Project description:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors.

Project description:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors. A total of 191 RCC FFPE samples are analyzed including 63 clear cell RCC (ccRCC), 57 papillary RCC (pRCC), 35 chromophobe RCC (chrRCC) and 36 oncocytoma (OC). Two-color array-comparative genomic hybdrization on custom designed using RCC DNA as test and normal sex-matched DNA as reference.

Project description:Introduction: Tuberculosis (TB) is a serious human disease caused by the bacterium Mycobacterium tuberculosis (Mtb). One third of the world’s population is estimated to be latently infected with Mtb. Exact mechanisms and properties of latent TB infection (LTBI) are not fully elucidated. In essence, the pathogen can enter a dormant or latent state characterized by limited growth and metabolism, resulting in absence of clinical symptoms in the host, and most importantly by increased phenotypic tolerance to the commonly used medications, thereby allowing indefinite persistence in the human body. This persistence is the main reason why the current treatment of new-onset pulmonary TB is very long, consisting of a six months therapy of four antibiotics (rifampicin, isoniazid, pyrazinamide, and ethambutol for the first two months, and only rifampicin and isoniazid for the last four months). Current state of the art: To fight TB globally and more efficiently, it is essential to shorten the treatment for TB with new drugs that are efficient also against LTBI. To facilitate discovery of such drugs, in vitro models for LTBI can be used for high throughput screening of chemical libraries. Current in vitro models such as nutrient starvation (Betts et al. 2002), nutrient depletion (Hampshire et al. 2004; Rifat, Bishai, and Karakousis 2009), progressive hypoxia (Wayne and Hayes 1996), nitric oxide treatment (Martin I. Voskuil et al. 2003) and multiple stress (Deb et al. 2009) mimic the dormant state of Mtb, but the technical settings and equipment required for these models obstruct high throughput applications. Proposed model: The streptomycin (STR)-starved 18b model (SS18b) is a simple and drug discovery efficient Mtb latency model successfully applicable to both in vitro and in vivo settings (Zhang et al. 2012). It is based on the Mtb strain 18b, which is a STR-dependent mutant that enters a viable but nonreplicating state in the absence of STR (Hashimoto 1955). Despite the successful model, we know very little about 18b. How well does SS18b mimic the bacterial response to the complex host-pathogen interactions underlying LTBI, and how does the SS18b response compare to that of other dormancy models are still open questions. In this work we analyse the complete genome of 18b and describe the transcriptomic response of 18b to STR depletion.

Project description:Introduction: Tuberculosis (TB) is a serious human disease caused by the bacterium Mycobacterium tuberculosis (Mtb). One third of the worldâs population is estimated to be latently infected with Mtb. Exact mechanisms and properties of latent TB infection (LTBI) are not fully elucidated. In essence, the pathogen can enter a dormant or latent state characterized by limited growth and metabolism, resulting in absence of clinical symptoms in the host, and most importantly by increased phenotypic tolerance to the commonly used medications, thereby allowing indefinite persistence in the human body. This persistence is the main reason why the current treatment of new-onset pulmonary TB is very long, consisting of a six months therapy of four antibiotics (rifampicin, isoniazid, pyrazinamide, and ethambutol for the first two months, and only rifampicin and isoniazid for the last four months). Current state of the art: To fight TB globally and more efficiently, it is essential to shorten the treatment for TB with new drugs that are efficient also against LTBI. To facilitate discovery of such drugs, in vitro models for LTBI can be used for high throughput screening of chemical libraries. Current in vitro models such as nutrient starvation (Betts et al. 2002), nutrient depletion (Hampshire et al. 2004; Rifat, Bishai, and Karakousis 2009), progressive hypoxia (Wayne and Hayes 1996), nitric oxide treatment (Martin I. Voskuil et al. 2003) and multiple stress (Deb et al. 2009) mimic the dormant state of Mtb, but the technical settings and equipment required for these models obstruct high throughput applications. Proposed model: The streptomycin (STR)-starved 18b model (SS18b) is a simple and drug discovery efficient Mtb latency model successfully applicable to both in vitro and in vivo settings (Zhang et al. 2012). It is based on the Mtb strain 18b, which is a STR-dependent mutant that enters a viable but nonreplicating state in the absence of STR (Hashimoto 1955). Despite the successful model, we know very little about 18b. How well does SS18b mimic the bacterial response to the complex host-pathogen interactions underlying LTBI, and how does the SS18b response compare to that of other dormancy models are still open questions. In this work we analyse the complete genome of 18b and describe the transcriptomic response of 18b to STR depletion. 12 samples were analyzed. Four biological replicates were sampled during the exponential growth phase, in presence of streptomycin. Four biological replicates derived after 2 weeks of streptomycin depletion, and two biological replicates derive after 4 weeks of streptomycin depletion. Two replicates derive from the resubmission of streptomycin after four weeks of depletion.

Project description:Background: Tuberculosis (TB) remains a major public health problem, especially in developing countries, with 1.5 million deaths annually worldwide. Antibiotics are commonly used in the treatment of bacterial infections. As with most drugs, antibiotic treatment can also alter host metabolism, leading to adverse side-effects. Antibiotics can also interfere with the immune system, indirectly through the disturbance of the body’s microbiota or directly by modulating the functions of immune cells. It is therefore important to understand how antibiotic treatment modulates immune cell functions. Here we aim to evaluate the impact of first-line anti-TB drugs on the response of human macrophages infected with Mycobacterium tuberculosis (MTB). Results: Human macrophages were stimulated with heat-killed Mycobacterium tuberculosis (hk-MTB) and treated with isoniazid (INH), rifampicin (RIF), ethambutol, pyrazinamide (PZA) or amikacin (AMK). After 24h of treatment, RNA was collected and we characterized the genome-wide gene expression profiles of drug-treated cells by RNAseq. 556, 752 and 7 genes were differentially expressed in hk-MTB-stimulated macrophages upon RIF, PZA and EMB treatment respectively, whereas in uninfected macrophages, 448 and 11 genes were differentially expressed upon RIF and PZA treatment respectively. No genes were differentially expressed upon INH and AMK treatment. We classified all modulated genes by performing gene-set enrichment analysis. The gene set regulated by PZA in infected macrophages was significantly enriched for genes involved in Integral to lumenal side of endoplasmic reticulum membrane, Cytokine-mediated signaling pathway or Interferon-gamma-mediated signaling pathway. In hk-MTB-stimulated macrophages treated by RIF, we found an enrichment in Endoplasmic reticulum unfolded protein response, NADP binding or Lipid metabolic process. Conclusions: Our results highlight the importance to understand how antibiotic treatment modulates macrophage (Mφ) functions, and more generally, how it impacts the host immune response.

			Action	DRS
	ERR2677390_1.fastq.gz	Fastqsanger.gz
	ERR2677390_2.fastq.gz	Fastqsanger.gz
	ERR2677400_1.fastq.gz	Fastqsanger.gz
	ERR2677400_2.fastq.gz	Fastqsanger.gz
	ERR2677410_1.fastq.gz	Fastqsanger.gz

Dataset Information

Resistance testing for Ethambutol and Pyrazinamide in multidrug-resistant tuberculosis clinical isolates

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets