Biomedical subjects

Michael Angelo

Publications and source records attributed to Michael Angelo.

3 recordsLinked to original sources

PubMed2006

An S-nitrosothiol (SNO) synthase function of hemoglobin that utilizes nitrite as a substrate.

Red blood cells (RBCs) act as O(2)-responsive transducers of vasodilator and vasoconstrictor activity in lungs and tissues by regulating the availability of nitric oxide (NO). Vasodilation by RBCs is impaired in diseases characterized by hypoxemia. We have proposed that the extent to which RBCs constrict vs. dilate vessels is, at least partly, controlled by a partitioning between NO bound to heme iron and to Cysbeta93 thiol of hemoglobin (Hb). Hemes sequester NO, whereas thiols deploy NO bioactivity. In recent work, we have suggested that specific micropopulations of NO-liganded Hb could support the chemistry of S-nitrosohemoglobin (SNO-Hb) formation. Here, by using nitrite as the source of NO, we demonstrate that a (T state) micropopulation of a heme-NO species, with spectral and chemical properties of Fe(III)NO, acts as a precursor to SNO-Hb formation, accompanying the allosteric transition of Hb to the R state. We also show that at physiological concentrations of nitrite and deoxyHb, a S-nitrosothiol precursor is formed within seconds and produces SNO-Hb in high yield upon its prompt exposure to O(2) or CO. Deoxygenation/reoxygenation cycling of oxyHb in the presence of physiological amounts of nitrite also efficiently produces SNO-Hb. In contrast, high amounts of nitrite or delays in reoxygenation inhibit the production of SNO-Hb. Collectively, our data provide evidence for a physiological S-nitrosothiol synthase activity of tetrameric Hb that depends on NO-Hb micropopulations and suggest that dysfunction of this activity may contribute to the pathophysiology of cardiopulmonary and blood disorders.

Hemoglobins↗

PubMed2002

Prediction of central nervous system embryonal tumour outcome based on gene expression.

Embryonal tumours of the central nervous system (CNS) represent a heterogeneous group of tumours about which little is known biologically, and whose diagnosis, on the basis of morphologic appearance alone, is controversial. Medulloblastomas, for example, are the most common malignant brain tumour of childhood, but their pathogenesis is unknown, their relationship to other embryonal CNS tumours is debated, and patients' response to therapy is difficult to predict. We approached these problems by developing a classification system based on DNA microarray gene expression data derived from 99 patient samples. Here we demonstrate that medulloblastomas are molecularly distinct from other brain tumours including primitive neuroectodermal tumours (PNETs), atypical teratoid/rhabdoid tumours (AT/RTs) and malignant gliomas. Previously unrecognized evidence supporting the derivation of medulloblastomas from cerebellar granule cells through activation of the Sonic Hedgehog (SHH) pathway was also revealed. We show further that the clinical outcome of children with medulloblastomas is highly predictable on the basis of the gene expression profiles of their tumours at diagnosis.

Adolescent↗

PubMed2002

Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning.

Diffuse large B-cell lymphoma (DLBCL), the most common lymphoid malignancy in adults, is curable in less than 50% of patients. Prognostic models based on pre-treatment characteristics, such as the International Prognostic Index (IPI), are currently used to predict outcome in DLBCL. However, clinical outcome models identify neither the molecular basis of clinical heterogeneity, nor specific therapeutic targets. We analyzed the expression of 6,817 genes in diagnostic tumor specimens from DLBCL patients who received cyclophosphamide, adriamycin, vincristine and prednisone (CHOP)-based chemotherapy, and applied a supervised learning prediction method to identify cured versus fatal or refractory disease. The algorithm classified two categories of patients with very different five-year overall survival rates (70% versus 12%). The model also effectively delineated patients within specific IPI risk categories who were likely to be cured or to die of their disease. Genes implicated in DLBCL outcome included some that regulate responses to B-cell-receptor signaling, critical serine/threonine phosphorylation pathways and apoptosis. Our data indicate that supervised learning classification techniques can predict outcome in DLBCL and identify rational targets for intervention.

Antineoplastic Combined Chemotherapy Protocols↗