Sublethal necroptosis signaling promotes inflammation and liver cancer.

It is currently not well known how necroptosis and necroptosis responses manifest in vivo. Here, we uncovered a molecular switch facilitating reprogramming between two alternative modes of necroptosis signaling in hepatocytes, fundamentally affecting immune responses and hepatocarcinogenesis. Concomitant necrosome and NF-κB activation in hepatocytes, which physiologically express low concentrations of receptor-interacting kinase 3 (RIPK3), did not lead to immediate cell death but forced them into a prolonged "sublethal" state with leaky membranes, functioning as secretory cells that released specific chemokines including CCL20 and MCP-1. This triggered hepatic cell proliferation as well as activation of procarcinogenic monocyte-derived macrophage cell clusters, contributing to hepatocarcinogenesis. In contrast, necrosome activation in hepatocytes with inactive NF-κB-signaling caused an accelerated execution of necroptosis, limiting alarmin release, and thereby preventing inflammation and hepatocarcinogenesis. Consistently, intratumoral NF-κB-necroptosis signatures were associated with poor prognosis in human hepatocarcinogenesis. Therefore, pharmacological reprogramming between these distinct forms of necroptosis may represent a promising strategy against hepatocellular carcinoma.

Comprehensive Molecular Portraits of Invasive Lobular Breast Cancer.

Invasive lobular carcinoma (ILC) is the second most prevalent histologic subtype of invasive breast cancer. Here, we comprehensively profiled 817 breast tumors, including 127 ILC, 490 ductal (IDC), and 88 mixed IDC/ILC. Besides E-cadherin loss, the best known ILC genetic hallmark, we identified mutations targeting PTEN, TBX3, and FOXA1 as ILC enriched features. PTEN loss associated with increased AKT phosphorylation, which was highest in ILC among all breast cancer subtypes. Spatially clustered FOXA1 mutations correlated with increased FOXA1 expression and activity. Conversely, GATA3 mutations and high expression characterized luminal A IDC, suggesting differential modulation of ER activity in ILC and IDC. Proliferation and immune-related signatures determined three ILC transcriptional subtypes associated with survival differences. Mixed IDC/ILC cases were molecularly classified as ILC-like and IDC-like revealing no true hybrid features. This multidimensional molecular atlas sheds new light on the genetic bases of ILC and provides potential clinical options.

Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy.

Heart failure encompasses a heterogeneous set of clinical features that converge on impaired cardiac contractile function1,2 and presents a growing public health concern. Previous work has highlighted changes in both transcription and protein expression in failing hearts3,4, but may overlook molecular changes in less prevalent cell types. Here we identify extensive molecular alterations in failing hearts at single-cell resolution by performing single-nucleus RNA sequencing of nearly 600,000 nuclei in left ventricle samples from 11 hearts with dilated cardiomyopathy and 15 hearts with hypertrophic cardiomyopathy as well as 16 non-failing hearts. The transcriptional profiles of dilated or hypertrophic cardiomyopathy hearts broadly converged at the tissue and cell-type level. Further, a subset of hearts from patients with cardiomyopathy harbour a unique population of activated fibroblasts that is almost entirely absent from non-failing samples. We performed a CRISPR-knockout screen in primary human cardiac fibroblasts to evaluate this fibrotic cell state transition; knockout of genes associated with fibroblast transition resulted in a reduction of myofibroblast cell-state transition upon TGFß1 stimulation for a subset of genes. Our results provide insights into the transcriptional diversity of the human heart in health and disease as well as new potential therapeutic targets and biomarkers for heart failure.

Spatial multi-omic map of human myocardial infarction.

Myocardial infarction is a leading cause of death worldwide1. Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality2. Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.

Matrix Metalloproteinase-7 in Urinary Extracellular Vesicles Identifies Rapid Disease Progression in Autosomal Dominant Polycystic Kidney Disease.

SIGNIFICANCE STATEMENT: There is an unmet need for biomarkers of disease progression in autosomal dominant polycystic kidney disease (ADPKD). This study investigated urinary extracellular vesicles (uEVs) as a source of such biomarkers. Proteomic analysis of uEVs identified matrix metalloproteinase 7 (MMP-7) as a biomarker predictive of rapid disease progression. In validation studies, MMP-7 was predictive in uEVs but not in whole urine, possibly because uEVs are primarily secreted by tubular epithelial cells. Indeed, single-nucleus RNA sequencing showed that MMP-7 was especially increased in proximal tubule and thick ascending limb cells, which were further characterized by a profibrotic phenotype. Together, these data suggest that MMP-7 is a biologically plausible and promising uEV biomarker for rapid disease progression in ADPKD. BACKGROUND: In ADPKD, there is an unmet need for early markers of rapid disease progression to facilitate counseling and selection for kidney-protective therapy. Our aim was to identify markers for rapid disease progression in uEVs. METHODS: Six paired case-control groups ( n =10-59/group) of cases with rapid disease progression and controls with stable disease were formed from two independent ADPKD cohorts, with matching by age, sex, total kidney volume, and genetic variant. Candidate uEV biomarkers were identified by mass spectrometry and further analyzed using immunoblotting and an ELISA. Single-nucleus RNA sequencing of healthy and ADPKD tissue was used to identify the cellular origin of the uEV biomarker. RESULTS: In the discovery proteomics experiments, the protein abundance of MMP-7 was significantly higher in uEVs of patients with rapid disease progression compared with stable disease. In the validation groups, a significant >2-fold increase in uEV-MMP-7 in patients with rapid disease progression was confirmed using immunoblotting. By contrast, no significant difference in MMP-7 was found in whole urine using ELISA. Compared with healthy kidney tissue, ADPKD tissue had significantly higher MMP-7 expression in proximal tubule and thick ascending limb cells with a profibrotic phenotype. CONCLUSIONS: Among patients with ADPKD, rapid disease progressors have higher uEV-associated MMP-7. Our findings also suggest that MMP-7 is a biologically plausible biomarker for more rapid disease progression.

Persistent transcriptional changes in cardiac adaptive immune cells following myocardial infarction: New evidence from the re-analysis of publicly available single cell and nuclei RNA-sequencing data sets.

INTRODUCTION: Chronic immunopathology contributes to the development of heart failure after a myocardial infarction. Both T and B cells of the adaptive immune system are present in the myocardium and have been suggested to be involved in post-MI immunopathology. METHODS: We analyzed the B and T cell populations isolated from previously published single cell RNA-sequencing data sets (PMID: 32130914, PMID: 35948637, PMID: 32971526 and PMID: 35926050), of the mouse and human heart, using differential expression analysis, functional enrichment analysis, gene regulatory inferences, and integration with autoimmune and cardiovascular GWAS. RESULTS: Already at baseline, mature effector B and T cells are present in the human and mouse heart, having increased activity in transcription factors maintaining tolerance (e.g. DEAF1, JDP2, SPI-B). Following MI, T cells upregulate pro-inflammatory transcript levels (e.g. Cd11, Gzmk, Prf1), while B cells upregulate activation markers (e.g. Il6, Il1rn, Ccl6) and collagen (e.g. Col5a2, Col4a1, Col1a2). Importantly, pro-inflammatory and fibrotic transcription factors (e.g. NFKB1, CREM, REL) remain active in T cells, while B cells maintain elevated activity in transcription factors related to immunoglobulin production (e.g. ERG, REL) in both mouse and human post-MI hearts. Notably, genes differentially expressed in post-MI T and B cells are associated with cardiovascular and autoimmune disease. CONCLUSION: These findings highlight the varied and time-dependent dynamic roles of post-MI T and B cells. They appear ready-to-go and are activated immediately after MI, thus participate in the acute wound healing response. However, they subsequently remain in a state of pro-inflammatory activation contributing to persistent immunopathology.

The role of desmoglein-2 in kidney disease.

Desmosomes are multi-protein cell-cell adhesion structures supporting cell stability and mechanical stress resilience of tissues, best described in skin and heart. The kidney is exposed to various mechanical stimuli and stress, yet little is known about kidney desmosomes. In healthy kidneys, we found desmosomal proteins located at the apical-junctional complex in tubular epithelial cells. In four different animal models and patient biopsies with various kidney diseases, desmosomal components were significantly upregulated and partly miss-localized outside of the apical-junctional complexes along the whole lateral tubular epithelial cell membrane. The most upregulated component was desmoglein-2 (Dsg2). Mice with constitutive tubular epithelial cell-specific deletion of Dsg2 developed normally, and other desmosomal components were not altered in these mice. When challenged with different types of tubular epithelial cell injury (unilateral ureteral obstruction, ischemia-reperfusion, and 2,8-dihydroxyadenine crystal nephropathy), we found increased tubular epithelial cell apoptosis, proliferation, tubular atrophy, and inflammation compared to wild-type mice in all models and time points. In vitro, silencing DSG2 via siRNA weakened cell-cell adhesion in HK-2 cells and increased cell death. Thus, our data show a prominent upregulation of desmosomal components in tubular cells across species and diseases and suggest a protective role of Dsg2 against various injurious stimuli.

MACA: marker-based automatic cell-type annotation for single-cell expression data.

SUMMARY: Accurately identifying cell types is a critical step in single-cell sequencing analyses. Here, we present marker-based automatic cell-type annotation (MACA), a new tool for annotating single-cell transcriptomics datasets. We developed MACA by testing four cell-type scoring methods with two public cell-marker databases as reference in six single-cell studies. MACA compares favorably to four existing marker-based cell-type annotation methods in terms of accuracy and speed. We show that MACA can annotate a large single-nuclei RNA-seq study in minutes on human hearts with ∼290K cells. MACA scales easily to large datasets and can broadly help experts to annotate cell types in single-cell transcriptomics datasets, and we envision MACA provides a new opportunity for integration and standardization of cell-type annotation across multiple datasets. AVAILABILITY AND IMPLEMENTATION: MACA is written in python and released under GNU General Public License v3.0. The source code is available at https://github.com/ImXman/MACA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dosimetric properties of thermoluminescent NaCl pellets from Khewra Salt Mines, Pakistan.

Thermoluminescence (TL) and extended dosimetric characteristics of naturally occurring sodium chloride (NaCl) salt were studied. Pellets were prepared from mined crystalline salt obtained from Khewra Salt Mines, Pakistan and irradiated from 1 mGy to 10,000 mGy using cobalt-60 gamma source. The TL response showed two dominant peaks around 125°C and 230°C, respectively, at low doses, with an additional peak in between at doses beyond 300 mGy. A linear and supra-linear TL response was observed between 1 mGy-100 mGy and 100 mGy-10 Gy dose ranges, respectively. During the first 24 h post-irradiation, the TL intensity dropped by 20%. A maximum angular dependence of up to 50% was observed between 0° to 360°. For photon energies between 33 keV and 1.25 MeV significant energy dependence was observed for photons < 100 keV only. Sample sensitivity increased with dose a qualitatively similar behaviour to TLD-200. Effective atomic number (Zeff ) of the sample (14.6) was comparable to TLD-200 (16.3). No significant dose rate effects (deviation for a cobalt-60 source within 3.5%) on the TL sensitivity of the sample were found. The lowest detectable dose limit (LDDL) for salt sample was found to be 0.8 mGy whereas the sample reproducibility test showed a maximum of ±11% deviation from the first value.

Characteristics, reasons and patterns of Road Traffic Injuries presenting in emergency department of a tertiary care public hospital in Karachi.

Objectives: To determine the main characteristics, reasons and patterns of road traffic injuries (RTIs) in a tertiary care public hospital of Karachi. Methods: It was a hospital based cross sectional study conducted in a public tertiary care hospital emergency department with a sample size 425 selected conveniently. Participants included the consenting victims or caretaking attendants of road traffic injuries (RTIs) from 25th May to 28th June in 2019. A structured questionnaire was developed after literature review and was translated into "Urdu" language. The questionnaire collected detailed information on socio-demographic characteristics, possible reasons of RTI's such as condition of vehicle, over speeding and breaking traffic rules. Data was analyzed by using software SPSS version 20. Results: Almost half of RTI victims (43.1%) belonged to the age group 18-29. Majority of the victims were males (86.6%). The most common vehicle involved was motorbike (87.50%) followed by Rickshaw (6.8%) and Car (2.4%). Majority of RTIs occurred on main road (75.30%). The most common sites of injuries were lower limb (64%), upper limb (37.60%) and head (32.2%). The severe injuries were significantly more likely to happen in events in which direct collision with other vehicle/thing was involved, road conditions were wet and pedestrian were crossing the road. Conclusion: Motorbikes were involved in majority of RTIs. Main reasons of RTIs included irresponsible road behaviors including over-speeding, careless road crossing, breaking the signal and riding on wet roads which lead to moderate to severe injuries in almost two thirds of participants.

Mechanistic insights into cancer cell killing through interaction of phosphodiesterase 3A and schlafen family member 12.

Cytotoxic molecules can kill cancer cells by disrupting critical cellular processes or by inducing novel activities. 6-(4-(Diethylamino)-3-nitrophenyl)-5-methyl-4,5-dihydropyridazin-3(2H)-one (DNMDP) is a small molecule that kills cancer cells by generation of novel activity. DNMDP induces complex formation between phosphodiesterase 3A (PDE3A) and schlafen family member 12 (SLFN12) and specifically kills cancer cells expressing elevated levels of these two proteins. Here, we examined the characteristics and covariates of the cancer cell response to DNMDP. On average, the sensitivity of human cancer cell lines to DNMDP is correlated with PDE3A expression levels. However, DNMDP could also bind the related protein, PDE3B, and PDE3B supported DNMDP sensitivity in the absence of PDE3A expression. Although inhibition of PDE3A catalytic activity did not account for DNMDP sensitivity, we found that expression of the catalytic domain of PDE3A in cancer cells lacking PDE3A is sufficient to confer sensitivity to DNMDP, and substitutions in the PDE3A active site abolish compound binding. Moreover, a genome-wide CRISPR screen identified the aryl hydrocarbon receptor-interacting protein (AIP), a co-chaperone protein, as required for response to DNMDP. We determined that AIP is also required for PDE3A-SLFN12 complex formation. Our results provide mechanistic insights into how DNMDP induces PDE3A-SLFN12 complex formation, thereby killing cancer cells with high levels of PDE3A and SLFN12 expression.

Clinical characteristic and epidemiological features of SARS CoV -2 disease patients from a COVID 19 designated Hospital in New Delhi.

SARS CoV -2 infection is rapidly evolving as a serious global pandemic. The present study describes the clinical characteristics of SARS CoV-2 infection patients. The Samples were subjected to RT - PCR or Rapid Antigen test for diagnosis of SARS CoV- 2. A cohort of 3745 patients with confirmed diagnosis of SARS CoV -2 infection in a tertiary care center in New Delhi, India were included in this study. Data was collected from offline and online medical records over a period of six months. Amongst 3745 SARS CoV -2 infected patients, 2245 (60%) were symptomatic and 1500 (40%) were asymptomatic. Most common presenting symptom was cough (49.3%) followed febrile episodes (47.1%), breathlessness (42.7%) and sore throat (35.1%). Cough along with breathlessness (24.1) was the most common combination of symptoms followed by fever with cough (22.7). The most common comorbidity found among symptomatic group was diabetes (42.5%) followed by hypertension (21.4%) and chronic kidney disease (18%). Comorbidities like diabetes mellitus, chronic diseases of lungs, heart and kidneys were found to be common in symptomatic group and this was found to be statistically significant (p<0.05). COVID-19 is an evolving disease and data from our study help in understanding the clinic-epidemiological profile of patients. This article is protected by copyright. All rights reserved.

Thermoluminescence study of pellets prepared using NaCl from Khewra Salt Mines in Pakistan.

In this study, the thermoluminescence characteristics of naturally occurring salt (NaCl) were assessed for the development of a radiation dosimeter. For this purpose, mined crystalline samples of salt were procured directly from Khewra salt mines in Pakistan. The samples were hand crushed, sieved, and compressed to pellets comparable in size to standard TLD chips, and irradiated to gamma radiation doses in the range of 5 mGy and 5000 mGy. Thermoluminescence (TL) response showed three main peaks in the glow curve around 115-130 °C, 150-170 °C, and 220-240 °C. A linear TL response was observed for the dose range of 5-100 mGy. The TL response became supra-linear for the dose ranges of 100-1000 mGy and 1000-5000 mGy. The Tm-Tstop method was applied to identify the overlapping peaks of the glow curve. Computerized glow curve deconvolution (CGCD) was then employed for the characterization of electron trap parameters such as frequency factor (s), activation energy (E), and the kinetic order (b), using General Order (GO) kinetics. The figure-of-merit (FOM) was found to be 1.08%, 0.94%, 0.77%, and 0.75%, at 500 mGy, 1 Gy, 2 Gy, and 5 Gy, respectively. The TL intensity faded by 20% within the first 24 h after irradiation and finally stabilized after two weeks. In addition, structural, morphological, and elemental analyses, were also performed using various analytical techniques. X-ray diffraction (XRD) showed that the salt crystallizes in a face-centered cubic structure. Scanning electron microscope (SEM) micrographs indicated that the crystallites are closely packed and cubic-shaped with non-uniform size, and mostly found in the agglomerated form. Similarly, the elemental analysis confirmed the presence of impurities such as Mg, Sr, S, K, O, and Ca, in the samples. The present study concludes that the pellets made from salt samples from Khewra mines have a potential for use as radiation dosimeters.

All-atom 3D structure prediction of transmembrane ß-barrel proteins from sequences.

Transmembrane ß-barrels (TMBs) carry out major functions in substrate transport and protein biogenesis but experimental determination of their 3D structure is challenging. Encouraged by successful de novo 3D structure prediction of globular and α-helical membrane proteins from sequence alignments alone, we developed an approach to predict the 3D structure of TMBs. The approach combines the maximum-entropy evolutionary coupling method for predicting residue contacts (EVfold) with a machine-learning approach (boctopus2) for predicting ß-strands in the barrel. In a blinded test for 19 TMB proteins of known structure that have a sufficient number of diverse homologous sequences available, this combined method (EVfold_bb) predicts hydrogen-bonded residue pairs between adjacent ß-strands at an accuracy of ∼70%. This accuracy is sufficient for the generation of all-atom 3D models. In the transmembrane barrel region, the average 3D structure accuracy [template-modeling (TM) score] of top-ranked models is 0.54 (ranging from 0.36 to 0.85), with a higher (44%) number of residue pairs in correct strand-strand registration than in earlier methods (18%). Although the nonbarrel regions are predicted less accurately overall, the evolutionary couplings identify some highly constrained loop residues and, for FecA protein, the barrel including the structure of a plug domain can be accurately modeled (TM score = 0.68). Lower prediction accuracy tends to be associated with insufficient sequence information and we therefore expect increasing numbers of ß-barrel families to become accessible to accurate 3D structure prediction as the number of available sequences increases.

Inclusion of dyad-repeat pattern improves topology prediction of transmembrane ß-barrel proteins.

UNLABELLED: : Accurate topology prediction of transmembrane ß-barrels is still an open question. Here, we present BOCTOPUS2, an improved topology prediction method for transmembrane ß-barrels that can also identify the barrel domain, predict the topology and identify the orientation of residues in transmembrane ß-strands. The major novelty of BOCTOPUS2 is the use of the dyad-repeat pattern of lipid and pore facing residues observed in transmembrane ß-barrels. In a cross-validation test on a benchmark set of 42 proteins, BOCTOPUS2 predicts the correct topology in 69% of the proteins, an improvement of more than 10% over the best earlier method (BOCTOPUS) and in addition, it produces significantly fewer erroneous predictions on non-transmembrane ß-barrel proteins. AVAILABILITY AND IMPLEMENTATION: BOCTOPUS2 webserver along with full dataset and source code is available at http://boctopus.bioinfo.se/ CONTACT: : arne@bioinfo.se SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PconsFold: improved contact predictions improve protein models.

MOTIVATION: Recently it has been shown that the quality of protein contact prediction from evolutionary information can be improved significantly if direct and indirect information is separated. Given sufficiently large protein families, the contact predictions contain sufficient information to predict the structure of many protein families. However, since the first studies contact prediction methods have improved. Here, we ask how much the final models are improved if improved contact predictions are used. RESULTS: In a small benchmark of 15 proteins, we show that the TM-scores of top-ranked models are improved by on average 33% using PconsFold compared with the original version of EVfold. In a larger benchmark, we find that the quality is improved with 15-30% when using PconsC in comparison with earlier contact prediction methods. Further, using Rosetta instead of CNS does not significantly improve global model accuracy, but the chemistry of models generated with Rosetta is improved. AVAILABILITY: PconsFold is a fully automated pipeline for ab initio protein structure prediction based on evolutionary information. PconsFold is based on PconsC contact prediction and uses the Rosetta folding protocol. Due to its modularity, the contact prediction tool can be easily exchanged. The source code of PconsFold is available on GitHub at https://www.github.com/ElofssonLab/pcons-fold under the MIT license. PconsC is available from http://c.pcons.net/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Harnessing Artificial Intelligence for Enhanced Renal Analysis: Automated Detection of Hydronephrosis and Precise Kidney Segmentation.

Background and objective: Hydronephrosis is essential in the diagnosis of renal colic. We automated the detection of hydronephrosis from ultrasound images to standardize the therapy and reduce the misdiagnosis of renal colic. Methods: Anonymously collected ultrasound images of human kidneys, both normal and hydronephrotic, were preprocessed for neural networks. Six "state of the art" models were trained and cross-validated for the detection of hydronephrosis, and two convolutional networks were used for kidney segmentation. In the testing phase, performance metrics included true positives, true negatives, false positives, false negatives, accuracy, and F1 score, while the evaluation of the segmentation task involved accuracy, precision, dice, jaccard, recall, and ASSD. Key findings and limitations: A total of 523 sonographic kidney images (423 nonhydronephrotic and 100 hydronephrotic) were collected from three different ultrasound devices. After training on this dataset, all models were used to evaluate 200 new ultrasound kidney images (142 nonhydronephrotic and 58 hydronephrotic kidneys). The highest validation accuracy (98.5%) was achieved by the AlexNet model (GoogLeNet 97%, AlexNet_v2 96%, ResNet50 96%, ResNet101 97.5%, and ResNet152 95%). The deeplabv3_resnet50 and deeplabv3_resnet101 reached a dice coefficient of 94.74% and 94.48%, respectively, on the task of automated kidney segmentation. The study is limited by analyzing only hydronephrosis, but this specific focus enabled high detection accuracy. Conclusions and clinical implications: We show that our automated ultrasound deep learning model can be trained and used to interpret and segmentate ultrasound images from different sources with high accuracy. This method will serve as an automated tool in the diagnostic algorithm of acute renal failure in the future. Patient summary: Hydronephrosis is crucial in the diagnosis of renal colic. Recent advances in artificial intelligence allow automated detection of hydronephrosis in ultrasound images with high accuracy. These methods will help standardize the diagnosis and treatment renal colic.