Endogenous insulin secretion and suppression during and after sepsis in critically ill patients: implications for tight glycemic control protocols

Introduction: Insulin infusions over 2 U/hr can suppress endogenous insulin secretion in healthy subjects 30-45% [1]. Virtually all tight glycaemic control (TGC) protocols deliver insulin via infusion. This study examines the impact of bolus delivery of insulin in TGC on the endogenous insulin secretion of critically ill patients. Methods: 18 patients from the Christchurch Hospital ICU enrolled in a prospective clinical trial studying sepsis each had two sets of blood samples assayed for insulin and C-peptide. The first set was taken at the commencement of the SPRINT TGC protocol for patients with suspected sepsis. The second set was taken when their SIRS score was consistently below 2. Each set had 4 samples taken at: -1, 10, 40 and 60 min following bolus delivery of insulin as required by SPRINT to capture endogenous insulin secretion during the bolus profile. Bolus size was dictated by the protocol, but was in the range 2-6 units. Model-based methods [2] were used to calculate the endogenous insulin secretion rate for each set of samples. The level of suppression was calculated as the ratio of the secretion rate between 5-15 mins (just after peak plasma insulin) and average of the 0-5 min (basal) and 15-60 min (return to basal) secretion rates identified

Insulin Infusion During Normoglycemia Modulates Insulin Secretion According to Whole-Body Insulin Sensitivity

OBJECTIVE—Glucose is the major stimulus for insulin release. Time course and amount of insulin secreted after glycemic stimulus are different between type 2 diabetes mellitus (T2DM) patients and healthy subjects. In rodents, it was demonstrated that insulin can modulate its own release. Previous studies in humans yielded contrasting results: Insulin was shown to have an enhancing effect, no effect, or a suppressive effect on its own secretion. Thus, we aimed to evaluate short-term effects of human insulin infusion on insulin secretion during normoglycemia in healthy humans and T2DM subjects of both sex. RESEARCH DESIGN AND METHODS—Hyperinsulinemic-isoglycemic clamps with whole-body insulin-sensitivity (M) and C-peptide measurements for insulin secretion modeling were performed in 65 insulin-sensitive (IS) subjects (45 6 1 year, BMI: 24.8 6 0.5 kg/m2), 17 insulin-resistant (IR) subjects (466 2 years, 28.16 1.3 kg/m2), and 20 T2DMpatients (566 2 years, 28.0 6 0.8 kg/m2; HbA1c = 6.7 6 0.1%). RESULTS—IS subjects (M = 8.8 6 0.3 mg z min21 z kg21) had higher (P, 0.00001) whole-body insulin sensitivity than IR subjects (M = 4.0 6 0.2) and T2DM patients (M = 4.3 6 0.5). Insulin secretion profiles during clamp were different (P, 0.00001) among the groups, in

neXtProt: a knowledge platform for human proteins

neXtProt (http://www.nextprot.org/) is a new human protein-centric knowledge platform. Developed at the Swiss Institute of Bioinformatics (SIB), it aims to help researchers answer questions relevant to human proteins. To achieve this goal, neXtProt is built on a corpus containing both curated knowledge originating from the UniProtKB/Swiss-Prot knowledgebase and carefully selected and filtered high-throughput data pertinent to human proteins. This article presents an overview of the database and the data integration process. We also lay out the key future directions of neXtProt that we consider the necessary steps to make neXtProt the one-stop-shop for all research projects focusing on human proteins

Spike pattern recognition by supervised classification in low dimensional embedding space

© The Author(s) 2016. This article is published with open access at Springerlink.com under the terms of the Creative Commons Attribution License 4.0, (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts’ manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min−1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.Peer reviewedFinal Published versio

Functional annotations of diabetes nephropathy susceptibility loci through analysis of genome-wide renal gene expression in rat models of diabetes mellitus

<p>Abstract</p> <p>Background</p> <p>Hyperglycaemia in diabetes mellitus (DM) alters gene expression regulation in various organs and contributes to long term vascular and renal complications. We aimed to generate novel renal genome-wide gene transcription data in rat models of diabetes in order to test the responsiveness to hyperglycaemia and renal structural changes of positional candidate genes at selected diabetic nephropathy (DN) susceptibility loci.</p> <p>Methods</p> <p>Both Affymetrix and Illumina technologies were used to identify significant quantitative changes in the abundance of over 15,000 transcripts in kidney of models of spontaneous (genetically determined) mild hyperglycaemia and insulin resistance (Goto-Kakizaki-GK) and experimentally induced severe hyperglycaemia (Wistar-Kyoto-WKY rats injected with streptozotocin [STZ]).</p> <p>Results</p> <p>Different patterns of transcription regulation in the two rat models of diabetes likely underlie the roles of genetic variants and hyperglycaemia severity. The impact of prolonged hyperglycaemia on gene expression changes was more profound in STZ-WKY rats than in GK rats and involved largely different sets of genes. These included genes already tested in genetic studies of DN and a large number of protein coding sequences of unknown function which can be considered as functional and, when they map to DN loci, positional candidates for DN. Further expression analysis of rat orthologs of human DN positional candidate genes provided functional annotations of known and novel genes that are responsive to hyperglycaemia and may contribute to renal functional and/or structural alterations.</p> <p>Conclusion</p> <p>Combining transcriptomics in animal models and comparative genomics provides important information to improve functional annotations of disease susceptibility loci in humans and experimental support for testing candidate genes in human genetics.</p

The UniProt-GO Annotation database in 2011

The GO annotation dataset provided by the UniProt Consortium (GOA: http://www.ebi.ac.uk/GOA) is a comprehensive set of evidenced-based associations between terms from the Gene Ontology resource and UniProtKB proteins. Currently supplying over 100 million annotations to 11 million proteins in more than 360 000 taxa, this resource has increased 2-fold over the last 2 years and has benefited from a wealth of checks to improve annotation correctness and consistency as well as now supplying a greater information content enabled by GO Consortium annotation format developments. Detailed, manual GO annotations obtained from the curation of peer-reviewed papers are directly contributed by all UniProt curators and supplemented with manual and electronic annotations from 36 model organism and domain-focused scientific resources. The inclusion of high-quality, automatic annotation predictions ensures the UniProt GO annotation dataset supplies functional information to a wide range of proteins, including those from poorly characterized, non-model organism species. UniProt GO annotations are freely available in a range of formats accessible by both file downloads and web-based views. In addition, the introduction of a new, normalized file format in 2010 has made for easier handling of the complete UniProt-GOA data set

The pancreas in human type 1 diabetes

Type 1 diabetes (T1D) is considered a disorder whose pathogenesis is autoimmune in origin, a notion drawn in large part from studies of human pancreata performed as far back as the 1960s. While studies of the genetics, epidemiology, and peripheral immunity in T1D have been subject to widespread analysis over the ensuing decades, efforts to understand the disorder through analysis of human pancreata have been far more limited. We have reviewed the published literature pertaining to the pathology of the human pancreas throughout all stages in the natural history of T1D. This effort uncovered a series of findings that challenge many dogmas ascribed to T1D and revealed data suggesting the marked heterogeneity in terms of its pathology. An improved understanding and appreciation for pancreatic pathology in T1D could lead to improved disease classification, an understanding of why the disorder occurs, and better therapies for disease prevention and management

Perspectives on tracking data reuse across biodata resources

c The Author(s) 2024. Published by Oxford University Press.Motivation: Data reuse is a common and vital practice in molecular biology and enables the knowledge gathered over recent decades to drive discovery and innovation in the life sciences. Much of this knowledge has been collated into molecular biology databases, such as UniProtKB, and these resources derive enormous value from sharing data among themselves. However, quantifying and documenting this kind of data reuse remains a challenge. Results: The article reports on a one-day virtual workshop hosted by the UniProt Consortium in March 2023, attended by representatives from biodata resources, experts in data management, and NIH program managers. Workshop discussions focused on strategies for tracking data reuse, best practices for reusing data, and the challenges associated with data reuse and tracking. Surveys and discussions showed that data reuse is widespread, but critical information for reproducibility is sometimes lacking. Challenges include costs of tracking data reuse, tensions between tracking data and open sharing, restrictive licenses, and difficulties in tracking commercial data use. Recommendations that emerged from the discussion include: development of standardized formats for documenting data reuse, education about the obstacles posed by restrictive licenses, and continued recognition by funding agencies that data management is a critical activity that requires dedicated resources

The Gene Ontology knowledgebase in 2023

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project