Pervasive Chromatin-RNA Binding Protein Interactions Enable RNA-Based Regulation of Transcription.

Increasing evidence suggests that transcriptional control and chromatin activities at large involve regulatory RNAs, which likely enlist specific RNA-binding proteins (RBPs). Although multiple RBPs have been implicated in transcription control, it has remained unclear how extensively RBPs directly act on chromatin. We embarked on a large-scale RBP ChIP-seq analysis, revealing widespread RBP presence in active chromatin regions in the human genome. Like transcription factors (TFs), RBPs also show strong preference for hotspots in the genome, particularly gene promoters, where their association is frequently linked to transcriptional output. Unsupervised clustering reveals extensive co-association between TFs and RBPs, as exemplified by YY1, a known RNA-dependent TF, and RBM25, an RBP involved in splicing regulation. Remarkably, RBM25 depletion attenuates all YY1-dependent activities, including chromatin binding, DNA looping, and transcription. We propose that various RBPs may enhance network interaction through harnessing regulatory RNAs to control transcription.

Shape memory polymer with programmable recovery onset.

Stimulus-responsive shape-shifting polymers1-3 have shown unique promise in emerging applications, including soft robotics4-7, medical devices8, aerospace structures9 and flexible electronics10. Their externally triggered shape-shifting behaviour offers on-demand controllability essential for many device applications. Ironically, accessing external triggers (for example, heating or light) under realistic scenarios has become the greatest bottleneck in demanding applications such as implantable medical devices8. Certain shape-shifting polymers rely on naturally present stimuli (for example, human body temperature for implantable devices)8 as triggers. Although they forgo the need for external stimulation, the ability to control recovery onset is also lost. Naturally triggered, yet actively controllable, shape-shifting behaviour is highly desirable but these two attributes are conflicting. Here we achieved this goal with a four-dimensional printable shape memory hydrogel that operates via phase separation, with its shape-shifting kinetics dominated by internal mass diffusion rather than by heat transport used for common shape memory polymers8-11. This hydrogel can undergo shape transformation at natural ambient temperature, critically with a recovery onset delay. This delay is programmable by altering the degree of phase separation during device programming, which offers a unique mechanism for shape-shifting control. Our naturally triggered shape memory polymer with a tunable recovery onset markedly lowers the barrier for device implementation.

Negative regulation of AMPK signaling by high glucose via E3 ubiquitin ligase MG53.

As a master regulator of metabolism, AMP-activated protein kinase (AMPK) is activated upon energy and glucose shortage but suppressed upon overnutrition. Exaggerated negative regulation of AMPK signaling by nutrient overload plays a crucial role in metabolic diseases. However, the mechanism underlying the negative regulation is poorly understood. Here, we demonstrate that high glucose represses AMPK signaling via MG53 (also called TRIM72) E3-ubiquitin-ligase-mediated AMPKα degradation and deactivation. Specifically, high-glucose-stimulated reactive oxygen species (ROS) signals AKT to phosphorylate AMPKα at S485/491, which facilitates the recruitment of MG53 and the subsequent ubiquitination and degradation of AMPKα. In addition, high glucose deactivates AMPK by ROS-dependent suppression of phosphorylation of AMPKα at T172. These findings not only delineate the mechanism underlying the impairment of AMPK signaling in overnutrition-related diseases but also highlight the significance of keeping the yin-yang balance of AMPK signaling in the maintenance of metabolic homeostasis.

A genetic program promotes C. elegans longevity at cold temperatures via a thermosensitive TRP channel.

Both poikilotherms and homeotherms live longer at lower body temperatures, highlighting a general role of temperature reduction in lifespan extension. However, the underlying mechanisms remain unclear. One prominent model is that cold temperatures reduce the rate of chemical reactions, thereby slowing the rate of aging. This view suggests that cold-dependent lifespan extension is simply a passive thermodynamic process. Here, we challenge this view in C. elegans by showing that genetic programs actively promote longevity at cold temperatures. We find that TRPA-1, a cold-sensitive TRP channel, detects temperature drop in the environment to extend lifespan. This effect requires cold-induced, TRPA-1-mediated calcium influx and a calcium-sensitive PKC that signals to the transcription factor DAF-16/FOXO. Human TRPA1 can functionally substitute for worm TRPA-1 in promoting longevity. Our results reveal a previously unrecognized function for TRP channels, link calcium signaling to longevity, and, importantly, demonstrate that genetic programs contribute to lifespan extension at cold temperatures.

SR proteins collaborate with 7SK and promoter-associated nascent RNA to release paused polymerase.

RNAP II is frequently paused near gene promoters in mammals, and its transition to productive elongation requires active recruitment of P-TEFb, a cyclin-dependent kinase for RNAP II and other key transcription elongation factors. A fraction of P-TEFb is sequestered in an inhibitory complex containing the 7SK noncoding RNA, but it has been unclear how P-TEFb is switched from the 7SK complex to RNAP II during transcription activation. We report that SRSF2 (also known as SC35, an SR-splicing factor) is part of the 7SK complex assembled at gene promoters and plays a direct role in transcription pause release. We demonstrate RNA-dependent, coordinated release of SRSF2 and P-TEFb from the 7SK complex and transcription activation via SRSF2 binding to promoter-associated nascent RNA. These findings reveal an unanticipated SR protein function, a role for promoter-proximal nascent RNA in gene activation, and an analogous mechanism to HIV Tat/TAR for activating cellular genes.

SMYD2-Methylated PPARγ Facilitates Hypoxia-Induced Pulmonary Hypertension by Activating Mitophagy.

BACKGROUND: Hyperproliferation of pulmonary arterial smooth muscle cells (PASMCs) and consequent pulmonary vascular remodeling are the crucial pathological features of pulmonary hypertension (PH). Protein methylation has been shown to be critically involved in PASMC proliferation and PH, but the underlying mechanism remains largely unknown. METHODS: PH animal models were generated by treating mice/rats with chronic hypoxia for 4 weeks. SMYD2-vTg mice (vascular smooth muscle cell-specific suppressor of variegation, enhancer of zeste, trithorax and myeloid Nervy DEAF-1 (deformed epidural auto-regulatory factor-1) domain-containing protein 2 transgenic) or wild-type rats and mice treated with LLY-507 (3-cyano-5-{2-[4-[2-(3-methylindol-1-yl)ethyl]piperazin-1-yl]-phenyl}-N-[(3-pyrrolidin-1-yl)propyl]benzamide) were used to investigate the function of SMYD2 (suppressor of variegation, enhancer of zeste, trithorax and myeloid Nervy DEAF-1 domain-containing protein 2) on PH development in vivo. Primary cultured rat PASMCs with SMYD2 knockdown or overexpression were used to explore the effects of SMYD2 on proliferation and to decipher the underlying mechanism. RESULTS: We demonstrated that the expression of the lysine methyltransferase SMYD2 was upregulated in the smooth muscle cells of pulmonary arteries from patients with PH and hypoxia-exposed rats/mice and in the cytoplasm of hypoxia-induced rat PASMCs. More importantly, targeted inhibition of SMYD2 by LLY-507 significantly attenuated hypoxia-induced pulmonary vascular remodeling and PH development in both male and female rats in vivo and reduced rat PASMC hyperproliferation in vitro. In contrast, SMYD2-vTg mice exhibited more severe PH phenotypes and related pathological changes than nontransgenic mice after 4 weeks of chronic hypoxia treatment. Furthermore, SMYD2 overexpression promoted, while SMYD2 knockdown suppressed, the proliferation of rat PASMCs by affecting the cell cycle checkpoint between S and G2 phases. Mechanistically, we revealed that SMYD2 directly interacted with and monomethylated PPARγ (peroxisome proliferator-activated receptor gamma) to inhibit the nuclear translocation and transcriptional activity of PPARγ, which further promoted mitophagy to facilitate PASMC proliferation and PH development. Furthermore, rosiglitazone, a PPARγ agonist, largely abolished the detrimental effects of SMYD2 overexpression on PASMC proliferation and PH. CONCLUSIONS: Our results demonstrated that SMYD2 monomethylates nonhistone PPARγ and inhibits its nuclear translocation and activation to accelerate PASMC proliferation and PH by triggering mitophagy, indicating that targeting SMYD2 or activating PPARγ are potential strategies for the prevention of PH.

A large-scale binding and functional map of human RNA-binding proteins.

Many proteins regulate the expression of genes by binding to specific regions encoded in the genome1. Here we introduce a new data set of RNA elements in the human genome that are recognized by RNA-binding proteins (RBPs), generated as part of the Encyclopedia of DNA Elements (ENCODE) project phase III. This class of regulatory elements functions only when transcribed into RNA, as they serve as the binding sites for RBPs that control post-transcriptional processes such as splicing, cleavage and polyadenylation, and the editing, localization, stability and translation of mRNAs. We describe the mapping and characterization of RNA elements recognized by a large collection of human RBPs in K562 and HepG2 cells. Integrative analyses using five assays identify RBP binding sites on RNA and chromatin in vivo, the in vitro binding preferences of RBPs, the function of RBP binding sites and the subcellular localization of RBPs, producing 1,223 replicated data sets for 356 RBPs. We describe the spectrum of RBP binding throughout the transcriptome and the connections between these interactions and various aspects of RNA biology, including RNA stability, splicing regulation and RNA localization. These data expand the catalogue of functional elements encoded in the human genome by the addition of a large set of elements that function at the RNA level by interacting with RBPs.

Pyrolysis-catalysis upcycling of waste plastic using a multilayer stainless-steel catalyst toward a circular economy.

Current un-sustainable plastic management is exacerbating plastic pollution, an urgent shift is thus needed to create a recycling society. Such recovering carbon (C) and hydrogen (H) from waste plastic has been considered as one practical route to achieve a circular economy. Here, we performed a simple pyrolysis-catalysis deconstruction of waste plastic via a monolithic multilayer stainless-steel mesh catalyst to produce multiwalled carbon nanotubes (MWCNTs) and H2, which are important carbon material and energy carrier to achieve sustainable development. Results revealed that the C and H recovery efficiencies were as high as 86% and 70%, respectively. The unique oxidation-reduction process and improvement of surface roughness led to efficient exposure of active sites, which increased MWCNTs by suppressing macromolecule hydrocarbons. The C recovery efficiency declined by only 5% after 10 cycles, proving the long-term employment of the catalyst. This catalyst can efficiently convert aromatics to MWCNTs by the vapor-solid-solid mechanism and demonstrate good universality in processing different kinds of waste plastics. The produced MWCNTs showed potential in applications of lithium-ion batteries and telecommunication. Owing to the economic profits and environmental benefits of the developed route, we highlighted its potential as a promising alternative to conventional incineration, simultaneously achieving the waste-to-resource strategy and circular economy.

Brain-gut communications via distinct neuroendocrine signals bidirectionally regulate longevity in C. elegans.

Tissue-tissue communications are integral to organismal aging, orchestrating a body-wide aging process. The brain plays a key role in this process by detecting and processing signals from the environment and then communicating them to distal tissues such as the gut to regulate longevity. How this is achieved, however, is poorly understood. Here, using Caenorhabditis elegans as a model, we identified two distinct neuroendocrine signaling circuits by which the worm nervous system senses cool and warm environmental temperatures through cool- and warm-sensitive neurons and then signals the gut to extend and shorten life span, respectively. The prolongevity "cool" circuit uses the small neurotransmitters glutamate and serotonin, whereas the anti-longevity "warm" circuit is mediated by insulin-like neuropeptides. Both types of neuroendocrine signals converge on the gut through their cognate receptors to differentially regulate the transcription factor DAF-16/FOXO, leading to opposing outcomes in longevity. Our study illustrates how the brain detects and processes environmental signals to bidirectionally regulate longevity by signaling the gut.

Long-read assembly and comparative evidence-based reanalysis of Cryptosporidium genome sequences reveal expanded transporter repertoire and duplication of entire chromosome ends including subtelomeric regions.

Cryptosporidiosis is a leading cause of waterborne diarrheal disease globally and an important contributor to mortality in infants and the immunosuppressed. Despite its importance, the Cryptosporidium community has only had access to a good, but incomplete, Cryptosporidium parvum IOWA reference genome sequence. Incomplete reference sequences hamper annotation, experimental design, and interpretation. We have generated a new C. parvum IOWA genome assembly supported by Pacific Biosciences (PacBio) and Oxford Nanopore long-read technologies and a new comparative and consistent genome annotation for three closely related species: C. parvum, Cryptosporidium hominis, and Cryptosporidium tyzzeri We made 1926 C. parvum annotation updates based on experimental evidence. They include new transporters, ncRNAs, introns, and altered gene structures. The new assembly and annotation revealed a complete Dnmt2 methylase ortholog. Comparative annotation between C. parvum, C. hominis, and C. tyzzeri revealed that most "missing" orthologs are found, suggesting that the biological differences between the species must result from gene copy number variation, differences in gene regulation, and single-nucleotide variants (SNVs). Using the new assembly and annotation as reference, 190 genes are identified as evolving under positive selection, including many not detected previously. The new C. parvum IOWA reference genome assembly is larger, gap free, and lacks ambiguous bases. This chromosomal assembly recovers all 16 chromosome ends, 13 of which are contiguously assembled. The three remaining chromosome ends are provisionally placed. These ends represent duplication of entire chromosome ends including subtelomeric regions revealing a new level of genome plasticity that will both inform and impact future research.

R-ChIP Using Inactive RNase H Reveals Dynamic Coupling of R-loops with Transcriptional Pausing at Gene Promoters.

R-loop, a three-stranded RNA/DNA structure, has been linked to induced genome instability and regulated gene expression. To enable precision analysis of R-loops in vivo, we develop an RNase-H-based approach; this reveals predominant R-loop formation near gene promoters with strong G/C skew and propensity to form G-quadruplex in non-template DNA, corroborating with all biochemically established properties of R-loops. Transcription perturbation experiments further indicate that R-loop induction correlates to transcriptional pausing. Interestingly, we note that most mapped R-loops are each linked to a nearby free RNA end; by using a ribozyme to co-transcriptionally cleave nascent RNA, we demonstrate that such a free RNA end coupled with a G/C-skewed sequence is necessary and sufficient to induce R-loop. These findings provide a topological solution for RNA invasion into duplex DNA and suggest an order for R-loop initiation and elongation in an opposite direction to that previously proposed.

Temperature Sensation: From Molecular Thermosensors to Neural Circuits and Coding Principles.

Temperature is a universal cue and regulates many essential processes ranging from enzymatic reactions to species migration. Due to the profound impact of temperature on physiology and behavior, animals and humans have evolved sophisticated mechanisms to detect temperature changes. Studies from animal models, such as mouse, Drosophila, and C. elegans, have revealed many exciting principles of thermosensation. For example, conserved molecular thermosensors, including thermosensitive channels and receptors, act as the initial detectors of temperature changes across taxa. Additionally, thermosensory neurons and circuits in different species appear to adopt similar logic to transduce and process temperature information. Here, we present the current understanding of thermosensation at the molecular and cellular levels. We also discuss the fundamental coding strategies of thermosensation at the circuit level. A thorough understanding of thermosensation not only provides key insights into sensory biology but also builds a foundation for developing better treatments for various sensory disorders.

Dual mode of DDX3X as an ATP-dependent RNA helicase and ATP-independent nucleic acid chaperone.

Human DDX3X, an important member of the DEAD-box family RNA helicases, plays a crucial role in RNA metabolism and is involved in cancer development, viral infection, and neurodegenerative disease. Although there have been many studies on the physiological functions of human DDX3X, issues regarding its exact targets and mechanisms of action remain unclear. In this study, we systematically characterized the biochemical activities and substrate specificity of DDX3X. The results demonstrate that DDX3X is a bidirectional RNA helicase to unwind RNA duplex and RNA-DNA hybrid driven by ATP. DDX3X also has nucleic acid annealing activity, especially for DNA. More importantly, it can function as a typical nucleic acid chaperone which destabilizes highly structured DNA and RNA in an ATP-independent manner and promotes their annealing to form a more stable structure. Further truncation mutations confirmed that the highly disordered N-tail and C-tail are critical for the biochemical activities of DDX3X. They are functionally complementary, with the N-tail being crucial. These results will shed new light on our understanding of the molecular mechanism of DDX3X in RNA metabolism and DNA repair, and have potential significance for the development of antiviral/anticancer drugs targeting DDX3X.

Chemical Looping Technology in Mild-Condition Ammonia Production: A Comprehensive Review and Analysis.

Ammonia is an efficient and clean hydrogen carrier that promises to tackle the increasing energy and environmental problems. However, more than 90% of ammonia is produced by the Haber-Bosch process, and its enormous energy consumption and CO2 emissions require the development of novel alternatives. Chemical looping technology can decouple the one-step ammonia synthesis reaction into separated nitridation and hydrogenation processes at atmospheric pressure, thereby achieving the mild ammonia synthesis based on renewable energy. The strategy of stepwise reactions circumvents the problem of competing adsorption of N2 and H2 /H2 O at the active sites and provides additive freedom for optimal regulation of sub-reactions. This review introduces the concept and mechanism of chemical looping ammonia production (CLAP), and comprehensively summarizes the state-of-art research from the perspective of reaction pathways and nitrogen carriers. The challenges faced by CLAP and strategies to address them in terms of nitrogen carriers, methods, equipment, and technological processes are also proposed.

MuSiC2: cell-type deconvolution for multi-condition bulk RNA-seq data.

Cell-type composition of intact bulk tissues can vary across samples. Deciphering cell-type composition and its changes during disease progression is an important step toward understanding disease pathogenesis. To infer cell-type composition, existing cell-type deconvolution methods for bulk RNA sequencing (RNA-seq) data often require matched single-cell RNA-seq (scRNA-seq) data, generated from samples with similar clinical conditions, as reference. However, due to the difficulty of obtaining scRNA-seq data in diseased samples, only limited scRNA-seq data in matched disease conditions are available. Using scRNA-seq reference to deconvolve bulk RNA-seq data from samples with different disease conditions may lead to a biased estimation of cell-type proportions. To overcome this limitation, we propose an iterative estimation procedure, MuSiC2, which is an extension of MuSiC, to perform deconvolution analysis of bulk RNA-seq data generated from samples with multiple clinical conditions where at least one condition is different from that of the scRNA-seq reference. Extensive benchmark evaluations indicated that MuSiC2 improved the accuracy of cell-type proportion estimates of bulk RNA-seq samples under different conditions as compared with the traditional MuSiC deconvolution. MuSiC2 was applied to two bulk RNA-seq datasets for deconvolution analysis, including one from human pancreatic islets and the other from human retina. We show that MuSiC2 improves current deconvolution methods and provides more accurate cell-type proportion estimates when the bulk and single-cell reference differ in clinical conditions. We believe the condition-specific cell-type composition estimates from MuSiC2 will facilitate the downstream analysis and help identify cellular targets of human diseases.

HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks.

Identifying new indications for drugs plays an essential role at many phases of drug research and development. Computational methods are regarded as an effective way to associate drugs with new indications. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering the biological knowledge of drugs and diseases, which are believed to be useful for improving the accuracy of drug repositioning. To this end, a novel heterogeneous information network (HIN) based model, namely HINGRL, is proposed to precisely identify new indications for drugs based on graph representation learning techniques. More specifically, HINGRL first constructs a HIN by integrating drug-disease, drug-protein and protein-disease biological networks with the biological knowledge of drugs and diseases. Then, different representation strategies are applied to learn the features of nodes in the HIN from the topological and biological perspectives. Finally, HINGRL adopts a Random Forest classifier to predict unknown drug-disease associations based on the integrated features of drugs and diseases obtained in the previous step. Experimental results demonstrate that HINGRL achieves the best performance on two real datasets when compared with state-of-the-art models. Besides, our case studies indicate that the simultaneous consideration of network topology and biological knowledge of drugs and diseases allows HINGRL to precisely predict drug-disease associations from a more comprehensive perspective. The promising performance of HINGRL also reveals that the utilization of rich heterogeneous information provides an alternative view for HINGRL to identify novel drug-disease associations especially for new diseases.

A geometric deep learning framework for drug repositioning over heterogeneous information networks.

Drug repositioning (DR) is a promising strategy to discover new indicators of approved drugs with artificial intelligence techniques, thus improving traditional drug discovery and development. However, most of DR computational methods fall short of taking into account the non-Euclidean nature of biomedical network data. To overcome this problem, a deep learning framework, namely DDAGDL, is proposed to predict drug-drug associations (DDAs) by using geometric deep learning (GDL) over heterogeneous information network (HIN). Incorporating complex biological information into the topological structure of HIN, DDAGDL effectively learns the smoothed representations of drugs and diseases with an attention mechanism. Experiment results demonstrate the superior performance of DDAGDL on three real-world datasets under 10-fold cross-validation when compared with state-of-the-art DR methods in terms of several evaluation metrics. Our case studies and molecular docking experiments indicate that DDAGDL is a promising DR tool that gains new insights into exploiting the geometric prior knowledge for improved efficacy.

iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network.

MOTIVATION: The task of predicting drug-target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from heterogeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the over-smoothing simulation when learning the latent representations of drugs and targets with their rich neighborhood information in HBIN, and thereby reduce the discriminative ability in DTI prediction. RESULTS: In this work, an improved graph representation learning method, namely iGRLDTI, is proposed to address the above issue by better capturing more discriminative representations of drugs and targets in a latent feature space. Specifically, iGRLDTI first constructs an HBIN by integrating the biological knowledge of drugs and targets with their interactions. After that, it adopts a node-dependent local smoothing strategy to adaptively decide the propagation depth of each biomolecule in HBIN, thus significantly alleviating over-smoothing by enhancing the discriminative ability of feature representations of drugs and targets. Finally, a Gradient Boosting Decision Tree classifier is used by iGRLDTI to predict novel DTIs. Experimental results demonstrate that iGRLDTI yields better performance that several state-of-the-art computational methods on the benchmark dataset. Besides, our case study indicates that iGRLDTI can successfully identify novel DTIs with more distinguishable features of drugs and targets. AVAILABILITY AND IMPLEMENTATION: Python codes and dataset are available at https://github.com/stevejobws/iGRLDTI/.

Prenatal Brain Maturation is Delayed in Neonates with Congenital Diaphragmatic Hernia.

OBJECTIVE: To assess brain development in fetuses with congenital diaphragmatic hernia (CDH) using a fetal Total Maturation Score (fTMS). STUDY DESIGN: This is a retrospective cohort study using data from a single-center clinical registry. Neonates with an antenatal diagnosis of CDH between 2014 and 2020 and prenatal brain magnetic resonance imaging (MRI) (n = 48) were included. We compared our study sample with historical healthy controls (n = 48). The relationship between fTMS and gestational age (GA), as well as the association between fTMS and key prenatal variables and placental pathologic findings, were evaluated. RESULTS: Compared with healthy controls, neonates with CDH had a significant delay in fTMS (P value <.001). Within the CDH cohort, there was no significant difference in fTMS based on CDH severity, intrathoracic liver position, right vs left CDH, sex, presence of abnormal echocardiogram findings, treatment with extracorporeal membrane oxygenation (ECMO), or in-hospital mortality. Placentas of neonates with CDH had a high proportion of fetal vascular malperfusion (56%) and chronic inflammation (67%), and relatively large placentas had a protective effect on prenatal brain maturation (P value = .025). CONCLUSIONS: Prenatal brain maturation in neonates with CDH is delayed. Placental pathology may influence fetal brain development. The etiology and clinical impact of prenatal brain immaturity in neonates with CDH warrant further investigation.

A standardized approach to evaluation and reporting of synovial histopathology in two surgically induced murine models of osteoarthritis.

OBJECTIVE: Synovial pathology has been linked to osteoarthritis (OA) pain in patients. Microscopic grading systems for synovial changes in human OA have been described, but a standardized approach for murine models of OA is needed. We sought to develop a reproducible approach and set of minimum recommendations for reporting of synovial histopathology in mouse models of OA. METHODS: Coronal and sagittal sections from male mouse knee joints subjected to destabilization of medial meniscus (DMM) or partial meniscectomy (PMX) were collected as part of other studies. Stains included Hematoxylin and Eosin (H&E), Toluidine Blue (T-Blue), and Safranin O/Fast Green (Saf-O). Four blinded readers graded pathological features (hyperplasia, cellularity, and fibrosis) at specific anatomic locations. Inter-reader agreement of each feature score was determined. RESULTS: There was acceptable to very good agreement when using 3-4 individual readers. After DMM and PMX, expected medial predominant changes in hyperplasia and cellularity were observed, with fibrosis noted at 12 weeks post-PMX. Synovial changes were consistent from section to section in the mid-joint area. When comparing stains, H&E and T-blue resulted in better agreement compared to Saf-O stain. CONCLUSIONS: To account for the pathologic and anatomic variability in synovial pathology and allow for a more standardized evaluation that can be compared across studies, we recommend evaluating a minimum set of 3 pathological features at standardized anatomic areas. Further, we suggest reporting individual feature scores separately before relying on a single summed "synovitis" score. H&E or T-blue are preferred, inter-reader agreement for each feature should be considered.