Deep Learning-Based Automated Measurement of Murine Bone Length in Radiographs.

Genetic mouse models of skeletal abnormalities have demonstrated promise in the identification of phenotypes relevant to human skeletal diseases. Traditionally, phenotypes are assessed by manually examining radiographs, a tedious and potentially error-prone process. In response, this study developed a deep learning-based model that streamlines the measurement of murine bone lengths from radiographs in an accurate and reproducible manner. A bone detection and measurement pipeline utilizing the Keypoint R-CNN algorithm with an EfficientNet-B3 feature extraction backbone was developed to detect murine bone positions and measure their lengths. The pipeline was developed utilizing 94 X-ray images with expert annotations on the start and end position of each murine bone. The accuracy of our pipeline was evaluated on an independent dataset test with 592 images, and further validated on a previously published dataset of 21,300 mouse radiographs. The results showed that our model performed comparably to humans in measuring tibia and femur lengths (R2 > 0.92, p-value = 0) and significantly outperformed humans in measuring pelvic lengths in terms of precision and consistency. Furthermore, the model improved the precision and consistency of genetic association mapping results, identifying significant associations between genetic mutations and skeletal phenotypes with reduced variability. This study demonstrates the feasibility and efficiency of automated murine bone length measurement in the identification of mouse models of abnormal skeletal phenotypes.

Bayesian hidden mark interaction model for detecting spatially variable genes in imaging-based spatially resolved transcriptomics data.

Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies on ad hoc kernels, or the energy-based Ising model, which requires gene expression to be measured on a lattice grid. To overcome these potential limitations, we developed a generalized energy-based framework to model gene expression measured from imaging-based SRT platforms, accommodating the irregular spatial distribution of measured cells. Our Bayesian model applies a zero-inflated negative binomial mixture model to dichotomize the raw count data, reducing noise. Additionally, we incorporate a geostatistical mark interaction model with a generalized energy function, where the interaction parameter is used to identify the spatial pattern. Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. We demonstrated the strength of our method on both simulated and real data. Our simulation study showed that our method captured various spatial patterns with high accuracy; moreover, analysis of a seqFISH dataset and a STARmap dataset established that our proposed method is able to identify genes with novel and strong spatial patterns.

Clinical Considerations and Outcomes of Robotic Urologic Surgery in Obese Patients.

Obesity is associated with many significant physiological changes. These considerations are important to surgery, especially in urological procedures. Obese patients often undergo surgical procedures and are at higher risk of complications. This investigation reviews physiological and anaesthesia considerations for obese and morbidly obese patients. In addition, urological surgeries and procedures should be considered for these higher risk patients. Clinical anaesthesiologists must use detailed assessment and, when appropriate, consultation in developing safe anaesthesia plans for these patients. Newer technologies have improved safety related to airway management, advanced airway devices, and regional anaesthesia with ultrasound-guided nerve blocks, which can reduce the need for opioids postoperatively. Recent developments in drug and monitoring technologies have also been developed and can be effective for obese and morbidly obese patients undergoing urological procedures and perioperative surgery, thus improving the likelihood of safety in this higher risk population.

GABAergic/Glycinergic and Glutamatergic Neurons Mediate Distinct Neurodevelopmental Phenotypes of STXBP1 Encephalopathy.

An increasing number of pathogenic variants in presynaptic proteins involved in the synaptic vesicle cycle are being discovered in neurodevelopmental disorders. The clinical features of these synaptic vesicle cycle disorders are diverse, but the most prevalent phenotypes include intellectual disability, epilepsy, movement disorders, cerebral visual impairment, and psychiatric symptoms ( Verhage and Sørensen, 2020; Bonnycastle et al., 2021; John et al., 2021; Melland et al., 2021). Among this growing list of synaptic vesicle cycle disorders, the most frequent is STXBP1 encephalopathy caused by de novo heterozygous pathogenic variants in syntaxin-binding protein 1 (STXBP1, also known as MUNC18-1; Verhage and Sørensen, 2020; John et al., 2021). STXBP1 is an essential protein for presynaptic neurotransmitter release. Its haploinsufficiency is the main disease mechanism and impairs both excitatory and inhibitory neurotransmitter release. However, the disease pathogenesis and cellular origins of the broad spectrum of neurological phenotypes are poorly understood. Here we generate cell type-specific Stxbp1 haploinsufficient male and female mice and show that Stxbp1 haploinsufficiency in GABAergic/glycinergic neurons causes developmental delay, epilepsy, and motor, cognitive, and psychiatric deficits, recapitulating majority of the phenotypes observed in the constitutive Stxbp1 haploinsufficient mice and STXBP1 encephalopathy. In contrast, Stxbp1 haploinsufficiency in glutamatergic neurons results in a small subset of cognitive and seizure phenotypes distinct from those caused by Stxbp1 haploinsufficiency in GABAergic/glycinergic neurons. Thus, the contrasting roles of excitatory and inhibitory signaling reveal GABAergic/glycinergic dysfunction as a key disease mechanism of STXBP1 encephalopathy and suggest the possibility to selectively modulate disease phenotypes by targeting specific neurotransmitter systems.

Infigratinib for the Treatment of Metastatic or Locally Advanced Cholangiocarcinoma With Known FGFR2 Gene Fusions or Rearrangements.

Cholangiocarcinoma (CCA) is an aggressive and diverse malignancy with a poor prognosis. Related to a typical indolent course of progression, most cases of CCA are metastatic or locally advanced at the time of presentation. For patients with nonresectable tumors or metastatic disease, the mainstay of treatment is comprehensive with combination chemotherapy. The first-line chemotherapeutic combination for the treatment of CCA are cisplatin and gemcitabine-based chemotherapies. However, many locally advanced and progressive CCA cases are refractory to first-line management. Within the past few years, the increase in the incidence of metastatic CCA and its poor prognosis has brought to light the need for novel therapeutic approaches to treatment. With advancements in next-generation genome sequencing, multiple molecular pathways have been identified in the pathogenesis of CCA and have shown great potential as alternative treatments in cases of CCA refractory to surgical resection. FGFR2 fusions or rearrangements have been identified in 10-16% of all intrahepatic CCA and are thought to serve as a pathway of resistance for a number of nonresectable and refractory cases of cholangiocarcinoma. A novel therapeutic agent that has been discussed is infigratinib, a selective, ATP-competitive inhibitor of fibroblast growth factor receptor 2 (FGFR2). In a phase 1 trial, infigratinib showed a safe profile and showed remarkable clinical efficacy in advanced CCA with FGFR2 fusions or rearrangements in phase II trials. As of May 2021, the Food and Drug Administration (FDA) approved infigratinib for CCA largely based on tumor response and duration of response. As of 2021, infigratinib, futibatinib, and pemigatinib, similar novel selective FGFR inhibitors, have been approved by the FDA for the treatment of locally advanced or metastatic CCA harboring FGFR2 gene mutations. The present investigation reviews the development of infigratinib in particular and its clinical efficacy compared to other available treatment options for cholangiocarcinoma. While the side effect profile of infigratinib is minimal, particularly GI side effects, when compared with futibatinib and pemigatinib, the overall response rate (ORR) and median overall survival (mOS) for infigratinib (ORR=23.1%, mOS=3.8 months) was significantly lower than futibatinib (ORR=35.8%, mOS=21.1 months) and pemigatinib (ORR=35.5%, mOS=21.1 months). While there is ample promise for the use of infigratinib as molecular-directed therapy in the treatment of CCA harboring FGFR2 mutations, there is an appropriate concern for patient-acquired resistance. The heterogeneous nature of FGFR mutations and the emergence of different resistance mechanisms emphasize a need for more agents to inhibit FGFR rearrangements effectively.

Osteosarcoma Explorer: A Data Commons With Clinical, Genomic, Protein, and Tissue Imaging Data for Osteosarcoma Research.

PURPOSE: Osteosarcoma research advancement requires enhanced data integration across different modalities and sources. Current osteosarcoma research, encompassing clinical, genomic, protein, and tissue imaging data, is hindered by the siloed landscape of data generation and storage. MATERIALS AND METHODS: Clinical, molecular profiling, and tissue imaging data for 573 patients with pediatric osteosarcoma were collected from four public and institutional sources. A common data model incorporating standardized terminology was created to facilitate the transformation, integration, and load of source data into a relational database. On the basis of this database, a data commons accompanied by a user-friendly web portal was developed, enabling various data exploration and analytics functions. RESULTS: The Osteosarcoma Explorer (OSE) was released to the public in 2021. Leveraging a comprehensive and harmonized data set on the backend, the OSE offers a wide range of functions, including Cohort Discovery, Patient Dashboard, Image Visualization, and Online Analysis. Since its initial release, the OSE has experienced an increasing utilization by the osteosarcoma research community and provided solid, continuous user support. To our knowledge, the OSE is the largest (N = 573) and most comprehensive research data commons for pediatric osteosarcoma, a rare disease. This project demonstrates an effective framework for data integration and data commons development that can be readily applied to other projects sharing similar goals. CONCLUSION: The OSE offers an online exploration and analysis platform for integrated clinical, molecular profiling, and tissue imaging data of osteosarcoma. Its underlying data model, database, and web framework support continuous expansion onto new data modalities and sources.

Artificial intelligence in mental healthcare: an overview and future perspectives.

Artificial intelligence is disrupting the field of mental healthcare through applications in computational psychiatry, which leverages quantitative techniques to inform our understanding, detection, and treatment of mental illnesses. This paper provides an overview of artificial intelligence technologies in modern mental healthcare and surveys recent advances made by researchers, focusing on the nascent field of digital psychiatry. We also consider the ethical implications of artificial intelligence playing a greater role in mental healthcare.

A Deep Learning Onion Peeling Approach to Measure Oral Epithelium Layer Number.

Head and neck squamous cell carcinoma (HNSCC), specifically in the oral cavity (oral squamous cell carcinoma, OSCC), is a common, complex cancer that significantly affects patients' quality of life. Early diagnosis typically improves prognoses yet relies on pathologist examination of histology images that exhibit high inter- and intra-observer variation. The advent of deep learning has automated this analysis, notably with object segmentation. However, techniques for automated oral dysplasia diagnosis have been limited to shape or cell stain information, without addressing the diagnostic potential in counting the number of cell layers in the oral epithelium. Our study attempts to address this gap by combining the existing U-Net and HD-Staining architectures for segmenting the oral epithelium and introducing a novel algorithm that we call Onion Peeling for counting the epithelium layer number. Experimental results show a close correlation between our algorithmic and expert manual layer counts, demonstrating the feasibility of automated layer counting. We also show the clinical relevance of oral epithelial layer number to grading oral dysplasia severity through survival analysis. Overall, our study shows that automated counting of oral epithelium layers can represent a potential addition to the digital pathology toolbox. Model generalizability and accuracy could be improved further with a larger training dataset.

Deep-Learning-Based Hepatic Ploidy Quantification Using H&E Histopathology Images.

Polyploidy, the duplication of the entire genome within a single cell, is a significant characteristic of cells in many tissues, including the liver. The quantification of hepatic ploidy typically relies on flow cytometry and immunofluorescence (IF) imaging, which are not widely available in clinical settings due to high financial and time costs. To improve accessibility for clinical samples, we developed a computational algorithm to quantify hepatic ploidy using hematoxylin-eosin (H&E) histopathology images, which are commonly obtained during routine clinical practice. Our algorithm uses a deep learning model to first segment and classify different types of cell nuclei in H&E images. It then determines cellular ploidy based on the relative distance between identified hepatocyte nuclei and determines nuclear ploidy using a fitted Gaussian mixture model. The algorithm can establish the total number of hepatocytes and their detailed ploidy information in a region of interest (ROI) on H&E images. This is the first successful attempt to automate ploidy analysis on H&E images. Our algorithm is expected to serve as an important tool for studying the role of polyploidy in human liver disease.

A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization.

Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research. However, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedures and produces massive data that capture tumor histologic details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nucleus segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nucleus segmentation and TME quantification within image patches. However, existing algorithms are computationally intensive and time consuming for WSI analysis. This study presents Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nucleus segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing WSI analysis methods in nucleus detection, classification accuracy, and computation time. We validated the advantages of the system on 3 different tissue types: lung cancer, liver cancer, and breast cancer. For breast cancer, nucleus features by HD-Yolo were more prognostically significant than both the estrogen receptor status by immunohistochemistry and the progesterone receptor status by immunohistochemistry. The WSI analysis pipeline and a real-time nucleus segmentation viewer are available at https://github.com/impromptuRong/hd_wsi.

Spatial Transcriptomics Arena (STAr): an Integrated Platform for Spatial Transcriptomics Methodology Research.

The emerging field of spatially resolved transcriptomics (SRT) has revolutionized biomedical research. SRT quantifies expression levels at different spatial locations, providing a new and powerful tool to interrogate novel biological insights. An essential question in the analysis of SRT data is to identify spatially variable (SV) genes; the expression levels of such genes have spatial variation across different tissues. SV genes usually play an important role in underlying biological mechanisms and tissue heterogeneity. Currently, several computational methods have been developed to detect such genes; however, there is a lack of unbiased assessment of these approaches to guide researchers in selecting the appropriate methods for their specific biomedical applications. In addition, it is difficult for researchers to implement different existing methods for either biological study or methodology development. Furthermore, currently available public SRT datasets are scattered across different websites and preprocessed in different ways, posing additional obstacles for quantitative researchers developing computational methods for SRT data analysis. To address these challenges, we designed Spatial Transcriptomics Arena (STAr), an open platform comprising 193 curated datasets from seven technologies, seven statistical methods, and analysis results. This resource allows users to retrieve high-quality datasets, apply or develop spatial gene detection methods, as well as browse and compare spatial gene analysis results. It also enables researchers to comprehensively evaluate SRT methodology research in both simulated and real datasets. Altogether, STAr is an integrated research resource intended to promote reproducible research and accelerate rigorous methodology development, which can eventually lead to an improved understanding of biological processes and diseases. STAr can be accessed at https://lce.biohpc.swmed.edu/star/ .

Bayesian Hidden Mark Interaction Model for Detecting Spatially Variable Genes in Imaging-Based Spatially Resolved Transcriptomics Data.

Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies on ad hoc kernels, or the energy-based Ising model, which requires gene expression to be measured on a lattice grid. To overcome these potential limitations, we developed a generalized energy-based framework to model gene expression measured from imaging-based SRT platforms, accommodating the irregular spatial distribution of measured cells. Our Bayesian model applies a zero-inflated negative binomial mixture model to dichotomize the raw count data, reducing noise. Additionally, we incorporate a geostatistical mark interaction model with a generalized energy function, where the interaction parameter is used to identify the spatial pattern. Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. We demonstrated the strength of our method on both simulated and real data. Our simulation study showed that our method captured various spatial patterns with high accuracy; moreover, analysis of a seqFISH dataset and a STARmap dataset established that our proposed method is able to identify genes with novel and strong spatial patterns.

Assessing Coronary Artery and Aortic Calcification in Patients with Prostate Cancer Using 18F-Sodium Fluoride PET/Computed Tomography.

The aim of this study was to assess coronary artery and aortic calcification in healthy controls, angina pectoris patients, and prostate cancer patients using 18F-sodium fluoride PET/computed tomography (NaF-PET/CT). A retrospective analysis compared 33 prostate cancer patients with 33 healthy subjects and 33 patients with angina pectoris. Increased target-to-background ratio (TBR) of the coronary arteries, ascending aorta, aortic arch, and descending aorta was observed in cancer patients compared to healthy controls but not compared to angina pectoris patients. These results demonstrate the feasibility of assessing vascular microcalcification with NaF-PET/CT, with significant differences in uptake according to comorbidities.

KLF5 governs sphingolipid metabolism and barrier function of the skin.

Stem cells are fundamental units of tissue remodeling whose functions are dictated by lineage-specific transcription factors. Home to epidermal stem cells and their upward-stratifying progenies, skin relies on its secretory functions to form the outermost protective barrier, of which a transcriptional orchestrator has been elusive. KLF5 is a Krüppel-like transcription factor broadly involved in development and regeneration whose lineage specificity, if any, remains unclear. Here we report KLF5 specifically marks the epidermis, and its deletion leads to skin barrier dysfunction in vivo. Lipid envelopes and secretory lamellar bodies are defective in KLF5-deficient skin, accompanied by preferential loss of complex sphingolipids. KLF5 binds to and transcriptionally regulates genes encoding rate-limiting sphingolipid metabolism enzymes. Remarkably, skin barrier defects elicited by KLF5 ablation can be rescued by dietary interventions. Finally, we found that KLF5 is widely suppressed in human diseases with disrupted epidermal secretion, and its regulation of sphingolipid metabolism is conserved in human skin. Altogether, we established KLF5 as a disease-relevant transcription factor governing sphingolipid metabolism and barrier function in the skin, likely representing a long-sought secretory lineage-defining factor across tissue types.

Temporal cell fate determination in the spinal cord is mediated by the duration of Notch signalling.

During neural development, progenitor cells generate different types of neurons in specific time windows. Despite the characterisation of many of the transcription factor networks involved in these differentiation events, the mechanism behind their temporal regulation is poorly understood. To address this question, we studied the temporal differentiation of the simple lateral floor plate (LFP) domain in the zebrafish spinal cord. LFP progenitors generate both early-born Kolmer-Agduhr" (KA") interneuron and late-born V3 interneuron populations. Analysis using a Notch signalling reporter demonstrates that these cell populations have distinct Notch signalling profiles. Not only do V3 progenitors receive higher total levels of Notch response, but they collect this response over a longer duration compared to KA" progenitors. To test whether the duration of Notch signalling determines the temporal cell fate specification, we combined a transgene that constitutively activates Notch signalling in the ventral spinal cord with a heat shock inducible Notch signalling terminator to switch off Notch response at any given time. Sustained Notch signalling results in expanded LFP progenitors while KA" and V3 interneurons fail to specify. Early termination of Notch signalling leads to exclusively KA" cell fate, despite the high level of Notch signalling, whereas late attenuation of Notch signalling drives only V3 cell fate. This suggests that the duration of Notch signalling, not simply the level, mediates cell fate specification. Interestingly, knockdown experiments reveal a role for the Notch ligand Jag2b in maintaining LFP progenitors and limiting their differentiation into KA" and V3 interneurons. Our results indicate that Notch signalling is required for neural progenitor maintenance while a specific attenuation timetable defines the fate of the postmitotic progeny.

Efficacy of Acupuncture in the Treatment of Chronic Abdominal Pain.

CONTEXT: Abdominal pain is a widespread complaint and is one of the common reasons leading patients to seek medical care, either in emergency situations or with their primary providers. While acute abdominal pain is a better defined, usually surgical condition, chronic abdominal pain requires longer, typically lifelong, therapy. Chronic abdominal pain may also present with acute flares and complications. Here we review seminal and novel evidence discussing the use of acupuncture in the treatment of abdominal pain, indications, and conditions that may benefit from this approach. EVIDENCE ACQUISITION: Chronic abdominal pain is a common complaint causing significant morbidity and disability and has a hefty price tag attached. Recent studies show it may be prevalent in as much as 25% of the adult population. It is defined as three episodes of severe abdominal pain over the course of three months. Chronic abdominal pain could be the result of chronicity of acute pain or of chronic pain syndromes, most commonly IBD syndromes and IBS. While a plethora of treatments exists for both conditions, these treatments usually fall short of complete symptom control, and there is a need for complementary measures to curb disability and increase the quality of life in these patients. Acupuncture is a form of integrative medicine that has long been used in Chinese and traditional medicine, based on the rebalancing of the patient's Qi, or Ying/Yang balance. It has been shown to be effective in treating several other conditions, and novel evidence may expand its use into other fields as well. Clinical trials studying acupuncture in chronic pain conditions have been promising, and recent evidence supports the use of abdominal pain in chronic abdominal pain conditions as well. Though not curative, acupuncture is a complementary approach that helps reduce symptoms and improved quality of life. CONCLUSIONS: Chronic abdominal pain is a widespread condition, mostly affected by the IBS and IBD spectrum. Etiologies are still being studied for these conditions, and while novel treatment approaches are absolute game changers for these patients, many continue to experience some level of symptoms and disability. Acupuncture may provide further alleviation of these symptoms in select patients, thus improving quality of life, reducing disability, and saving healthcare dollars. It is a largely safe and inexpensive method that may significantly contribute to the quality of life of selected patients.

Air quality monitoring using mobile microscopy and machine learning.

Rapid, accurate and high-throughput sizing and quantification of particulate matter (PM) in air is crucial for monitoring and improving air quality. In fact, particles in air with a diameter of ≤2.5 µm have been classified as carcinogenic by the World Health Organization. Here we present a field-portable cost-effective platform for high-throughput quantification of particulate matter using computational lens-free microscopy and machine-learning. This platform, termed c-Air, is also integrated with a smartphone application for device control and display of results. This mobile device rapidly screens 6.5 L of air in 30 s and generates microscopic images of the aerosols in air. It provides statistics of the particle size and density distribution with a sizing accuracy of ~93%. We tested this mobile platform by measuring the air quality at different indoor and outdoor environments and measurement times, and compared our results to those of an Environmental Protection Agency-approved device based on beta-attenuation monitoring, which showed strong correlation to c-Air measurements. Furthermore, we used c-Air to map the air quality around Los Angeles International Airport (LAX) over 24 h to confirm that the impact of LAX on increased PM concentration was present even at >7 km away from the airport, especially along the direction of landing flights. With its machine-learning-based computational microscopy interface, c-Air can be adaptively tailored to detect specific particles in air, for example, various types of pollen and mold and provide a cost-effective mobile solution for highly accurate and distributed sensing of air quality.

A long noncoding RNA protects the heart from pathological hypertrophy.

The role of long noncoding RNA (lncRNA) in adult hearts is unknown; also unclear is how lncRNA modulates nucleosome remodelling. An estimated 70% of mouse genes undergo antisense transcription, including myosin heavy chain 7 (Myh7), which encodes molecular motor proteins for heart contraction. Here we identify a cluster of lncRNA transcripts from Myh7 loci and demonstrate a new lncRNA-chromatin mechanism for heart failure. In mice, these transcripts, which we named myosin heavy-chain-associated RNA transcripts (Myheart, or Mhrt), are cardiac-specific and abundant in adult hearts. Pathological stress activates the Brg1-Hdac-Parp chromatin repressor complex to inhibit Mhrt transcription in the heart. Such stress-induced Mhrt repression is essential for cardiomyopathy to develop: restoring Mhrt to the pre-stress level protects the heart from hypertrophy and failure. Mhrt antagonizes the function of Brg1, a chromatin-remodelling factor that is activated by stress to trigger aberrant gene expression and cardiac myopathy. Mhrt prevents Brg1 from recognizing its genomic DNA targets, thus inhibiting chromatin targeting and gene regulation by Brg1. It does so by binding to the helicase domain of Brg1, a domain that is crucial for tethering Brg1 to chromatinized DNA targets. Brg1 helicase has dual nucleic-acid-binding specificities: it is capable of binding lncRNA (Mhrt) and chromatinized--but not naked--DNA. This dual-binding feature of helicase enables a competitive inhibition mechanism by which Mhrt sequesters Brg1 from its genomic DNA targets to prevent chromatin remodelling. A Mhrt-Brg1 feedback circuit is thus crucial for heart function. Human MHRT also originates from MYH7 loci and is repressed in various types of myopathic hearts, suggesting a conserved lncRNA mechanism in human cardiomyopathy. Our studies identify a cardioprotective lncRNA, define a new targeting mechanism for ATP-dependent chromatin-remodelling factors, and establish a new paradigm for lncRNA-chromatin interaction.

Structure of the first representative of Pfam family PF09410 (DUF2006) reveals a structural signature of the calycin superfamily that suggests a role in lipid metabolism.

The first structural representative of the domain of unknown function DUF2006 family, also known as Pfam family PF09410, comprises a lipocalin-like fold with domain duplication. The finding of the calycin signature in the N-terminal domain, combined with remote sequence similarity to two other protein families (PF07143 and PF08622) implicated in isoprenoid metabolism and the oxidative stress response, support an involvement in lipid metabolism. Clusters of conserved residues that interact with ligand mimetics suggest that the binding and regulation sites map to the N-terminal domain and to the interdomain interface, respectively.

The structure of SSO2064, the first representative of Pfam family PF01796, reveals a novel two-domain zinc-ribbon OB-fold architecture with a potential acyl-CoA-binding role.

SSO2064 is the first structural representative of PF01796 (DUF35), a large prokaryotic family with a wide phylogenetic distribution. The structure reveals a novel two-domain architecture comprising an N-terminal, rubredoxin-like, zinc ribbon and a C-terminal, oligonucleotide/oligosaccharide-binding (OB) fold domain. Additional N-terminal helical segments may be involved in protein-protein interactions. Domain architectures, genomic context analysis and functional evidence from certain bacterial representatives of this family suggest that these proteins form a novel fatty-acid-binding component that is involved in the biosynthesis of lipids and polyketide antibiotics and that they possibly function as acyl-CoA-binding proteins. This structure has led to a re-evaluation of the DUF35 family, which has now been split into two entries in the latest Pfam release (v.24.0).