ADAM-17 Activity and Its Relation to ACE2: Implications for Severe COVID-19.

There is a lack of studies aiming to assess cellular a disintegrin and metalloproteinase-17 (ADAM-17) activity in COVID-19 patients and the eventual associations with the shedding of membrane-bound angiotensin-converting enzyme 2 (mACE2). In addition, studies that investigate the relationship between ACE2 and ADAM-17 gene expressions in organs infected by SARS-CoV-2 are lacking. We used data from the Massachusetts general hospital COVID-19 study (306 COVID-19 patients and 78 symptomatic controls) to investigate the association between plasma levels of 33 different ADAM-17 substrates and COVID-19 severity and mortality. As a surrogate of cellular ADAM-17 activity, an ADAM-17 substrate score was calculated. The associations between soluble ACE2 (sACE2) and the ADAM-17 substrate score, renin, key inflammatory markers, and lung injury markers were investigated. Furthermore, we used data from the Genotype-Tissue Expression (GTEx) database to evaluate ADAM-17 and ACE2 gene expressions by age and sex in ages between 20-80 years. We found that increased ADAM-17 activity, as estimated by the ADAM-17 substrates score, was associated with COVID-19 severity (p = 0.001). ADAM-17 activity was also associated with increased mortality but did not reach statistical significance (p = 0.06). Soluble ACE2 showed the strongest positive correlation with the ADAM-17 substrate score, follow by renin, interleukin-6, and lung injury biomarkers. The ratio of ADAM-17 to ACE2 gene expression was highest in the lung. This study indicates that increased ADAM-17 activity is associated with severe COVID-19. Our findings also indicate that there may a bidirectional relationship between membrane-bound ACE2 shedding via increased ADAM-17 activity, dysregulated renin-angiotensin system (RAS) and immune signaling. Additionally, differences in ACE2 and ADAM-17 gene expressions between different tissues may be of importance in explaining why the lung is the organ most severely affected by COVID-19, but this requires further evaluation in prospective studies.

Genetic Susceptibility to Mood Disorders and Risk of Stroke: A Polygenic Risk Score and Mendelian Randomization Study.

BACKGROUND: Mood disorders and strokes are often comorbid, and their health toll worldwide is huge. This study characterizes prognostic and causal roles of mood disorders in stroke. METHODS: We tested if genetic susceptibilities for mood disorders were associated with all strokes, ischemic strokes in the Malmö Diet and Cancer cohort (24 631 individuals with a median follow-up of 21.3 (interquartile range: 16.6-23.2) years. We further examined the causal effects for mood disorders on all strokes and ischemic strokes using summary statistics from large genome-wide association studies of mood disorders (up to 609 424 individuals, Psychiatric Genomics Consortium), all strokes and ischemic strokes (up to 446 696 individuals, MEGASTROKE Consortium). RESULTS: Among 24 366 stroke-free participants at baseline, 2632 individuals developed strokes, 2172 of them ischemic, during follow-up. After properly adjusting for well-known risk factors, participants in the highest quintile of polygenic risk scores for mood disorders had 1.45× (95% CI, 1.21-1.74) higher risk of strokes and 1.44× (95% CI, 1.18-1.76) higher risk of ischemic strokes compared with the lowest quintile in women. Mendelian randomization analyses suggested that mood disorders had a causal effect on strokes (odds ratio, 1.07 [95% CI, 1.03-1.11]) and ischemic strokes (odds ratio, 1.09 [95% CI, 1.04-1.13]). CONCLUSIONS: Our results suggest a causal role of mood disorders in the risk of stroke. High-risk women could be identified early in life using polygenic risk scores to ultimately prevent mood disorders and strokes.

Genome-wide association study identifies 16 genomic regions associated with circulating cytokines at birth.

Circulating inflammatory markers are essential to human health and disease, and they are often dysregulated or malfunctioning in cancers as well as in cardiovascular, metabolic, immunologic and neuropsychiatric disorders. However, the genetic contribution to the physiological variation of levels of circulating inflammatory markers is largely unknown. Here we report the results of a genome-wide genetic study of blood concentration of ten cytokines, including the hitherto unexplored calcium-binding protein (S100B). The study leverages a unique sample of neonatal blood spots from 9,459 Danish subjects from the iPSYCH initiative. We estimate the SNP-heritability of marker levels as ranging from essentially zero for Erythropoietin (EPO) up to 73% for S100B. We identify and replicate 16 associated genomic regions (p < 5 x 10-9), of which four are novel. We show that the associated variants map to enhancer elements, suggesting a possible transcriptional effect of genomic variants on the cytokine levels. The identification of the genetic architecture underlying the basic levels of cytokines is likely to prompt studies investigating the relationship between cytokines and complex disease. Our results also suggest that the genetic architecture of cytokines is stable from neonatal to adult life.

Interferon regulatory factor-5-dependent CD11c+ macrophages contribute to the formation of rupture-prone atherosclerotic plaques.

AIMS: Inflammation is a key factor in atherosclerosis. The transcription factor interferon regulatory factor-5 (IRF5) drives macrophages towards a pro-inflammatory state. We investigated the role of IRF5 in human atherosclerosis and plaque stability. METHODS AND RESULTS: Bulk RNA sequencing from the Carotid Plaque Imaging Project biobank were used to mine associations between major macrophage associated genes and transcription factors and human symptomatic carotid disease. Immunohistochemistry, proximity extension assays, and Helios cytometry by time of flight (CyTOF) were used for validation. The effect of IRF5 deficiency on carotid plaque phenotype and rupture in ApoE-/- mice was studied in an inducible model of plaque rupture. Interferon regulatory factor-5 and ITGAX/CD11c were identified as the macrophage associated genes with the strongest associations with symptomatic carotid disease. Expression of IRF5 and ITGAX/CD11c correlated with the vulnerability index, pro-inflammatory plaque cytokine levels, necrotic core area, and with each other. Macrophages were the predominant CD11c-expressing immune cells in the plaque by CyTOF and immunohistochemistry. Interferon regulatory factor-5 immunopositive areas were predominantly found within CD11c+ areas with a predilection for the shoulder region, the area of the human plaque most prone to rupture. Accordingly, an inducible plaque rupture model of ApoE-/-Irf5-/- mice had significantly lower frequencies of carotid plaque ruptures, smaller necrotic cores, and less CD11c+ macrophages than their IRF5-competent counterparts. CONCLUSION: Using complementary evidence from data from human carotid endarterectomies and a murine model of inducible rupture of carotid artery plaque in IRF5-deficient mice, we demonstrate a mechanistic link between the pro-inflammatory transcription factor IRF5, macrophage phenotype, plaque inflammation, and its vulnerability to rupture.

Genetic Predisposition to Elevated Levels of Circulating ADAM17 Is Associated with the Risk of Severe COVID-19.

High levels of ADAM17 activity have emerged as an important mediator in severe COVID-19. This study aims to characterize eventual causal relationships between ADAM17 and COVID-19. Using Mendelian randomization analyses, we examined the causal effects of circulating ADAM17 on COVID-19 outcomes using summary statistics from large, genome-wide association studies of ADAM17 (up to 35,559 individuals) from the Icelandic Cancer Project and deCODE genetics, as well as critically ill COVID-19 patients (cases: 13,769; controls: 1,072,442), hospitalized COVID-19 patients (cases: 32,519; controls: 2,062,805) and reported SARS-CoV-2 infections (cases: 122,616; controls: 2,475,240) from the COVID-19 Host Genetics Initiative. The Mendelian randomization (MR) analyses demonstrated that a 1 standard deviation increase in genetically determined circulating ADAM17 (extracellular domain) was associated with an increased risk of developing critical ill COVID-19 (odds ratio [OR] = 1.26, 95% confidence interval [CI]:1.03-1.55). The multivariable MR analysis suggested a direct causal role of circulating ADAM17 (extracellular domain) in the risk of developing critical COVID-19 (OR = 1.09; 95% CI:1.01-1.17) when accounting for body mass index. No causal effect for the cytoplasmic domain of ADAM17 on COVID-19 was observed. Our results suggest that an increased genetic susceptibility to elevated levels of circulating ADAM17 (extracellular domain) is associated with a higher risk of suffering from severe COVID-19, strengthening the idea that the timely selective inhibition of ADAM17 could be a potential therapeutic target worthy of investigation.

Osteomodulin Gene Expression Is Associated With Plaque Calcification, Stability, and Fewer Cardiovascular Events in the CPIP Cohort.

BACKGROUND: Stable atherosclerotic plaques are characterized by thick fibrous caps of smooth muscle cells, collagen, and macrocalcifications. Identifying factors of plaque stability is necessary to design drugs to prevent plaque rupture and symptoms. Osteomodulin, originally identified in bones, is expressed by bone synthesizing osteoblasts and involved in mineralization. In the present study, we analyzed osteomodulin expression in human carotid plaques, its link with plaque phenotype, calcification, and future cardiovascular events. METHODS: Osteomodulin gene expression (OMD; n=82) was determined by RNA sequencing and osteomodulin protein levels by immunohistochemistry (n=45) in carotid plaques obtained by endarterectomy from patients with or without cerebrovascular symptoms from the CPIP (Carotid Plaque Imaging Project) cohort, Skåne University Hospital, Sweden. Plaque components were assessed by immunohistochemistry, RNA sequencing, and multiplex analysis. Patients were followed for cardiovascular events or cardiovascular death during a median of 57 or 70 months, respectively, using national registers. RESULTS: OMD levels were increased in plaques from asymptomatic patients compared to symptomatics. High OMD levels were associated with fewer cardiovascular events during follow-up. OMD correlated positively with smooth muscle α-actin (ACTA2; r=0.73, P=10-13) and collagen (COL1A2; r=0.4, P=0.0002), but inversely with CD68 gene expression (r=-0.67, P=10-11), lipids (r=-0.37, P=0.001), intraplaque hemorrhage (r=-0.32, P=0.010), inflammatory cytokine, and matrix metalloproteinase plaque contents. OMD was positively associated with MSX2 (Msh Homeobox 2) (r=0.32, P=0.003), a marker of preosteoblast differentiation, BMP4 (bone morphogenetic protein) (r=0.50, P=0.000002) and BMP6 (r=0.47, P=0.000007), plaque calcification (r=0.35, P=0.016), and was strongly upregulated in osteogenically stimulated smooth muscle cells, which was further increased upon BMP stimulation. Osteomodulin protein was present in calcified regions. Osteomodulin protein levels were associated with plaque calcification (r=0.41, P=0.006) and increased in macrocalcified plaques. CONCLUSIONS: These data show that osteomodulin mRNA and protein levels are associated with plaque calcification in human atherosclerosis. Furthermore, osteomodulin mRNA, but not protein levels, is associated with plaque stability.

Polygenic scores for low lung function and the future risk of adverse health outcomes.

AIMS: Reduced lung function and adverse health outcomes are often observed. This study characterizes genetic susceptibility for reduced lung function and risk of developing a range of adverse health outcomes. METHODS: We studied 27,438 middle-aged adults from the Malmö Diet and Cancer study (MDCS), followed up to 28.8 years. Trait-specific Polygenic scores (PGS) for forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) were constructed for each participant using MDCS genetic data and summary statistics from the latest GWAS of lung function. Linear regression models and cox proportional hazards regression models were used to assess associations between adverse health outcomes and lung function-PGS. RESULTS: FEV1-PGS and FVC-PGS were significantly associated with mean sBP at baseline after adjustments (FEV1-PGS Q1 (highest PGS = highest lung function): 140.7mmHg vs. Q4: 141.5mmHg, p-value 0.008). A low FVC-PGS was significantly associated with the risk of future diabetic events after adjustments (Q4 vs. Q1 HR: 1.22 (CI 1.12-1.32), p-trend < 0.001) and had added value to risk prediction models for diabetes. Low FEV1-PGS was significantly associated with future coronary events (Q4 vs. Q1 HR: 1.13 (CI: 1.04-1.22), p-trend 0.008). No significant association was found between PGS and sudden cardiac death, chronic kidney disease or all-cause mortality. Results remained largely unchanged in a subgroup of subjects when further adjusted for apolipoproteins. CONCLUSION: Genetic susceptibility for reduced lung function is associated with higher sBP, increased risk of diabetes and to a lesser extent, future coronary events, suggesting etiological roles of lung function on these outcomes. Using PGS, high-risk groups could be early detected to implement early lifestyle changes to mitigate the risk.

The diagnostic value of PET/CT for the lymph node metastasis in Asian patients with non-small cell lung cancer: A meta-analysis.

OBJECTIVE: To evaluate the accuracy of positron emission tomography/computed tomography (PET/CT) in the diagnosis of lymph node metastasis in non-small cell lung cancer (NSCLC) by a method of meta-analysis. MATERIALS AND METHODS: A comprehensive search of PubMed, Cochrane Library, China National Knowledge Infrastructure (CNKI), and Embase data bases was conducted to collect literature about PET/CT diagnosing lymph node metastasis of NSCLC up to December 1, 2021. Stata 15.0 software was used for the calculation of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). The publication bias was evaluated by Deeks' funnel plot. RESULTS: A total of 25 studies were enrolled, including 2,458 patients with NSCLC. The pooled sensitivity of PET/CT for diagnosing lymph node metastasis in NSCLC was 0.68 (95%CI: 0.61-0.75), the pooled specificity being 0.93 (95%CI: 0.89-0.95). Likelihood ratio syntheses gave an overall PLR of 9.4 (95%CI: 6.3-13.9), and NLR of 0.34 (95%CI: 0.28-0.41). The pooled DOR was 28 (95%CI: 19-40). The summary receiver operating characteristic curve showed the area under the curve of 0.88 (95%CI: 0.84-0.90). CONCLUSION: Positron emission tomography/CT has a good value in the diagnosis of lymph node metastasis of NSCLC, with specificity excellent. Positron emission tomography/CT can be used as one of the main imaging diagnosis methods for lymph node metastasis in patients with NSCLC.

Hyperglycaemia-associated Caspase-3 predicts diabetes and coronary artery disease events.

BACKGROUND: Apoptosis is central in both diabetes and atherosclerosis, linked to pancreatic beta cell death and plaque progression. Circulating Caspase-3 has also been associated with diabetes and coronary calcium score. Here, we explored if soluble Caspase-3 (sCaspase-3) is associated with cardio-metabolic risk factors and predicts incidence of diabetes and coronary artery disease (CAD). METHODS: Clinical data and plasma from 4637 individuals from the Malmö Diet and Cancer cohort were studied. Plasma sCaspase-3 was measured by a Proximity Extension Assay. National registers were used to identify diabetes and CAD events during follow-up. Type 2 diabetes risk variants and expression quantitative trait loci (eQTL) for sCaspase-3 were retrieved from the DIAGRAM consortium and the Genotype-Tissue Expression project. RESULTS: HbA1c was the factor with the strongest association with sCaspase-3 (r = 0.18, P = 1.3x10-36 ). During follow-up 666 individuals developed diabetes and 648 individuals suffered from CAD. Increasing sCaspase-3 was associated with a higher risk of developing diabetes (hazard ratio (HR) 1.18 per 1unit; P = 7 × 10-5 ) and CAD (HR 1.2 per 1 unit, P = 1 × 10-4 ) during follow-up. A genetic variant rs60780116, located upstream of CASP3, showed strong association with type 2 diabetes (OR 1.06, 95%CI 1.04-1.07, P = 8.4 × 10-11 ). An eQTL was identified between this variant and gene expression of CASP3, where the allele positively correlated with type 2 diabetes was associated with increased CASP3 expression in blood. CONCLUSIONS: The present study provides evidence for plasma sCaspase-3 as a marker of cardio-metabolic risk factors and as a predictor of future diabetes and CAD in a cohort without cardiovascular disease or diabetes at baseline.

Association of TIM-1 (T-Cell Immunoglobulin and Mucin Domain 1) With Incidence of Stroke.

OBJECTIVE: The aim of this study was to investigate if there is a causal relationship between circulating levels of TIM-1 (T-cell immunoglobulin and mucin domain 1) and incidence of stroke. Approach and Results: Plasma TIM-1 was analyzed in 4591 subjects (40% men; mean age, 57.5 years) attending the Malmö Diet and Cancer Study. Incidence of stroke was studied in relation to TIM-1 levels during a mean of 19.5 years follow-up. Genetic variants associated with TIM-1 (pQTLs [protein quantitative trait loci]) were examined, and a 2-sample Mendelian randomization analysis was performed to explore the role of TIM-1 in stroke using summary statistics from our pQTLs and the MEGASTROKE consortium. A total of 416 stroke events occurred during follow-up, of which 338 were ischemic strokes. After risk factor adjustment, TIM-1 was associated with increased incidence of all-cause stroke (hazards ratio for third versus first tertile, 1.44 [95% CI, 1.10-1.87]; P for trend, 0.004), and ischemic stroke (hazards ratio, 1.42 [95% CI, 1.06-1.90]; P for trend, 0.011). Nineteen independent lead SNPs, located in three genomic risk loci showed significant associations with TIM-1 (P<5×10-8). A 2-sample Mendelian Randomization analysis suggested a causal effect of TIM-1 on stroke (ß=0.083, P=0.0004) and ischemic stroke (ß=0.102, P=7.7×10-5). CONCLUSIONS: Plasma level of TIM-1 is associated with incidence of stroke. The genetic analyses suggest that this could be a causal relationship.

Automatic differentiation of thyroid scintigram by deep convolutional neural network: a dual center study.

BACKGROUND: 99mTc-pertechnetate thyroid scintigraphy is a valid complementary avenue for evaluating thyroid disease in the clinic, the image feature of thyroid scintigram is relatively simple but the interpretation still has a moderate consistency among physicians. Thus, we aimed to develop an artificial intelligence (AI) system to automatically classify the four patterns of thyroid scintigram. METHODS: We collected 3087 thyroid scintigrams from center 1 to construct the training dataset (n = 2468) and internal validating dataset (n = 619), and another 302 cases from center 2 as external validating datasets. Four pre-trained neural networks that included ResNet50, DenseNet169, InceptionV3, and InceptionResNetV2 were implemented to construct AI models. The models were trained separately with transfer learning. We evaluated each model's performance with metrics as following: accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), recall, precision, and F1-score. RESULTS: The overall accuracy of four pre-trained neural networks in classifying four common uptake patterns of thyroid scintigrams all exceeded 90%, and the InceptionV3 stands out from others. It reached the highest performance with an overall accuracy of 92.73% for internal validation and 87.75% for external validation, respectively. As for each category of thyroid scintigrams, the area under the receiver operator characteristic curve (AUC) was 0.986 for 'diffusely increased,' 0.997 for 'diffusely decreased,' 0.998 for 'focal increased,' and 0.945 for 'heterogeneous uptake' in internal validation, respectively. Accordingly, the corresponding performances also obtained an ideal result of 0.939, 1.000, 0.974, and 0.915 in external validation, respectively. CONCLUSIONS: Deep convolutional neural network-based AI model represented considerable performance in the classification of thyroid scintigrams, which may help physicians improve the interpretation of thyroid scintigrams more consistently and efficiently.

Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models.

The volume of high throughput screening data has considerably increased since the beginning of the automated biochemical and cell-based assays era. This information-rich data source provides tremendous repurposing opportunities for data mining. It was recently shown that biochemical or cell-based assay results can be compiled into so-called high-throughput fingerprints (HTSFPs) as a new type of descriptor describing molecular bioactivity profiles which can be applied in virtual screening, iterative screening, and target deconvolution. However, so far, studies around HTSFPs and machine learning have mainly focused on predicting the outcome of molecules in single high-throughput assays, and no one has reported the modeling of compounds' biochemical assay activities toward a panel of target proteins. In this article, we aim at comparing how our in-house HTSFPs perform at this when combined with multitask deep learning versus the single task support vector machine method both in terms of hit identification and of scaffold hopping potential. Performances obtained from the two HTSFP models were reported with respect to the performances of multitask deep learning and support vector machine models built with the structural descriptors ECFP. Moreover, we investigated the effect of high throughput screening false positives and negatives on the performance of the generated models. Our results showed that the two fingerprints yielded in similar performances and diverse hits with very little overlap, thus demonstrating the orthogonality of bioactivity profile-based descriptors with structural descriptors. Therefore, modeling compound activity data using ECFPs together with HTSFPs increases the scaffold hopping potential of the predictive models.

Altered expression of DLG1-AS1 distinguished papillary thyroid carcinoma from benign thyroid nodules.

BACKGROUND: Benign thyroid nodules (BTN) are frequently diagnosed as papillary thyroid carcinoma (PTC), leading to unnecessary treatment. We found that plasma lncRNA DLG1-AS1 was upregulated in PTC patients but not in BTN patients and healthy controls. METHODS: In this study DLG1-AS1 and miR-199a-3p in plasma of both PTC patients and BTN patients were detected by qPCR. ROC curve analysis was performed for diagnostic analysis. Overexpression experiments were performed to analyze the interaction between DLG1-AS1 and miR-199a-3p. CCK-8 assay was performed to analyze cell proliferation. RESULTS: In this study, upregulation of DLG1-AS1 distinguished PTC patients from BTN patients and healthy controls. Plasma miR-199a-3p was downregulated in PTC patients compared with healthy controls and BTN patients. Plasma levels of miR-199a-3p were inversely correlated in PTC patients, but not in BTN patients and healthy controls. miR-199a-3p overexpression failed to significantly affect DLG1-AS1, while DLG1-AS1 overexpression resulted in downregulated miR-199a-3p, In addition, DLG1-AS1 overexpression promoted the proliferation of PTC cells. miR-199a-3p overexpression played an opposite role and attenuated the effects of DLG1-AS1 overexpression. CONCLUSIONS: Therefore, DLG1-AS1 may promote PTC by downregulating miR-199a-3p.

Applying Mondrian Cross-Conformal Prediction To Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets.

Conformal prediction has been proposed as a more rigorous way to define prediction confidence compared to other application domain concepts that have earlier been used for QSAR modeling. One main advantage of such a method is that it provides a prediction region potentially with multiple predicted labels, which contrasts to the single valued (regression) or single label (classification) output predictions by standard QSAR modeling algorithms. Standard conformal prediction might not be suitable for imbalanced data sets. Therefore, Mondrian cross-conformal prediction (MCCP) which combines the Mondrian inductive conformal prediction with cross-fold calibration sets has been introduced. In this study, the MCCP method was applied to 18 publicly available data sets that have various imbalance levels varying from 1:10 to 1:1000 (ratio of active/inactive compounds). Our results show that MCCP in general performed well on bioactivity data sets with various imbalance levels. More importantly, the method not only provides confidence of prediction and prediction regions compared to standard machine learning methods but also produces valid predictions for the minority class. In addition, a compound similarity based nonconformity measure was investigated. Our results demonstrate that although it gives valid predictions, its efficiency is much worse than that of model dependent metrics.

Loss of TFB1M results in mitochondrial dysfunction that leads to impaired insulin secretion and diabetes.

We have previously identified transcription factor B1 mitochondrial (TFB1M) as a type 2 diabetes (T2D) risk gene, using human and mouse genetics. To further understand the function of TFB1M and how it is associated with T2D, we created a ß-cell-specific knockout of Tfb1m, which gradually developed diabetes. Prior to the onset of diabetes, ß-Tfb1m(-/-) mice exhibited retarded glucose clearance owing to impaired insulin secretion. ß-Tfb1m(-/-) islets released less insulin in response to fuels, contained less insulin and secretory granules and displayed reduced ß-cell mass. Moreover, mitochondria in Tfb1m-deficient ß-cells were more abundant with disrupted architecture. TFB1M is known to control mitochondrial protein translation by adenine dimethylation of 12S ribosomal RNA (rRNA). Here, we found that the levels of TFB1M and mitochondrial-encoded proteins, mitochondrial 12S rRNA methylation, ATP production and oxygen consumption were reduced in ß-Tfb1m(-/-) islets. Furthermore, the levels of reactive oxygen species (ROS) in response to cellular stress were increased whereas induction of defense mechanisms was attenuated. We also show increased apoptosis and necrosis as well as infiltration of macrophages and CD4(+) cells in the islets. Taken together, our findings demonstrate that Tfb1m-deficiency in ß-cells caused mitochondrial dysfunction and subsequently diabetes owing to combined loss of ß-cell function and mass. These observations reflect pathogenetic processes in human islets: using RNA sequencing, we found that the TFB1M risk variant exhibited a negative gene-dosage effect on islet TFB1M mRNA levels, as well as insulin secretion. Our findings highlight the role of mitochondrial dysfunction in impairments of ß-cell function and mass, the hallmarks of T2D.

PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif.

Knowledge of subcellular localizations (SCLs) of plant proteins relates to their functions and aids in understanding the regulation of biological processes at the cellular level. We present PlantLoc, a highly accurate and fast webserver for predicting the multi-label SCLs of plant proteins. The PlantLoc server has two innovative characters: building localization motif libraries by a recursive method without alignment and Gene Ontology information; and establishing simple architecture for rapidly and accurately identifying plant protein SCLs without a machine learning algorithm. PlantLoc provides predicted SCLs results, confidence estimates and which is the substantiality motif and where it is located on the sequence. PlantLoc achieved the highest accuracy (overall accuracy of 80.8%) of identification of plant protein SCLs as benchmarked by using a new test dataset compared other plant SCL prediction webservers. The ability of PlantLoc to predict multiple sites was also significantly higher than for any other webserver. The predicted substantiality motifs of queries also have great potential for analysis of relationships with protein functional regions. The PlantLoc server is available at http://cal.tongji.edu.cn/PlantLoc/.

Retrieving backbone string neighbors provides insights into structural modeling of membrane proteins.

Identification of protein structural neighbors to a query is fundamental in structure and function prediction. Here we present BS-align, a systematic method to retrieve backbone string neighbors from primary sequences as templates for protein modeling. The backbone conformation of a protein is represented by the backbone string, as defined in Ramachandran space. The backbone string of a query can be accurately predicted by two innovative technologies: a knowledge-driven sequence alignment and encoding of a backbone string element profile. Then, the predicted backbone string is employed to align against a backbone string database and retrieve a set of backbone string neighbors. The backbone string neighbors were shown to be close to native structures of query proteins. BS-align was successfully employed to predict models of 10 membrane proteins with lengths ranging between 229 and 595 residues, and whose high-resolution structural determinations were difficult to elucidate both by experiment and prediction. The obtained TM-scores and root mean square deviations of the models confirmed that the models based on the backbone string neighbors retrieved by the BS-align were very close to the native membrane structures although the query and the neighbor shared a very low sequence identity. The backbone string system represents a new road for the prediction of protein structure from sequence, and suggests that the similarity of the backbone string would be more informative than describing a protein as belonging to a fold.

DSP: a protein shape string and its profile prediction server.

Many studies have demonstrated that shape string is an extremely important structure representation, since it is more complete than the classical secondary structure. The shape string provides detailed information also in the regions denoted random coil. But few services are provided for systematic analysis of protein shape string. To fill this gap, we have developed an accurate shape string predictor based on two innovative technologies: a knowledge-driven sequence alignment and a sequence shape string profile method. The performance on blind test data demonstrates that the proposed method can be used for accurate prediction of protein shape string. The DSP server provides both predicted shape string and sequence shape string profile for each query sequence. Using this information, the users can compare protein structure or display protein evolution in shape string space. The DSP server is available at both http://cheminfo.tongji.edu.cn/dsp/ and its main mirror http://chemcenter.tongji.edu.cn/dsp/.

Atherosclerotic plaque features relevant to rupture-risk detected by clinical photon-counting CT ex vivo: a proof-of-concept study.

BACKGROUND: To identify subjects with rupture-prone atherosclerotic plaques before thrombotic events occur is an unmet clinical need. Thus, this proof-of-concept study aims to determine which rupture-prone plaque features can be detected using clinically available photon-counting computed tomography (PCCT). METHODS: In this retrospective study, advanced atherosclerotic plaques (ex vivo, paraffin-embedded) from the Carotid Plaque Imaging Project were scanned by PCCT with reconstructed energy levels (45, 70, 120, 190 keV). Density in HU was measured in 97 regions of interest (ROIs) representing rupture-prone plaque features as demonstrated by histopathology (thrombus, lipid core, necrosis, fibrosis, intraplaque haemorrhage, calcium). The relationship between HU and energy was then assessed using a mixed-effects model for each plaque feature. RESULTS: Plaques from five men (age 79 ± 8 [mean ± standard deviation]) were included in the study. Comparing differences in coefficients (b1diff) of matched ROIs on plaque images obtained by PCCT and histology confirmed that calcium was distinguishable from all other analysed features. Of greater novelty, additional rupture-prone plaque features proved discernible from each other, particularly when comparing haemorrhage with fibrous cap (p = 0.017), lipids (p = 0.003) and necrosis (p = 0.004) and thrombus compared to fibrosis (p = 0.048), fibrous cap (p = 0.028), lipids (p = 0.015) and necrosis (p = 0.017). CONCLUSIONS: Clinically available PCCT detects not only calcification, but also other rupture-prone features of human carotid plaques ex vivo. RELEVANCE STATEMENT: Improved atherosclerotic plaque characterisation by photon-counting CT provides the ability to distinguish not only calcium, but also rupture-prone plaque features such as haemorrhage and thrombus. This may potentially improve monitoring and risk stratification of atherosclerotic patients in order to prevent strokes. KEY POINTS: • CT of atherosclerotic plaques mainly detects calcium. • Many components, such as intra-plaque haemorrhage and lipids, determine increased plaque rupture risk. • Ex vivo carotid plaque photon-counting CT distinguishes haemorrhage and thrombus. • Improved plaque photon-counting CT evaluation may refine risk stratification accuracy to prevent strokes.

The Gly82Ser polymorphism in the receptor for advanced glycation endproducts increases the risk for coronary events in the general population.

The receptor for advanced glycation endproducts (RAGE) has pro-inflammatory and pro-atherogenic effects. Low plasma levels of soluble RAGE (sRAGE), a decoy receptor for RAGE ligands, have been associated with increased risk for major adverse coronary events (MACE) in the general population. We performed a genome-wide association study to identify genetic determinants of plasma sRAGE in 4338 individuals from the cardiovascular arm of the Malmö Diet and Cancer study (MDC-CV). Further, we explored the associations between these genetic variants, incident first-time MACE and mortality in 24,640 unrelated individuals of European ancestry from the MDC cohort. The minor alleles of four single nucleotide polymorphisms (SNPs): rs2070600, rs204993, rs116653040, and rs7306778 were independently associated with lower plasma sRAGE. The minor T (vs. C) allele of rs2070600 was associated with increased risk for MACE [HR 1.13 95% CI (1.02-1.25), P = 0.016]. Neither SNP was associated with mortality. This is the largest study to demonstrate a link between a genetic sRAGE determinant and CV risk. Only rs2070600, which enhances RAGE function by inducing a Gly82Ser polymorphism in the ligand-binding domain, was associated with MACE. The lack of associations with incident MACE for the other sRAGE-lowering SNPs suggests that this functional RAGE modification is central for the observed relationship.