How rotating ATP synthases can modulate membrane structure

F1Fo-ATP synthase (ATP synthase) is a central membrane protein that synthetizes most of the ATP in the cell through a rotational movement driven by a proton gradient across the hosting membrane. In mitochondria, ATP synthases can form dimers through specific interactions between some subunits of the protein. The dimeric form of ATP synthase provides the protein with a spontaneous curvature that sustain their arrangement at the rim of the high-curvature edges of mitochondrial membrane (cristae). Also, a direct interaction with cardiolipin, a lipid present in the inner mitochondrial membrane, induces the dimerization of ATP synthase molecules along cristae. The deletion of those biochemical interactions abolishes the protein dimerization producing an altered mitochondrial function and morphology. Mechanically, membrane bending is one of the key deformation modes by which mitochondrial membranes can be shaped. In particular, bending rigidity and spontaneous curvature are important physical factors for membrane remodelling. Here, we discuss a complementary mechanism whereby the rotatory movement of the ATP synthase might modify the mechanical properties of lipid bilayers and contribute to the formation and regulation of the membrane invaginations

Behavioral immune landscapes of inflammation.

Transcriptional or proteomic profiling of individual cells have revolutionized interpretation of biological phenomena by providing cellular landscapes of healthy and diseased tissues. These approaches, however, fail to describe dynamic scenarios in which cells can change their biochemical properties and downstream “behavioral” outputs every few seconds or minutes. Here, we used 4D live imaging to record tens to hundreds of morpho-kinetic parameters describing the dynamism of individual leukocytes at sites of active inflammation. By analyzing over 100,000 reconstructions of cell shapes and tracks over time, we obtained behavioral descriptors of individual cells and used these high-dimensional datasets to build behavioral landscapes. These landscapes recognized leukocyte identities in the inflamed skin and trachea, and inside blood vessels uncovered a continuum of neutrophil states, including a large, sessile state that was embraced by the underlying endothelium and associated with pathogenic inflammation. Behavioral in vivo screening of thousands of cells from 24 different mouse mutants identified the kinase Fgr as a driver of this pathogenic state, and genetic or pharmacological interference of Fgr protected from inflammatory injury. Thus, behavioral landscapes report unique biological properties of dynamic environments at high cellular, spatial and temporal resolution.pre-print4302 K

Depression symptomatology and diagnosis: discordance between patients and physicians in primary care settings

<p>Abstract</p> <p>Background</p> <p>To examine the agreement between depression symptoms using an assessment tool (PHQ-9), and physician documentation of the same symptoms during a clinic visit, and then to examine how the presence of these symptoms affects depression diagnosis in primary care settings.</p> <p>Methods</p> <p>Interviewer administered surveys and medical record reviews. A total of 304 participants were recruited from 2321 participants screened for depression at two large urban primary care community settings.</p> <p>Results</p> <p>Of the 2321 participants screened for depression 304 were positive for depression and of these 75.3% (n = 229) were significantly depressed (PHQ-9 score ≥ 10). Of these, 31.0% were diagnosed by a physician with a depressive disorder. A total of 57.6% (n = 175) of study participants had both significant depression symptoms and functional impairment. Of these 37.7% were diagnosed by physicians as depressed. Cohen's Kappa analysis, used to determine the agreement between depression symptoms elicited using the PHQ-9 and physician documentation of these symptoms showed only slight agreement (0.001–0.101) for all depression symptoms using standard agreement rating scales. Further analysis showed that only suicidal ideation and hypersomnia or insomnia were associated with an increased likelihood of physician depression diagnosis (OR 5.41 P sig < .01 and (OR 2.02 P sig < .05 respectively). Other depression symptoms and chronic medical conditions had no affect on physician depression diagnosis.</p> <p>Conclusion</p> <p>Two-thirds of individuals with depression are undiagnosed in primary care settings. While functional impairment increases the rate of physician diagnosis of depression, the agreement between a structured assessment and physician elicited and or documented symptoms during a clinical encounter is very low. Suicidality, hypersomnia and insomnia are associated with an increase in the rate of depression diagnosis even when physician and self report of the symptom differ. Interventions that emphasize the use of routine structured screening of primary care patients might also improve the rate of diagnosis of depression in these settings. Further studies are needed to explore depression symptom assessment during physician patient encounter in primary care settings.</p

Cancer data quality and harmonization in Europe: the experience of the BENCHISTA Project – international benchmarking of childhood cancer survival by stage

IntroductionVariation in stage at diagnosis of childhood cancers (CC) may explain differences in survival rates observed across geographical regions. The BENCHISTA project aims to understand these differences and to encourage the application of the Toronto Staging Guidelines (TG) by Population-Based Cancer Registries (PBCRs) to the most common solid paediatric cancers.MethodsPBCRs within and outside Europe were invited to participate and identify all cases of Neuroblastoma, Wilms Tumour, Medulloblastoma, Ewing Sarcoma, Rhabdomyosarcoma and Osteosarcoma diagnosed in a consecutive three-year period (2014-2017) and apply TG at diagnosis. Other non-stage prognostic factors, treatment, progression/recurrence, and cause of death information were collected as optional variables. A minimum of three-year follow-up was required. To standardise TG application by PBCRs, on-line workshops led by six tumour-specific clinical experts were held. To understand the role of data availability and quality, a survey focused on data collection/sharing processes and a quality assurance exercise were generated. To support data harmonization and query resolution a dedicated email and a question-and-answers bank were created.Results67 PBCRs from 28 countries participated and provided a maximally de-personalized, patient-level dataset. For 26 PBCRs, data format and ethical approval obtained by the two sponsoring institutions (UCL and INT) was sufficient for data sharing. 41 participating PBCRs required a Data Transfer Agreement (DTA) to comply with data protection regulations. Due to heterogeneity found in legal aspects, 18 months were spent on finalizing the DTA. The data collection survey was answered by 68 respondents from 63 PBCRs; 44% of them confirmed the ability to re-consult a clinician in cases where stage ascertainment was difficult/uncertain. Of the total participating PBCRs, 75% completed the staging quality assurance exercise, with a median correct answer proportion of 92% [range: 70% (rhabdomyosarcoma) to 100% (Wilms tumour)].ConclusionDifferences in interpretation and processes required to harmonize general data protection regulations across countries were encountered causing delays in data transfer. Despite challenges, the BENCHISTA Project has established a large collaboration between PBCRs and clinicians to collect detailed and standardised TG at a population-level enhancing the understanding of the reasons for variation in overall survival rates for CC, stimulate research and improve national/regional child health plans

Variable selection for nonlinear dimensionality reduction of biological datasets through bootstrapping of correlation networks.

Identifying the most relevant variables or features in massive datasets for dimensionality reduction can lead to improved and more informative display, faster computation times, and more explainable models of complex systems. Despite significant advances and available algorithms, this task generally remains challenging, especially in unsupervised settings. In this work, we propose a method that constructs correlation networks using all intervening variables and then selects the most informative ones based on network bootstrapping. The method can be applied in both supervised and unsupervised scenarios. We demonstrate its functionality by applying Uniform Manifold Approximation and Projection for dimensionality reduction to several high-dimensional biological datasets, derived from 4D live imaging recordings of hundreds of morpho-kinetic variables, describing the dynamics of thousands of individual leukocytes at sites of prominent inflammation. We compare our method with other standard ones in the field, such as Principal Component Analysis and Elastic Net, showing that it outperforms them. The proposed method can be employed in a wide range of applications, encompassing data analysis and machine learning.This research has been supported by grants awarded to G.F.C. by the Spanish Ministerio de Ciencia e Innovación and the European Union NextGenerationEU/PRTR, MCIN/AEI/10.13039/501100011033 (grant numbers TED2021-132296B-C55, PDC2022-133520-I00 and PID2022- 142341OB-I00). D.G.A., Spain is supported by a research contract with reference 2023-CDT-11616 (from project with grant number TED2021- 132296B-C55). A.H. was supported by RTI2018-095497-B-I00 from Ministerio de Ciencia e Innovación (MCIN), Spain, HR17_00527 from Fundación La Caixa, Spain, Transatlantic Network of Excellence, Spain (TNE-18CVD04) from the Leducq Foundation, and FET-OPEN (no. 861878) from the European Comission. M.P.-S. is supported by a Federation of European Biochemical Societies, Spain, the EMBO ALTF (no. 1142–2020) long-term fellowship and from MICINN, Spain (RYC2021- 033511-I). J.S. is supported by a fellowship (PRE2019-089130) from MICINN, Spain. The CNIC is supported by the MCIN and the Pro-CNIC Foundation, Spain.S

Integrated modeling of labile and glycated hemoglobin with glucose for enhanced diabetes detection and short-term monitoring

Summary: Metabolic biomarkers, particularly glycated hemoglobin and fasting plasma glucose, are pivotal in the diagnosis and control of diabetes mellitus. Despite their importance, they exhibit limitations in assessing short-term glucose variations. In this study, we propose labile hemoglobin as an additional biomarker, providing insightful perspectives into these fluctuations. By utilizing datasets from 40,652 retrospective general participants and conducting glucose tolerance tests on 60 prospective pediatric subjects, we explored the relationship between plasma glucose and labile hemoglobin. A mathematical model was developed to encapsulate short-term glucose kinetics in the pediatric group. Applying dimensionality reduction techniques, we successfully identified participant subclusters, facilitating the differentiation between diabetic and non-diabetic individuals. Intriguingly, by integrating labile hemoglobin measurements with plasma glucose values, we were able to predict the likelihood of diabetes in pediatric subjects, underscoring the potential of labile hemoglobin as a significant glycemic biomarker for diabetes research