Aid Drug Development Research Articles

The Promise use of Hybrid PET/MRI

Hybrid PET/MRI is a cutting-edge imaging modality that integrates the molecular imaging capabilities of Positron Emission Tomography (PET) with the superior anatomical and functional imaging strengths of Magnetic Resonance Imaging (MRI). This integration offers significant promise in enhancing diagnostic accuracy, reducing radiation exposure, and providing comprehensive imaging in a single session. Clinically, hybrid PET/MRI has shown particular value in oncology, neurology, and cardiology, offering improved tumor characterization, early detection of neurodegenerative diseases, and detailed cardiac assessments. In the research domain, it provides critical insights into physiological and pathological processes, aiding drug development and the study of disease mechanisms. Despite its high cost, complexity, and limited availability, ongoing technological advancements and the integration of artificial intelligence are expected to expand its clinical applications and improve its practicality. Hybrid PET/MRI thus represents a promising advancement in medical imaging, with the potential to significantly impact personalized and precision medicine.

Diseases Caused by Faulty Gene Expression. There are Several Hereditary Diseases that may be Entirely Cured if Detected and Treated Early Enough

Many genes and environmental factors often work together to cause a disease. The identification of significant genetic factors is useful for both medical (by aiding drug development and personalised therapy) and scientific (by shedding light on mechanistic and evolutionary aspects of illness) purposes. Linkage analysis (which joins loci that have a propensity to be inherited together) and association studies are two of the many genetic methods that have shown correlations between illnesses and particular sections of the genome (mapping correlation between alleles at different loci). Several hundreds of genes are examined in these types of studies, much too numerous to be tested experimentally as potential disease genes. The use of computer methods to assess the possibility of individual genes within a certain chromosomal area being disease genes is thus quite useful. Many diseases' susceptibilities have been demonstrated to a fair degree. changes in the rate at which genes are expressed in various cell types. In instance, if a gene or gene cluster is more common in sick individuals than in healthy people, it's likely that the gene plays a role in illness. Microarray studies were the major method for detecting the differences in expression levels.

Mice Hepatic Organoids for Modeling Nonalcoholic Fatty Liver Disease and Drug Response.

Nonalcoholic fatty liver disease (NAFLD) is a serious disease. There are no specific drugs for it, in part because of the lack of effective models to aid drug development. However, it has been shown that three-dimensional organoid culture systems can reproduce the organ structure and maintain the gene expression profile of the original tissue. Therefore, we aimed to construct NAFLD models from liver organoids for pharmacological and mechanism studies. We successfully observed morphological changes in normal liver tissue in mouse liver organoids with positive albumin (ALB) expression and potential for differentiation toward hepatocyte-like cells. The mRNA expression of the hepatocyte markers ALB and hepatocyte nuclear factor 4 alpha increased after liver organoid differentiation. We observed free fatty acid (FFA)-induced lipid accumulation in organoids with significant increases in alanine aminotransferase, aspartate aminotransferase, total bilirubin, and triglyceride levels. Moreover, FFA-induced inflammatory cytokines (interleukin-6, tumor necrosis factor-α, and nitric oxide) and fibrosis indicators (collagen type I α1 and laminin α1) were also increased. In addition, RNA sequencing results showed that the expression of key genes [nucleotide oligomerization domain-like receptor (NLR) family apoptosis inhibitory protein, interferon regulatory factor (IRF) 3, and IRF7] involved in NAFLD metabolic abnormalities and insulin resistance in the NLR signaling pathway was altered after FFA induction of the liver organoids. Finally, we found that JC2-11 and lanifibranor limited the FFA-induced increase in oil-red lipid droplets, liver damage, inflammation, and liver fibrosis. In conclusion, tissue structure, gene expression, and the response of mouse liver organoids to drugs can partially mimic invivo liver tissue. Liver organoids can successfully construct NAFLD models for drug discovery research.

Simultaneous Protein Colorful Imaging via Raman Signal Classification.

Protein imaging aids diagnosis and drug development by revealing protein-drug interactions or protein levels. However, the challenges of imaging multiple proteins, reduced sensitivity, and high reliance on specific protein properties such as Raman peaks or refractive index hinder the understanding. Here, we introduce multiprotein colorful imaging through Raman signal classification. Our method utilized machine learning-assisted classification of Raman signals, which are the distinctive features of label-free proteins. As a result, three types of proteins could be imaged simultaneously. In addition, we could quantify individual proteins from a mixture of multiple proteins over a wide detection range (10 fg/mL-1 μg/mL). These results showed a 1000-fold improvement in sensitivity and a 30-fold increase in the upper limit of detection compared to existing methods. These advances will enhance our understanding of biology and facilitate the development of disease diagnoses and treatments.

A hepatitis B virus (HBV) sequence variation graph improves alignment and sample-specific consensus sequence construction.

Nearly 300 million individuals live with chronic hepatitis B virus (HBV) infection (CHB), for which no curative therapy is available. As viral diversity is associated with pathogenesis and immunological control of infection, improved methods to characterize this diversity could aid drug development efforts. Conventionally, viral sequencing data are mapped/aligned to a reference genome, and only the aligned sequences are retained for analysis. Thus, reference selection is critical, yet selecting the most representative reference a priori remains difficult. We investigate an alternative pangenome approach which can combine multiple reference sequences into a graph which can be used during alignment. Using simulated short-read sequencing data generated from publicly available HBV genomes and real sequencing data from an individual living with CHB, we demonstrate alignment to a phylogenetically representative 'genome graph' can improve alignment, avoid issues of reference ambiguity, and facilitate the construction of sample-specific consensus sequences more genetically similar to the individual's infection. Graph-based methods can, therefore, improve efforts to characterize the genetics of viral pathogens, including HBV, and have broader implications in host-pathogen research.

Biomarkers in clinical epidemiology studies.

This paper discusses the use of biomarkers in clinical practice and biomedical research. Biomarkers are measurable characteristics that can be used to indicate the presence or absence of a disease or to track the progression of a disease. They can also be used to predict how a patient will respond to a particular treatment. Biomarkers have enriched clinical practice and disease prognosis by providing measurable characteristics that indicate biological processes. They offer valuable insights into disease susceptibility, progression, and treatment response, aiding drug development and personalized medicine. However, developing and implementing biomarkers come with challenges that must be addressed. Rigorous testing, standardization of assays, and consideration of ethical factors are crucial in ensuring the reliability and validity of biomarkers. Reliability is vital in biomarker research. It ensures accurate measurements by preventing biases and facilitating robust correlations with outcomes. Conversely, validation examines which and how many biomarkers correspond to theoretical constructs and external criteria, establishing their predictive value. Multiple biomarkers are sometimes necessary to represent the complex relationship between exposure and disease outcomes accurately. Susceptibility factors are pivotal in disease states' complex interaction among genetic and environmental factors. Gaining a comprehensive understanding of these factors is essential for effectively interpreting biomarker data and maximizing their clinical usefulness. Using well-validated biomarkers can improve diagnoses, more effective treatment evaluations, and enhanced disease prediction. This, in turn, will contribute to better patient outcomes and drive progress in medicine.

Dissecting the shared genetic landscape of anxiety, depression, and schizophrenia

BackgroundNumerous studies highlight the genetic underpinnings of mental disorders comorbidity, particularly in anxiety, depression, and schizophrenia. However, their shared genetic loci are not well understood. Our study employs Mendelian randomization (MR) and colocalization analyses, alongside multi-omics data, to uncover potential genetic targets for these conditions, thereby informing therapeutic and drug development strategies.MethodsWe utilized the Consortium for Linkage Disequilibrium Score Regression (LDSC) and Mendelian Randomization (MR) analysis to investigate genetic correlations among anxiety, depression, and schizophrenia. Utilizing GTEx V8 eQTL and deCODE Genetics pQTL data, we performed a three-step summary-data-based Mendelian randomization (SMR) and protein–protein interaction analysis. This helped assess causal and comorbid loci for these disorders and determine if identified loci share coincidental variations with psychiatric diseases. Additionally, phenome-wide association studies, drug prediction, and molecular docking validated potential drug targets.ResultsWe found genetic correlations between anxiety, depression, and schizophrenia, and under a meta-analysis of MR from multiple databases, the causal relationships among these disorders are supported. Based on this, three-step SMR and colocalization analyses identified ITIH3 and CCS as being related to the risk of developing depression, while CTSS and DNPH1 are related to the onset of schizophrenia. BTN3A1, PSMB4, and TIMP4 were identified as comorbidity loci for both disorders. Molecules that could not be determined through colocalization analysis were also presented. Drug prediction and molecular docking showed that some drugs and proteins have good binding affinity and available structural data.ConclusionsOur study indicates genetic correlations and shared risk loci between anxiety, depression, and schizophrenia. These findings offer insights into the underlying mechanisms of their comorbidities and aid in drug development.

Development and validation of an LC-MS/MS method for quantifying NAD+ and related metabolites in mice sciatic nerves and its application to a nerve injury animal model

Recent studies highlight the pivotal roles of Nicotinamide adenine dinucleotide (NAD+) and its metabolites in aging and neurodegeneration. Accurate quantification of NAD+ and its metabolite levels in cells or tissues is crucial for advancing biochemical research and interventions targeting aging and neurodegenerative diseases. This study presents an accurate, precise, and rapid LC-MS/MS method using a surrogate matrix to quantify endogenous substances NAD+, nicotinamide mononucleotide (NMN), nicotinamide (NAM), adenosine diphosphate ribose (ADPR), and cyclic adenosine diphosphate ribose (cADPR) concentrations in mice sciatic nerves. Considering the properties of the phosphate groups in the analytes, the column and mobile phase were systematically optimized. These five polar analytes exhibited excellent analytical performance and baseline separation within 5 min on an Atlantis Premier BEH C18 AX column, with methylene phosphonic acid as a mobile phase additive. Enhanced sensitivity addressed the challenges posed by the small sample size of mice sciatic nerve and low NMN and cADPR detection. The method was fully validated, with linear correlation coefficients exceeding 0.992, precision (%relative standard deviation, RSD) values within 8.8%, and accuracy values between 92.2% and 107.3%, suggesting good reproducibility. Analytical recoveries in spiked and diluted matrix ranged from 87.8% to 104.7%, indicating the suitability of water as a surrogate matrix. Application of the method to quantify NAD+ and its metabolite levels in normal and injured mice sciatic nerve identified cADPR as a sensitive biomarker in the nerve injury model. This method is anticipated to deepen our understanding of the connections between NAD+ and its metabolites in health and disease, potentially improving diagnoses of various neurological disorders and aiding drug development for aging and neurodegenerative diseases.

Spheroids formation in large drops suspended in superhydrophobic paper cones.

The utilization of 3D cell culture for spheroid formation holds significant implications in cancer research, contributing to a fundamental understanding of the disease and aiding drug development. Conventional methods such as the hanging drop technique and other alternatives encounter limitations due to smaller drop volumes, leading to nutrient starvation and restricted culture duration. In this study, we present a straightforward approach to creating superhydrophobic paper cones capable of accommodating large volumes of culture media drops. These paper cones have sterility, autoclavability, and bacterial repellent properties. Leveraging these attributes, we successfully generate large spheroids of ovarian cancer cells and, as a proof of concept, conduct drug screening to assess the impact of carboplatin. Thus, our method enables the preparation of flexible superhydrophobic surfaces for laboratory applications in an expeditious manner, exemplified here through spheroid formation and drug screening demonstrations.

High-Resolution NMR Structures of Intrastrand Hairpins Formed by CTG Trinucleotide Repeats.

The CAG and CTG trinucleotide repeat expansions cause more than 10 human neurodegenerative diseases. Intrastrand hairpins formed by trinucleotide repeats contribute to repeat expansions, establishing them as potential drug targets. High-resolution structural determination of CAG and CTG hairpins poses as a long-standing goal to aid drug development, yet it has not been realized due to the intrinsic conformational flexibility of repetitive sequences. We herein investigate the solution structures of CTG hairpins using nuclear magnetic resonance (NMR) spectroscopy and found that four CTG repeats with a clamping G-C base pair was able to form a stable hairpin structure. We determine the first solution NMR structure of dG(CTG)4C hairpin and decipher a type I folding geometry of the TGCT tetraloop, wherein the two thymine residues form a T·T loop-closing base pair and the first three loop residues continuously stack. We further reveal that the CTG hairpin can be bound and stabilized by a small-molecule ligand, and the binding interferes with replication of a DNA template containing CTG repeats. Our determined high-resolution structures lay an important foundation for studying molecular interactions between native CTG hairpins and ligands, and benefit drug development for trinucleotide repeat expansion diseases.

Emerging perspectives of synaptic biomarkers in ALS and FTD.

Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Dementia (FTD) are debilitating neurodegenerative diseases with shared pathological features like transactive response DNA-binding protein of 43 kDa (TDP-43) inclusions and genetic mutations. Both diseases involve synaptic dysfunction, contributing to their clinical features. Synaptic biomarkers, representing proteins associated with synaptic function or structure, offer insights into disease mechanisms, progression, and treatment responses. These biomarkers can detect disease early, track its progression, and evaluate therapeutic efficacy. ALS is characterized by elevated neurofilament light chain (NfL) levels in cerebrospinal fluid (CSF) and blood, correlating with disease progression. TDP-43 is another key ALS biomarker, its mislocalization linked to synaptic dysfunction. In FTD, TDP-43 and tau proteins are studied as biomarkers. Synaptic biomarkers like neuronal pentraxins (NPs), including neuronal pentraxin 2 (NPTX2), and neuronal pentraxin receptor (NPTXR), offer insights into FTD pathology and cognitive decline. Advanced technologies, like machine learning (ML) and artificial intelligence (AI), aid biomarker discovery and drug development. Challenges in this research include technological limitations in detection, variability across patients, and translating findings from animal models. ML/AI can accelerate discovery by analyzing complex data and predicting disease outcomes. Synaptic biomarkers offer early disease detection, personalized treatment strategies, and insights into disease mechanisms. While challenges persist, technological advancements and interdisciplinary efforts promise to revolutionize the understanding and management of ALS and FTD. This review will explore the present comprehension of synaptic biomarkers in ALS and FTD and discuss their significance and emphasize the prospects and obstacles.

Functional engineering of human iPSC-derived parasympathetic neurons enhances responsiveness to gastrointestinal hormones.

Food-derived biological signals are transmitted to the brain via peripheral nerves through the paracrine activity of gastrointestinal (GI) hormones. The signal transduction circuit of the brain-gut axis has been analyzed in animals; however, species-related differences and animal welfare concerns necessitate investigation using in vitro human experimental models. Here, we focused on the receptors of five GI hormones (CCK, GLP1, GLP2, PYY, and serotonin (5-HT)), and established human induced pluripotent stem cell (iPSC) lines that functionally expressed each receptor. Compared to the original iPSCs, iPSCs expressing one of the receptors did not show any differences in global mRNA expression, genomic stability, or differentiation capacities of the three germ layers. We induced parasympathetic neurons from these established iPSC lines to assess vagus nerve activity. We generated GI hormone receptor-expressing neurons (CCKAR, GLP1R, and NPY2R-neuron) and tested their responsiveness to each ligand using Ca2+ imaging and microelectrode array recording. GI hormone receptor-expressing neurons (GLP2R and HTR3A) were generated directly by gene induction into iPSC-derived peripheral nerve progenitors. These receptor-expressing neurons promise to contribute to a better understanding of how the body responds to GI hormones via the brain-gut axis, aid in drug development, and offer an alternative to animal studies.

Correlation between PFS and OS in FL: Implications for Clinical Trial Endpoint Selection

Introduction: Follicular lymphoma (FL) comprises 15% of all lymphoma cases and 70% of indolent lymphomas. Cytotoxic, targeted, and novel treatments lead to prolonged remission periods, with a median overall survival (OS) of greater than 10 years. For this reason, surrogate endpoints for OS are frequently used in clinical trials of FL to allow for quicker results and shorter time to drug approvals, as well as to offset the likely cost and difficulty in funding long term trials with OS as a primary endpoint. A frequently used surrogate endpoint is progression free survival (PFS); however, PFS has not been previously validated as a surrogate endpoint for OS in the treatment of FL. Previous systematic reviews of multiple trials did not have mature enough data to draw definitive conclusions (Zhu et al, AAPS J 2017). Methods: To evaluate the correlation between PFS and OS in FL trials, we conducted a comprehensive systematic review. We included all completed phase 2 or 3 clinical trials with posted results on ClinicalTrials.gov from the inception of the database until July 2023. Additionally, we manually added trials that were completed before the database inception in February 2000 culled from HemOnc.org. For inclusion, randomized controlled trials reporting a hazard ratio for both PFS and OS were considered. We performed statistical analyses, utilizing unadjusted linear regression weighted by sample size to evaluate the association between hazard ratios for PFS and OS. Results: A total of 169 randomized controlled trials for FL were initially identified. Upon screening, 74 randomized trials reported rates for both PFS and OS. 20 trials with 22 interventional arms that included hazard ratios were eligible for inclusion in final analysis. 21 (96%) phase 3 trials and 1 (4%) randomized phase 2 trial were included, encompassing 10,729 participants. Among the included interventional arms, 12 (55%) were in the first-line setting and 10 (45%) were in the refractory or relapsed setting. 14 (64%) arms demonstrated a PFS benefit, and 4 (18%) demonstrated both a PFS and OS benefit. Disease characteristics and treatment outcomes are included in Table 1. The correlation coefficient between PFS and OS was 0.558 (95% CI 0.545-0.571), indicating a weak association. The coefficient of determination was 0.311 (95% CI: 0.08-0.52) suggesting 31.1% of OS variance could be explained by changes in PFS (Figure 1). Discussion: PFS showed a weak correlation with OS in FL trials, challenging its role as a surrogate endpoint. Factors such as patient heterogeneity, treatment regimens, subsequent therapies, and treatment-related complications may contribute to the discrepancy. While PFS aids regulatory approval and drug development, our study suggests that PFS may not be an appropriate surrogate endpoint for OS in the treatment of FL, and alternative surrogate endpoints should be pursued. Possible alternative endpoints though will need to be validated using similar systematic methods given often contradictory findings between individual studies. For example, progression within 24 months of initial treatment (POD 24) was a valid surrogate endpoint for OS in the PRIMA study, however this was not the case in the GALLIUM study (Bachi et al., Blood Adv 2021). Other surrogate endpoints such as complete response rate at 30 months (CR30) have demonstrated strong correlation with PFS however their association with OS has yet to be established (Shi el, JCO 2016).

An In Vitro Model for Cocrystal Dissolution with Simultaneous Surface and Bulk Precipitation.

Cocrystals can be promising means of overcoming the poor aqueous solubility of many drugs. However, precipitation of the stable drug at the cocrystal surface or in the bulk medium is often provoked during cocrystal dissolution due to high drug supersaturation, which prevents sustaining high drug concentrations for enhanced bioavailability. There is a need for predictive in vitro models that can accurately describe this cocrystal dissolution-supersaturation-precipitation (DSP) process to aid drug development and formulation design. Consideration of surface precipitation is often essential for such models given the strong impact of surface precipitation on the drug concentration during cocrystal dissolution. However, DSP models that can explicitly account for the effect of surface precipitation are currently lacking. This work presents a population balance-based model to describe in vitro cocrystal DSP behavior, which accounts for cocrystal dissolution, surface precipitation, and bulk precipitation. Dissolution experiments with carbamazepine-succinic acid cocrystals are conducted for model development and validation. The developed model captures all of the principal experimental trends and predicts the dose-dependent DSP behavior outside the regression data set with reasonable accuracy. The results show that surface precipitation is an essential component of the model. Finally, the new model is integrated with numerical optimization to illustrate how it can be used to identify an optimal dose, particle size, and amount of predissolved coformer.

Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study.

Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery.

Machine learning aided drug development: Assessing improvement of drug efficiency by correlation of solubility in supercritical solvent for nanomedicine preparation

In this study, the solubility of phenytoin and the supercritical CO2 (solvent) density were investigated using three different models: Multilayer Perceptron (MLP), NU-SVR, and Poisson Regressor (POR). The models were optimized using the BA algorithm. The inputs for the models were temperature (T) in Kelvin and pressure (P) in bar, while the outputs were the solubility (y) of phenytoin and the CO2 density. For the solubility prediction, the MLP model achieved an impressive R2 score of 0.998 with a MSE of 0.051, a MAE of 0.200, and a MAPE of 0.080. The NU-SVR model also performed well with an R2 score of 0.966, MSE of 1.094, MAE of 0.708, and MAPE of 0.194. The POR model yielded an R2 score of 0.957, MSE of 0.959, MAE of 0.765, and MAPE of 0.337. For the CO2 density prediction, the MLP model achieved an excellent R2 score of 0.998 with an MSE of 75.232, MAE of 5.866, and MAPE of 0.018. The NU-SVR model performed reasonably well with an R2 score of 0.858, MSE of 3853.3, MAE of 49.697, and MAPE of 0.119. The POR model yielded an R2 score of 0.839, MSE of 4012.3, MAE of 52.297, and MAPE of 0.140. These results reveal that the MLP model provides highly reliable forecasts for both the solubility of phenytoin and the CO2 density.

In silico profiling of nonsynonymous SNPs of fat mass and obesity-associated gene: possible impacts on the treatment of non-alcoholic fatty liver disease

BackgroundNonalcoholic fatty liver, or NAFLD, is the most common chronic liver ailment. It is characterized by excessive fat deposition in hepatocytes of individuals who consume little or no alcohol and are unaffected by specific liver damaging factors. It is also associated with extrahepatic manifestations such as chronic kidney disease, cardiovascular disease, and sleep apnea. The global burden of NAFLD is increasing at an alarming rate. However, no pharmacologically approved drugs against NAFLD are available owing to their complex pathophysiology. Genome-wide association studies have uncovered SNPs in the fat mass and obesity-associated gene (FTO) that are robustly associated with obesity and higher BMI. The prevalence of NAFLD increases in parallel with the increasing prevalence of obesity. Since FTO might play a crucial role in NAFLD development, the current study identified five potentially deleterious mutations from 383 ns-SNPs in the human FTO gene using various in silico tools.MethodsThis study aims to identify potentially deleterious nonsynonymous SNPs (ns-SNPs) employing various in silico tools. Additionally, molecular modeling approaches further studied the structural changes caused by identified SNPs. Moreover, molecular dynamics studies finally investigated the binding potentials of the phytochemicals resveratrol, rosmarinic acid, and capsaicin with different mutant forms of FTO.ResultsThe current investigation has five potentially deleterious mutations from 383 ns-SNPs in the human FTO gene using various in silico tools. The present study identified five nsSNPs of the human gene FTO, Gly103Asp, Arg96Pro, Tyr295Cys, and Arg322Gln, with an apparent connection to the disease condition. Modulation of demethylation activity by phytomolecule scanning explains the hepatoprotective action of molecules. The current investigation also suggested that predicted mutations did not affect the binding ability of three polyphenols: rosamarinic acid, resveratrol, and capsaicin.ConclusionThis study showed that the predicted mutations in FTO did not affect the binding of three polyphenols. Thus, these three molecules can significantly aid drug development against FTO and NAFLD.

Structural insights into viral genome replication by the severe fever with thrombocytopenia syndrome virus L protein.

Severe fever with thrombocytopenia syndrome virus (SFTSV) is a phenuivirus that has rapidly become endemic in several East Asian countries. The large (L) protein of SFTSV, which includes the RNA-dependent RNA polymerase (RdRp), is responsible for catalysing viral genome replication and transcription. Here, we present 5 cryo-electron microscopy (cryo-EM) structures of the L protein in several states of the genome replication process, from pre-initiation to late-stage elongation, at a resolution of up to 2.6 Å. We identify how the L protein binds the 5' viral RNA in a hook-like conformation and show how the distal 5' and 3' RNA ends form a duplex positioning the 3' RNA terminus in the RdRp active site ready for initiation. We also observe the L protein stalled in the early and late stages of elongation with the RdRp core accommodating a 10-bp product-template duplex. This duplex ultimately splits with the template binding to a designated 3' secondary binding site. The structural data and observations are complemented by in vitro biochemical and cell-based mini-replicon assays. Altogether, our data provide novel key insights into the mechanism of viral genome replication by the SFTSV L protein and will aid drug development against segmented negative-strand RNA viruses.

A biomimetic renal fibrosis progression model on-chip evaluates anti-fibrotic effects longitudinally in a dynamic fibrogenic niche.

Although renal fibrosis can advance chronic kidney disease and progressively lead to end-stage renal failure, no effective anti-fibrotic drugs have been clinically approved. To aid drug development, we developed a biomimetic renal fibrosis progression model on-chip to evaluate anti-fibrotic effects of natural killer cell-derived extracellular vesicles and pirfenidone (PFD) across different fibrotic stages. First, the dynamic interplay between fibroblasts and kidney-derived extracellular matrix (ECM) resembling the fibrogenic niche on-chip demonstrated that myofibroblasts induced by stiff ECM in 3 days were reversed to fibroblasts by switching to soft ECM, which was within 2, but not 7 days. Second, PFD significantly down-regulated the expression of α-SMA in NRK-49F in medium ECM, as opposed to stiff ECM. Third, a study in rats showed that early administration of PFD significantly inhibited renal fibrosis in terms of the expression levels of α-SMA and YAP. Taken together, both on-chip and animal models indicate the importance of early anti-fibrotic intervention for checking the progression of renal fibrosis. Therefore, this renal fibrosis progression on-chip with a feature of recapitulating dynamic biochemical and biophysical cues can be readily used to assess anti-fibrotic candidates and to explore the tipping point when the fibrotic fate can be rescued for better medical intervention.

Engineered matrix microenvironments reveal the heterogeneity of liver sinusoidal endothelial cell phenotypic responses.

Fibrosis is one of the hallmarks of chronic liver disease and is associated with aberrant wound healing. Changes in the composition of the liver microenvironment during fibrosis result in a complex crosstalk of extracellular cues that promote altered behaviors in the cell types that comprise the liver sinusoid, particularly liver sinusoidal endothelial cells (LSECs). Recently, it has been observed that LSECs may sustain injury before other fibrogenesis-associated cells of the sinusoid, implicating LSECs as key actors in the fibrotic cascade. A high-throughput cellular microarray platform was used to deconstruct the collective influences of defined combinations of extracellular matrix (ECM) proteins, substrate stiffness, and soluble factors on primary human LSEC phenotype in vitro. We observed remarkable heterogeneity in LSEC phenotype as a function of stiffness, ECM, and soluble factor context. LYVE-1 and CD-31 expressions were highest on 1 kPa substrates, and the VE-cadherin junction localization was highest on 25 kPa substrates. Also, LSECs formed distinct spatial patterns of LYVE-1 expression, with LYVE-1+ cells observed in the center of multicellular domains, and pattern size regulated by microenvironmental context. ECM composition also influenced a substantial dynamic range of expression levels for all markers, and the collagen type IV was observed to promote elevated expressions of LYVE-1, VE-cadherin, and CD-31. These studies highlight key microenvironmental regulators of LSEC phenotype and reveal unique spatial patterning of the sinusoidal marker LYVE-1. Furthermore, these data provide insight into understanding more precisely how LSECs respond to fibrotic microenvironments, which will aid drug development and identification of targets to treat liver fibrosis.