The current version of BrainProt includes six domains, which provide simplified, robust, and comprehensive data visualization to understand the human brain and its diseases/disorders based on proteomics, transcriptomics, public data curation, and integration strategies. Firstly, the HBDA (Human Brain Disease Atlas), index and navigator of BrainProt provides a resource table for 56 brain diseases. Secondly, Brain Disease Marker Curator (BDMC) and Brain Disease Drug Finder (BDDF) include a total of 20,202 diseases associated genes, more than 1,30,000 Chemical Target interactions, and around 2,145 Clinical Trial Information for more than 50 Brain Diseases. Thirdly, Brain Disease Transcriptome Map (BDTM) and Brain Disease Proteome Map (BDPM) integrate multi-omics data for 11 and 6 alarming brain diseases respectively. Currently, these two domains feature an expressional profile of 52 datasets, information of 1,868 samples, 3,657 DEPs, and 6,256 DEGs. Lastly, BrainProt also modifies and integrates the proteome and phosphoproteome data of the Inter-hemispheric Brain Proteome Map (IBPM). Overall, BrainProt is the first knowledgebase that connects different omics level information of brain diseases and provides a powerful scoring-based ranking platform to select and identify brain disease-associated markers, along with exploration of clinical trials, and drugs/chemical compounds to accelerate the identification of new disease markers and novel therapeutic strategies. The objectives of BrainProt are to support and follow the footsteps of the HBPP (Human Brain Proteome Project) by integrating different datasets to unravel the complexity of Human Brain and its associated diseases.

neuroscience↗

bioRxiv2023

RNA splicing modulator induces peripheral neuropathy with increased neurofilament light chain levels via p53 signaling

RNA splicing modulators are a new class of small molecules with the potential to modify the expression levels of proteins. A recent clinical trial investigating the splicing modulator branaplam for Huntingtons disease to lower huntingtin levels was terminated due to the development of peripheral neuropathy. Here, we describe how branaplam leads to this adverse effect. On a cellular level, branaplam disrupts neurite integrity reflected by elevated neurofilament light chain levels in human induced pluripotent stem cell (iPSC)-derived motor neurons (iPSC-MN). Branaplam does not target neuropathy-associated genes. However, transcription factor binding site enrichment analysis indicates p53 activation. P53 activation upon branaplam treatment in iPSC-MN is linked to increased nucleolar stress, thereby enhanced expression of the neurotoxic p53-target gene BBC3. These findings illustrate that RNA splicing modulators may have clinically relevant off-target effects, implying the necessity of comprehensive pre-screening in human models prior to executing clinical trials. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=188 SRC="FIGDIR/small/551640v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@9b2cbdorg.highwire.dtl.DTLVardef@1964746org.highwire.dtl.DTLVardef@121612corg.highwire.dtl.DTLVardef@72c6a6_HPS_FORMAT_FIGEXP M_FIG C_FIG One Sentence SummaryPredicting side effects of RNA splicing modulator branaplam leading to neurotoxicity via nucleolar stress, p53 activation, and axonal degeneration.

pharmacology and toxicology↗