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To address large gaps in our understanding of the molecular regulation of articular and growth plate cartilage development in humans, we used our directed differentiation approach to generate these distinct cartilage tissues from human embryonic stem cells. The resulting transcriptomic profiles of hESC-derived articular and growth plate chondrocytes were similar to fetal epiphyseal and growth plate chondrocytes, with respect to genes both known and previously unknown to cartilage biology. With the goal to characterize the regulatory landscapes accompanying these respective transcriptomes, we mapped chromatin accessibility in hESC-derived chondrocyte lineages, and mouse embryonic chondrocytes, using ATAC-sequencing. Integration of the expression dataset with the differentially accessible genomic regions revealed lineage-specific gene regulatory networks. We validated functional interactions of two transcription factors (RUNX2 in growth plate chondrocytes and RELA in articular chondrocytes) with their predicted genomic targets. The maps we provide thus represent a framework for probing regulatory interactions governing chondrocyte differentiation. This work constitutes a substantial step towards comprehensive and comparative molecular characterizations of distinct chondrogenic lineages, and sheds new light on human cartilage development and biology.

A tendon’s ordered extracellular matrix (ECM) is integral for transmitting force and highly prone to injury. How tendon cells, or tenocytes, embedded within this dense ECM mobilize and contribute to healing is unknown. Here, we identify a specialized Axin2+ population in mouse and human tendons that remains latent in homeostasis yet initiates the healing response and serves as a major source of tendon progenitors. Axin2+ tenocytes readily expand in vitro and express stem cell markers. In vivo, Axin2+-descendants are major functional contributors to repair: Axin2+ tenocytes proliferate, acquire injury responsive states, and re-adopt a tenocyte fate post-injury. Specific loss of Wnt secretion in Axin2+ cells alters their progenitor identity, disrupts their activation upon injury, and severely compromises any healing response. Our work highlights an unusual paradigm, wherein quiescent Axin2+ tenocytes self-regulate their identity and mobilization upon injury and provide the key initiating signal to organize a tendon-wide healing response.

Individuals infected with the SARS-CoV-2 virus present with a wide variety of phenotypes ranging from asymptomatic to severe and even lethal outcomes. Past research has revealed a genetic haplotype on chromosome 3 that entered the human population via introgression from Neanderthals as the strongest genetic risk factor for the severe COVID-19 phenotype. However, the specific variants along this introgressed haplotype that contribute to this risk and the biological mechanisms that are involved remain unclear. Here, we assess the variants present on the risk haplotype for their likelihood of driving the severe COVID-19 phenotype. We do this by first exploring their impact on the regulation of genes involved in COVID-19 infection using a variety of population genetics and functional genomics tools. We then perform an locus-specific massively parallel reporter assay to individually assess the regulatory potential of each allele on the haplotype in a multipotent immune-related cell line. We ultimately reduce the set of over 600 linked genetic variants to identify 4 introgressed alleles that are strong functional candidates for driving the association between this locus and severe COVID-19. These variants likely drive the locus’ impact on severity by putatively modulating the regulation of two critical chemokine receptor genes: CCR1 and CCR5. These alleles are ideal targets for future functional investigations into the interaction between host genomics and COVID-19 outcomes.

The angiotensin-converting enzyme 2 (ACE2) protein has been highly studied as a key catalytic regulator of the renin-angiotensin system (RAS), involved in fluid homeostasis and blood pressure modulation. In addition to its important physiological role as a broadly-expressed membrane-bound protein, ACE2 serves as a cell-surface receptor for some viruses - most notably, coronaviruses such as SARS-CoV and SARS-CoV-2. Differing levels of ACE2 expression may impact viral susceptibility and subsequent changes to expression may be a pathogenic mechanism of disease risk and manifestation. Therefore, an improved understanding of howACE2expression is regulated at the genomic and transcriptional level may help us understand not only how the effects of pre-existing conditions (e.g., chronic obstructive pulmonary disease) may manifest with increased COVID-19 incidence, but also the mechanisms that regulate ACE2 levels following viral infection. Here, we initially perform bioinformatic analyses of several datasets to generate hypotheses aboutACE2gene-regulatory mechanisms in the context of immune signaling and chronic oxidative stress. We then identify putative non-coding regulatory elements withinACE2intronic regions as potential determinants ofACE2expression activity. We perform functional validation of our computational predictions in vitro via targeted CRISPR-Cas9 deletions of the identifiedACE2 cis-regulatory elements in the context of both immunological stimulation and oxidative stress conditions. We demonstrate that intronicACE2regulatory elements are responsive to both immune signaling and oxidative-stress pathways, and this contributes to our understanding of how expression of this gene may be modulated at both baseline and during immune challenge. Our work supports the further pursuit of these putative mechanisms in our understanding, prevention, and treatment of infection and disease caused by ACE2-utilizing viruses such as SARS-CoV, SARS-CoV-2, and future emerging SARS-related viruses.Author SummaryThe recent emergence of the virus SARS-CoV-2 which has caused the COVID-19 pandemic has prompted scientists to intensively study how the virus enters human host cells. This work has revealed a key protein, ACE2, that acts as a receptor permitting the virus to infect cells. Much research has focused on how the virus physically interacts with ACE2, yet little is known on how ACE2 is turned on or off in human cells at the level of the DNA molecule. Understanding this level of regulation may offer additional ways to prevent or lower viral entry into human hosts. Here, we have examined the control of theACE2gene, the DNA sequence that instructs ACE2 protein receptor formation, and we have done so in the context of immune stimulation. We have indeed identified a number of DNA on/off switches forACE2that appear responsive to immuno-logical and oxidative stress. These switches may fine-tune howACE2is turned on or off before, during, and/or after infection by SARS-CoV-2 or other related coronaviruses. Our studies help pave the way for additional functional studies on these switches, and their potential therapeutic targeting in the future.

ObjectiveDevelop a bone shape measure that reflects the extent of cartilage loss and bone flattening in knee osteoarthritis (OA) and test it against estimates of disease severity.MethodsA fast region-based convolutional neural network was trained to crop the knee joints in sagittal dual-echo steady state MRI sequences obtained from the Osteoarthritis Initiative (OAI). Publicly available annotations of the cartilage and menisci were used as references to annotate the tibia and the femur in 61 knees. Another deep neural network (U-Net) was developed to learn these annotations. Model predictions were compared with radiologist-driven annotations on an independent test set (27 knees). The U-Net was applied to automatically extract the knee joint structures on the larger OAI dataset (9,434 knees). We defined subchondral bone length (SBL), a novel shape measure characterizing the extent of overlying cartilage and bone flattening, and examined its relationship with radiographic joint space narrowing (JSN), concurrent WOMAC pain and disability as well as subsequent partial or total knee replacement (KR). Odds ratios for each outcome were estimated using relative changes in SBL on the OAI dataset into quartiles.ResultMean SBL values for knees with JSN were consistently different from knees without JSN. Greater changes of SBL from baseline were associated with greater pain and disability. For knees with medial or lateral JSN, the odds ratios between lowest and highest quartiles corresponding to SBL changes for future KR were 5.68 (95% CI:[3.90,8.27]) and 7.19 (95% CI:[3.71,13.95]), respectively.ConclusionSBL quantified OA status based on JSN severity. It has promise as an imaging marker in predicting clinical and structural OA outcomes.

Background and objectiveIt remains difficult to characterize pain in knee joints with osteoarthritis solely by radiographic findings. We sought to understand how advanced machine learning methods such as deep neural networks can be used to analyze raw MRI scans and predict bilateral knee pain, independent of other risk factors.MethodsWe developed a deep learning framework to associate information from MRI slices taken from the left and right knees of subjects from the Osteoarthritis Initiative with bilateral knee pain. Model training was performed by first extracting features from two-dimensional (2D) sagittal intermediate-weighted turbo spin echo slices. The extracted features from all the 2D slices were subsequently combined to directly associate using a fused deep neural network with the output of interest as a binary classification problem.ResultsThe deep learning model resulted in predicting bilateral knee pain on test data with 70.1% mean accuracy, 51.3% mean sensitivity, and 81.6% mean specificity. Systematic analysis of the predictions on the test data revealed that the model performance was consistent across subjects of different Kellgren-Lawrence grades.ConclusionThe study demonstrates a proof of principle that a machine learning approach can be applied to associate MR images with bilateral knee pain.SIGNIFICANCE AND INNOVATIONKnee pain is typically considered as an early indicator of osteoarthritis (OA) risk. Emerging evidence suggests that MRI changes are linked to pre-clinical OA, thus underscoring the need for building image-based models to predict knee pain. We leveraged a state-of-the-art machine learning approach to associate raw MR images with bilateral knee pain, independent of other risk factors.

Given GDF5 involvement in hip development, and osteoarthritis (OA) and developmental hip dysplasia (DDH) risk, here we sought to assess the role(s) of GDF5 and its regulatory sequence on the development of hip morphology linked to injury risk. The brachypodism (bp) mouse, which harbors a Gdf5 inactivating mutation, was used to survey how Gdf5 loss of function impacts the development of hip morphology. Two transgenic Gdf5 reporter BAC lines were used to assess the spatiotemporal expression of Gdf5 regulatory sequences. Each BAC line was also used to assess the functional roles of upstream and downstream sequence on hip morphology. bp/bp mice had shorter femora with smaller femoral heads and necks as well as larger alpha angles, smaller anterior offsets, and smaller acetabula, compared to bp/+ mice (pGdf5 drove strong prenatal (E17) expression and low postnatal (6 months) expression across regions of femoral head and acetabulum. Conversely, upstream regulatory sequences drove very low expression at E17 and no detectable expression at 6 months. Importantly, downstream, but not upstream Gdf5 regulatory sequences fully restored all the key morphologic features disrupted in bp/bp mice. Hip morphology is profoundly affected by Gdf5 absence, and downstream regulatory sequences mediate its effects by controlling Gdf5 expression during development. This downstream region contains numerous enhancers harboring risk variants related to hip OA, DDH, and dislocation. We posit that subtle alterations to morphology driven by changes in downstream regulatory sequence underlie this locus’ role in hip injury risk.