High-dimensional in situ proteomics imaging to assess γδ T cells in spatial biology

This study presents a high-dimensional immunohistochemistry approach to assess human γδ T cell subsets in their native tissue microenvironments at spatial resolution, a hitherto unmet scientific goal due to the lack of established antibodies and required technology. We report an integrated approach based on multiplexed imaging and bioinformatic analysis to identify γδ T cells, characterize their phenotypes, and analyze the composition of their microenvironment. Twenty-eight γδ T cell microenvironments were identified in tissue samples from fresh frozen human colon and colorectal cancer where interaction partners of the immune system, but also cancer cells were discovered in close proximity to γδ T cells, visualizing their potential contributions to cancer immunosurveillance. While this proof-of-principle study demonstrates the potential of this cutting-edge technology to assess γδ T cell heterogeneity and to investigate their microenvironment, future comprehensive studies are warranted to associate phenotypes and microenvironment profiles with features such as relevant clinical characteristics.

Corrigendum: A review on deep learning applications in highly multiplexed tissue imaging data analysis

[This corrects the article DOI: 10.3389/fbinf.2023.1159381.].

S3-CIMA: Supervised spatial single-cell image analysis for identifying disease-associated cell-type compositions in tissue

The spatial organization of various cell types within the tissue microenvironment is a key element for the formation of physiological and pathological processes, including cancer and autoimmune diseases. Here, we present S3-CIMA, a weakly supervised convolutional neural network model that enables the detection of disease-specific microenvironment compositions from high-dimensional proteomic imaging data. We demonstrate the utility of this approach by determining cancer outcome- and cellular-signaling-specific spatial cell-state compositions in highly multiplexed fluorescence microscopy data of the tumor microenvironment in colorectal cancer. Moreover, we use S3-CIMA to identify disease-onset-specific changes of the pancreatic tissue microenvironment in type 1 diabetes using imaging mass-cytometry data. We evaluated S3-CIMA as a powerful tool to discover novel disease-associated spatial cellular interactions from currently available and future spatial biology datasets.

Tumor-associated macrophages trigger MAIT cell dysfunction at the HCC invasive margin

Mucosal-associated invariant T (MAIT) cells represent an abundant innate-like T cell subtype in the human liver. MAIT cells are assigned crucial roles in regulating immunity and inflammation, yet their role in liver cancer remains elusive. Here, we present a MAIT cell-centered profiling of hepatocellular carcinoma (HCC) using scRNA-seq, flow cytometry, and co-detection by indexing (CODEX) imaging of paired patient samples. These analyses highlight the heterogeneity and dysfunctionality of MAIT cells in HCC and their defective capacity to infiltrate liver tumors. Machine-learning tools were used to dissect the spatial cellular interaction network within the MAIT cell neighborhood. Co-localization in the adjacent liver and interaction between niche-occupying CSF1R+PD-L1+ tumor-associated macrophages (TAMs) and MAIT cells was identified as a key regulatory element of MAIT cell dysfunction. Perturbation of this cell-cell interaction in ex vivo co-culture studies using patient samples and murine models reinvigorated MAIT cell cytotoxicity. These studies suggest that aPD-1/aPD-L1 therapies target MAIT cells in HCC patients.

A review on deep learning applications in highly multiplexed tissue imaging data analysis

Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.

Dynamics of Melanoma-Associated Epitope-Specific CD8+ T Cells in the Blood Correlate With Clinical Outcome Under PD-1 Blockade

Immune checkpoint blockade (ICB) is standard-of-care for patients with metastatic melanoma. It may re-invigorate T cells recognizing tumors, and several tumor antigens have been identified as potential targets. However, little is known about the dynamics of tumor antigen-specific T cells in the circulation, which might provide valuable information on ICB responses in a minimally invasive manner. Here, we investigated individual signatures composed of up to 167 different melanoma-associated epitope (MAE)-specific CD8+ T cells in the blood of stage IV melanoma patients before and during anti-PD-1 treatment, using a peptide-loaded multimer-based high-throughput approach. Additionally, checkpoint receptor expression patterns on T cell subsets and frequencies of myeloid-derived suppressor cells and regulatory T cells were quantified by flow cytometry. Regression analysis using the MAE-specific CD8+ T cell populations was applied to identify those that correlated with overall survival (OS). The abundance of MAE-specific CD8+ T cell populations, as well as their dynamics under therapy, varied between patients. Those with a dominant increase of these T cell populations during PD-1 ICB had a longer OS and progression-free survival than those with decreasing or balanced signatures. Patients with a dominantly increased MAE-specific CD8+ T cell signature also exhibited an increase in TIM-3+ and LAG-3+ T cells. From these results, we created a model predicting improved/reduced OS by combining data on dynamics of the three most informative MAE-specific CD8+ T cell populations. Our results provide insights into the dynamics of circulating MAE-specific CD8+ T cell populations during ICB, and should contribute to a better understanding of biomarkers of response and anti-cancer mechanisms.

ATRT-19. Functional genomics reveal distinct modulators of response to CDK4/6 inhibitors in ATRTs

Brain tumors are the leading cause of cancer-related deaths in children, and atypical teratoid rhabdoid tumors (ATRTs) are among the most common aggressive brain tumors in infants. With no standard-of-care treatment so far, ATRTs continue to have relatively low survival estimates, illustrating the urgent need for more efficacious treatment options. We have previously used genome-wide CRISPR/Cas9 knockout screens in combination with small-molecule drug assays to identify targetable vulnerabilities in ATRTs. CDK4/6 inhibitors, among the most promising drugs in our study with direct translational potential, are capable of inhibiting tumor growth due to mutual exclusive dependency of ATRTs on either CDK4 or CDK6. We here used genome-wide loss-of-function and gain-of-function strategies to identify modulators of response to CDK4/6 inhibition in ATRTs. Of note, while some well-known resistance mechanisms such as loss of RB1 or FBXW7 are shared by ATRT cell lines, we have also identified modulators of response to CDK4/6 inhibition with opposing effects across ATRT cell lines. As such, loss of AMBRA1, a recently described master regulator of D type cyclins, can either oppose the effects of or synergize with CDK4/6 inhibitors based on the cellular background. We are currently using a proteomics approach to further delineate the mechanism driving this functional heterogeneity of AMBRA1 in ATRTs. Our study will therefore provide deeper insights into the response of ATRTs to CDK4/6 inhibitors, which represent one of the most promising class of targeted agents for the treatment of ATRTs.

Reconstruction and analysis of circRNA-miRNA-mRNA network in the pathology of lung cancer

BACKGROUND

The presented study aimed to gain insights into the pathogenesis of lung cancer (LC) and provide novel bio-markers for LC by building a regulatory circular (circ) RNAmicro (mi) RNAmRNA network.

MATERIALS AND METHODS

High-throughput sequencing data of circRNAs, miRNAs and mRNAs related to LC originated from GEO (Gene Expression Omnibus) database, and the differential expressions of circRNAs, miRNAs and mRNAs were screened with R language Limma. The circRNA-miRNA and miRNA-mRNA pairs were used to build the ceRNA network. The functions of differential expression circRNAs were elucidated by performing the functional enrichment analysis on GO and KEGG. Furthermore, the selected LC prognostic genes were verified by tissue chips and immunohistochemistry (IHC).

RESULTS

On the whole, 20 downregulated circRNAs, 55 upregulated miRNAs and 243 downregulated mRNAs were identified in LC. Lastly, a circRNA-miRNA-mRNA ceRNA network was built, which was composed of 2 circRNAs, 2 miRNAs, and 2 mRNAs. As indicated from the analysis based on public databases and IHC, the differential genes (i.e., FXYD1 and SEMA5A) in this network acted as LC prognostic factors. As confirmed by performing IHC and survival analyses, FXYD1 and SEMA5A expressions in LC were downregulated, and their expressions displayed a relationship to the overall survival (OS) of LC cases.

CONCLUSIONS

This study presents novel insights into the role of circRNAs in the development of LC via the ceRNA mechanism. The identified FXYD1 and SEMA5A gene could act as novel and vital LC prognostic indicators.

Abstract P021: Highly multiplexed spatial analysis of the HCC tumor immune microenvironment using CODEX imaging

Introduction: Hepatocellular Carcinoma (HCC) is a leading cause of cancer-related death and can be considered a prototype of inflammation-derived cancer arising from chronic liver injury. The cell composition of the HCC tumor immune microenvironment (TiME) has a major impact on cancer biology as the TME can have divergent capacities on tumor initiation, progress, and response to therapy. Recent development of multi-omics and single-cell technologies help us to comprehensively quantify the cellular heterogeneity and spatial organization of the TiME and to further our understanding of antitumor immunity.

Methods: Multiplexed immunofluorescence microscopy and high-dimensional flow cytometry was used to analyze immune cell infiltration in primary human liver cancer samples. We developed and validated a comprehensive 37-plex antibody panel for immunofluorescence imaging of human fresh frozen HCC samples. We applied highly multiplexed co-detection by indexing (CODEX) technology to simultaneously profile in situ expression of 37 proteins at sub-cellular resolution in 15 HCC patient samples using whole slide scanning. We established an image analysis pipeline to quantify all major cell populations in the human liver using supervised manual gating and unsupervised clustering algorithms. Proximity and nearest neighbor calculations as well as infiltration analysis was performed using HALO quantitative image analysis software.

Results: Using high-dimensional flow cytometry and parallel spatially resolved quantitative analysis of multiplexed immunofluorescence microscopy images, we profiled the single-cell pathology landscape for human HCC. The translation from raw images to numerical output was successfully established. In situ phenotyping of 4,500,000 single cells (including 1,500,000 CD45+ immune cells) allowed for the quantification of cell phenotype clusters, differential analysis of activation markers and spatial features of each individual cell. CODEX imaging revealed detailed composition of the immune cell niche in human liver cancer tissue allowing for further distinct spatial analysis including infiltration analysis and nearest-neighbor analysis. We found impaired infiltration of CD163+ macrophages, granulocytes, CD8+ T cells, NK cells and MAIT cells into human HCC tumors compared to unaffected liver tissue. whereas regulatory T cells accumulated in tumor tissue. Flow cytometry data correlated well with image-based immune phenotyping. Beyond that, whole slide imaging allowed for the identification of the tumor-to-liver interface as a unique site of immune cell inhibition.

Conclusion: Here, we demonstrate that spatially resolved, single-cell analysis of human liver cancer tissue allows for the in-depth characterization of the immune cell composition of HCC. This tool can be used for biomarker research, to determine cellular functional states in intact tissue and to spatially and functionally quantify interactions between immune cells in the context of hepatocarcinogenesis.

Citation Format: Benjamin Ruf, Noemi Kedei, Matthias Bruhns, Sepideh Babaei, Vanessa V. Catania, Simon Wabitsch, Chi Ma, Bernd Heinrich, Varun Subramanyam, Merrill K. Stovroff, Layla T. Greten, Alexander Kroemer, Manfred Claassen, Tim F. Greten, Firouzeh Korangy. Highly multiplexed spatial analysis of the HCC tumor immune microenvironment using CODEX imaging [abstract]. In: Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; 2021 Oct 5-6. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(1 Suppl):Abstract nr P021.

EXTH-69. FUNCTIONAL GENOMICS UNCOVER GENETIC DEPENDENCIES IN ATRTS

Brain tumors are the leading cause of cancer-related deaths in children. Embryonal brain tumors including medulloblastoma and atypical teratoid rhabdoid tumors (ATRTs) account for 15% of all primary brain and CNS tumors under the age of 14 years, with ATRTs being most prevalent in infants. Despite intensive research efforts, survival estimates for ATRT patients stay relatively low as compared to other tumor entities with a median survival of around 17 months. We here describe genome-wide CRISPR/Cas9 knockout screens in combination with small-molecule drug assays to identify targetable vulnerabilities in ATRTs. Based on functional genomic screening revealing ATRT context-specific genetic vulnerabilities (n = 671 genes), we successfully generated a small-molecule library that shows preferential activity in ATRT cells as compared to a broad selection of other human cancer cell lines. Of note, none of these drugs differentially affect ATRT cells from distinct molecular subgroups, suggesting that top candidate inhibitors might serve as pan-ATRT therapeutic avenues. CDK4/6 inhibitors, among the most potent drugs in our library, are capable of inhibiting tumor growth due to mutual exclusive dependency of ATRTs on either CDK4 or CDK6. Our approach might serve as a blueprint for fostering the identification of functionally-instructed therapeutic strategies in other incurable diseases beyond ATRT, whose genomic profiles also lack actionable alterations so far.

Targeting extracellular and juxtamembrane FGFR2 mutations in chemotherapy-refractory cholangiocarcinoma

Intrahepatic cholangiocarcinoma (iCCA) has emerged as a promising candidate for precision medicine, especially in the case of activating FGFR2 gene fusions. In addition to fusions, a considerable fraction of iCCA patients reveals FGFR2 mutations, which might lead to uncontrolled activation of the FGFR2 pathway but are mostly of unknown functional significance. A current challenge for molecular tumor boards (MTB) is to predict the functional consequences of such FGFR2 alterations to guide potential treatment decisions. We report two iCCA patients with extracellular and juxtamembrane FGFR2 mutations. After in silico investigation of the alterations and identification of activated FGFR2 downstream targets in tumor specimens by immunohistochemistry and transcriptome analysis, the MTB recommended treatment with an FGFR-inhibiting tyrosine kinase inhibitor. Both patients developed a rapidly detectable and prolonged partial response to treatment. These two cases suggest an approach to characterize further detected FGFR2 mutations in iCCA to enable patients´ selection for a successful application of the FGFR -inhibiting drugs.

P–285 Controlling semi-invasive activity of human endometriotic stromal cells by inhibiting NF-kB signaling pathway using aloe-emodin or aspirin

Study question

Does inhibiting nuclear factor-kB (NF-kB) signaling by aloe-emodin (AE) or aspirin (Asp), as anti-inflammatory compounds, suppress the invasive activity of stage IV human endometriotic stromal cells?

Summary answer

Eutopic endometriotic stromal cells (EuESCs) seem to have a semi-invasive activity which is largely suppressed by AE or Asp.

What is known already

Inflammation and its master regulator, NF-kB, have been implicated in the development of endometriosis. Inhibition of NF-kB pathway using small molecules ameliorated disease progression and reduced the lesion size; nevertheless, underlying mechanism is not fully understood.

Study design, size, duration

In this cross-sectional study, a total of 8 infertile patients with proven endometriosis and 8 women without endometriosis (Control group) undergoing infertility treatment cycles, were enrolled between October 2018 and December 2019. The invasiveness of collected endometriotic stromal cells before and after treatment with AE or Asp, was analyzed and compared with the control group.

Participants/materials, setting, methods

The eutopic endometriotic and healthy endometrial biopsies were digested and the single cells were cultured. Gene and protein expression of proliferation, adhesion, and invasion markers of eutopic endometriotic stromal cells (EuESCs) with and without treatment with AE or Asp, as well as control endometrial stromal cells (CESCs) were analyzed using q-PCR and immunofluorescence staining, respectively. Cell migration capacity was assessed by wound closure assay.

Main results and the role of chance

We observed an association between NF-kB overexpression and higher proliferation/adhesion capacity in EuESCs. TNF-α, as a known NF-kB inducer, further potentiated this association. EuESCs at stage IV, displayed silent invasive and migratory behaviors. Pretreatment of EuESCs with AE or Asp significantly attenuated NF-kB expression and reduced proliferative, adhesive, invasive and migratory activity.

Limitations, reasons for caution

Due to some adverse effects observed following treatment with AE or Asp on the normal activity of EuESCs, more investigations on possible toxicity of the treatment, must be considered.

Wider implications of the findings: We suggest that both Asp and AE (as potent NF-kB inhibitors) may be useful as a supplement to conventional endometriosis treatments.

Trial registration number

Not applicable

Distinct immunological signatures discriminate severe COVID-19 from non-SARS-CoV-2-driven critical pneumonia

Immune profiling of COVID-19 patients has identified numerous alterations in both innate and adaptive immunity. However, whether those changes are specific to SARS-CoV-2 or driven by a general inflammatory response shared across severely ill pneumonia patients remains unknown. Here, we compared the immune profile of severe COVID-19 with non-SARS-CoV-2 pneumonia ICU patients using longitudinal, high-dimensional single-cell spectral cytometry and algorithm-guided analysis. COVID-19 and non-SARS-CoV-2 pneumonia both showed increased emergency myelopoiesis and displayed features of adaptive immune paralysis. However, pathological immune signatures suggestive of T cell exhaustion were exclusive to COVID-19. The integration of single-cell profiling with a predicted binding capacity of SARS-CoV-2 peptides to the patients' HLA profile further linked the COVID-19 immunopathology to impaired virus recognition. Toward clinical translation, circulating NKT cell frequency was identified as a predictive biomarker for patient outcome. Our comparative immune map serves to delineate treatment strategies to interfere with the immunopathologic cascade exclusive to severe COVID-19.

Expression Quantitative Trait Locus Analysis in Systemic Sclerosis Identifies New Candidate Genes Associated With Multiple Aspects of Disease Pathology

OBJECTIVE

To identify the genetic variants that affect gene expression (expression quantitative trait loci [eQTLs]) in systemic sclerosis (SSc) and to investigate their role in the pathogenesis of the disease.

METHODS

We performed an eQTL analysis using whole-blood sequencing data from 333 SSc patients and 524 controls and integrated them with SSc genome-wide association study (GWAS) data. We integrated our findings from expression modeling, differential expression analysis, and transcription factor binding site enrichment with key clinical features of SSc.

RESULTS

We detected 49,123 validated cis-eQTLs from 4,539 SSc-associated single-nucleotide polymorphisms (SNPs) (P_GWAS < 10^-5 ). A total of 1,436 genes were within 1 Mb of the 4,539 SSc-associated SNPs. Of those 1,436 genes, 565 were detected as having ≥1 eQTL with an SSc-associated SNP. We developed a strategy to prioritize disease-associated genes based on their expression variance explained by SSc eQTLs (r² > 0.05). As a result, 233 candidates were identified, 134 (58%) of them associated with hallmarks of SSc and 105 (45%) of them differentially expressed in the blood cells, skin, or lung tissue of SSc patients. Transcription factor binding site analysis revealed enriched motifs of 24 transcription factors (5%) among SSc eQTLs, 5 of which were found to be differentially regulated in the blood cells (ELF1 and MGA), skin (KLF4 and ID4), and lungs (TBX4) of SSc patients. Ten candidate genes (4%) can be targeted by approved medications for immune-mediated diseases, of which only 3 have been tested in clinical trials in patients with SSc.

CONCLUSION

The findings of the present study indicate a new layer to the molecular complexity of SSc, contributing to a better understanding of the pathogenesis of the disease.

Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases

OBJECTIVE

Clinical heterogeneity, a hallmark of systemic autoimmune diseases, impedes early diagnosis and effective treatment, issues that may be addressed if patients could be classified into groups defined by molecular pattern. This study was undertaken to identify molecular clusters for reclassifying systemic autoimmune diseases independently of clinical diagnosis.

METHODS

Unsupervised clustering of integrated whole blood transcriptome and methylome cross-sectional data on 955 patients with 7 systemic autoimmune diseases and 267 healthy controls was undertaken. In addition, an inception cohort was prospectively followed up for 6 or 14 months to validate the results and analyze whether or not cluster assignment changed over time.

RESULTS

Four clusters were identified and validated. Three were pathologic, representing "inflammatory," "lymphoid," and "interferon" patterns. Each included all diagnoses and was defined by genetic, clinical, serologic, and cellular features. A fourth cluster with no specific molecular pattern was associated with low disease activity and included healthy controls. A longitudinal and independent inception cohort showed a relapse-remission pattern, where patients remained in their pathologic cluster, moving only to the healthy one, thus showing that the molecular clusters remained stable over time and that single pathogenic molecular signatures characterized each individual patient.

CONCLUSION

Patients with systemic autoimmune diseases can be jointly stratified into 3 stable disease clusters with specific molecular patterns differentiating different molecular disease mechanisms. These results have important implications for future clinical trials and the study of nonresponse to therapy, marking a paradigm shift in our view of systemic autoimmune diseases.

Intrarenal activation of adaptive immune effectors is associated with tubular damage and impaired renal function in lupus nephritis

Objectives

Chronic renal impairment remains a feared complication of lupus nephritis (LN). The present work aimed at identifying mechanisms and markers of disease severity in renal tissue samples from patients with LN.

Methods

We performed high-throughput transcriptomic studies (Illumina HumanHT-12 v4 Expression BeadChip) on archived kidney biopsies from 32 patients with LN and eight controls (pretransplant donors). Histological staging (glomerular and tubular scores) and immunohistochemistry experiments were performed on the same and on a replication set of 37 LN kidney biopsy samples.

Results

A group of LN samples was identified by unsupervised clustering studies based on their gene expression features, that is, the overexpression of transcripts involved in antigen presentation, T and B cell activation. These samples were characterised by a significantly lower estimated glomerular filtration rate (eGFR) at the time of biopsy (T0) compared with the other systemic lupus erythematosus samples. Yet, apparent disease duration at T0, double-stranded DNA antibody titres at T0 and other relevant characteristics (serum C3, proteinuria, histological scores, numbers of previous flares) were not different between groups.

Immunohistochemistry studies confirmed the association between interstitial infiltration by adaptive immune effectors and decreased renal function in the same and in a replication group of LN kidney biopsies. This was associated with transcriptomic, histological and immunohistochemical evidence of renal tubular cell involvement.

Conclusion

Interstitial infiltration of LN kidney biopsies by adaptive immune effectors is associated with impaired renal tubular cell function and decreased eGFR. These results open new perspectives in evaluating and treating patients with LN, focusing on intrarenal mechanisms of immune cell activation.

Production and Characterization of a Novel Monoclonal Antibody Against Human Sortilin

Sortilin, as a member of Vps10p-domain sorting receptor family, is overexpressed in a number of malignancies, including ovarian carcinoma. Antibodies against sortilin may contribute to further clarification of sortilin functional activities in signal transduction, intracellular sorting of proteins, and endocytosis. The aim of this study was to produce a monoclonal antibody against a synthetic peptide derived from extracellular N-terminal region of sortilin to be used as a tool for investigating sortilin characteristics in ovarian carcinoma. A synthetic peptide derived from the last 50 amino acids of extracellular domain of sortilin protein was selected and conjugated to keyhole limpet hemocyanin and used to immunize mice. The anti-sortilin monoclonal antibody (MAb), clone 2D8, was purified from supernatant of final hybridoma clone using peptide-affinity chromatography column. Reactivity of antibody with the immunizing peptide was assessed in ELISA. Furthermore, flow cytometry and Western blot analyses were used to investigate the reactivity of antibody with its target in a panel of ovarian carcinoma cell lines or tissues. MAb 2D8 was able to recognize the coated immunizing peptide in ELISA and detect its protein target, sortilin, in flow cytometry and Western blot analyses. The achieved data suggest that the developed monoclonal antibody may be applicable as a research tool for detection of sortilin protein in Western blot as well as flow cytometry tests.

Compassionate behaviours of clinical nurses in Iran: an ethnographic study

AIM

The aim of this research was to explore compassionate behaviour in Iranian nurses.

BACKGROUND

Nurses provide high-quality care with compassion. It is useful to improve relationships between nurses and patients to give them more confidence in the care process. It is important to consider how compassion is demonstrated by nurses and to explore different compassionate behaviours in different cultures.

METHODS

This ethnographic study was conducted with 20 nurses and 12 patients in six medical and four surgical wards in Iran in 2014-2015. Data were collected through fieldwork observations and semi-structured deep interviews and field notes. We analysed data using constant comparison based on Strauss and Corbin method.

RESULTS

Results of the present study showed four cultural themes; love expression and compassion in the form of non-verbal emotional behaviours, empathy with others, emotional supports of patients at bedside and non-caring behaviours.

CONCLUSION

The findings of this study described compassionate behaviours in nurses. Compassionate behaviours of nurses are closely related to the culture issues. These are formed by communication between nurses and patients, and nurses and patients' families during nursing care. Thus improving compassionate behaviours is highly significant in nursing.

IMPLICATIONS FOR NURSING AND/HEALTH POLICY

Nurses, instructors and policy-makers can use the results of this study. They should consider compassionate care as an important component of patient-centred care. Compassion should be considered in nursing and practical educational programmes. To develop the compassionate care, it is vital to make changes in the dominant philosophy and culture of caring environments. Nurses should receive appropriate training with focus on holistic and patient-centred approaches.

Physalis alkekengi and Alhagi maurorum ameliorate the side effect of cisplatin-induced nephrotoxicity

Cisplatin is frequently being used for the treatment of different tumors, although the application of this agent is associated with nephrotoxicity. Here, we explored the antioxidant and anti-inflammatory activities of Physalis alkekengi and Alhagi maurorum; 400 mg kg(-1) per day P. alkekengi and 100 mg kg(-1) per day A. maurorum were administered in rats, orally for 10 days after a single dose of 7 mg kg(-1) intraperitoneal cisplatin. The concentrations of creatinine, urea-nitrogen, and relative and absolute excretion of sodium/potassium were evaluated before/after therapy. Levels of malondialdehyde (MDA) and ferric-reducing antioxidant power (FRAP) were measured to assess the oxidative stress induced by cisplatin. Moreover, tissues sections were used for histological analyses and evaluation of the degree of tissue damage. Cisplatin increased serum levels of creatinine and urea-nitrogen, relative/absolute excretion of sodium/potassium, and MDA, whereas decreased FRAP level. Interestingly, P. alkekengi or A. maurorum were able to reduce the level of the renal function markers as well as the levels of sodium/potassium. This effect was more pronounced by P. alkekengi. Moreover, cisplatin induced pathological damage in kidney, whereas treatment with these agents improved this condition. Our findings demonstrate the potential therapeutic impact of P. alkekengi and A. maurorum for improving cisplatin-induced nephrotoxicity, supporting further investigations on the novel potential clinical application of these agents for patients being treated with cisplatin to ameliorate cisplatin-induced nephrotoxicity.

Enhanced skin penetration of lidocaine through encapsulation into nanoethosomes and nanostructured lipid carriers: a comparative study

Lipid based nanoparticles have become a major research object in topical drug delivery to enable drugs to pass the stratum corneum and reach the desired skin layer. The present investigation deals with the encapsulation of lidoacine into nanostructured lipid carriers (NLCs) and nanoethosomes for improving its dermal delivery and consequently local anesthetic efficacy. Concurrently these two topical delivery systems were compared. Lidocaine-loaded NLCs and nanoethosomes were characterized by various techniques and used for an in vitro skin penetration study using excised rat skin and Franz diffusion cells. The nanoparticles were tracked in the skin by following the Rhodamine-labled nanocarriers under fluorescent microscopy. Optimized lidocaine-loaded NLCs (size 96 nm, zeta potential -13.7 mV, encapsulation efficiency (EE) % 69.86% and loading capacity (LC) % 10.47%) and nanoethosomes (size 105.4 nm, zeta potential -33.6 mV, EE 40.14% and LC 8.02%) were chosen for a skin drug delivery study. Higher skin drug deposition of NLCs and nanoethosomal formulations compared to lidocaine hydroalcoholic solution represented a better localization of the drug in the skin. NLC formulation showed the lowest entered drug in the receptor phase of Franz diffusion cell in comparison with nanoethosomes and hydroalcoholic solution confirming the highest skin accumulation of drug. Both colloidal systems showed superiority over the drug solution for dermal delivery of lidocaine, however, NLC exhibited more promising characteristics than nanoethosomes regarding drug loading and skin targeted delivery.

Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex

The three dimensional conformation of the genome in the cell nucleus influences important biological processes such as gene expression regulation. Recent studies have shown a strong correlation between chromatin interactions and gene co-expression. However, predicting gene co-expression from frequent long-range chromatin interactions remains challenging. We address this by characterizing the topology of the cortical chromatin interaction network using scale-aware topological measures. We demonstrate that based on these characterizations it is possible to accurately predict spatial co-expression between genes in the mouse cortex. Consistent with previous findings, we find that the chromatin interaction profile of a gene-pair is a good predictor of their spatial co-expression. However, the accuracy of the prediction can be substantially improved when chromatin interactions are described using scale-aware topological measures of the multi-resolution chromatin interaction network. We conclude that, for co-expression prediction, it is necessary to take into account different levels of chromatin interactions ranging from direct interaction between genes (i.e. small-scale) to chromatin compartment interactions (i.e. large-scale).

Regulatory elements can target genes over large genomic distances through long-range chromatin interactions. These interactions arise as a result of the three-dimensional (3D) conformation of chromosomes in the cell nucleus. This 3D conformation can also result in the co-localization of co-regulated genes. To investigate this, we asked whether genome-wide chromatin interactions can predict co-expression patterns of genes. To address this question, we characterized 3D interactions between genes, captured by Hi-C measurements, by a network, termed chromatin interaction network (CIN). We applied scale-aware topological measures to the network to comprehensively characterize the chromatin interactions at different scales, ranging from direct interaction between gene pairs to chromatin compartment interactions. We then used multi-scale chromatin interactions to predict spatial co-expression patterns in the mouse cortex. The results show that the prediction performance improves when scale-aware topological measures of the multi-resolution chromatin interaction network are used.

3D hotspots of recurrent retroviral insertions reveal long-range interactions with cancer genes

Genomically distal mutations can contribute to the deregulation of cancer genes by engaging in chromatin interactions. To study this, we overlay viral cancer-causing insertions obtained in a murine retroviral insertional mutagenesis screen with genome-wide chromatin conformation capture data. Here we find that insertions tend to cluster in 3D hotspots within the nucleus. The identified hotspots are significantly enriched for known cancer genes, and bear the expected characteristics of bona fide regulatory interactions, such as enrichment for transcription factor-binding sites. In addition, we observe a striking pattern of mutual exclusive integration. This is an indication that insertions in these loci target the same gene, either in their linear genomic vicinity or in their 3D spatial vicinity. Our findings shed new light on the repertoire of targets obtained from insertional mutagenesis screening and underline the importance of considering the genome as a 3D structure when studying effects of genomic perturbations.

Integration of gene expression and DNA-methylation profiles improves molecular subtype classification in acute myeloid leukemia

Background

Acute Myeloid Leukemia (AML) is characterized by various cytogenetic and molecular abnormalities. Detection of these abnormalities is important in the risk-classification of patients but requires laborious experimentation. Various studies showed that gene expression profiles (GEP), and the gene signatures derived from GEP, can be used for the prediction of subtypes in AML. Similarly, successful prediction was also achieved by exploiting DNA-methylation profiles (DMP). There are, however, no studies that compared classification accuracy and performance between GEP and DMP, neither are there studies that integrated both types of data to determine whether predictive power can be improved.

Approach

Here, we used 344 well-characterized AML samples for which both gene expression and DNA-methylation profiles are available. We created three different classification strategies including early, late and no integration of these datasets and used them to predict AML subtypes using a logistic regression model with Lasso regularization.

Results

We illustrate that both gene expression and DNA-methylation profiles contain distinct patterns that contribute to discriminating AML subtypes and that an integration strategy can exploit these patterns to achieve synergy between both data types. We show that concatenation of features from both data sets, i.e. early integration, improves the predictive power compared to classifiers trained on GEP or DMP alone. A more sophisticated strategy, i.e. the late integration strategy, employs a two-layer classifier which outperforms the early integration strategy.

Conclusion

We demonstrate that prediction of known cytogenetic and molecular abnormalities in AML can be further improved by integrating GEP and DMP profiles.

Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion

Background

Delineating the molecular drivers of cancer, i.e. determining cancer genes and the pathways which they deregulate, is an important challenge in cancer research. In this study, we aim to identify pathways of frequently mutated genes by exploiting their network neighborhood encoded in the protein-protein interaction network. To this end, we introduce a multi-scale diffusion kernel and apply it to a large collection of murine retroviral insertional mutagenesis data. The diffusion strength plays the role of scale parameter, determining the size of the network neighborhood that is taken into account. As a result, in addition to detecting genes with frequent mutations in their genomic vicinity, we find genes that harbor frequent mutations in their interaction network context.

Results

We identify densely connected components of known and putatively novel cancer genes and demonstrate that they are strongly enriched for cancer related pathways across the diffusion scales. Moreover, the mutations in the clusters exhibit a significant pattern of mutual exclusion, supporting the conjecture that such genes are functionally linked. Using multi-scale diffusion kernel, various infrequently mutated genes are found to harbor significant numbers of mutations in their interaction network neighborhood. Many of them are well-known cancer genes.

Conclusions

The results demonstrate the importance of defining recurrent mutations while taking into account the interaction network context. Importantly, the putative cancer genes and networks detected in this study are found to be significant at different diffusion scales, confirming the necessity of a multi-scale analysis.

Unilateral absence of latissimus dorsi muscle

Latissimus dorsi (LD) is the broadest muscle of the back responsible for extension and adduction of shoulder. The authors report a case of isolated unilateral absence of the latissimus dorsi muscle observed during an ablative surgical procedure and flap reconstruction. The left LD muscle was completely absent in our patient and no tendon fibers belonging to this muscle could be observable on further dissection. The surrounding muscle anatomy was normal and in place suggesting a developmental etiology for its absence. Awareness of this possible variation is of importance in considering reconstructive options.

Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks

The supervised learning of recurrent neural networks well-suited for prediction of protein secondary structures from the underlying amino acids sequence is studied. Modular reciprocal recurrent neural networks (MRR-NN) are proposed to model the strong correlations between adjacent secondary structure elements. Besides, a multilayer bidirectional recurrent neural network (MBR-NN) is introduced to capture the long-range intramolecular interactions between amino acids in formation of the secondary structure. The final modular prediction system is devised based on the interactive integration of the MRR-NN and the MBR-NN structures to arbitrarily engage the neighboring effects of the secondary structure types concurrent with memorizing the sequential dependencies of amino acids along the protein chain. The advanced combined network augments the percentage accuracy (Q₃) to 79.36% and boosts the segment overlap (SOV) up to 70.09% when tested on the PSIPRED dataset in three-fold cross-validation.

Administration of exogenous endothelin-1 following vascular balloon injury: early and late effects on intimal hyperplasia

Administration of exogenous endothelin-1 (ET-1) has been shown to stimulate neointimal hyperplasia following arterial balloon angioplasty (BA). However, the specific effects of ET-1 on the cellular and extracellular matrix response of the vessel wall after balloon injury and the persistence of these ET-1 effects have not been studied. The objectives of this study were to determine the acute (1 week) and long term (10 weeks) effects of administering exogenous ET-1 after arterial BA on neointimal hyperplasia, collagen synthesis and content, cellular proliferation, and ET(A) and ET(B) receptor expression. Thirty-one rabbits were randomized to receive subcutaneous ET-1 (500 pmol/kg/day for 1 week) or placebo time-release pellets and sacrificed at either 1 or 10 weeks after BA. At 1 week, there was a significant two-fold increase in intimal cross-sectional area (CSA) in ET-1 treated animals compared with placebo. ET-1 treated animals showed significant increases in collagen synthesis (ten-fold) and collagen content (three-fold) compared to placebo treated animals. ET-1 treated animals also had a significant increase (two-fold) in proliferation rates. In addition, ET(A) and ET(B) receptor expression were significantly upregulated in ET-1 treated animals. By 10 weeks these stimulatory effects on intimal CSA and collagen content were no longer evident with a 'catch up' phenomenon observed in the placebo treated animals. Similarly, ET(A) and ET(B) mRNA levels had declined significantly in both groups. Therefore, exogenous ET-1 acutely stimulates extracellular and cellular processes including increased expression of ET(A) and ET(B) receptors contributing to intimal hyperplasia. However, these effects are transient and not maintained long term after withdrawal of exogenous ET-1 stimulation.

Differential effects of pentaerythritol tetranitrate and nitroglycerin on the development of tolerance and evidence of lipid peroxidation: a human in vivo study

OBJECTIVES

We investigated the development of nitrate tolerance after continuous exposure to nitroglycerin (GTN) as compared with pentaerythritol tetranitrate (PETN) in humans.

BACKGROUND

Sustained therapy with GTN causes tolerance and has been associated with increased production of free oxygen radicals by the endothelium. Pentaerythritol tetranitrate is an organic nitrate that has been used in the therapy of angina. There have been no investigations concerning the development of tolerance to PETN in humans. Animal investigations suggested that continuous therapy with PETN does not cause increased free radical production or hemodynamic tolerance.

METHODS

We randomized 30 healthy volunteers to continuous GTN (0.6 mg/h/24 h), long-acting PETN (60 mg orally three times a day) or no treatment (control group) for seven days. We studied systemic blood pressure responses and venous volume responses to GTN with strain-gauge plethysmography. The levels of cytotoxic aldehydes and isoprostanes were measured as markers of free radical-mediated lipid peroxidation.

RESULTS

Tolerance, as demonstrated by blood pressure and forearm plethysmography, developed in the GTN group and was absent in the PETN group (p < 0.05). Therapy with GTN was associated with a significant increase in plasma markers of lipid peroxidation. This response was not observed in those treated with PETN (isoprostanes: control: 38 +/- 5; GTN: 59 +/- 6; PETN: 38 +/- 3 microg/ml; p < 0.005).

CONCLUSIONS

Treatment with PETN does not cause tolerance and is not associated with evidence of increased free radical production.

Blockade of endothelin receptors markedly reduces atherosclerosis in LDL receptor deficient mice: role of endothelin in macrophage foam cell formation

OBJECTIVE

We evaluated the direct effects of long-term blockade of ET(A) and ET(B) receptors using a mixed endothelin (ET) receptor antagonist, LU224332, in the low density lipoprotein receptor (LDL-R) knockout mouse model of atherosclerosis.

METHODS

Four groups of LDL-R deficient mice were studied: control mice fed normal chow (group I); mice fed a high cholesterol (HC, 1.25%) diet alone (group II), HC fed animals treated with LU224332 (group III); and mice fed normal chow treated with the LU compound (group IV). All treatments were continued for 8 weeks at which time the animals were sacrificed and the aortae were removed and stained with oil red O. Atherosclerotic area (AA) was determined by quantitative morphometry and normalized relative to total aortic area (TA).

RESULTS

Cholesterol feeding resulted in a marked increased in total plasma cholesterol ( approximately 15 fold) and widespread aortic atherosclerosis (AA/TA: group I: 0.013+/-0.007; group II: 0.33+/-0. 11; P<0.001). Atherosclerotic lesions were characterized by immunohistochemistry as consisting mainly of macrophages which also showed high levels of ET-1 expression. Treatment with ET antagonist significantly reduced the development of atherosclerosis (AA/TA: group III: 0.19+/-0.07, P<0.01 vs. group II), without altering plasma cholesterol levels and blood pressure. The direct effect of LU224332 on macrophage activation and foam-cell formation was determined in vitro using a human macrophage cell line, THP-1. Treatment of the THP-1 cells with LU224332 significantly reduced cholesterol ester and triacylglycerol accumulation and foam-cell formation on exposure to oxidized LDL (P<0.01 and P<0.05, respectively).

CONCLUSION

We conclude that a nonselective ET receptor antagonist substantially inhibited the development of atherosclerosis in a genetic model of hyperlipidemia, possibly by inhibiting macrophage foam-cell formation, suggesting a role for these agents in the treatment and prevention of atherosclerotic vascular disease.

Homocysteine, lipoprotein(a), and restenosis after percutaneous transluminal coronary angioplasty: a prospective study

BACKGROUND

Restenosis complicates 30% to 40% of angioplasty procedures and may be unrelated to traditional coronary risk factors. Homocysteine, lipoprotein(a), and methylenetetrahydrofolate reductase (MTHFR 677T) (a genetic determinant of plasma homocysteine concentrations) are novel risk factors for coronary artery disease. Their roles in restenosis are unclear, and the potential synergism between homocysteine and lipoprotein(a) has not previously been studied. The objective of this study was to determine the relations among homocysteine, lipoprotein (a), MTHFR 677T, and restenosis after percutaneous transluminal coronary angioplasty.

METHODS

This prospective study enrolled patients with successful elective percutaneous transluminal coronary angioplasty or stenting of a single, de novo, native coronary lesion. Fasting blood was drawn the morning of the procedure for homocysteine, lipoprotein(a), and MTHFR 677T. Follow-up angiography was performed 6 months after the procedure or earlier if clinically indicated. All cineangiograms were analyzed quantitatively.

RESULTS

A total of 144 (92%) of 156 eligible patients underwent follow-up coronary angiography. The overall angiographic restenosis rate (residual stenosis >50%) was 31%. Mean homocysteine concentration was 10.1 +/- 3.7 micromol/L. Plasma homocysteine concentrations were not significantly different in patients with or without angiographic restenosis (9.6 +/- 3.3 vs 10.3 +/- 3.8 micromol/L; P =.31). Mean lipoprotein(a) concentration was 21.2 +/- 20.1 mg/dL. Plasma lipoprotein(a) concentrations were not significantly different in patients with or without restenosis (21.9 +/- 21.8 vs 20.9 +/- 19.5 mg/dL). Homozygosity for MTHFR 677T was present in 6.5% and was not associated with increased restenosis. No interaction between homocysteine and lipoprotein(a) was detected.

CONCLUSIONS

Homocysteine, lipoprotein(a), and MTHFR 677T are not associated with restenosis after percutaneous transluminal coronary angioplasty.

Role of nitric oxide in the angiogenic response in vitro to basic fibroblast growth factor

Angiogenesis is a complex process that involves the activation of quiescent endothelial cells (ECs) to a proliferative and migratory phenotype and, subsequently, their redifferentiation to form vascular tubes. We hypothesized that NO contributes to angiogenesis by terminating the proliferative action of angiogenic growth factors and initiating a genetic program of EC differentiation. Human umbilical vein ECs (HUVECs) and calf pulmonary artery ECs (CPAECs) were grown directly on plastic dishes or on three-dimensional fibrin matrices. In the absence of fibrin, treatment with NO-donor compounds, such as S-nitroso-N-acetylpenicillamine (SNAP, 0.1 and 0.4 mmol/L), produced a dose-dependent inhibition of proliferation in both cell lines, whereas the inhibition of endogenous NO production using NG-nitro-L-arginine methyl ester (L-NAME, 1 mmol/L) or NG-monomethyl-L-arginine (L-NMMA, 1 mmol/L) significantly increased proliferation of the CPAECs. The addition of basic fibroblast growth factor (bFGF, 30 ng/mL) increased the expression of endothelial NO synthase mRNA and the production of NO in both cell types when cultured on three-dimensional fibrin gels and produced profound morphological changes characterized by the appearance of extensive capillary-like vascular structures and the loss of EC monolayers. These changes were quantified by measuring total tube length per low-power field (x100), and a differentiation index was derived using the ratio of tube length over area covered by residual EC monolayer. In the absence of additional angiogenic factors, the differentiation index was low for both HUVECs and CPAECs (control, 1.16+/-0.19 and 2.07+/-0.87, respectively). Treatment with bFGF increased the differentiation index significantly in both cell types (10.59+/-2.03 and 20.02+/-5.01 for HUVECs and CPAECs, respectively; P<.05 versus control), and the addition of SNAP (0.4 mmol/L) mimicked the angiogenic response to bFGF (8.57+/-1.34 and 12.20+/-3.49 for HUVECs and CPAECs, respectively; P<.05 versus control). Moreover, L-NAME inhibited EC tube formation in response to bFGF in a dose-response manner, consistent with a role of endogenous NO production in EC differentiation in this angiogenic model. These findings suggest that NO may act as a crucial signal in the angiogenic response to bFGF, terminating the proliferative actions of angiogenic growth factors and promoting EC differentiation into vascular tubes.