scholarly journals Gene expression patterns in blood leukocytes discriminate patients with acute infections

Blood ◽  
2006 ◽  
Vol 109 (5) ◽  
pp. 2066-2077 ◽  
Author(s):  
Octavio Ramilo ◽  
Windy Allman ◽  
Wendy Chung ◽  
Asuncion Mejias ◽  
Monica Ardura ◽  
...  

Abstract Each infectious agent represents a unique combination of pathogen-associated molecular patterns that interact with specific pattern-recognition receptors expressed on immune cells. Therefore, we surmised that the blood immune cells of individuals with different infections might bear discriminative transcriptional signatures. Gene expression profiles were obtained for 131 peripheral blood samples from pediatric patients with acute infections caused by influenza A virus, Gram-negative (Escherichia coli) or Gram-positive (Staphylococcus aureus and Streptococcus pneumoniae) bacteria. Thirty-five genes were identified that best discriminate patients with influenza A virus infection from patients with either E coli or S pneumoniae infection. These genes classified with 95% accuracy (35 of 37 samples) an independent set of patients with either influenza A, E coli, or S pneumoniae infection. A different signature discriminated patients with E coli versus S aureus infections with 85% accuracy (34 of 40). Furthermore, distinctive gene expression patterns were observed in patients presenting with respiratory infections of different etiologies. Thus, microarray analyses of patient peripheral blood leukocytes might assist in the differential diagnosis of infectious diseases.

Harmful Algae ◽  
2016 ◽  
Vol 57 ◽  
pp. 35-38 ◽  
Author(s):  
Maria-Cecilia Lopez ◽  
Ricardo F. Ungaro ◽  
Henry V. Baker ◽  
Lyle L. Moldawer ◽  
Alison Robertson ◽  
...  

2015 ◽  
Vol 64 (11) ◽  
pp. 1437-1447 ◽  
Author(s):  
Sara J. Felts ◽  
Virginia P. Van Keulen ◽  
Adam D. Scheid ◽  
Kathleen S. Allen ◽  
Renee K. Bradshaw ◽  
...  

SLEEP ◽  
2011 ◽  
Vol 34 (2) ◽  
pp. 153-160 ◽  
Author(s):  
Abdelnaby Khalyfa ◽  
Sina A. Gharib ◽  
Jinkwan Kim ◽  
Oscar Sans Capdevila ◽  
Leila Kheirandish-Gozal ◽  
...  

2020 ◽  
Vol 20 ◽  
Author(s):  
Zsuzsanna Molnár ◽  
Zsófia Bánlaki ◽  
Anikó Somogyi ◽  
Zoltán Herold ◽  
Magdolna Herold ◽  
...  

Background: Type 2 diabetes (T2DM) and colorectal cancer (CRC) are both known to modulate gene expression patterns in peripheral blood leukocytes (PBLs). Objective : As T2DM has been shown to increase the incidence of CRC, we were prompted to check whether diabetes affects mRNA signatures in PBLs isolated from CRC patients. Methods : 22 patients were recruited to the study and classified into four cohorts (healthy controls; T2DM; CRC; CRC and T2DM). Relative expression levels of 573 cell signaling gene transcripts were determined by reverse transcription real-time PCR assays run on low-density OpenArray platforms. Enrichment analysis was performed with the g:GOSt profiling tool to order differentially expressed genes into functional pathways. Results : 49 genes were found to be significantly up- or downregulated in tumorous diabetic individuals as compared to tumor-free diabetic controls, while 11 transcripts were differentially regulated in patients with CRC versus healthy, tumor-free and non-diabetic controls. Importantly, these gene sets were completely distinct, implying that diabetes exerts profound influence on the transcription of signaling genes in CRC. The top 5 genes showing most significant expression differences in both contexts were PCK2, MAPK9, CCND1, HMBS, TLR3 (p≤ 0.0040) and CREBBP, PPIA, NFKBIL1, MDM2 and SELPLG (p0.0121), respectively. Functional analysis revealed that most significantly affected pathways were cytokine, interleukin and PI3K/Akt/mTOR signaling cascades as well as mitotic regulation. Conclusions : We propose that differentially expressed genes listed above might be potential biomarkers of CRC and should be studied further on larger patient groups. Diabetes might promote colorectal carcinogenesis by impairing signaling pathways in PBLs.


2019 ◽  
Author(s):  
Wenfa Ng

Although various immune cells could infiltrate the cellular and tissue environment surrounding a tumor, the tumor microenvironment nevertheless presents immunosuppressive conditions unfavorable for immune cells to conduct large scale attack on cancer cells. For example, T-cells that make it to the tumor microenvironment are typically non-functional in containing tumor growth. On the other hand, macrophages could infiltrate the tumor microenvironment and is an important cell type modulated by and which also modulates the tumor. Specifically, two variants of macrophages with different phenotypes are known to exhibit close interactions with tumors. Known as M1 and M2 macrophages, they present dichotomously different signals to the tumor. Specifically, M1 macrophages control tumor growth while M2 macrophages promote tumor growth. Thus, from a treatment perspective, it would be desirable to tune the phenotypes and cell differentiation program of macrophages towards the M1 subset. To do that, differential gene expression of macrophages in the M1 and M2 lineages must be understood. Such a goal could be achieved with the profiling of tumor associated macrophages from tumor biopsy samples for gene expression patterns characteristic of the two dominant macrophage lineages. Single cell RNA-sequencing conducted after flow cytometry sorting of M1 and M2 macrophages would highlight gene expression patterns associated with each lineage, and the cellular differentiation programs that prompted entry into particular macrophage subtype. Knowledge of gene expression pattern associated with each macrophage lineage is not useful for tuning their differentiation state unless specific transcription factor that trigger the regulon could be identified. To this end, transcription factors that have been upregulated in the differentiation program could be profiled from the transcriptome data, and help inform the design of vectors for targeted overexpression of specific transcription factor for modulating cellular differentiation of macrophage. Given their low immunogenicity, adeno-associated virus (AAV) could serve as vectors for ferrying the gene cassette containing specific transcription factors into macrophages. Delivery methods for the AAV could be via targeted local infusion of vectors to tumors or through the systemic circulation, but the latter approach would result in lower transfection efficiency. Collectively, possibility exists of tuning the differentiation state of macrophage associated with tumors for enabling tumor controlling lineage to be dominant. Such immuno-targeted therapy would harness the body’s macrophages for controlling tumor growth and represents a treatment option that may yield fewer side effects compared to conventional chemotherapy. But, identification of genes that control lineage-specific differentiation program and the delivery of gene cassette to macrophages for modulating their differentiation remain key challenges.


2019 ◽  
Author(s):  
Wenfa Ng

Although various immune cells could infiltrate the cellular and tissue environment surrounding a tumor, the tumor microenvironment nevertheless presents immunosuppressive conditions unfavorable for immune cells to conduct large scale attack on cancer cells. For example, T-cells that make it to the tumor microenvironment are typically non-functional in containing tumor growth. On the other hand, macrophages could infiltrate the tumor microenvironment and is an important cell type modulated by and which also modulates the tumor. Specifically, two variants of macrophages with different phenotypes are known to exhibit close interactions with tumors. Known as M1 and M2 macrophages, they present dichotomously different signals to the tumor. Specifically, M1 macrophages control tumor growth while M2 macrophages promote tumor growth. Thus, from a treatment perspective, it would be desirable to tune the phenotypes and cell differentiation program of macrophages towards the M1 subset. To do that, differential gene expression of macrophages in the M1 and M2 lineages must be understood. Such a goal could be achieved with the profiling of tumor associated macrophages from tumor biopsy samples for gene expression patterns characteristic of the two dominant macrophage lineages. Single cell RNA-sequencing conducted after flow cytometry sorting of M1 and M2 macrophages would highlight gene expression patterns associated with each lineage, and the cellular differentiation programs that prompted entry into particular macrophage subtype. Knowledge of gene expression pattern associated with each macrophage lineage is not useful for tuning their differentiation state unless specific transcription factor that trigger the regulon could be identified. To this end, transcription factors that have been upregulated in the differentiation program could be profiled from the transcriptome data, and help inform the design of vectors for targeted overexpression of specific transcription factor for modulating cellular differentiation of macrophage. Given their low immunogenicity, adeno-associated virus (AAV) could serve as vectors for ferrying the gene cassette containing specific transcription factors into macrophages. Delivery methods for the AAV could be via targeted local infusion of vectors to tumors or through the systemic circulation, but the latter approach would result in lower transfection efficiency. Collectively, possibility exists of tuning the differentiation state of macrophage associated with tumors for enabling tumor controlling lineage to be dominant. Such immuno-targeted therapy would harness the body’s macrophages for controlling tumor growth and represents a treatment option that may yield fewer side effects compared to conventional chemotherapy. But, identification of genes that control lineage-specific differentiation program and the delivery of gene cassette to macrophages for modulating their differentiation remain key challenges.


PLoS ONE ◽  
2009 ◽  
Vol 4 (9) ◽  
pp. e7037 ◽  
Author(s):  
Peter R. Sinnaeve ◽  
Mark P. Donahue ◽  
Peter Grass ◽  
David Seo ◽  
Jacky Vonderscher ◽  
...  

2005 ◽  
Vol 7 (5) ◽  
Author(s):  
Praveen Sharma ◽  
Narinder S Sahni ◽  
Robert Tibshirani ◽  
Per Skaane ◽  
Petter Urdal ◽  
...  

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