ost in promiscuity? An evolutionary and biochemical evaluation of HSD10 function in cardiolipin metabolism

Multifunctional proteins are challenging as it can be difficult to confirm pathomechanisms associated with disease-causing genetic variants. The human 17β-hydroxysteroid dehydrogenase 10 (HSD10) is a moonlighting enzyme with at least two structurally and catalytically unrelated functions. HSD10 disease was originally described as a disorder of isoleucine metabolism, but the clinical manifestations were subsequently shown to be linked to impaired mtDNA transcript processing due to deficient function of HSD10 in the mtRNase P complex. A surprisingly large number of other, mostly enzymatic and potentially clinically relevant functions have been attributed to HSD10. Recently, HSD10 was reported to exhibit phospholipase C-like activity towards cardiolipins (CL), important mitochondrial phospholipids. To assess the physiological role of the proposed CL-cleaving function, we studied CL architectures in living cells and patient fibroblasts in different genetic backgrounds and lipid environments using our well-established LC–MS/MS cardiolipidomic pipeline. These experiments revealed no measurable effect on CLs, indicating that HSD10 does not have a physiologically relevant function towards CL metabolism. Evolutionary constraints could explain the broad range of reported substrates for HSD10 in vitro. The combination of an essential structural with a non-essential enzymatic function in the same protein could direct the evolutionary trajectory towards improvement of the former, thereby increasing the flexibility of the binding pocket, which is consistent with the results presented here

A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease

A proteomic survival predictor for COVID-19 patients in intensive care

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care

A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease

A survey of the arthropod pests associated with commercial pomegranates, Punica granatum (Lythraceae), in South Africa

Little local information is available on pomegranate cultivation, especially on the pests affecting production and fruit quality. An extensive survey of the arthropods associated with commercial pomegranates, Punica granatum (Lythraceae), was therefore conducted over a period of 20 months from September 2006 to April 2008 in selected production orchards. Twelve orchards in nine districts of the Western Cape Province were inspected monthly and two spot surveys were done in the other major pomegranate-growing areas of the Northern Cape, North West, Mpumalanga and Limpopo Provinces of South Africa. A total of 35 insects and one mite species of economic importance was recorded. Of these, the most serious pests were the false codling moth, Thaumatotibia leucotreta (Meyrick), which damaged the fruit, the long tailed mealybug, Pseudococcus longispinus (Targioni Tozzetti), and various thrips species that affected both leaves and fruit. Small weevils of undescribed species were found to cause stunted growth due to larval and adult feeding on young shoots.Articl

Weevils causing damage to commercial pomegranates, Punica granatum (Lythraceae), in South Africa

[No abstract available]Articl

Mediterranean black fig fly, Silba adipata McAlpine (Diptera: Lonchaeidae), recorded from South Africa

[No abstract available]Articl

A survey of the arthropod pests and plant parasitic nematodes associated with commercial figs, Ficus carica (Moraceae), in South Africa

[No abstract available]Articl