C. elegans feed yolk to their young in a form of primitive lactation

The nematode Caenorhabditis elegans exhibits rapid senescence that is promoted by the insulin/IGF-1 signalling (IIS) pathway via regulated processes that are poorly understood. IIS also promotes production of yolk for egg provisioning, which in post-reproductive animals continues in an apparently futile fashion, supported by destructive repurposing of intestinal biomass that contributes to senescence. Here we show that post-reproductive mothers vent yolk which can be consumed by larvae and promotes their growth. This implies that later yolk production is not futile; instead vented yolk functions similarly to milk. Moreover, yolk venting is promoted by IIS. These findings suggest that a self-destructive, lactation-like process effects resource transfer from postreproductive C. elegans mothers to offspring, in a fashion reminiscent of semelparous organisms that reproduce in a single, suicidal burst. That this process is promoted by IIS provides insights into how and why IIS shortens lifespan in C. elegans

Clinical Amyloid Typing by Proteomics: Performance Evaluation and Data Sharing Between Two Centres

Amyloidosis is a relatively rare human disease caused by the deposition of abnormal protein fibres in the extracellular space of various tissues, impairing their normal function. Proteomic analysis of patients' biopsies, developed by Dogan and colleagues at the Mayo Clinic, has become crucial for clinical diagnosis and for identifying the amyloid type. Currently, the proteomic approach is routinely used at National Amyloidosis Centre (NAC, London, UK) and Istituto di Tecnologie Biomediche-Consiglio Nazionale delle Ricerche (ITB-CNR, Milan, Italy). Both centres are members of the European Proteomics Amyloid Network (EPAN), which was established with the aim of sharing and discussing best practice in the application of amyloid proteomics. One of the EPAN's activities was to evaluate the quality and the confidence of the results achieved using different software and algorithms for protein identification. In this paper, we report the comparison of proteomics results obtained by sharing NAC proteomics data with the ITB-CNR centre. Mass spectrometric raw data were analysed using different software platforms including Mascot, Scaffold, Proteome Discoverer, Sequest and bespoke algorithms developed for an accurate and immediate amyloid protein identification. Our study showed a high concordance of the obtained results, suggesting a good accuracy of the different bioinformatics tools used in the respective centres. In conclusion, inter-centre data exchange is a worthwhile approach for testing and validating the performance of software platforms and the accuracy of results, and is particularly important where the proteomics data contribute to a clinical diagnosis

Diagnostic amyloid proteomics: experience of the UK National Amyloidosis Centre

Systemic amyloidosis is a serious disease which is caused when normal circulating proteins misfold and aggregate extracellularly as insoluble fibrillary deposits throughout the body. This commonly results in cardiac, renal and neurological damage. The tissue target, progression and outcome of the disease depends on the type of protein forming the fibril deposit, and its correct identification is central to determining therapy. Proteomics is now used routinely in our centre to type amyloid; over the past 7 years we have examined over 2000 clinical samples. Proteomics results are linked directly to our patient database using a simple algorithm to automatically highlight the most likely amyloidogenic protein. Whilst the approach has proved very successful, we have encountered a number of challenges, including poor sample recovery, limited enzymatic digestion, the presence of multiple amyloidogenic proteins and the identification of pathogenic variants. Our proteomics procedures and approaches to resolving difficult issues are outlined

Clinical ApoA-IV amyloid is associated with fibrillogenic signal sequence

Apolipoprotein A-IV amyloidosis is an uncommon form of the disease normally resulting in renal and cardiac dysfunction. ApoA-IV amyloidosis was identified in sixteen patients attending the National Amyloidosis Centre and in eight clinical samples received for histology review. Unexpectedly, proteomics identified the presence of ApoA-IV signal sequence residues (p.18–43 to p.20–43) in 16/24 trypsin-digested amyloid deposits, but in only 1/266 non-ApoA-IV amyloid samples examined. These additional signal residues were also detected in the cardiac sample from the Swedish patient in which ApoA-IV amyloid was first described, and in plasma from a single cardiac ApoA-IV amyloidosis patient. The most common signal-containing peptide observed in ApoA-IV amyloid, p.20–43 and to far lesser extent the N-terminal peptide, p.21–43 were fibrillogenic in vitro at physiological pH generating Congo red positive fibrils. The addition of a single signal-derived alanine residue to the N-terminus has resulted in markedly increased fibrillogenesis. If this effect translates to the mature circulating protein in vivo, then the presence of signal may result in preferential deposition as amyloid, perhaps acting as seed for the main circulating native form of the protein; it may also influence other ApoA-IV associated pathologies

Proteomic Analysis for the Diagnosis of Fibrinogen Aα-Chain Amyloidosis

INTRODUCTION: Hereditary fibrinogen Aα-chain (AFib) amyloidosis is a relatively uncommon renal disease associated with a small number of pathogenic fibrinogen Aα (FibA) variants; wild-type FibA normally does not result in amyloid deposition. Proteomics is now routinely used to identify the amyloid type in clinical samples, and we report here our algorithm for identification of FibA in amyloid. METHODS: Proteomics data from 1001 Congo red–positive patient samples were examined using the Mascot search engine to interrogate the Swiss-Prot database and generate protein identity scores. An algorithm was applied to identify FibA as the amyloid protein based on Mascot scores. FibA variants were identified by appending the known amyloidogenic variant sequences to the Swiss-Prot database. RESULTS: AFib amyloid was identified by proteomics in 64 renal samples based on the Mascot scores relative to other amyloid proteins, the presence of a pathogenic variant, and coverage of the p.449-621 sequence. Contamination by blood could be excluded from a comparison of the FibA score with that of the fibrinogen β and γ chains. The proteomics results were consistent with the clinical diagnosis. Four additional renal samples did not fulfill all the criteria using the algorithm but were adjudged as AFib amyloid based on a full assessment of the clinical and biochemical results. CONCLUSION: AFib amyloid can be identified reliably in glomerular amyloid by proteomics using a score-based algorithm. Proteomics data should be used as a guide to AFib diagnosis, with the results considered together with all available clinical and laboratory information

Misidentification of transthyretin and immunoglobulin variants by proteomics due to methyl lysine formation in formalin-fixed paraffin-embedded amyloid tissue

Proteomics is becoming the de facto gold standard for identifying amyloid proteins and is now used routinely in a number of centres. The technique is compound class independent and offers the added ability to identify variant and modified proteins. We re-examined proteomics results from a number of formalin-fixed paraffin-embedded amyloid samples, which were positive for transthyretin (TTR) by immunohistochemistry and proteomics, using the UniProt human protein database modified to include TTR variants. The amyloidogenic variant, V122I TTR, was incorrectly identified in 26/27 wild-type and non-V122I variant samples due to its close mass spectral similarity with the methyl lysine-modified WT peptide [126KMe]105-127 (p.[146 KMe]125-147) generated during formalin fixation. Similarly, the methyl lysine peptide, [50KMe]43-59, from immunoglobulin lambda light chain constant region was also misidentified as arising from a rare myeloma-derived lambda variant V49I. These processing-derived modifications are not present in fresh cardiac tissue, non-fixed fat nor serum and do not materially affect the identification of amyloid proteins. They could result in the incorrect assignment of a variant, and this may have consequences for the immediate family who will require genetic counselling and potentially early clinical intervention. As proteomics becomes a routine clinical test for amyloidosis, it becomes important to be aware of potentially confounding issues such as formalin-mediated lysine methylation, and how these may influence diagnosis and possibly treatment

Rhamnolipids: diversity of structures, microbial origins and roles

Rhamnolipids are glycolipidic biosurfactants produced by various bacterial species. They were initially found as exoproducts of the opportunistic pathogen Pseudomonas aeruginosa and described as a mixture of four congeners: α-L-rhamnopyranosyl-α-L-rhamnopyranosyl-β-hydroxydecanoyl-β-hydroxydecanoate (Rha-Rha-C10-C10), α-L-rhamnopyranosyl-α-L-rhamnopyranosyl-β-hydroxydecanoate (Rha-Rha-C10), as well as their mono-rhamnolipid congeners Rha-C10-C10 and Rha-C10. The development of more sensitive analytical techniques has lead to the further discovery of a wide diversity of rhamnolipid congeners and homologues (about 60) that are produced at different concentrations by various Pseudomonas species and by bacteria belonging to other families, classes, or even phyla. For example, various Burkholderia species have been shown to produce rhamnolipids that have longer alkyl chains than those produced by P. aeruginosa. In P. aeruginosa, three genes, carried on two distinct operons, code for the enzymes responsible for the final steps of rhamnolipid synthesis: one operon carries the rhlAB genes and the other rhlC. Genes highly similar to rhlA, rhlB, and rhlC have also been found in various Burkholderia species but grouped within one putative operon, and they have been shown to be required for rhamnolipid production as well. The exact physiological function of these secondary metabolites is still unclear. Most identified activities are derived from the surface activity, wetting ability, detergency, and other amphipathic-related properties of these molecules. Indeed, rhamnolipids promote the uptake and biodegradation of poorly soluble substrates, act as immune modulators and virulence factors, have antimicrobial activities, and are involved in surface motility and in bacterial biofilm development

Pharmacology and therapeutic implications of current drugs for type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is a global epidemic that poses a major challenge to health-care systems. Improving metabolic control to approach normal glycaemia (where practical) greatly benefits long-term prognoses and justifies early, effective, sustained and safety-conscious intervention. Improvements in the understanding of the complex pathogenesis of T2DM have underpinned the development of glucose-lowering therapies with complementary mechanisms of action, which have expanded treatment options and facilitated individualized management strategies. Over the past decade, several new classes of glucose-lowering agents have been licensed, including glucagon-like peptide 1 receptor (GLP-1R) agonists, dipeptidyl peptidase 4 (DPP-4) inhibitors and sodium/glucose cotransporter 2 (SGLT2) inhibitors. These agents can be used individually or in combination with well-established treatments such as biguanides, sulfonylureas and thiazolidinediones. Although novel agents have potential advantages including low risk of hypoglycaemia and help with weight control, long-term safety has yet to be established. In this Review, we assess the pharmacokinetics, pharmacodynamics and safety profiles, including cardiovascular safety, of currently available therapies for management of hyperglycaemia in patients with T2DM within the context of disease pathogenesis and natural history. In addition, we briefly describe treatment algorithms for patients with T2DM and lessons from present therapies to inform the development of future therapies