Protein networks linking Warburg and reverse Warburg effects to cancer cell metabolism.

It was 80 years after the Otto Warburg discovery of aerobic glycolysis, a major hallmark in the understanding of cancer. The Warburg effect is the preference of cancer cell for glycolysis that produces lactate even when sufficient oxygen is provided. "reverse Warburg effect" refers to the interstitial tissue communications with adjacent epithelium, that in the process of carcinogenesis, is needed to be explored. Among these cell-cell communications, the contact between epithelial cells; between epithelial cells and matrix; and between fibroblasts and inflammatory cells in the underlying matrix. Cancer involves dysregulation of Warburg and reverse Warburg cellular metabolic pathways. How these gene and protein-based regulatory mechanisms have functioned has been the basis for this review. The importance of the Warburg in oxidative phosphorylation suppression, with increased glycolysis in cancer growth and proliferation is emphasized. Studies that are directed at pathways that would be expected to shift cell metabolism to an increased oxidation and to a decrease in glycolysis are emphasized. Key enzymes required for oxidative phosphorylation, and affect the inhibition of fatty acid metabolism and glutamine dependence are conferred. The findings are of special interest to cancer pharmacotherapy. Studies described in this review are concerned with the effects of therapeutic modalities that are intimately related to the Warburg effect. These interactions described may be helpful as adjuvant therapy in controlling the process of proliferation and metastasis.

Diabetes-induced Proteome Changes Throughout Development.

BACKGROUND: Diabetes Mellitus (DM) is a multisystemic disease involving the homeostasis of insulin secretion by the pancreatic islet beta cells (ß-cells). It is associated with hypertension, renal disease, and arterial and arteriolar vascular diseases. DISCUSSION: The classification of diabetes is identified as type 1 (gene linked ß-cell destruction in childhood) and type 2 (late onset associated with ß-cell overload and insulin resistance in peripheral tissues. Type 1 diabetes is characterized by insulin deficiency, type 2 diabetes by both insulin deficiency and insulin resistance. The former is a genetically programmed loss of insulin secretion whereas the latter constitutes a disruption of the homeostatic relationship between the opposing activity of ß- cell insulin and alpha cell (α-cell) glucagon of the Islets of Langerhans. The condition could also occur in pregnancy, as a prenatal occurring event, possibly triggered by the hormonal changes of pregnancy combined with ß-cell overload. This review discusses the molecular basis of the biomolecular changes that occur with respect to glucose homeostasis and related diseases in DM. The underlying link between pancreatic, renal, and microvascular diseases in DM is based on oxidative stress and the Unfolded Protein Response (UPR). CONCLUSION: Studying proteome changes in diabetes can deepen our understanding of the biomolecular basis of disease and help us acquire more efficient therapies.

A shared comparison of diabetes mellitus and neurodegenerative disorders.

Diabetes mellitus (DM), one of the most prevalent metabolic diseases in the world population, is associated with a number of comorbid conditions including obesity, pancreatic endocrine changes, and renal and cardio-cerebrovascular alterations, coupled with peripheral neuropathy and neurodegenerative disease, some of these disorders are bundled into metabolic syndrome. Type 1 DM (T1DM) is an autoimmune disease that destroys the insulin-secreting islet cells. Type 2 DM (T2DM) is diabetes that is associated with an imbalance in the glucagon/insulin homeostasis that leads to the formation of amyloid deposits in the brain, pancreatic islet cells, and possibly in the kidney glomerulus. There are several layers of molecular pathologic alterations that contribute to the DM metabolic pathophysiology and its associated neuropathic manifestations. In this review, we describe the general signature metabolic features of DM and the cross-talk with neurodegeneration. We will assess the underlying molecular key players associated with DM-induced neuropathic disorders that are associated with both T1DM and T2DM. In this context, we will highlight the role of tau and amyloid protein deposits in the brain as well in the pancreatic islet cells, and possibly in the kidney glomerulus. Furthermore, we will discuss the central role of mitochondria, oxidative stress, and the unfolded protein response in mediating the DM-associated neuropathic degeneration. This study will elucidate the relationship between DM and neurodegeneration which may account for the evolution of other neurodegenerative diseases, particularly Alzheimer's disease and Parkinson's disease as discussed later.

Mechanisms of disordered neurodegenerative function: concepts and facts about the different roles of the protein kinase RNA-like endoplasmic reticulum kinase (PERK).

Neurodegenerative diseases, such as Alzheimer's disease, Huntington's disease, Parkinson's disease, prion disease, and amyotrophic lateral sclerosis, are a dissimilar group of disorders that share a hallmark feature of accumulation of abnormal intraneuronal or extraneuronal misfolded/unfolded protein and are classified as protein misfolding disorders. Cellular and endoplasmic reticulum (ER) stress activates multiple signaling cascades of the unfolded protein response (UPR). Consequently, translational and transcriptional alterations in target gene expression occur in response directed toward restoring the ER capacity of proteostasis and reestablishing the cellular homeostasis. Evidences from in vitro and in vivo disease models indicate that disruption of ER homeostasis causes abnormal protein aggregation that leads to synaptic and neuronal dysfunction. However, the exact mechanism by which it contributes to disease progression and pathophysiological changes remains vague. Downstream signaling pathways of UPR are fully integrated, yet with diverse unexpected outcomes in different disease models. Three well-identified ER stress sensors have been implicated in UPR, namely, inositol requiring enzyme 1, protein kinase RNA-activated-like ER kinase (PERK), and activating transcription factor 6. Although it cannot be denied that each of the involved stress sensor initiates a distinct downstream signaling pathway, it becomes increasingly clear that shared pathways are crucial in determining whether or not the UPR will guide the cells toward adaptive prosurvival or proapoptotic responses. We review a body of work on the mechanism of neurodegenerative diseases based on oxidative stress and cell death pathways with emphasis on the role of PERK.

What role does the stress response have in congestive heart failure?

This review is concerned with cardiac malfunction as a result of an imbalance in protein proteostasis, the homeostatic balance between protein removal and regeneration in a long remodeling process involving the endoplasmic reticulum (ER) and the unfolded protein response (UPR). The importance of this is of special significance with regard to cardiac function as a high energy requiring muscular organ that has a high oxygen requirement and is highly dependent on mitochondria. The importance of mitochondria is not only concerned with high energy dependence on mitochondrial electron transport, but it also has a role in the signaling between the mitochondria and the ER under stress. Proteins made in the ER are folded as a result of sulfhydryl groups (-SH) and attractive and repulsive reactions in the tertiary structure. We discuss how this matters with respect to an imbalance between muscle breakdown and repair in a stressful environment, especially as a result of oxidative and nitrosative byproducts of mitochondrial activity. The normal repair is a remodeling, but under this circumstance, the cell undergoes or even lysosomal "self eating" autophagy, or even necrosis instead of apoptosis. We shall discuss the relationship of the UPR pathway to chronic congestive heart failure (CHF).

A shared comparison of diabetes mellitus and neurodegenerative disorders.

Diabetes mellitus (DM) is one of the most common diseases in the world population, associated with obesity, pancreatic endocrine changes, cardiovascular disease, renal glomerular disease, cerebrovascular disease, peripheral neuropathy, neurodegenerative disease, retinal disease, sleep apnea, some of which are bundled into the metabolic syndrome. The main characteristic of this disease is hyperglycemia, and often with albuminuria. Nevertheless, the classic features, with ketoacidosis in the extreme, are only a first layer of description of this condition. The description of the islet cells of the endocrine pancreas was first described by Opie, and the discovery of insulin by tying off the exocrine pancreatic ducts followed. We later find that the ß-cells secrete insulin and glucagon, which synchronously stimulate or suppress glycogenolysis, and that insulin is essential for glucose intake into the cell. There are yet two other layers for our understanding of diabetes and the effects of its dysfunction, which is the basis for understanding the system-wide expression of the disease. We describe the molecular basis for the central nervous system neuropathic diseases that are associated with both Type 1 DM (T1DM) and Type 2 DM (T2DM), but more so with T2DM. T2DM is an autoimmune disease that destroys the insulin secreting islet cells. T2DM is the diabetes that is associated with an imbalance in the glucagon/insulin homeostasis that leads to the formation of amyloid deposits in the brain, pancreatic islet cells, and possibly the kidney glomerulus.

Endocrine Imbalance Associated With Proteome Changes in Diabetes.

The dynamics of cellular metabolism involves rapid interactions between proteins and nucleic acids, proteins and proteins, and signaling. These involve the interactions with respect to the sulfur bond, noncovalent electrostatic interactions, protein structure stabilization and protein-ligand binding, weak electrostatic interactions in proteins, oxygen radicals that initiate a change in conformation and a chain of events. We review a development in molecular medicine that is a very promising work in progress. We also review the current and future research methods involving mitochondria. Long-term effects of diabetes include glycation of proteins, for example, glycohemoglobin (HbA1c), increased risk of cardiovascular diseases, atherosclerosis, retinopathy, nephropathy, and neurological dysfunctions. Tissues are exposed to significant quantities of highly reactive chemical species including nitric oxide • NO and reactive oxygen species ROS over months to years, to an extent generated by mitochondrial activities. The reactions of • NO can be broadly discussed with reference to three main processes which control their fate in biological systems: (1) diffusion and intra-cellular consumption; (2) autooxidation to form nitrous anhydride N2 O3 ; and (3) reaction with superoxide O2• - to form peroxynitrite ONOO-. Reactive nitrogen species produced by macrophages and neutrophils in the interstitial space, with emphasis on • NO, N2 O3 , ONOO-, and nitrogen dioxide radicals • NO2 generate protein and DNA damage. Serum thiol (-SH) groups act as an important extracellular scavenger of peroxides and are therefore helpful in protecting the surrounding tissues. The events described here are a homeostatic endocrine imbalance that is associated with proteostasis. The advances we have seen in untangling this web of interactions are sure to continue at a breathtaking pace. J. Cell. Biochem. 118: 3569-3576, 2017. © 2017 Wiley Periodicals, Inc.

Biomarkers of stress-mediated metabolic deregulation in diabetes mellitus.

This review illustrates the relationship of oxidative and nitrative stress to diabetes mellitus and its complications. This is of considerable interest because diabetes mellitus is a lifetime systemic metabolic disease that may have childhood or adult onset and affects not only a triad of pancreatic islet cell insulin, pituitary insulin-like growth hormone, and liver steatosis, it has a long-term association with adiposity, atherosclerosis, coronary vascular disease, kidney disease of the nature afferent arteriolar sclerosis and nodular glomerulosclerosis, cerebrovascular disease, and amyloid deposition in the pancreas and kidney. Only at the end of the 20th century do we gain insight into oxidative and nitrative stress and their consequences. Of special interest here is the fact that reactive oxygen and nitrogen radicals are with us generated throughout the life cycle, and the roles for glutathione and Fe3+ are key elements in the metabolic picture, which brings into the picture dietary factors. More research is required to demonstrate the clinical relivance of naturally-occuring whole-food antioxidants in ameliorating human diabetic complications in vivo.

Plasma Transthyretin as a Biomarker of Lean Body Mass and Catabolic States.

Plasma transthyretin (TTR) is a plasma protein secreted by the liver that circulates bound to retinol-binding protein 4 (RBP4) and its retinol ligand. TTR is the sole plasma protein that reveals from birth to old age evolutionary patterns that are closely superimposable to those of lean body mass (LBM) and thus works as the best surrogate analyte of LBM. Any alteration in energy-to-protein balance impairs the accretion of LBM reserves and causes early depression of TTR production. In acute inflammatory states, cytokines induce urinary leakage of nitrogenous catabolites, deplete LBM stores, and cause an abrupt decrease in TTR and RBP4 concentrations. As a result, thyroxine and retinol ligands are released in free form, creating a second frontline that strengthens that primarily initiated by cytokines. Malnutrition and inflammation thus keep in check TTR and RBP4 secretion by using distinct and unrelated physiologic pathways, but they operate in concert to downregulate LBM stores. The biomarker complex integrates these opposite mechanisms at any time and thereby constitutes an ideally suited tool to determine residual LBM resources still available for metabolic responses, hence predicting outcomes of the most interwoven disease conditions.

Downsizing of lean body mass is a key determinant of Alzheimer's disease.

Lean body mass (LBM) encompasses all metabolically active organs distributed into visceral and structural tissue compartments and collecting the bulk of N and K stores of the human body. Transthyretin (TTR) is a plasma protein mainly secreted by the liver within a trimolecular TTR-RBP-retinol complex revealing from birth to old age strikingly similar evolutionary patterns with LBM in health and disease. TTR is also synthesized by the choroid plexus along distinct regulatory pathways. Chronic dietary methionine (Met) deprivation or cytokine-induced inflammatory disorders generates LBM downsizing following differentiated physiopathological processes. Met-restricted regimens downregulate the transsulfuration cascade causing upstream elevation of homocysteine (Hcy) safeguarding Met homeostasis and downstream drop of hydrogen sulfide (H2S) impairing anti-oxidative capacities. Elderly persons constitute a vulnerable population group exposed to increasing Hcy burden and declining H2S protection, notably in plant-eating communities or in the course of inflammatory illnesses. Appropriate correction of defective protein status and eradication of inflammatory processes may restore an appropriate LBM size allowing the hepatic production of the retinol circulating complex to resume, in contrast with the refractory choroidal TTR secretory process. As a result of improved health status, augmented concentrations of plasma-derived TTR and retinol may reach the cerebrospinal fluid and dismantle senile amyloid plaques, contributing to the prevention or the delay of the onset of neurodegenerative events in elderly subjects at risk of Alzheimer's disease.

The automated malnutrition assessment.

OBJECTIVE: We propose an automated nutritional assessment algorithm that provides a method for malnutrition risk prediction with high accuracy and reliability. METHODS: The database used for this study was a file of 432 patients, where each patient was described by 4 laboratory parameters and 11 clinical parameters. A malnutrition risk assessment of low (1), moderate (2), or high (3) was assigned by a dietitian for each patient. An algorithm for data organization and classification using characteristic metrics for each patient was developed. For each patient, the algorithm characterized the patients' unique profile and built a characteristic metric to identify similar patients who were mapped into a classification. For each patient, the algorithm characterized the patients' classification. RESULTS: The algorithm assigned a malnutrition risk level for different training sizes that were taken from the data. Our method resulted in average errors (distance between the automated score and the real score) of 0.386, 0.3507, 0.3454, 0.34, and 0.2907 for the 10%, 30%, 50%, 70%, and 90% training sizes, respectively. Our method outperformed the compared method even when our method used a smaller training set than the compared method. In addition, we showed that the laboratory parameters themselves were sufficient for the automated risk prediction and organized the patients into clusters that corresponded to low-, low-moderate-, moderate-, moderate-high-, and high-risk areas. The organization and visualization methods provided a tool for the exploration and navigation of the data points. CONCLUSION: The problem of rapidly identifying risk and severity of malnutrition is crucial for minimizing medical and surgical complications. These are not easily performed or adequately expedited. We characterized for each patient a unique profile and mapped similar patients into a classification. We also found that the laboratory parameters were sufficient for the automated risk prediction.

The increasing role for the laboratory in nutritional assessment.

The laboratory role in nutritional management of the patient has seen remarkable growth while there have been dramatic changes in technology over the last 25 years, and it is bound to be transformative in the near term. This editorial is an overview of the importance of the laboratory as an active participant in nutritional care.

A systematized interdisciplinary nutritional care plan results in improved clinical outcomes.

OBJECTIVE: This study investigated identification and treatment of patients at-risk for malnutrition and extended inpatient length of stay. DESIGN: Data were collected retrospectively from the medical records for a period of 6 months. The records were reviewed for (1) adherence to RD recommendation, (2) decreasing serum albumin during hospital stay, (3) length of hospital stay, (4) readmission within 30 days, (5) age, (6) gender, (7) past medical history, (8) primary and secondary diagnoses, (9) the presence or absence of a diet order and (10) medications. SUBJECTS AND PARTICIPANTS: Medical records were reviewed for diagnoses associated with nutrition-related complications. Patient's records were excluded for length of stay less than 4 days, or in-hospital death. RESULTS: The mean LOS was 10 days shorter when the advice was followed (p=0.0074). CONCLUSIONS: Patients at high nutritional risk have a shorter LOS and have fewer complications when the RD advice is followed.

Measurement of granulocyte maturation may improve the early diagnosis of the septic state.

BACKGROUND: Sepsis is a costly diagnosis in hospitalized patients. Failure to diagnose sepsis in a timely manner creates a potential financial and safety hazard. The use of transthyretin, C-reactive protein and procalcitonin measurement as early markers of systemic inflammatory response syndrome (SIRS) and sepsis in association with admission of emergency department patients to the intensive care unit (ICU) has been studied. In these studies the SIRS criteria as well as the use of an elevated neutrophil count with granulocyte precursors (left shift) have proved to be problematic. Despite the validity of procalcitonin measurement (PCT, Brahms) in the early diagnosis of SIRS the cost and time for testing are limiting considerations. Immature granulocyte (IG) measurement has been proposed as a more readily available indicator of the presence of granulocyte precursors (left shift). METHODS: This study calibrates and validates the measurement of granulocyte maturation [Immature granulocytes (IG)] to the identification of sepsis, a study carried out on a Sysmex Analyzer, model XE 2100 (Kobe, Japan). The Sysmex IG parameter is a crucial measure of immature granulocyte counts and includes metamyelocytes and myelocytes, but not band neutrophils. RESULTS AND CONCLUSIONS: We found agreement with previous work that designated an IG measurement cut-off of 3.2 as optimal. The analysis was then carried a step further with a multivariable discriminator.

Enhancing the diagnostic performance of troponins in the acute care setting.

BACKGROUND: Current guidelines define cardiac troponin I (TnI) as an indicator of necrosis when the concentration exceeds the 99% upper limit of a healthy reference population, a reference value near the assay's lowest detectable level. We assessed the utility of a modified TnI cutoff point derived from a population at low risk for coronary artery disease (CAD) and evaluated its utility in determining acute myocardial infarction (MI). METHODS: A modified TnI cutoff point was derived by the receiver operating characteristic (ROC) curve from 737 consecutive patients who underwent serial TnI measurements for exclusion of MI. Creatinine kinase isoenzyme MB (CK-MB) evolutionary change was used to define MI. The new derived cutoff point was validated using another subset of 320 patients who were evaluated for MI. RESULTS: ROC-derived TnI cutoff point (A) was 0.65 µg/L, and its performance was compared to the recommended cutoff point ([B] 0.15 µg/L). Cutoff point A had greater specificity (94.5% vs. 86.9%, p < 0.001) but slightly lower sensitivity (96.5% vs. 100%, p < 0.01). Cutoff point A provided significantly greater positive predictive value (PPV) for MI (74.1% vs. 55.5%, p < 0.0001) and fewer false-positive errors, while preserving comparable negative predictive value (NPV) (98.9% vs. 100%). CONCLUSION: A higher cutoff point derived from a reference population of patients at low risk for CAD may improve the TnI performance assay. The PPV for diagnosis of MI was significantly higher and false-positive values were fewer without affecting the NPV. The more reliable diagnosis of MI may have resulted, which, in turn, may have significant clinical and economic implications.