Sex differences in patellar facet shape among healthy and osteoarthritic cohorts.

OBJECTIVE: Patellofemoral osteoarthritis (OA) may be more common in females than males. Reasons for this are not fully understood, but sex differences in patellar morphology may help explain this phenomenon. We quantified differences in patellar morphology between males and females in healthy and patellofemoral OA populations. DESIGN: A total of 97 (50F, 47M) healthy and 67 (40F, 27M) OA knees were scanned via computed tomography. OA individuals were on a waitlist for total knee replacement. Patella 3D models were segmented and 2D measurements were recorded: patellar width and height, lateral and medial facet width, and surface area. Medial and lateral facet surface topography was mapped using 81 points to describe 3D articular surface shape. Sex and group differences were assessed using Procrustes analysis of variance (ANOVA). Data were ordinated using Principal Component Analysis. RESULTS: Differences in patellar 2D measurements between healthy and OA individuals were smaller than were differences between males and females from healthy and OA groups. Sex and healthy/OA differences were most pronounced for medial facet shape, which featured a posteriorly-curving facet and taller, narrower facet shape in males compared to females. Lateral facet shape variance was higher in OA cohorts compared to healthy groups. CONCLUSIONS: Medial and lateral facet shapes showed different patterning of variation by sex and healthy/OA status. Lateral facet shape may be of interest in future models of OA risk in the patellofemoral joint, here showing increased magnitudes of variance associated with increased severity of disease (patellofemoral Kellgren and Lawrence score).

Inhibition of the eukaryotic initiation factor-2-α kinase PERK decreases risk of autoimmune diabetes in mice.

Preventing the onset of autoimmune type 1 diabetes (T1D) is feasible through pharmacological interventions that target molecular stress-responsive mechanisms. Cellular stresses, such as nutrient deficiency, viral infection, or unfolded proteins, trigger the integrated stress response (ISR), which curtails protein synthesis by phosphorylating eIF2α. In T1D, maladaptive unfolded protein response (UPR) in insulin-producing beta cells renders these cells susceptible to autoimmunity. We found that inhibition of the eIF2α kinase PERK, a common component of the UPR and ISR, reversed the mRNA translation block in stressed human islets and delayed the onset of diabetes, reduced islet inflammation, and preserved ß cell mass in T1D-susceptible mice. Single-cell RNA sequencing of islets from PERK-inhibited mice showed reductions in the UPR and PERK signaling pathways and alterations in antigen processing and presentation pathways in ß cells. Spatial proteomics of islets from these mice showed an increase in the immune checkpoint protein PD-L1 in ß cells. Golgi membrane protein 1, whose levels increased following PERK inhibition in human islets and EndoC-ßH1 human ß cells, interacted with and stabilized PD-L1. Collectively, our studies show that PERK activity enhances ß cell immunogenicity, and inhibition of PERK may offer a strategy to prevent or delay the development of T1D.

Inhibition of the Eukaryotic Initiation Factor-2-α Kinase PERK Decreases Risk of Autoimmune Diabetes in Mice.

Preventing the onset of autoimmune type 1 diabetes (T1D) is feasible through pharmacological interventions that target molecular stress-responsive mechanisms. Cellular stresses, such as nutrient deficiency, viral infection, or unfolded proteins, trigger the integrated stress response (ISR), which curtails protein synthesis by phosphorylating eIF2α. In T1D, maladaptive unfolded protein response (UPR) in insulin-producing ß cells renders these cells susceptible to autoimmunity. We show that inhibition of the eIF2α kinase PERK, a common component of the UPR and ISR, reverses the mRNA translation block in stressed human islets and delays the onset of diabetes, reduces islet inflammation, and preserves ß cell mass in T1D-susceptible mice. Single-cell RNA sequencing of islets from PERK-inhibited mice shows reductions in the UPR and PERK signaling pathways and alterations in antigen processing and presentation pathways in ß cells. Spatial proteomics of islets from these mice shows an increase in the immune checkpoint protein PD-L1 in ß cells. Golgi membrane protein 1, whose levels increase following PERK inhibition in human islets and EndoC-ßH1 human ß cells, interacts with and stabilizes PD-L1. Collectively, our studies show that PERK activity enhances ß cell immunogenicity, and inhibition of PERK may offer a strategy to prevent or delay the development of T1D.

Proteogenomic characterization of difficult-to-treat breast cancer with tumor cells enriched through laser microdissection.

BACKGROUND: Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer death among women globally. Despite advances, there is considerable variation in clinical outcomes for patients with non-luminal A tumors, classified as difficult-to-treat breast cancers (DTBC). This study aims to delineate the proteogenomic landscape of DTBC tumors compared to luminal A (LumA) tumors. METHODS: We retrospectively collected a total of 117 untreated primary breast tumor specimens, focusing on DTBC subtypes. Breast tumors were processed by laser microdissection (LMD) to enrich tumor cells. DNA, RNA, and protein were simultaneously extracted from each tumor preparation, followed by whole genome sequencing, paired-end RNA sequencing, global proteomics and phosphoproteomics. Differential feature analysis, pathway analysis and survival analysis were performed to better understand DTBC and investigate biomarkers. RESULTS: We observed distinct variations in gene mutations, structural variations, and chromosomal alterations between DTBC and LumA breast tumors. DTBC tumors predominantly had more mutations in TP53, PLXNB3, Zinc finger genes, and fewer mutations in SDC2, CDH1, PIK3CA, SVIL, and PTEN. Notably, Cytoband 1q21, which contains numerous cell proliferation-related genes, was significantly amplified in the DTBC tumors. LMD successfully minimized stromal components and increased RNA-protein concordance, as evidenced by stromal score comparisons and proteomic analysis. Distinct DTBC and LumA-enriched clusters were observed by proteomic and phosphoproteomic clustering analysis, some with survival differences. Phosphoproteomics identified two distinct phosphoproteomic profiles for high relapse-risk and low relapse-risk basal-like tumors, involving several genes known to be associated with breast cancer oncogenesis and progression, including KIAA1522, DCK, FOXO3, MYO9B, ARID1A, EPRS, ZC3HAV1, and RBM14. Lastly, an integrated pathway analysis of multi-omics data highlighted a robust enrichment of proliferation pathways in DTBC tumors. CONCLUSIONS: This study provides an integrated proteogenomic characterization of DTBC vs LumA with tumor cells enriched through laser microdissection. We identified many common features of DTBC tumors and the phosphopeptides that could serve as potential biomarkers for high/low relapse-risk basal-like BC and possibly guide treatment selections.

Development of an International Standard Set of Outcomes and Measurement Methods for Routine Practice for Adults with Epilepsy: The International Consortium for Health Outcomes Measurement Consensus Recommendations.

At present, there is no internationally accepted set of core outcomes or measurement methods for epilepsy clinical practice. Therefore, the International Consortium for Health Outcomes Measurement (ICHOM) convened an international working group of experts in epilepsy, people with epilepsy and their representatives to develop minimum sets of standardized outcomes and outcomes measurement methods for clinical practice that support patient-clinician decision-making and quality improvement. Consensus methods identified 20 core outcomes. Measurement tools were recommended based on their evidence of strong clinical measurement properties, feasibility, and cross-cultural applicability. The essential outcomes included many non-seizure outcomes: anxiety, depression, suicidality, memory and attention, sleep quality, functional status, and the social impact of epilepsy. The proposed set will facilitate the implementation of the use of patient-centered outcomes in daily practice, ensuring holistic care. They also encourage harmonization of outcome measurement, and if widely implemented should reduce the heterogeneity of outcome measurement, accelerate comparative research, and facilitate quality improvement efforts.

Development of an International Standard Set of Outcomes and Measurement Methods for Routine Practice for Infants, Children, and Adolescents with Epilepsy: The International Consortium for Health Outcomes Measurement Consensus Recommendations.

At present, there is no internationally accepted set of core outcomes or measurement methods for epilepsy clinical practice. The International Consortium for Health Outcomes Measurement (ICHOM) convened an international working group of experts in epilepsy, people with epilepsy, and their representatives to develop minimum sets of standardized outcomes and outcome measurement methods for clinical practice. Using modified Delphi consensus methods with consecutive rounds of online voting over 12 months, a core set of outcomes and corresponding measurement tool packages to capture the outcomes were identified for infants, children, and adolescents with epilepsy. Consensus methods identified 20 core outcomes. In addition to the outcomes identified for the ICHOM Epilepsy adult standard set, behavioral, motor, and cognitive/language development outcomes were voted as essential for all infants and children with epilepsy. The proposed set of outcomes and measurement methods will facilitate the implementation of the use of patient-centered outcomes in daily practice.

Improving epilepsy diagnosis across the lifespan: approaches and innovations.

Epilepsy diagnosis is often delayed or inaccurate, exposing people to ongoing seizures and their substantial consequences until effective treatment is initiated. Important factors contributing to this problem include delayed recognition of seizure symptoms by patients and eyewitnesses; cultural, geographical, and financial barriers to seeking health care; and missed or delayed diagnosis by health-care providers. Epilepsy diagnosis involves several steps. The first step is recognition of epileptic seizures; next is classification of epilepsy type and whether an epilepsy syndrome is present; finally, the underlying epilepsy-associated comorbidities and potential causes must be identified, which differ across the lifespan. Clinical history, elicited from patients and eyewitnesses, is a fundamental component of the diagnostic pathway. Recent technological advances, including smartphone videography and genetic testing, are increasingly used in routine practice. Innovations in technology, such as artificial intelligence, could provide new possibilities for directly and indirectly detecting epilepsy and might make valuable contributions to diagnostic algorithms in the future.

A compendium of multi-omics data illuminating host responses to lethal human virus infections.

Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host response to infection generated from 45 individual experiments involving human viruses from the Orthomyxoviridae, Filoviridae, Flaviviridae, and Coronaviridae families. Analogous experimental designs were implemented across human or mouse host model systems, longitudinal samples were collected over defined time courses, and global multi-omics data (transcriptomics, proteomics, metabolomics, and lipidomics) were acquired by microarray, RNA sequencing, or mass spectrometry analyses. For comparison, we have included transcriptomics datasets from cells treated with type I and type II human interferon. Raw multi-omics data and metadata were deposited in public repositories, and we provide a central location linking the raw data with experimental metadata and ready-to-use, quality-controlled, statistically processed multi-omics datasets not previously available in any public repository. This compendium of infection-induced host response data for reuse will be useful for those endeavouring to understand viral disease pathophysiology and network biology.

Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images.

Light-sheet microscopy has made possible the 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual undertaking. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through optimizing transfer learning. We fine-tuned the existing TrailMap model using expert-labeled data from noradrenergic axonal structures in the mouse brain. By changing the cross-entropy weights and using augmentation, we demonstrate a generally improved adjusted F1-score over using the originally trained TrailMap model within our test datasets.

Regulation of ß-cell death by ADP-ribosylhydrolase ARH3 via lipid signaling in insulitis.

BACKGROUND: Lipids are regulators of insulitis and ß-cell death in type 1 diabetes development, but the underlying mechanisms are poorly understood. Here, we investigated how the islet lipid composition and downstream signaling regulate ß-cell death. METHODS: We performed lipidomics using three models of insulitis: human islets and EndoC-ßH1 ß cells treated with the pro-inflammatory cytokines interlukine-1ß and interferon-γ, and islets from pre-diabetic non-obese mice. We also performed mass spectrometry and fluorescence imaging to determine the localization of lipids and enzyme in islets. RNAi, apoptotic assay, and qPCR were performed to determine the role of a specific factor in lipid-mediated cytokine signaling. RESULTS: Across all three models, lipidomic analyses showed a consistent increase of lysophosphatidylcholine species and phosphatidylcholines with polyunsaturated fatty acids and a reduction of triacylglycerol species. Imaging assays showed that phosphatidylcholines with polyunsaturated fatty acids and their hydrolyzing enzyme phospholipase PLA2G6 are enriched in islets. In downstream signaling, omega-3 fatty acids reduce cytokine-induced ß-cell death by improving the expression of ADP-ribosylhydrolase ARH3. The mechanism involves omega-3 fatty acid-mediated reduction of the histone methylation polycomb complex PRC2 component Suz12, upregulating the expression of Arh3, which in turn decreases cell apoptosis. CONCLUSIONS: Our data provide insights into the change of lipidomics landscape in ß cells during insulitis and identify a protective mechanism by omega-3 fatty acids. Video Abstract.

Decrease in multiple complement proteins associated with development of islet autoimmunity and type 1 diabetes.

Type 1 diabetes (T1D) is a chronic condition caused by autoimmune destruction of the insulin-producing pancreatic ß cells. While it is known that gene-environment interactions play a key role in triggering the autoimmune process leading to T1D, the pathogenic mechanism leading to the appearance of islet autoantibodies-biomarkers of autoimmunity-is poorly understood. Here we show that disruption of the complement system precedes the detection of islet autoantibodies and persists through disease onset. Our results suggest that children who exhibit islet autoimmunity and progress to clinical T1D have lower complement protein levels relative to those who do not progress within a similar time frame. Thus, the complement pathway, an understudied mechanistic and therapeutic target in T1D, merits increased attention for use as protein biomarkers of prediction and potentially prevention of T1D.

A Composite Biomarker Signature of Type 1 Diabetes Risk Identified via Augmentation of Parallel Multi-Omics Data from a Small Cohort.

Background: Biomarkers of early pathogenesis of type 1 diabetes (T1D) are crucial to enable effective prevention measures in at-risk populations before significant damage occurs to their insulin producing beta-cell mass. We recently introduced the concept of integrated parallel multi-omics and employed a novel data augmentation approach which identified promising candidate biomarkers from a small cohort of high-risk T1D subjects. We now validate selected biomarkers to generate a potential composite signature of T1D risk. Methods: Twelve candidate biomarkers, which were identified in the augmented data and selected based on their fold-change relative to healthy controls and cross-reference to proteomics data previously obtained in the expansive TEDDY and DAISY cohorts, were measured in the original samples by ELISA. Results: All 12 biomarkers had established connections with lipid/lipoprotein metabolism, immune function, inflammation, and diabetes, but only 7 were found to be markedly changed in the high-risk subjects compared to the healthy controls: ApoC1 and PON1 were reduced while CETP, CD36, FGFR1, IGHM, PCSK9, SOD1, and VCAM1 were elevated. Conclusions: Results further highlight the promise of our data augmentation approach in unmasking important patterns and pathologically significant features in parallel multi-omics datasets obtained from small sample cohorts to facilitate the identification of promising candidate T1D biomarkers for downstream validation. They also support the potential utility of a composite biomarker signature of T1D risk characterized by the changes in the above markers.

Mapping microhabitats of lignocellulose decomposition by a microbial consortium.

The leaf-cutter ant fungal garden ecosystem is a naturally evolved model system for efficient plant biomass degradation. Degradation processes mediated by the symbiotic fungus Leucoagaricus gongylophorus are difficult to characterize due to dynamic metabolisms and spatial complexity of the system. Herein, we performed microscale imaging across 12-µm-thick adjacent sections of Atta cephalotes fungal gardens and applied a metabolome-informed proteome imaging approach to map lignin degradation. This approach combines two spatial multiomics mass spectrometry modalities that enabled us to visualize colocalized metabolites and proteins across and through the fungal garden. Spatially profiled metabolites revealed an accumulation of lignin-related products, outlining morphologically unique lignin microhabitats. Metaproteomic analyses of these microhabitats revealed carbohydrate-degrading enzymes, indicating a prominent fungal role in lignocellulose decomposition. Integration of metabolome-informed proteome imaging data provides a comprehensive view of underlying biological pathways to inform our understanding of metabolic fungal pathways in plant matter degradation within the micrometer-scale environment.

Clinical practice applicability and relevance to non-specialists of a paediatric EEG online learning tool.

BACKGROUND: Paediatric electroencephalography (EEG) training is inadequate amongst healthcare practitioners and technicians managing children with epilepsy in sub-Saharan Africa. An entry level handbook was developed for healthcare practitioners in sub-Saharan Africa and subsequently made globally accessible via the International Child Neurology Teaching Network. AIM: To investigate the usefulness of a paediatric online EEG handbook. METHOD: A survey of the ICNApedia online EEG handbook was circulated (December 2021-June 2022), to all 108 handbook registered participants (39 countries) via the research electronic data capture (REDCap) from the University of Cape Town (UCT). RESULTS: Fifty participants from 25 countries responded: 8 from high income, 16 upper-middle income, 21 lower-middle income and 5 from low-income. 32 (64%) fully and 18 (36%) partially completed the survey. 35/50 (70%) had completed the handbook and seven respondents had partially completed the handbook. Responses supported the handbook as a good entry point to learn EEGs, especially for paediatrics. Likert scale ratings supported the handbook as relevant for gaining/enhancing knowledge and improving diagnosis and management of patients with confidence. The handbook was considered user friendly, comprehensible, and provided a practical experience. For improving EEG reading skills the handbook helped skills development via reinforcement and good illustrations. 29/32 (90%) of respondents confirmed that they are using learnt skills from the handbook in their current work. CONCLUSION: In resource limited settings non-specialist clinicians often provide extended services including EEG interpretation. The survey supports that the handbook is supporting this niche skills area, especially for the accessibility of knowledge gained. The handbook will continue to be adapted in-line with survey feedback.

What is allosteric regulation? Exploring the exceptions that prove the rule!

"Allosteric" was first introduced to mean the other site (i.e., a site distinct from the active or orthosteric site), an adjective for "regulation" to imply a regulatory outcome resulting from ligand binding at another site. That original idea outlines a system with two ligand-binding events at two distinct locations on a macromolecule (originally a protein system), which defines a four-state energy cycle. An allosteric energy cycle provides a quantifiable allosteric coupling constant and focuses our attention on the unique properties of the four equilibrated protein complexes that constitute the energy cycle. Because many observed phenomena have been referenced as "allosteric regulation" in the literature, the goal of this work is to use literature examples to explore which systems are and are not consistent with the two-ligand thermodynamic energy cycle-based definition of allosteric regulation. We emphasize the need for consistent language so comparisons can be made among the ever-increasing number of allosteric systems. Building on the mutually exclusive natures of an energy cycle definition of allosteric regulation versus classic two-state models, we conclude our discussion by outlining how the often-proposed Rube-Goldberg-like mechanisms are likely inconsistent with an energy cycle definition of allosteric regulation.

Which terms should be used to describe medications used in the treatment of seizure disorders? An ILAE position paper.

A variety of terms, such as "antiepileptic," "anticonvulsant," and "antiseizure" have been historically applied to medications for the treatment of seizure disorders. Terminology is important because using terms that do not accurately reflect the action of specific treatments may result in a misunderstanding of their effects and inappropriate use. The present International League Against Epilepsy (ILAE) position paper used a Delphi approach to develop recommendations on English-language terminology applicable to pharmacological agents currently approved for treating seizure disorders. There was consensus that these medications should be collectively named "antiseizure medications". This term accurately reflects their primarily symptomatic effect against seizures and reduces the possibility of health care practitioners, patients, or caregivers having undue expectations or an incorrect understanding of the real action of these medications. The term "antiseizure" to describe these agents does not exclude the possibility of beneficial effects on the course of the disease and comorbidities that result from the downstream effects of seizures, whenever these beneficial effects can be explained solely by the suppression of seizure activity. It is acknowledged that other treatments, mostly under development, can exert direct favorable actions on the underlying disease or its progression, by having "antiepileptogenic" or "disease-modifying" effects. A more-refined terminology to describe precisely these actions needs to be developed.

Assessing Decision Support Tools for Mitigating Tail Biting in Pork Production: Current Progress and Future Directions.

Tail biting (TB) in pigs is a complex issue that can be caused by multiple factors, making it difficult to determine the exact etiology on a case-by-case basis. As such, it is often difficult to pinpoint the reason, or set of reasons, for TB events, Decision Support Tools (DSTs) can be used to identify possible risk factors of TB on farms and provide suitable courses of action. The aim of this review was to identify DSTs that could be used to predict the risk of TB behavior. Additionally, technologies that can be used to support DSTs, with monitoring and tracking the prevalence of TB behaviors, are reviewed. Using the PRISMA methodology to identify sources, the applied selection process found nine DSTs related to TB in pigs. All support tools relied on secondary information, either by way of the scientific literature or expert opinions, to determine risk factors for TB predictions. Only one DST was validated by external sources, seven were self-assessed by original developers, and one presented no evidence of validation. This analysis better understands the limitations of DSTs and highlights an opportunity for the development of DSTs that rely on objective data derived from the environment, animals, and humans simultaneously to predict TB risks. Moreover, an opportunity exists for the incorporation of monitoring technologies for TB detection into a DST.

Genetic variants in the complement system and their potential link in the aetiology of type 1 diabetes.

Type 1 diabetes is an autoimmune disease in which one's own immune system destroys insulin-secreting beta cells in the pancreas. This process results in life-long dependence on exogenous insulin for survival. Both genetic and environmental factors play a role in disease initiation, progression, and ultimate clinical diagnosis of type 1 diabetes. This review will provide background on the natural history of type 1 diabetes and the role of genetic factors involved in the complement system, as several recent studies have identified changes in levels of these proteins as the disease evolves from pre-clinical through to clinically apparent disease.