Protein quality control in the nucleus

In their natural environment, cells are regularly exposed to various stress conditions that may lead to protein misfolding, but also in the absence of stress, misfolded proteins occur as the result of mutations or failures during protein synthesis. Since such partially denatured proteins are prone to aggregate, cells have evolved several elaborate quality control systems to deal with these potentially toxic proteins. First, various molecular chaperones will seize the misfolded protein and either attempt to refold the protein or target it for degradation via the ubiquitin-proteasome system. The degradation of misfolded proteins is clearly compartmentalized, so unique degradation pathways exist for misfolded proteins depending on whether their subcellular localization is ER/secretory, mitochondrial, cytosolic or nuclear. Recent studies, mainly in yeast, have shown that the nucleus appears to be particularly active in protein quality control. Thus, specific ubiquitin-protein ligases located in the nucleus, target not only misfolded nuclear proteins, but also various misfolded cytosolic proteins which are transported to the nucleus prior to their degradation. In comparison, much less is known about these mechanisms in mammalian cells. Here we highlight recent advances in our understanding of nuclear protein quality control, in particular regarding substrate recognition and proteasomal degradation

Co-Chaperones in Targeting and Delivery of Misfolded Proteins to the 26S Proteasome

Protein homeostasis (proteostasis) is essential for the cell and is maintained by a highly conserved protein quality control (PQC) system, which triages newly synthesized, mislocalized and misfolded proteins. The ubiquitin-proteasome system (UPS), molecular chaperones, and co-chaperones are vital PQC elements that work together to facilitate degradation of misfolded and toxic protein species through the 26S proteasome. However, the underlying mechanisms are complex and remain partly unclear. Here, we provide an overview of the current knowledge on the co-chaperones that directly take part in targeting and delivery of PQC substrates for degradation. While J-domain proteins (JDPs) target substrates for the heat shock protein 70 (HSP70) chaperones, nucleotide-exchange factors (NEFs) deliver HSP70-bound substrates to the proteasome. So far, three NEFs have been established in proteasomal delivery: HSP110 and the ubiquitin-like (UBL) domain proteins BAG-1 and BAG-6, the latter acting as a chaperone itself and carrying its substrates directly to the proteasome. A better understanding of the individual delivery pathways will improve our ability to regulate the triage, and thus regulate the fate of aberrant proteins involved in cell stress and disease, examples of which are given throughout the review

Polyautoimmunity in patients with cutaneous lupus erythematosus:A nationwide sex- and age-matched cohort study from Denmark

BACKGROUND: Polyautoimmunity is defined as having 2 or more autoimmune diseases. Little is known about polyautoimmunity in patients with cutaneous lupus erythematosus (CLE).OBJECTIVES: To estimate prevalence and 5-year incidence of non-lupus erythematosus (LE) autoimmune diseases in patients with CLE.METHODS: Patients with CLE were identified In the Danish National Patient Registry and each patient was age- and sex-matched with 10 general population controls. Outcome information on non-LE autoimmune diseases was obtained by register-linkage between Danish National Patient Registry and the National Prescription Register. The risk ratio (RR) for prevalent non-LE autoimmune disease at time of CLE diagnosis was calculated in modified Poisson regression; and hazard ratios (HRs) for incident non-LE autoimmune disease were estimated in Cox regression analyses.RESULTS: Overall, 1674 patients with CLE had a higher prevalence of a non-LE autoimmune disease than the comparators (18.5 vs 7.9%; RR 2.4; 95% CI, 2.1 to 2.6). Correspondingly, the cumulative incidence of a non-LE autoimmune disease during 5 years of follow-up was increased for the patients with CLE: HR 3.5 (95% CI, 3.0 to 4.0).LIMITATIONS: Risk of detection and misclassification bias, mainly pertaining to the CLE group.CONCLUSION: Patients with CLE had higher prevalence and 5-year cumulative incidence of a non-LE autoimmune disease than the general population.</p

Lithium induced hypercalcemia:an expert opinion and management algorithm

Background: Lithium is the gold standard prophylactic treatment for bipolar disorder. Most clinical practice guidelines recommend regular calcium assessments as part of monitoring lithium treatment, but easy-to-implement specific management strategies in the event of abnormal calcium levels are lacking. Methods: Based on a narrative review of the effects of lithium on calcium and parathyroid hormone (PTH) homeostasis and its clinical implications, experts developed a step-by-step algorithm to guide the initial management of emergent hypercalcemia during lithium treatment. Results: In the event of albumin-corrected plasma calcium levels above the upper limit, PTH and calcium levels should be measured after two weeks. Measurement of PTH and calcium levels should preferably be repeated after one month in case of normal or high PTH level, and after one week in case of low PTH level, independently of calcium levels. Calcium levels above 2.8 mmol/l may require a more acute approach. If PTH and calcium levels are normalized, repeated measurements are suggested after six months. In case of persistent PTH and calcium abnormalities, referral to an endocrinologist is suggested since further examination may be needed. Conclusions: Standardized consensus driven management may diminish the potential risk of clinicians avoiding the use of lithium because of uncertainties about managing side-effects and consequently hindering some patients from receiving an optimal treatment

A new tool to assess Clinical Diversity In Meta‐analyses (CDIM) of interventions

OBJECTIVE: To develop and validate Clinical Diversity In Meta-analyses (CDIM), a new tool for assessing clinical diversity between trials in meta-analyses of interventions.STUDY DESIGN AND SETTING: The development of CDIM was based on consensus work informed by empirical literature and expertise. We drafted the CDIM tool, refined it, and validated CDIM for interrater scale reliability and agreement in three groups.RESULTS: CDIM measures clinical diversity on a scale that includes four domains with 11 items overall: setting (time of conduct/country development status/units type); population (age, sex, patient inclusion criteria/baseline disease severity, comorbidities); interventions (intervention intensity/strength/duration of intervention, timing, control intervention, cointerventions); and outcome (definition of outcome, timing of outcome assessment). The CDIM is completed in two steps: first two authors independently assess clinical diversity in the four domains. Second, after agreeing upon scores of individual items a consensus score is achieved. Interrater scale reliability and agreement ranged from moderate to almost perfect depending on the type of raters.CONCLUSION: CDIM is the first tool developed for assessing clinical diversity in meta-analyses of interventions. We found CDIM to be a reliable tool for assessing clinical diversity among trials in meta-analysis.</p

Identifying Molecular Effects of Diet through Systems Biology: Influence of Herring Diet on Sterol Metabolism and Protein Turnover in Mice

BACKGROUND: Changes in lifestyle have resulted in an epidemic development of obesity-related diseases that challenge the healthcare systems worldwide. To develop strategies to tackle this problem the focus is on diet to prevent the development of obesity-associated diseases such as cardiovascular disease (CVD). This will require methods for linking nutrient intake with specific metabolic processes in different tissues. METHODOLOGY/PRINCIPAL FINDING: Low-density lipoprotein receptor-deficient (Ldlr -/-) mice were fed a high fat/high sugar diet to mimic a westernized diet, being a major reason for development of obesity and atherosclerosis. The diets were supplemented with either beef or herring, and matched in macronutrient contents. Body composition, plasma lipids and aortic lesion areas were measured. Transcriptomes of metabolically important tissues, e.g. liver, muscle and adipose tissue were analyzed by an integrated approach with metabolic networks to directly map the metabolic effects of diet in these different tissues. Our analysis revealed a reduction in sterol metabolism and protein turnover at the transcriptional level in herring-fed mice. CONCLUSION: This study shows that an integrated analysis of transcriptome data using metabolic networks resulted in the identification of signature pathways. This could not have been achieved using standard clustering methods. In particular, this systems biology analysis could enrich the information content of biomedical or nutritional data where subtle changes in several tissues together affects body metabolism or disease progression. This could be applied to improve diets for subjects exposed to health risks associated with obesity

Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations

Accurate methods to assess the pathogenicity of mutations are needed to fully leverage the possibilities of genome sequencing in diagnosis. Current data-driven and bioinformatics approaches are, however, limited by the large number of new variations found in each newly sequenced genome, and often do not provide direct mechanistic insight. Here we demonstrate, for the first time, that saturation mutagenesis, biophysical modeling and co-variation analysis, performed in silico, can predict the abundance, metabolic stability, and function of proteins inside living cells. As a model system, we selected the human mismatch repair protein, MSH2, where missense variants are known to cause the hereditary cancer predisposition disease, known as Lynch syndrome. We show that the majority of disease-causing MSH2 mutations give rise to folding defects and proteasome-dependent degradation rather than inherent loss of function, and accordingly our in silico modeling data accurately identifies disease-causing mutations and outperforms the traditionally used genetic disease predictors. Thus, in conclusion, in silico biophysical modeling should be considered for making genotype-phenotype predictions and for diagnosis of Lynch syndrome, and perhaps other hereditary diseases

Soluble CD14 in cerebrospinal fluid is associated with markers of inflammation and axonal damage in untreated HIV-infected patients: a retrospective cross-sectional study

Background: HIV-associated cognitive impairment has declined since the introduction of combination antiretroviral treatment (cART). However, milder forms of cognitive impairment persist. Inflammation in the cerebrospinal fluid (CSF) has been associated with cognitive impairment, and CSF neurofilament light chain protein (NFL) and CSF neopterin concentrations are increased in those patients. Microbial translocation in HIV infection has been suggested to contribute to chronic inflammation, and lipopolysaccharide (LPS) and soluble CD14 (sCD14) are markers of microbial translocation and the resulting monocyte activation, respectively. We hypothesised that microbial translocation contributes to inflammation and axonal damage in the central nervous system (CNS) in untreated HIV infection. / Methods: We analyzed paired samples of plasma and CSF from 62 HIV-infected, untreated patients without cognitive symptoms from Sahlgrenska University Hospital, Gothenburg, Sweden. Measurements of neopterin and NFL in CSF were available from previous studies. Plasma and CSF sCD14 was measured using ELISA (R&D, Minneapolis, MN), and plasma and CSF LPS was measured using LAL colorimetric assay (Lonza, Walkersville, MD, USA). Univariate and multivariate regression analyses were performed. / Results: LPS in plasma was associated with plasma sCD14 (r = 0.31, P = 0.015), and plasma sCD14 was associated with CSF sCD14 (r = 0.32, P = 0.012). Furthermore, CSF sCD14 was associated with NFL (r = 0.32, P = 0.031) and neopterin (r = 0.32, P = 0.012) in CSF. LPS was not detectable in CSF. In a multivariate regression model CSF sCD14 remained associated with NFL and neopterin after adjusting for age, CD4+ cell count, and HIV RNA in CSF. / Conclusions: In a group of untreated, HIV-infected patients LPS was associated with sCD14 in plasma, and plasma sCD14 was associated CSF sCD14. CSF sCD14 were associated with markers of CNS inflammation and axonal damage. This suggest that microbial translocation might be a driver of systemic and CNS inflammation. However, LPS was not detectable in the CSF, and since sCD14 is a marker of monocyte activation sCD14 may be increased due to other causes than microbial translocation. Further studies regarding cognitive impairment and biomarkers are warranted to fully understand causality