Diabetes mellitus (DM) is a group of metabolic diseases marked by hyperglycemia, which increases the risk of systemic infections. DM patients are at greater risk of hospitalization and mortality from bacterial, viral, and fungal infections. Poor glycemic control can result in skin, blood, bone, urinary, gastrointestinal, and respiratory tract infections and recurrent infections. Therefore, the evidence that infections play a critical role in DM progression and the hazard ratio for a person with DM dying from any infection is higher. Early diagnosis and better glycemic control can help prevent infections and improve treatment outcomes. Perhaps, half (49.7%) of the people living with DM are undiagnosed, resulting in a higher frequency of infections induced by the hyperglycemic milieu that favors immune dysfunction. Novel diagnostic and therapeutic markers for glycemic control and infection prevention are desirable. High-throughput blood-based immunoassays that screen infections and hyperglycemia are required to guide timely interventions and efficiently monitor treatment responses. The present review aims to collect information on the most common infections associated with DM, their origin, pathogenesis, and the potential of immunoproteomics assays in the early diagnosis of the infections. While infections are common in DM, their role in glycemic control and disease pathogenesis is poorly described. Nevertheless, more research is required to identify novel diagnostic and prognostic markers to understand DM pathogenesis and management of infections. Precise monitoring of diabetic infections by immunoproteomics may provide novel insights into disease pathogenesis and healthy prognosis.

Background and purpose: Idiopathic facial palsy (IFP) accounts for over 60% of peripheral facial palsy (FP) cases. The cause of IFP remains to be determined. Possible etiologies are nerve swelling due to inflammation and/or viral infection. In this study, we applied an integrative mass spectrometry approach to identify possibly altered protein patterns in the cerebrospinal fluid (CSF) of IFP patients. Methods:We obtained CSF samples from 34 patients with FP. In four patients, varicellazoster virus was the cause (VZV-FP). Among the 30 patients diagnosed with IFP, 17 had normal CSF parameters, five had slightly elevated CSF cell counts and normal or elevated CSF protein, and eight had normal CSF cell counts but elevated CSF protein.Five patients with primary headache served as controls. All samples were tested for viral pathogens by PCR and subjected to liquid chromatography tandem mass spectrometry and bioinformatics analysis and multiplex cytokine/chemokine arrays.Results: All CSF samples, except those from VZV-FP patients, were negative for all tested pathogens. The protein composition of CSF samples from IFP patients with normal CSF was comparable to controls. IFP patients with elevated CSF protein showed dysregulated proteins involved in inflammatory pathways, findings which were similar to those in VZV-FP patients. Multiplex analysis revealed similarly elevated cytokine levels in the CSF of IFP patients with elevated CSF protein and VZV-FP. Conclusions:Our study revealed a subgroup of IFP patients with elevated CSF protein that showed upregulated inflammatory pathways, suggesting an inflammatory/infectious cause. However, no evidence for an inflammatory cause was found in IFP patients with normal CSF.

A balance between the synthesis and degradation of proteins is referred to as protein turnover, which is crucial for cellular protein homeostasis. Proteome-wide analysis of protein turnover in adipocytes, which are well-known for their role in energy storage and their link to obesity and metabolism disorders, is yet to be conducted. Thus, with this objective in mind, our investigation utilized a comparative analysis of time-dependent SILAC labeling to assess protein turnover in 3T3-L1 adipocytes, spanning a period of 0 to 144 h. We observed that relatively faster or slower protein half-lives in several protein groups were associated with the PPARγ signaling pathway, energy metabolism, extracellular matrix, ubiquitin–proteasome system, RNA splicing, Golgi complex, and lysosome. It is anticipated that these protein half-life profiles will provide greater clarity on the life cycle of adipocyte proteome and shed light on how they maintain protein homeostasis.