Deletion of the mRNA endonuclease Regnase-1 promotes NK cell anti-tumor activity via OCT2-dependent transcription of Ifng.

Limited infiltration and activity of natural killer (NK) and T cells within the tumor microenvironment (TME) correlate with poor immunotherapy responses. Here, we examined the role of the endonuclease Regnase-1 on NK cell anti-tumor activity. NK cell-specific deletion of Regnase-1 (Reg1ΔNK) augmented cytolytic activity and interferon-gamma (IFN-γ) production in vitro and increased intra-tumoral accumulation of Reg1ΔNK-NK cells in vivo, reducing tumor growth dependent on IFN-γ. Transcriptional changes in Reg1ΔNK-NK cells included elevated IFN-γ expression, cytolytic effectors, and the chemokine receptor CXCR6. IFN-γ induced expression of the CXCR6 ligand CXCL16 on myeloid cells, promoting further recruitment of Reg1ΔNK-NK cells. Mechanistically, Regnase-1 deletion increased its targets, the transcriptional regulators OCT2 and IκBζ, following interleukin (IL)-12 and IL-18 stimulation, and the resulting OCT2-IκBζ-NF-κB complex induced Ifng transcription. Silencing Regnase-1 in human NK cells increased the expression of IFNG and POU2F2. Our findings highlight NK cell dysfunction in the TME and propose that targeting Regnase-1 could augment active NK cell persistence for cancer immunotherapy.

Intercellular crosstalk in adult dental pulp is mediated by heparin-binding growth factors Pleiotrophin and Midkine.

BACKGROUND: In-depth knowledge of the cellular and molecular composition of dental pulp (DP) and the crosstalk between DP cells that drive tissue homeostasis are not well understood. To address these questions, we performed a comparative analysis of publicly available single-cell transcriptomes of healthy adult human DP to 5 other reference tissues: peripheral blood mononuclear cells, bone marrow, adipose tissue, lung, and skin. RESULTS: Our analysis revealed that DP resident cells have a unique gene expression profile when compared to the reference tissues, and that DP fibroblasts are the main cell type contributing to this expression profile. Genes coding for pleiotrophin (PTN) and midkine (MDK), homologous heparin-binding growth-factors, possessed the highest differential expression levels in DP fibroblasts. In addition, we identified extensive crosstalk between DP fibroblasts and several other DP resident cells, including Schwann cells, mesenchymal stem cells and odontoblasts, mediated by PTN and MDK. CONCLUSIONS: DP fibroblasts emerge as unappreciated players in DP homeostasis, mainly through their crosstalk with glial cells. These findings suggest that fibroblast-derived growth factors possess major regulatory functions and thus have a potential role as dental therapeutic targets.

Dual Mobility Cups as the Routine Choice in Total Hip Arthroplasty.

Background and Objectives: Total hip arthroplasty (THA) is considered the most successful surgical procedure in orthopedics. However, dislocation remains the main indication for surgical revision. New designs of dual mobility cups (DMC) have lowered the classical complications and have extended the indications of DMC in elective surgeries. Our aim is to assess the trend of DMC indications in THA as well as the incidence of their dislocation. Materials and Methods: We retrospectively reviewed all patients undergoing THA with DMC during the years 2015 and 2021. The original indication for DMC included patients sustaining neck of femur fractures (NOF#) and associated risk factors for dislocations. Five years later, DMC was considered our standard of care in total hip arthroplasty. The approach (anterolateral or posterolateral) was chosen by the surgeon according to his/her preferences, as was the implant. Data collected included patients' demographics, diagnosis, admission time, surgical approach, cup models, and inclination and complications. Patients sustaining a hip dislocation were prospectively reviewed and assessed for treatment received, new dislocations, and need for surgical revision. Two groups were created for the analysis according to the presence or absence of dislocation during follow-up. Results: In the analysis, 531 arthroplasties were included (mean age 72.2 years) with a mean follow-up of 2.86 years. The trend of indications for DMC increased from 16% of THA in 2015 to 78% of THA in 2021. We found a total of 8 dislocations (1.5%), none of them associated with elective surgery. Closed reduction was unsatisfactory in four cases (50%). There was one case of intraprosthetic dislocation. Dislocations were associated to smaller heads (22 mm) (1.5% vs. 25%, p = 0.008) and cups (51.2 mm vs. 48.7 mm, p = 0.038) and posterior approach (62.5% vs. 37.5%, p = 0.011). Conclusion: Dual mobility cups are a great option to reduce the risk of dislocation after a THA both in the neck of femur fractures and elective cases. The use of an anterolateral approach in THA after a neck or femur fracture might considerably decrease the risk of dislocation.

Analysis of early failure of Biotronik Linox Smart implantable cardioverter-defibrillator leads: A comparative study of three defibrillator leads.

BACKGROUND AND OBJECTIVES: Early failure of Biotronik Linox and Linox Smart leads (Biotronik, Berlin, Germany) has been reported in numerous recent publications. The aim of this study was to assess the performance of this lead compared with that of two other contemporary leads. METHODS: We conducted an ambispective study of all consecutive first implantations of defibrillator leads carried out in our center: Endotak (model 148, 158, Boston Scientific, Marlborough, MA, USA) (n = 173), Sprint Quattro (model 6644, 6947, Medtronic, Dublin, Ireland) (n = 145), and Linox Smart (Biotronik, model SD 65/16) (n = 120). RESULTS: During a median follow-up of 4.6 ± 2.1 years, failure occurred in nine Linox Smart (7.5%), one Endotak Reliance (0.6%), and no Sprint Quattro leads. The survival probability of the Linox Smart group was significantly lower than that of the Endotak and Sprint Quattro groups measured by the log-rank test (Linox vs Endotak; P < 0.001 and Linox vs Sprint Quattro; P < 0.001). Nonphysiological signals not due to external interference were observed in all Linox Smart leads, with normal parameters and without visible anomalies on chest x-ray. CONCLUSIONS: In this single-center experience, the survival rate of Linox Smart leads was 88% at 5 years of follow-up, which was significantly lower than that of the other leads. Comprehensive vigilance of Linox Smart leads, including home monitoring, may be advisable to facilitate early detection of lead failure and avoid inappropriate shocks.

Systematic identification of transcriptional regulatory modules from protein-protein interaction networks.

Transcription factors (TFs) combine with co-factors to form transcriptional regulatory modules (TRMs) that regulate gene expression programs with spatiotemporal specificity. Here we present a novel and generic method (rTRM) for the reconstruction of TRMs that integrates genomic information from TF binding, cell type-specific gene expression and protein-protein interactions. rTRM was applied to reconstruct the TRMs specific for embryonic stem cells (ESC) and hematopoietic stem cells (HSC), neural progenitor cells, trophoblast stem cells and distinct types of terminally differentiated CD4(+) T cells. The ESC and HSC TRM predictions were highly precise, yielding 77 and 96 proteins, of which ∼75% have been independently shown to be involved in the regulation of these cell types. Furthermore, rTRM successfully identified a large number of bridging proteins with known roles in ESCs and HSCs, which could not have been identified using genomic approaches alone, as they lack the ability to bind specific DNA sequences. This highlights the advantage of rTRM over other methods that ignore PPI information, as proteins need to interact with other proteins to form complexes and perform specific functions. The prediction and experimental validation of the co-factors that endow master regulatory TFs with the capacity to select specific genomic sites, modulate the local epigenetic profile and integrate multiple signals will provide important mechanistic insights not only into how such TFs operate, but also into abnormal transcriptional states leading to disease.

Network analysis in the investigation of chronic respiratory diseases. From basics to application.

Chronic respiratory diseases are complex multifactorial disorders whose pathogenesis depends on the interplay between host and environmental factors. To fully understand them and to identify novel treatments, a holistic approach that integrates multiple types and levels of clinical and biological data is necessary. Toward this end, the application of systems biology-based strategies, in particular, network analysis, offers great potential. These systems-based approaches rely heavily on computational methods that can be challenging for the nonspecialist. Accordingly, this Pulmonary Perspective: (1) outlines the basic concepts of networks in biology and the fundamentals of network analysis, and (2) discusses recent applications of network analysis to understand respiratory diseases. The intent of this Perspective is to provide readers with increased understanding of the strengths and weaknesses of network analysis methods as well as their usefulness in addressing research questions involving chronic respiratory diseases.

Distinct transcriptional regulatory modules underlie STAT3's cell type-independent and cell type-specific functions.

Transcription factors (TFs) regulate gene expression by binding to short DNA sequence motifs, yet their binding specificities alone cannot explain how certain TFs drive a diversity of biological processes. In order to investigate the factors that control the functions of the pleiotropic TF STAT3, we studied its genome-wide binding patterns in four different cell types: embryonic stem cells, CD4(+) T cells, macrophages and AtT-20 cells. We describe for the first time two distinct modes of STAT3 binding. First, a small cell type-independent mode represented by a set of 35 evolutionarily conserved STAT3-binding sites that collectively regulate STAT3's own functions and cell growth. We show that STAT3 is recruited to sites with E2F1 already pre-bound before STAT3 activation. Second, a series of different transcriptional regulatory modules (TRMs) assemble around STAT3 to drive distinct transcriptional programs in the four cell types. These modules recognize cell type-specific binding sites and are associated with factors particular to each cell type. Our study illustrates the versatility of STAT3 to regulate both universal- and cell type-specific functions by means of distinct TRMs, a mechanism that might be common to other pleiotropic TFs.

The repertoires of ubiquitinating and deubiquitinating enzymes in eukaryotic genomes.

Reversible protein ubiquitination regulates virtually all known cellular activities. Here, we present a quantitatively evaluated and broadly applicable method to predict eukaryotic ubiquitinating enzymes (UBE) and deubiquitinating enzymes (DUB) and its application to 50 distinct genomes belonging to four of the five major phylogenetic supergroups of eukaryotes: unikonts (including metazoans, fungi, choanozoa, and amoebozoa), excavates, chromalveolates, and plants. Our method relies on a collection of profile hidden Markov models, and we demonstrate its superior performance (coverage and classification accuracy >99%) by identifying approximately 25% and approximately 35% additional UBE and DUB genes in yeast and human, which had not been reported before. In yeast, we predict 85 UBE and 24 DUB genes, for 814 UBE and 107 DUB genes in the human genome. Most UBE and DUB families are present in all eukaryotic lineages, with plants and animals harboring massively enlarged repertoires of ubiquitin ligases. Unicellular organisms, on the other hand, typically harbor less than 300 UBEs and less than 40 DUBs per genome. Ninety-one UBE/DUB genes are orthologous across all four eukaryotic supergroups, and these likely represent a primordial core of enzymes of the ubiquitination system probably dating back to the first eukaryotes approximately 2 billion years ago. Our genome-wide predictions are available through the Database of Ubiquitinating and Deubiquitinating Enzymes (www.DUDE-db.org), where users can also perform advanced sequence and phylogenetic analyses and submit their own predictions.

Semaphorin 6D tunes amygdalar circuits for emotional, metabolic, and inflammatory outputs.

Regulated neural-metabolic-inflammatory responses are essential for maintaining physiological homeostasis. However, the molecular machinery that coordinates neural, metabolic, and inflammatory responses is largely unknown. Here, we show that semaphorin 6D (SEMA6D) coordinates anxiogenic, metabolic, and inflammatory outputs from the amygdala by maintaining synaptic homeostasis. Using genome-wide approaches, we identify SEMA6D as a pleiotropic gene for both psychiatric and metabolic traits in human. Sema6d deficiency increases anxiety in mice. When fed a high-fat diet, Sema6d-/- mice display attenuated obesity and enhanced myelopoiesis compared with control mice due to higher sympathetic activity via the ß3-adrenergic receptor. Genetic manipulation and spatial and single-nucleus transcriptomics reveal that SEMA6D in amygdalar interneurons is responsible for regulating anxiogenic and autonomic responses. Mechanistically, SEMA6D is required for synaptic maturation and γ-aminobutyric acid transmission. These results demonstrate that SEMA6D is important for the normal functioning of the neural circuits in the amygdala, coupling emotional, metabolic, and inflammatory responses.

Evolutionary expansion of the Ras switch regulatory module in eukaryotes.

Ras proteins control many aspects of eukaryotic cell homeostasis by switching between active (GTP-bound) and inactive (GDP-bound) conformations, a reaction catalyzed by GTPase exchange factors (GEF) and GTPase activating proteins (GAP) regulators, respectively. Here, we show that the complexity, measured as number of genes, of the canonical Ras switch genetic system (including Ras, RasGEF, RasGAP and RapGAP families) from 24 eukaryotic organisms is correlated with their genome size and is inversely correlated to their evolutionary distances from humans. Moreover, different gene subfamilies within the Ras switch have contributed unevenly to the module's expansion and speciation processes during eukaryote evolution. The Ras system remarkably reduced its genetic expansion after the split of the Euteleostomi clade and presently looks practically crystallized in mammals. Supporting evidence points to gene duplication as the predominant mechanism generating functional diversity in the Ras system, stressing the leading role of gene duplication in the Ras family expansion. Domain fusion and alternative splicing are significant sources of functional diversity in the GAP and GEF families but their contribution is limited in the Ras family. An evolutionary model of the Ras system expansion is proposed suggesting an inherent 'decision making' topology with the GEF input signal integrated by a homologous molecular mechanism and bifurcation in GAP signaling propagation.

A universal tool for predicting differentially active features in single-cell and spatial genomics data.

With the growing complexity of single-cell and spatial genomics data, there is an increasing importance of unbiased and efficient exploratory data analysis tools. One common exploratory data analysis step is the prediction of genes with different levels of activity in a subset of cells or locations inside a tissue. We previously developed singleCellHaystack, a method for predicting differentially expressed genes from single-cell transcriptome data, without relying on comparisons between clusters of cells. Here we present an update to singleCellHaystack, which is now a universally applicable method for predicting differentially active features: (1) singleCellHaystack now accepts continuous features that can be RNA or protein expression, chromatin accessibility or module scores from single-cell, spatial and even bulk genomics data, and (2) it can handle 1D trajectories, 2-3D spatial coordinates, as well as higher-dimensional latent spaces as input coordinates. Performance has been drastically improved, with up to ten times reduction in computational time and scalability to millions of cells, making singleCellHaystack a suitable tool for exploratory analysis of atlas level datasets. singleCellHaystack is available as packages in both R and Python.

Use of a structural deformity index as a predictor of severity among trauma victims in motor vehicle crashes.

BACKGROUND: Information obtained from vehicle crash scenes, called kinematics, may prove useful in the management of victims and may complement anatomical and physiological findings. OBJECTIVES: In addition to analyzing the significance of age, gender, position occupied in the vehicle, the use of restraint systems, and ejection from the vehicle, the objective was to carry out a preliminary study of what we have defined as the Structural Deformity Index (SDI) to verify its usefulness in predicting injury severity at the scene of a motor vehicle crash. The index consists of various parameters that can be easily identified at the crash scene. METHOD: An historical cohort of vehicle occupants involved in crashes in the Navarra province of Spain from January 1, 2001 to December 31, 2002 was studied. Information was collected from the database of the Navarra Severe Trauma Victim group study. Bivariate statistical analysis and multivariate logistic regression models were employed for statistical management. RESULTS: There were 212 vehicle occupants identified. Significant differences in severity of injury, and of mortality, were observed based on age, ejection from the vehicle, and a high SDI. Logistic regression showed significant differences in injury severity by age (odds ratio [OR] 6.55, 95% confidence interval [CI] 1.6-26.7) and high SDI (OR 1.84, 95% CI 1-3.3), as well as differences in the patient death rate by age (OR 6.92, 95% CI 1.2-38.9) and high SDI (OR 3.28, 95% CI 1.5-6.8). CONCLUSIONS: The SDI is useful to the first responders, enabling them to alert and transmit objective, reliable information to the emergency coordination center, thus efficiently activating health care resources. In addition, use of the SDI may assist prehospital and hospital health care providers to suspect the presence of particular serious injuries when anatomical and physiological criteria are not definitive.

Thyroid hormone regulators in human cerebral cortex development.

Brain development is critically dependent on the timely supply of thyroid hormones. The thyroid hormone transporters are central to the action of thyroid hormones in the brain, facilitating their passage through the blood-brain barrier. Mutations of the monocarboxylate transporter 8 (MCT8) cause the Allan-Herndon-Dudley syndrome, with altered thyroid hormone concentrations in the blood and profound neurological impairment and intellectual deficit. Mouse disease models have revealed interplay between transport, deiodination, and availability of T3 to receptors in specific cells. However, the mouse models are not satisfactory, given the fundamental differences between the mouse and human brains. The goal of the present work is to review human neocortex development in the context of thyroid pathophysiology. Recent developments in single-cell transcriptomic approaches aimed at the human brain make it possible to profile the expression of thyroid hormone regulators in single-cell RNA-Seq datasets of the developing human neocortex. The data provide novel insights into the specific cellular expression of thyroid hormone transporters, deiodinases, and receptors.

Unbiased integration of single cell transcriptome replicates.

Single cell transcriptomic approaches are becoming mainstream, with replicate experiments commonly performed with the same single cell technology. Methods that enable integration of these datasets by removing batch effects while preserving biological information are required for unbiased data interpretation. Here, we introduce Canek for this purpose. Canek leverages information from mutual nearest neighbor to combine local linear corrections with cell-specific non-linear corrections within a fuzzy logic framework. Using a combination of real and synthetic datasets, we show that Canek corrects batch effects while introducing the least amount of bias compared with competing methods. Canek is computationally efficient and can easily integrate thousands of single-cell transcriptomes from replicated experiments.

Single-Cell Transcriptome Profiling of Thyroid Hormone Effectors in the Human Fetal Neocortex: Expression of SLCO1C1, DIO2, and THRB in Specific Cell Types.

Background: Thyroid hormones are crucial for brain development, acting through the thyroid hormone nuclear receptors (TR)α1 and ß to control gene expression. Triiodothyronine (T3), the receptor-ligand, is transported into the brain from the blood by the monocarboxylate transporter 8 (MCT8). Another source of brain T3 is from the local deiodination of thyroxine (T4) by type 2 deiodinase (DIO2). While these mechanisms are very similar in mice and humans, important species-specific differences confound our understanding of disease using mouse models. To fill this knowledge gap on thyroid hormone action in the human fetal brain, we analyzed the expression of transporters, DIO2, and TRs, which we call thyroid hormone effectors, at single-cell resolution. Methods: We analyzed publicly available single-cell transcriptome data sets of isolated cerebral cortex neural cells from three different studies, with expression data from 393 to almost 40,000 cells. We generated Uniform Manifold Approximation and Projection scatterplots and cell clusters to identify differentially expressed genes between clusters, and correlated their gene signatures with the expression of thyroid effectors. Results: The radial glia, mainly the outer radial glia, and astrocytes coexpress SLCO1C1 and DIO2, indicating close cooperation between the T4 transporter OATP1C1 and DIO2 in local T3 formation. Strikingly, THRB was mainly present in two classes of interneurons: a majority expressing CALB2/calretinin, from the caudal ganglionic eminence, and in somatostatin-expressing interneurons from the medial ganglionic eminence. By contrast, many cell types express SLC16A2 and THRA. Conclusions:SLCO1C1 and DIO2 coexpression in the outer radial glia, the universal stem cell of the cerebral cortex, highlights the likely importance of brain-generated T3 in neurogenesis. The unique expression of THRB in discrete subsets of interneurons is a novel finding whose pathophysiological meaning deserves further investigation.

GeneRegionScan: a Bioconductor package for probe-level analysis of specific, small regions of the genome.

SUMMARY: Whole-genome microarrays allow us to interrogate the entire transcriptome of a cell. Affymetrix microarrays are constructed using several probes that match to different regions of a gene and a summarization step reduces this complexity into a single value, representing the expression level of the gene or the expression level of an exon in the case of exon arrays. However, this simplification eliminates information that might be useful when focusing on specific genes of interest. To address these limitations, we present a software package for the R platform that allows detailed analysis of expression at the probe level. The package matches the probe sequences against a target gene sequence (either mRNA or DNA) and shows the expression levels of each probe along the gene. It also features functions to fit a linear regression based on several genetic models that enables study of the relationship between gene expression and genotype. AVAILABILITY AND IMPLEMENTATION: The software is implemented as a platform-independent R package available through the Bioconductor repository at http://www.bioconductor.org/. It is licensed as GPL 2.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data.

A common analysis of single-cell sequencing data includes clustering of cells and identifying differentially expressed genes (DEGs). How cell clusters are defined has important consequences for downstream analyses and the interpretation of results, but is often not straightforward. To address this difficulty, we present singleCellHaystack, a method that enables the prediction of DEGs without relying on explicit clustering of cells. Our method uses Kullback-Leibler divergence to find genes that are expressed in subsets of cells that are non-randomly positioned in a multidimensional space. Comparisons with existing DEG prediction approaches on artificial datasets show that singleCellHaystack has higher accuracy. We illustrate the usage of singleCellHaystack through applications on 136 real transcriptome datasets and a spatial transcriptomics dataset. We demonstrate that our method is a fast and accurate approach for DEG prediction in single-cell data. singleCellHaystack is implemented as an R package and is available from CRAN and GitHub.

Methods for sequence and structural analysis of B and T cell receptor repertoires.

B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context.