Tissue-specific Information Research Articles

MethylSeqLogo: DNA methylation smart sequence logos

BackgroundSome transcription factors, MYC for example, bind sites of potentially methylated DNA. This may increase binding specificity as such sites are (1) highly under-represented in the genome, and (2) offer additional, tissue specific information in the form of hypo- or hyper-methylation. Fortunately, bisulfite sequencing data can be used to investigate this phenomenon.MethodWe developed MethylSeqLogo, an extension of sequence logos which includes new elements to indicate DNA methylation and under-represented dimers in each position of a set binding sites. Our method displays information from both DNA strands, and takes into account the sequence context (CpG or other) and genome region (promoter versus whole genome) appropriate to properly assess the expected background dimer frequency and level of methylation. MethylSeqLogo preserves sequence logo semantics—the relative height of nucleotides within a column represents their proportion in the binding sites, while the absolute height of each column represents information (relative entropy) and the height of all columns added together represents total informationResultsWe present figures illustrating the utility of using MethylSeqLogo to summarize data from several CpG binding transcription factors. The logos show that unmethylated CpG binding sites are a feature of transcription factors such as MYC and ZBTB33, while some other CpG binding transcription factors, such as CEBPB, appear methylation neutral.ConclusionsOur software enables users to explore bisulfite and ChIP sequencing data sets—and in the process obtain publication quality figures.

Extracellular vesicles carry transcriptional 'dark matter' revealing tissue-specific information.

From eukaryotes to prokaryotes, all cells secrete extracellular vesicles (EVs) as part of their regular homeostasis, intercellular communication, and cargo disposal. Accumulating evidence suggests that small EVs carry functional small RNAs, potentially serving as extracellular messengers and liquid-biopsy markers. Yet, the complete transcriptomic landscape of EV-associated small RNAs during disease progression is poorly delineated due to critical limitations including the protocols used for sequencing, suboptimal alignment of short reads (20-50nt), and uncharacterized genome annotations-often denoted as the 'dark matter' of the genome. In this study, we investigate the EV-associated small unannotated RNAs that arise from endogenous genes and are part of the genomic 'dark matter', which may play a key emerging role in regulating gene expression and translational mechanisms. To address this, we created a distinct small RNAseq dataset from human prostate cancer & benign tissues, and EVs derived from blood (pre- & post-prostatectomy), urine, and human prostate carcinoma epithelial cell line. We then developed an unsupervised data-based bioinformatic pipeline that recognizes biologically relevant transcriptional signals irrespective of their genomic annotation. Using this approach, we discovered distinct EV-RNA expression patterns emerging from the un-annotated genomic regions (UGRs) of the transcriptomes associated with tissue-specific phenotypes. We have named these novel EV-associated small RNAs as 'EV-UGRs' or "EV-dark matter". Here, we demonstrate that EV-UGR gene expressions are downregulated by ∼100 fold (FDR<0.05) in the circulating serum EVs from aggressive prostate cancer subjects. Remarkably, these EV-UGRs expression signatures were regained (upregulated) after radical prostatectomy in the same follow-up patients. Finally, we developed a stem-loop RT-qPCR assay that validated prostate cancer-specific EV-UGRs for selective fluid-based diagnostics. Overall, using an unsupervised data driven approach, we investigate the 'dark matter' of EV-transcriptome and demonstrate that EV-UGRs carry tissue-specific Information that significantly alters pre- and post-prostatectomy in the prostate cancer patients. Although further validation in randomized clinical trials is required, this new class of EV-RNAs hold promise in liquid-biopsy by avoiding highly invasive biopsy procedures in prostate cancer.

Comparative Gene Signature of Nociceptors Innervating Mouse Molar Teeth, Cranial Meninges, and Cornea.

The trigeminal ganglion (TG) collects afferent sensory information from various tissues. Recent large-scale RNA sequencing of neurons of the TG and dorsal root ganglion has revealed a variety of functionally distinct neuronal subpopulations, but organ-specific information is lacking. To link transcriptomic and tissue-specific information, we labeled small-diameter neurons of 3 specific subpopulations of the TG by local application of lipophilic carbocyanine dyes to their innervation site in the dental pulp, cornea, and meninges (dura mater). We then collected mRNA-sequencing data from fluorescent neurons. Differentially expressed genes (DEGs) were analyzed and subjected to downstream gene set enrichment analysis (GSEA), and ion channel profiling was performed. A total of 10,903 genes were mapped to the mouse genome (>500 reads). DEG analysis revealed 18 and 81 genes with differential expression (log 2 fold change > 2, Padj < .05) in primary afferent neurons innervating the dental pulp (dental primary afferent neurons [DPAN]) compared to those innervating the meninges (meningeal primary afferent neurons [MPAN]) and the cornea (corneal primary afferent neurons [CPAN]). We found 250 and 292 genes differentially expressed in MPAN as compared to DPAN and to CPAN, and 21 and 12 in CPAN as compared to DPAN and MPAN. Scn2b had the highest log 2 fold change when comparing DPAN versus MPAN and Mmp12 was the most prominent DEG when comparing DPAN versus CPAN and, CPAN versus MPAN. GSEA revealed genes of the immune and mitochondrial oxidative phosphorylation system for the DPAN versus MPAN comparison, cilium- and ribosome-related genes for the CPAN versus DPAN comparison, and respirasome, immune cell- and ribosome-related gene sets for the CPAN versus MPAN comparison. DEG analysis for ion channels revealed no significant differences between the neurons set except for the sodium voltage-gated channel beta subunit 2, Scn2b . However, in each tissue a few ion channels turned up with robust number of reads. In DPAN, these were Cacna1b , Trpv2 , Cnga4 , Hcn1 , and Hcn3 , in CPAN Trpa1 , Trpv1 , Cacna1a , and Kcnk13 and in MPAN Trpv2 and Scn11a . Our study uncovers previously unknown differences in gene expression between sensory neuron subpopulations from the dental pulp, cornea, and dura mater and provides the basis for functional studies, including the investigation of ion channel function and their suitability as targets for tissue-specific analgesia.

Reducing annotation burden in MR: A novel MR-contrast guided contrastive learning approach for image segmentation.

Contrastive learning, a successful form of representational learning, has shown promising results in pretraining deep learning (DL) models for downstream tasks. When working with limited annotation data, as in medical image segmentation tasks, learning domain-specific local representations can further improve the performance of DL models. In this work, we extend the contrastive learning framework to utilize domain-specific contrast information from unlabeled Magnetic Resonance (MR) images to improve the performance of downstream MR image segmentation tasks in the presence of limited labeled data. The contrast in MR images is controlled by underlying tissue properties (e.g., T1 or T2) and image acquisition parameters. We hypothesize that learning to discriminate local representations based on underlying tissue properties should improve subsequent segmentation tasks on MR images. We propose a novel constrained contrastive learning (CCL) strategy that uses tissue-specific information via a constraint map to define positive and negative local neighborhoods for contrastive learning, embedding this information in the representational space during pretraining. For a given MR contrast image, the proposed strategy uses local signal characteristics (constraint map) across a set of related multi-contrast MR images as a surrogate for underlying tissue information. We demonstrate the utility of the approach for downstream: (1) multi-organ segmentation tasks in T2-weighted images where a DL model learns T2 information with constraint maps from a set of 2D multi-echo T2-weighted images (n=101) and (2) tumor segmentation tasks in multi-parametric images from the public brain tumor segmentation (BraTS) (n=80) dataset where DL models learn T1 and T2 information from multi-parametric BraTS images. Performance is evaluated on downstream multi-label segmentation tasks with limited data in (1) T2-weighted images of the abdomen from an in-house Radial-T2 (Train/Test=30/20), (2) public Cartesian-T2 (Train/Test=6/12) dataset, and (3) multi-parametric MR images from the public brain tumor segmentation dataset (BraTS) (Train/Test=40/50). The performance of the proposed CCL strategy is compared to state-of-the-art self-supervised contrastive learning techniques. In each task, a model is also trained using all available labeled data for supervised baseline performance. The proposed CCL strategy consistently yielded improved Dice scores, Precision, and Recall metrics, and reduced HD95 values across all segmentation tasks. We also observed performance comparable to the baseline with reduced annotation effort. The t-SNE visualization of features for T2-weighted images demonstrates its ability to embed T2 information in the representational space. On the BraTS dataset, we also observed that using an appropriate multi-contrast space to learn T1+T2, T1, or T2 information during pretraining further improved the performance of tumor segmentation tasks. Learning to embed tissue-specific information that controls MR image contrast with the proposed constrained contrastive learning improved the performance of DL models on subsequent segmentation tasks compared to conventional self-supervised contrastive learning techniques. The use of such domain-specific local representations could help understand, improve performance, and mitigate the scarcity of labeled data in MR image segmentation tasks.

Information-Rich Multi-Functional OCT for Adult Zebrafish Intra- and Extracranial Imaging.

The zebrafish serves as a valuable animal model for both intra- and extracranial research, particularly in relation to the brain and skull. To effectively investigate the development and regeneration of adult zebrafish, a versatile in vivo imaging technique capable of showing both intra- and extracranial conditions is essential. In this paper, we utilized a high-resolution multi-functional optical coherence tomography (OCT) to obtain rich intra- and extracranial imaging outcomes of adult zebrafish, encompassing pigmentation distribution, tissue-specific information, cranial vascular imaging, and the monitoring of traumatic brain injury (TBI). Notably, it is the first that the channels through the zebrafish cranial suture, which may have a crucial function in maintaining the patency of the cranial sutures, have been observed. Rich imaging results demonstrated that a high-resolution multi-functional OCT system can provide a wealth of novel and interpretable biological information for intra- and extracranial studies of adult zebrafish.

Automated T1 and T2 mapping segmentation on cardiovascular magnetic resonance imaging using deep learning.

Structural and functional heart abnormalities can be examined non-invasively with cardiac magnetic resonance imaging (CMR). Thanks to the development of MR devices, diagnostic scans can capture more and more relevant information about possible heart diseases. T1 and T2 mapping are such novel technology, providing tissue specific information even without the administration of contrast material. Artificial intelligence solutions based on deep learning have demonstrated state-of-the-art results in many application areas, including medical imaging. More specifically, automated tools applied at cine sequences have revolutionized volumetric CMR reporting in the past five years. Applying deep learning models to T1 and T2 mapping images can similarly improve the efficiency of post-processing pipelines and consequently facilitate diagnostic processes. In this paper, we introduce a deep learning model for myocardium segmentation trained on over 7,000 raw CMR images from 262 subjects of heterogeneous disease etiology. The data were labeled by three experts. As part of the evaluation, Dice score and Hausdorff distance among experts is calculated, and the expert consensus is compared with the model's predictions. Our deep learning method achieves 86% mean Dice score, while contours provided by three experts on the same data show 90% mean Dice score. The method's accuracy is consistent across epicardial and endocardial contours, and on basal, midventricular slices, with only 5% lower results on apical slices, which are often challenging even for experts. We trained and evaluated a deep learning based segmentation model on 262 heterogeneous CMR cases. Applying deep neural networks to T1 and T2 mapping could similarly improve diagnostic practices. Using the fine details of T1 and T2 mapping images and high-quality labels, the objective of this research is to approach human segmentation accuracy with deep learning.

Identifying Tissue- and Cohort-Specific RNA Regulatory Modules in Cancer Cells Using Multitask Learning.

Simple SummaryUnderstanding the underlying biological mechanisms of primary tumors is crucial for predicting how tumors respond to therapies and exploring accurate treatment strategies. miRNA–mRNA interactions have a major effect on many biological processes that are important in the formation and progression of cancer. In this study, we introduced a computational pipeline to extract tissue- and cohort-specific miRNA–mRNA regulatory modules of multiple cancer types from the same origin using miRNA and mRNA expression profiles of primary tumors. Our model identified regulatory modules of underlying cancer types (i.e., cohort-specific) and shared regulatory modules between cohorts (i.e., tissue-specific).MicroRNA (miRNA) alterations significantly impact the formation and progression of human cancers. miRNAs interact with messenger RNAs (mRNAs) to facilitate degradation or translational repression. Thus, identifying miRNA–mRNA regulatory modules in cohorts of primary tumor tissues are fundamental for understanding the biology of tumor heterogeneity and precise diagnosis and treatment. We established a multitask learning sparse regularized factor regression (MSRFR) method to determine key tissue- and cohort-specific miRNA–mRNA regulatory modules from expression profiles of tumors. MSRFR simultaneously models the sparse relationship between miRNAs and mRNAs and extracts tissue- and cohort-specific miRNA–mRNA regulatory modules separately. We tested the model’s ability to determine cohort-specific regulatory modules of multiple cancer cohorts from the same tissue and their underlying tissue-specific regulatory modules by extracting similarities between cancer cohorts (i.e., blood, kidney, and lung). We also detected tissue-specific and cohort-specific signatures in the corresponding regulatory modules by comparing our findings from various other tissues. We show that MSRFR effectively determines cancer-related miRNAs in cohort-specific regulatory modules, distinguishes tissue- and cohort-specific regulatory modules from each other, and extracts tissue-specific information from different cohorts of disease-related tissue. Our findings indicate that the MSRFR model can support current efforts in precision medicine to define tumor-specific miRNA–mRNA signatures.

Multi-Omics and Artificial Intelligence-Guided Data Integration in Chronic Liver Disease: Prospects and Challenges for Precision Medicine.

Chronic liver disease (CLD) is a significant planetary health burden. CLD includes a broad range of liver pathologies from different causes, for example, hepatitis B virus infection, fatty liver disease, hepatocellular carcinoma, and nonalcoholic fatty liver disease or the metabolic associated fatty liver disease. Biomarker and diagnostic discovery, and new molecular targets for precision treatments are timely and sorely needed in CLD. In this context, multi-omics data integration is increasingly being facilitated by artificial intelligence (AI) and attendant digital transformation of systems science. While the digital transformation of multi-omics integrative analyses is still in its infancy, there are noteworthy prospects, hope, and challenges for diagnostic and therapeutic innovation in CLD. This expert review aims at the emerging knowledge frontiers as well as gaps in multi-omics data integration at bulk tissue levels, and those including single cell-level data, gut microbiome data, and finally, those incorporating tissue-specific information. We refer to AI and related digital transformation of the CLD research and development field whenever possible. This review of the emerging frontiers at the intersection of systems science and digital transformation informs future roadmaps to bridge digital technology discovery and clinical omics applications to benefit planetary health and patients with CLD.

Identification of VEGFA-centric temporal hypoxia-responsive dynamic cardiopulmonary network biomarkers

AimsHypoxia, a pathophysiological condition, is profound in several cardiopulmonary diseases (CPD). Every individual's lethality to a hypoxia state differs in terms of hypoxia exposure time, dosage units and dependent on the individual's genetic makeup. Most of the proposed markers for CPD were generally aim to distinguish disease samples from normal samples. Although, as per the 2018 GOLD guidelines, clinically useful biomarkers for several cardio pulmonary disease patients in stable condition have yet to be identified. We attempt to address these key issues through the identification of Dynamic Network Biomarkers (DNB) to detect hypoxia induced early warning signals of CPD before the catastrophic deterioration. Materials and methodsThe human microvascular endothelial tissues microarray datasets (GSE11341) of lung and cardiac expose to hypoxia (1% O2) for 3, 24 and 48 h were retrieved from the public repository. The time dependent differentially expressed genes were subjected to tissue specificity and promoter analysis to filtrate the noise levels in the networks and to dissect the tissue specific hypoxia induced genes. These filtered out genes were used to construct the dynamic segmentation networks. The hypoxia induced dynamic differentially expressed genes were validated in the lung and heart tissues of male rats. These rats were exposed to hypobaric hypoxia (simulated altitude of 25,000 or PO2 - 282 mm of Hg) progressively for 3, 24 and 48 h. Key findingsTo identify the temporal key genes regulated in hypoxia, we ranked the dominant genes based on their consolidated topological features from tissue specific networks, time dependent networks and dynamic networks. Overall topological ranking described VEGFA as a single node dynamic hub and strongly communicated with tissue specific genes to carry forward their tissue specific information. We named this type of VEGFAcentric dynamic networks as “V-DNBs”. As a proof of principle, our methodology helped us to identify the V-DNBs specific for lung and cardiac tissues namely V-DNBL and V-DNBC respectively. SignificanceOur experimental studies identified VEGFA, SLC2A3, ADM and ENO2 as the minimum and sufficient candidates of V-DNBL. The dynamic expression patterns could be readily exploited to capture the pre disease state of hypoxia induced pulmonary vascular remodelling. Whereas in V-DNBC the minimum and sufficient candidates are VEGFA, SCL2A3, ADM, NDRG1, ENO2 and BHLHE40. The time dependent single node expansion indicates V-DNBC could also be the pre disease state pathological hallmark for hypoxia-associated cardiovascular remodelling. The network cross-talk and expression pattern between V-DNBL and V-DNBC are completely distinct. On the other hand, the great clinical advantage of V-DNBs for pre disease predictions, a set of samples during the healthy condition should suffice. Future clinical studies might further shed light on the predictive power of V-DNBs as prognostic and diagnostic biomarkers for CPD.

Tissue characteristics of the athlete"s heart: differentiation of physiological and pathological hypertrophy using parametric T1 and T2 mapping

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Research, Development and Innovation Fund of Hungary Background Intensive physical exercise leads to structural and functional cardiac adaptation termed athlete’s heart. Cardiac magnetic resonance (CMR) has an important role in the differentiation of physiological adaptation and pathological conditions. Beside the precise measurement of the ventricular volumes, mass, and function, it provides tissue specific information. Recently, native T1 mapping technique has been applied as a non-contrast method to detect myocardial fibrosis. Previous studies suggested that native T1 mapping can identify myocardial pathology before other CMR imaging techniques. T2 mapping values are elevated in case of myocardial edema. Purpose The aim of our study was to investigate the differences in CMR characteristics especially the native T1 and T2 mapping values of highly trained healthy athletes, healthy controls and patients with hypertrophic cardiomyopathy (HCM). Methods A total of 43 healthy athletes (water polo, swimming, football, 22 ± 8 training hours/week), 27 non-athlete healthy control and 25 HCM patients were involved in the study. Our inclusion criteria were: age &amp;gt;18 years, in the athlete group &amp;gt;7 training hours per week . We evaluated the left ventricular (LV) end-systolic, end-diastolic (EDVi) and stroke volume (SVi) index, mass index (LVMi), ejection fraction (EF) and maximal end-diastolic wall thickness (EDWT). In a basal short axis slice the native T1 and T2 mapping values were evaluated. Results Athletes had significantly higher LV volumes compared to the control and HCM group (LVEDVi 114 ± 13 vs. 86 ± 11; 84 ± 15 ml/m2, LVSVi 64 ± 7 vs. 51 ± 7; 54 ± 10 ml/m2, respectively, p &amp;lt; 0.0001). HCM patients had the highest LVMi (72 ± 14 g/m2) and EDWT (18 ± 4 mm) compared to athletes and controls, athletes had higher LVMi (60 ± 11 vs. 42 ± 8 g/m2) and EDWT (10 ± 2 vs. 8 ± 1 mm) compared to the controls (p &amp;lt; 0.001). The native T1 mapping values differed significantly in the three groups, athletes had the lowest, HCM patients had the highest T1 values (athletes: 956 ± 19 ms, controls: 971 ± 20 ms, HCM patients: 993 ± 39 ms; p &amp;lt; 0.0001). There was no difference in the T2 mapping values between athletes and controls (44 ± 2 vs. 43 ± 2 ms), HCM patients had higher T2 values (45 ± 2 ms) compared to the other two groups (p &amp;lt; 0.01). Conclusion Intensive and regular training may lead to tissue specific changes of the myocardium. T1 and T2 mapping are potentially useful tools for differentiating between athlete"s heart and patients with hypertrophic cardiomyopathy. Abstract Figure. T1 mapping in HCM and athlete

Diagnostic and prognostic impact of cardiac magnetic resonance, including scar quantification and strain imaging in patients with malignant ventricular arrhythmias and nonobstructed coronary arteries

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Development and Innovation Fund of Hungary, National Research, Development and Innovation Office Background In case of malignant ventricular arrhythmias (VA) and nonobstructed coronary arteries, the differential diagnosis of the underlying diseases is still challenging, due to the board spectrum of possible causes. Cardiac magnetic resonance (CMR) provides functional, morphological and tissue specific information, including necrotic and scar-tissue. Aims We aimed to assess the diagnostic and prognostic implications of CMR parameters including global strain values and myocardial scar in patients after ventricular fibrillation (VF) or sustained ventricular tachycardia (SVT) and nonobstructed coronary arteries. Methods Between 2011 and 2019, 99 patients (42 ±17 years, 54 male) presenting with VF or SVT and nonobstructed coronary arteries, who underwent CMR examination before secondary prevention implantable cardioverter defibrillator (ICD) implantation were included in our study. Post-processing included feature-tracking strain analysis and left ventricular (LV) scar quantification. Patients were followed for the combined endpoint of all-cause-mortality and appropriate ICD therapy. Results CMR examination proved structural myocardial disease in 72%: dilated (n = 21), arrhythmogenic (n = 11), hypertrophic cardiomyopathy (n = 7) and other cardiomyopathies (n = 3). We found LGE patterns showing chronic myocardial infarction (n = 4), suggesting chronic myocarditis (n = 4) and aspecific nonischemic scar formation (n = 14). In 7 cases aspecific structural alterations without scar formation were detected. Overall, myocardial scar was found in 52%, with an average extent of 12 ± 8% of the LV myocardium. The CMR examination changed the clinical diagnosis in 55% of the patients. During a median follow-up at 2 years, 6 patients died and 42 experienced appropriate ICD therapy. We found an association between cardiac events and the presence of structural abnormality and myocardial scar (logrank: 4,553, p &amp;lt; 0.05 and 8.375, p &amp;lt;0.01). On Cox proportional-hazards modell LV ejection fraction, LV stroke volume index, the presence of structural abnormality, the presence and extent of myocardial scar, global LV strain parameters including longitudinal and circumferential strain, and a global left ventricular dssynchrony parameter (mechanical dispersion) were univariate predictors of the combined endpoint of all-cause-mortality and appropriate ICD therapy(p &amp;lt; 0.05). Conclusion CMR performed in patients after malignant VA and nonobstructed coronary arteries not only establishes the diagnosis in a high proportion of patients, but may also provide additional prognostic factors. This may indicate that CMR could play a complementary role in the risk stratification in this patient population. Abstract Figure.

Cardiac magnetic resonance features of acute myocarditis presenting as acute coronary syndrome

Abstract Background Clinical presentation of myocarditis varies, one specific form of myocarditis appears with the clinical signs of acute coronary syndrome (ACS). Cardiac magnetic resonance (CMR) is an important method for assessing ventricular function and morphology, additionally provides accurate tissue specific and functional information of the heart. Aims Our aim was to investigate the characteristics, and prognosis of myocarditis presenting with ACS symptoms. Methods 113 patients with the clinical signs of ACS but nonobstructed coronary arteries in whom the CMR revealed acute myocarditis were included in our study. CMR was performed in acute phase and at 3–6-month follow-up. Left ventricular (LV) volumes, mass and strain parameters expressing myocardial deformity were determined. Additional images were taken to represent tissue specific information. Relationships between laboratory and CMR parameters were investigated. Parameters predicting changes in LV ejection fraction (LVEF) were analyzed by logistic regression. Results A total of 113 patients with myocarditis (98 males, 31±11 years) underwent acute and follow-up CMR. Sixty two patients reported fever or infection before the beginning of their complaints, most commonly gastroenteritis (33%) and pharyngitis (32%). The creatinine kinase MB value measured in the acute phase showed positive correlation with the extent of necrosis, and the global longitudinal- and circumferential strain. The extent of the LV necrosis showed negative correlation with LVEF and positive correlation with global circumferential strain (GCS) (p&amp;lt;0.05). On the control CMR examination LVEF and all global strain values improved, fibrosis persisted in 82% of cases but shrank (15±11 vs 5±4 g) and LV mass decreased (p&amp;lt;0.01) compared to the acute phase. Compared to the acute phase, 21% of the patients had lower LVEF on the follow-up CMR. Lower initial LVEF, worse acute GCS, and greater LV necrosis were independent predictors of LVEF reduction in the logistic regression model. During a median follow-up of 6-years of patients treated at our clinic (n=39) no patient suffered cardiac death, heart failure, or documented ventricular arrhythmia but 21% of them had recurrent myocarditis. Conclusion Myocarditis mimicking ACS affects predominantly young men and shows functional improvement and good prognosis on follow-up, but it may reoccur in some cases. The reduction of LV function on control CMR may be predicted by worse initial LVEF, GCS, and a larger LV scar. Strain, LGE in acute phase and follow-up Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Project no. NVKP_16-1-2016-0017 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the NVKP_16 funding scheme. This project was supported by a grant from the National Research, Development and Innovation Office (NKFIH) of Hungary (K 120277).

Structural and functional cardiac changes after transplantation: experiences of the first year of the prospective Heart-TIming CMR substudy

Abstract Background In case of heart transplantation (HTX) the heart is affected by several factors e.g. ischaemia/reperfusion, denervation, immunosuppression. During the adaptation, the heart may show marked temporal changes in terms of myocardial mechanics, function and tissue characteristics. To better understand cardiac temporal characteristics after orthotopic bicaval HTX we started the prospective Heart-TIming (Transplantation Imaging) trial in January 2018. Purpose In our CMR substudy we aimed to evaluate the physiological structural and functional left and right ventricular characteristics and their temporal changes after HTX using cardiac magnetic resonance. Methods As part of the study HTX patients underwent CMR at one, three, six and twelve months after HTX (n=49; 53±11y, 39 male). Cine images, T2-weighted, native T1 and T2 mapping, late gadolinium enhancement (LGE) and adenosine stress perfusion (at 1 and 12 month) images were acquired. In order to describe physiological characteristics of the transplanted heart we excluded pts with significant coronary artery disease, ischaemic scar, ≥Grade II allograft rejection from this present study (n=9). We evaluated the left (LV) and right ventricular (RV) ejection fractions (EF), volumes, masses (M) and the global LV strain values: longitudinal (GLS), circumferential (GCS) strain and the standard deviation (SD) of the peak longitudinal strain (LS) and the mechanical dispersion. In a basal short axis slice the native T1 and T2 mapping values were evaluated. We compared baseline CMR parameters to age and gender matched healthy controls (n=20; 48±10y, 16 male), and analyzed the temporal changes after HTX. Results Comparing the HTX patients' CMR parameters at one month with normal controls, HTX patients had lower end-diastolic volumes (LVEDVi: 74±15 vs 89±13 ml/m2; RVEDVi: 72±16 vs 89±15 ml/m2 p&amp;lt;0.05), stroke volumes (LVSVi: 45±7 vs 55±8 ml/m2, RVSVi: 43±8 vs 54±8 ml/m2, p&amp;lt;0.0001), higher LVMi (63±2 vs 55±3 g/m2, p&amp;lt;0.05), increased SD of peak LS (14±2 vs 10±2, p&amp;lt;0.0001) and more pronounced mechanical dispersion (18±5 vs 12±4, p&amp;lt;0.0001). The native T1 mapping values were significantly higher in HTX pts (1007±40 vs 975±24 ms, p&amp;lt;0,01). Examining temporal changes in HTX pts we found a decrease in LVMi (66±14 vs 59±10 g/m2, p&amp;lt;0.01) already at three months. At 12 months LVMi decreased further, less negative GLS (−25±4 vs −20±4, p&amp;lt;0.01) and GCS (−38±7 vs −34±4, p&amp;lt;0.05), and lower SD of the peak LS (14±2 vs 11±2, p&amp;lt;0.01) were measured. Conclusions Understanding the temporal changes of cardiac mechanics, function and tissue characteristics, furthermore the establishment of physiological values may help in the early, noninvasive identification of pathological changes in HTX pts. Tissue specific information in HTX pts Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Project no. NVKP_16-1-2016-0017 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the NVKP_16 funding scheme. Supported by the ÚNKP-18-3-IV New National Excellence Program of Human Capacities.

The diagnostic value of cardiac magnetic resonance in athletes with suspected structural myocardial diseases

Abstract Introduction Sudden cardiac death (SCD) is the leading cause of death in athletes occurring usually during intensive training. Cardiac magnetic resonance (CMR) is a reliable technique to assess ventricular volumes and function. Furthermore, it provides tissue-specific information and has a crucial role in detecting structural myocardial diseases. Aim We aimed to investigate the prevalence of myocardial structural heart diseases and the etiology of sudden cardiac death in highly trained athletes and their outcome during follow-up. Method We examined athletes (training ≥6 hours/week) who underwent CMR due to suspected structural myocardial disease at Semmelweis University Heart and Vascular Center between 2009 and 2019. Cine movie images and late gadolinium enhanced (LGE) images were performed. Athletes with structural myocardial alterations were followed for the endpoint of all-cause-mortality. Results CMR was performed on a total of 338 athletes (280 male, 24±11 age). The indications for CMR were as follows: aborted sudden cardiac death/sustained ventricular tachycardia (SVT) (4%), ECG alterations (36%), echocardiographic alterations (32%), positive family history of SCD or cardiomyopathies (CMP) (3%), and patients' complaints, e.g. palpitation, syncope, dyspnoea, chest complaints (25%). CMR confirmed structural myocardial disease in 82 athletes with the following distribution: 20 hypertrophic (HCM), 10 arrhythmogenic (AC), 8 dilated (DCM), and 7 non-compact (NCCMP) CMP. The CMR images of three patients indicated Fabry disease. We found post-myocardial infarction scars in 7 cases, and atypical non-ischemic scars in 28 athletes. Besides pathological conditions, we identified minor alterations in 58 patients (51 male, 25±12 age) such as: increased trabeculation, nonspecific LGE in left ventricular insertion point and myocardial crypts. Among athletes examined after aborted sudden cardiac death or SVT we found structural heart disease in 11 males and one female: AC (n=7), HCM (n=1), NCCMP (n=1) and atypical non-ischemic scars (n=3, in two patients the localisation was lateral subepicardial) were diagnosed. During the median follow up of five years one patient died in whom CMR showed lateral scar formation and only mildly reduced left ventricular ejection fraction (50%). Conclusions The most common structural alteration was non-ischaemic scar, the most common CMP was HCM, and the leading cause of sudden cardiac death or SVT in our competitive athletes was AC and lateral subepicardial scar formation. LGE pattern in various cardiomyopathies Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Project no. NVKP_16-1-2016-0017 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the NVKP_16 funding scheme. This project was supported by a grant from the National Research, Development and Innovation Office (NKFIH) of Hungary (K 120277).

TS-GOEA: a web tool for tissue-specific gene set enrichment analysis based on gene ontology

BackgroundThe Gene Ontology (GO) knowledgebase is the world’s largest source of information on the functions of genes. Since the beginning of GO project, various tools have been developed to perform GO enrichment analysis experiments. GO enrichment analysis has become a commonly used method of gene function analysis. Existing GO enrichment analysis tools do not consider tissue-specific information, although this information is very important to current research.ResultsIn this paper, we built an easy-to-use web tool called TS−GOEA that allows users to easily perform experiments based on tissue-specific GO enrichment analysis. TS−GOEA uses strict threshold statistical method for GO enrichment analysis, and provides statistical tests to improve the reliability of the analysis results. Meanwhile, TS−GOEA provides tools to compare different experimental results, which is convenient for users to compare the experimental results. To evaluate its performance, we tested the genes associated with platelet disease with TS−GOEA.ConclusionsTS−GOEA is an effective GO analysis tool with unique features. The experimental results show that our method has better performance and provides a useful supplement for the existing GO enrichment analysis tools. TS−GOEA is available at http://120.77.47.2:5678.

Tissue-specific mouse mRNA isoform networks

Alternative Splicing produces multiple mRNA isoforms of genes which have important diverse roles such as regulation of gene expression, human heritable diseases, and response to environmental stresses. However, little has been done to assign functions at the mRNA isoform level. Functional networks, where the interactions are quantified by their probability of being involved in the same biological process are typically generated at the gene level. We use a diverse array of tissue-specific RNA-seq datasets and sequence information to train random forest models that predict the functional networks. Since there is no mRNA isoform-level gold standard, we use single isoform genes co-annotated to Gene Ontology biological process annotations, Kyoto Encyclopedia of Genes and Genomes pathways, BioCyc pathways and protein-protein interactions as functionally related (positive pair). To generate the non-functional pairs (negative pair), we use the Gene Ontology annotations tagged with “NOT” qualifier. We describe 17 Tissue-spEcific mrNa iSoform functIOnal Networks (TENSION) following a leave-one-tissue-out strategy in addition to an organism level reference functional network for mouse. We validate our predictions by comparing its performance with previous methods, randomized positive and negative class labels, updated Gene Ontology annotations, and by literature evidence. We demonstrate the ability of our networks to reveal tissue-specific functional differences of the isoforms of the same genes. All scripts and data from TENSION are available at: https://doi.org/10.25380/iastate.c.4275191.

Promoter conservation in HDACs points to functional implications

BackgroundHistone deacetylases (HDACs) are the proteins responsible for removing the acetyl group from lysine residues of core histones in chromosomes, a crucial component of gene regulation. Eleven known HDACs exist in humans and most other vertebrates. While the basic function of HDACs has been well characterized and new discoveries are still being made, the transcriptional regulation of their corresponding genes is still poorly understood.ResultsHere, we conducted a computational analysis of the eleven HDAC promoter sequences in 25 vertebrate species to determine whether transcription factor binding sites (TFBSs) are conserved in HDAC evolution, and if so, whether they provide useful information about HDAC expression and function. Furthermore, we used tissue-specific information of transcription factors to investigate the potential expression patterns of HDACs in different human tissues based on their transcription factor binding sites. We found that the TFBS profiles of most of the HDACs were well conserved in closely related species for all HDAC promoters except HDAC7 and HDAC10. HDAC5 had particularly strong conservation across over half of the species studied, with nearly identical profiles in the primate species. Our comparisons of TFBSs with the tissue specific gene expression profiles of their corresponding TFs showed that most HDACs had the ability to be ubiquitously expressed. A few HDAC promoters exhibited the potential for preferential expression in certain tissues, most notably HDAC11 in gall bladder, while HDAC9 seemed to have less propensity for expression in the nervous system.ConclusionsIn general, we found evolutionary conservation in HDAC promoters that seems to be more prominent for the ubiquitously expressed HDACs. In turn, when conservation did not follow usual phylogeny, human TFBS patterns indicated possible functional relevance. While we found that HDACs appear to uniformly expressed, we confirm that the functional differences in HDACs may be less a matter of location of activity than a question of which proteins and which acetyl groups they may be acting on.

Revealing hemodynamic heterogeneity of gliomas based on signal profile features of dynamic susceptibility contrast-enhanced MRI.

Dynamic susceptibility contrast enhanced magnetic resonance imaging (DSC MRI) is widely used for studying blood perfusion in brain tumors. While the time-dependent change of MRI signals related to the concentration of the tracer is used to derive the hemodynamic parameters such as regional blood volume and flow into tumors, the tissue-specific information associated with variations in profiles of signal time course is often overlooked. We report a new approach of combining model free independent component analysis (ICA) identification of specific signal profiles of DSC MRI time course data and extraction of the features from those time course profiles to interrogate time course data followed by calculating the region specific blood volume based on selected individual time courses. Based on the retrospective analysis of DSC MRI data from 38 patients with pathology confirmed low (n = 18) and high (n = 20) grade gliomas, the results reveal the spatially defined intra-tumoral hemodynamic heterogeneity of brain tumors based on features of time course profiles. The hemodynamic heterogeneity as measured by the number of independent components of time course data is associated with the tumor grade. Using 8 selected signal profile features, machine-learning trained algorithm, e.g., logistic regression, was able to differentiate pathology confirmed low intra-tumoral and high grade gliomas with an accuracy of 86.7%. Furthermore, the new method can potentially extract more tumor physiological information from DSC MRI comparing to the traditional model-based analysis and morphological analysis of tumor heterogeneity, thus may improve the characterizations of gliomas for better diagnosis and treatment decisions.

Assessment of tissue-specific multifactor effects in environmental –omics studies of heterogeneous biological samples: Combining hyperspectral image information and chemometrics

The use of hyperspectral imaging techniques in biological studies has increased in the recent years. Hyperspectral images (HSI) provide chemical information and preserve the morphology and original structure of heterogeneous biological samples, which can be potentially useful in environmental –omics studies when effects due to several factors, e.g., contaminant exposure, phenotype,…, at a specific tissue level need to be investigated. Yet, no available strategies exist to exploit adequately this kind of information.This work offers a novel chemometric strategy to pass from the raw image information to useful knowledge in terms of statistical assessment of the multifactor effects of interest in –omic studies. To do so, unmixing of the hyperspectral image measurement is carried out to provide tissue-specific information. Afterwards, several specific ANOVA-Simultaneous Component Analysis (ASCA) models are generated to properly assess and interpret the diverse effect of the factors of interest on the spectral fingerprints of the different tissues characterized.The unmixing step is performed by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) on multisets of biological images related to each studied condition and provides reliable HSI spectral signatures and related image maps for each specific tissue in the regions imaged. The variability associated with these signatures within a population is obtained through an MCR-based resampling step on representative pixel subsets of the images analyzed. All spectral fingerprints obtained for a particular tissue in the different conditions studied are used to obtain the related ASCA model that will help to assess the significance of the factors studied on the tissue and, if relevant, to describe the associated fingerprint modifications.The potential of the approach is assessed in a real case of study linked to the investigation of the effect of exposure time to chlorpyrifos-oxon (CPO) on ocular tissues of different phenotypes of zebrafish larvae from Raman HSI of eye cryosections. The study allowed the characterization of melanin, crystalline and internal eye tissue and the phenotype, exposure time and the interaction of the two factors were found to be significant in the changes found in all kind of tissues. Factor-related changes in the spectral fingerprint were described and interpreted per each kind of tissue characterized.

Inhomogeneity of Cellulose Microfibril Assembly in Plant Cell Walls Revealed with Sum Frequency Generation Microscopy.

Sum frequency generation (SFG) vibrational spectroscopy can selectively detect and analyze noncentrosymmetric components interspersed in amorphous matrices; this principle has been used for studies of nanoscale structure and mesoscale assembly of cellulose in plant cell walls. However, the spectral information averaged over a large area or volume cannot provide regiospecific or tissue-specific information of different cells in plants. This study demonstrates spatially resolved SFG analysis and imaging by combining a broad-band SFG spectroscopy system with an optical microscope. The system was designed to irradiate both narrow-band 800 nm and broad-band tunable IR beams through a single reflective objective lens, but from opposite sides of the surface normal direction of the sample. The developed technique was used to reveal inhomogeneous distributions of cellulose microfibrils within single cell walls, such as cotton fibers and onion epidermis as well as among different tissues in Arabidopsis inflorescence stems and bamboo culms. SFG microscopy can be used for vibrational spectroscopic imaging of other biological systems in complement to conventional Fourier transform infrared spectroscopy and confocal Raman microscopy.