Assembly and Translocation of a CRISPR-Cas Primed Acquisition Complex.

CRISPR-Cas systems confer an adaptive immunity against viruses. Following viral injection, Cas1-Cas2 integrates segments of the viral genome (spacers) into the CRISPR locus. In type I CRISPR-Cas systems, efficient "primed" spacer acquisition and viral degradation (interference) require both the Cascade complex and the Cas3 helicase/nuclease. Here, we present single-molecule characterization of the Thermobifida fusca (Tfu) primed acquisition complex (PAC). We show that TfuCascade rapidly samples non-specific DNA via facilitated one-dimensional diffusion. Cas3 loads at target-bound Cascade and the Cascade/Cas3 complex translocates via a looped DNA intermediate. Cascade/Cas3 complexes stall at diverse protein roadblocks, resulting in a double strand break at the stall site. In contrast, Cas1-Cas2 samples DNA transiently via 3D collisions. Moreover, Cas1-Cas2 associates with Cascade and translocates with Cascade/Cas3, forming the PAC. PACs can displace different protein roadblocks, suggesting a mechanism for long-range spacer acquisition. This work provides a molecular basis for the coordinated steps in CRISPR-based adaptive immunity.

The evolution of metazoan shelterin.

The mammalian telomeric shelterin complex-comprised of TRF1, TRF2, Rap1, TIN2, TPP1, and POT1-blocks the DNA damage response at chromosome ends and interacts with telomerase and the CST complex to regulate telomere length. The evolutionary origins of shelterin are unclear, partly because unicellular organisms have distinct telomeric proteins. Here, we describe the evolution of metazoan shelterin, showing that TRF1 emerged in vertebrates upon duplication of a TRF2-like ancestor. TRF1 and TRF2 diverged rapidly during vertebrate evolution through the acquisition of new domains and interacting factors. Vertebrate shelterin is also distinguished by the presence of an HJRL domain in the split C-terminal OB fold of POT1, whereas invertebrate POT1s carry inserts of variable nature. Importantly, the data reveal that, apart from the primate and rodent POT1 orthologs, all metazoan POT1s are predicted to have a fourth OB fold at their N termini. Therefore, we propose that POT1 arose from a four-OB-fold ancestor, most likely an RPA70-like protein. This analysis provides insights into the biology of shelterin and its evolution from ancestral telomeric DNA-binding proteins.

RPA Phosphorylation Inhibits DNA Resection.

Genetic recombination in all kingdoms of life initiates when helicases and nucleases process (resect) the free DNA ends to expose single-stranded DNA (ssDNA) overhangs. Resection regulation in bacteria is programmed by a DNA sequence, but a general mechanism limiting resection in eukaryotes has remained elusive. Using single-molecule imaging of reconstituted human DNA repair factors, we identify phosphorylated RPA (pRPA) as a negative resection regulator. Bloom's syndrome (BLM) helicase together with exonuclease 1 (EXO1) and DNA2 nucleases catalyze kilobase-length DNA resection on nucleosome-coated DNA. The resulting ssDNA is rapidly bound by RPA, which further stimulates DNA resection. RPA is phosphorylated during resection as part of the DNA damage response (DDR). Remarkably, pRPA inhibits DNA resection in cellular assays and in vitro via inhibition of BLM helicase. pRPA suppresses BLM initiation at DNA ends and promotes the intrinsic helicase strand-switching activity. These findings establish that pRPA provides a feedback loop between DNA resection and the DDR.

MBNL1 Regulates Programmed Postnatal Switching Between Regenerative and Differentiated Cardiac States.

BACKGROUND: Discovering determinants of cardiomyocyte maturity is critical for deeply understanding the maintenance of differentiated states and potentially reawakening endogenous regenerative programs in adult mammalian hearts as a therapeutic strategy. Forced dedifferentiation paired with oncogene expression is sufficient to drive cardiac regeneration, but elucidation of endogenous developmental regulators of the switch between regenerative and mature cardiomyocyte cell states is necessary for optimal design of regenerative approaches for heart disease. MBNL1 (muscleblind-like 1) regulates fibroblast, thymocyte, and erythroid differentiation and proliferation. Hence, we examined whether MBNL1 promotes and maintains mature cardiomyocyte states while antagonizing cardiomyocyte proliferation. METHODS: MBNL1 gain- and loss-of-function mouse models were studied at several developmental time points and in surgical models of heart regeneration. Multi-omics approaches were combined with biochemical, histological, and in vitro assays to determine the mechanisms through which MBNL1 exerts its effects. RESULTS: MBNL1 is coexpressed with a maturation-association genetic program in the heart and is regulated by the MEIS1/calcineurin signaling axis. Targeted MBNL1 overexpression early in development prematurely transitioned cardiomyocytes to hypertrophic growth, hypoplasia, and dysfunction, whereas loss of MBNL1 function increased cardiomyocyte cell cycle entry and proliferation through altered cell cycle inhibitor transcript stability. Moreover, MBNL1-dependent stabilization of estrogen-related receptor signaling was essential for maintaining cardiomyocyte maturity in adult myocytes. In accordance with these data, modulating MBNL1 dose tuned the temporal window of neonatal cardiac regeneration, where increased MBNL1 expression arrested myocyte proliferation and regeneration and MBNL1 deletion promoted regenerative states with prolonged myocyte proliferation. However, MBNL1 deficiency was insufficient to promote regeneration in the adult heart because of cell cycle checkpoint activation. CONCLUSIONS: Here, MBNL1 was identified as an essential regulator of cardiomyocyte differentiated states, their developmental switch from hyperplastic to hypertrophic growth, and their regenerative potential through controlling an entire maturation program by stabilizing adult myocyte mRNAs during postnatal development and throughout adulthood. Targeting loss of cardiomyocyte maturity and downregulation of cell cycle inhibitors through MBNL1 deletion was not sufficient to promote adult regeneration.

Caveolin-1 promotes mitochondrial health and limits mitochondrial ROS through ROCK/AMPK regulation of basal mitophagic flux.

Caveolin-1 (CAV1), the main structural component of caveolae, is phosphorylated at tyrosine-14 (pCAV1), regulates signal transduction, mechanotransduction, and mitochondrial function, and plays contrasting roles in cancer progression. We report that CRISPR/Cas9 knockout (KO) of CAV1 increases mitochondrial oxidative phosphorylation, increases mitochondrial potential, and reduces ROS in MDA-MB-231 triple-negative breast cancer cells. Supporting a role for pCAV1, these effects are reversed upon expression of CAV1 phosphomimetic CAV1 Y14D but not non-phosphorylatable CAV1 Y14F. pCAV1 is a known effector of Rho-associated kinase (ROCK) signaling and ROCK1/2 signaling mediates CAV1 promotion of increased mitochondrial potential and decreased ROS production in MDA-MB-231 cells. CAV1/ROCK control of mitochondrial potential and ROS is caveolae-independent as similar results were observed in PC3 prostate cancer cells lacking caveolae. Increased mitochondrial health and reduced ROS in CAV1 KO MDA-MB-231 cells were reversed by knockdown of the autophagy protein ATG5, mitophagy regulator PINK1 or the mitochondrial fission protein Drp1 and therefore due to mitophagy. Use of the mitoKeima mitophagy probe confirmed that CAV1 signaling through ROCK inhibited basal mitophagic flux. Activation of AMPK, a major mitochondrial homeostasis protein inhibited by ROCK, is inhibited by CAV1-ROCK signaling and mediates the increased mitochondrial potential, decreased ROS, and decreased basal mitophagy flux observed in wild-type MDA-MB-231 cells. CAV1 regulation of mitochondrial health and ROS in cancer cells therefore occurs via ROCK-dependent inhibition of AMPK. This study therefore links pCAV1 signaling activity at the plasma membrane with its regulation of mitochondrial activity and cancer cell metabolism through control of mitophagy.

Single-Molecule Imaging Reveals How Mre11-Rad50-Nbs1 Initiates DNA Break Repair.

DNA double-strand break (DSB) repair is essential for maintaining our genomes. Mre11-Rad50-Nbs1 (MRN) and Ku70-Ku80 (Ku) direct distinct DSB repair pathways, but the interplay between these complexes at a DSB remains unclear. Here, we use high-throughput single-molecule microscopy to show that MRN searches for free DNA ends by one-dimensional facilitated diffusion, even on nucleosome-coated DNA. Rad50 binds homoduplex DNA and promotes facilitated diffusion, whereas Mre11 is required for DNA end recognition and nuclease activities. MRN gains access to occluded DNA ends by removing Ku or other DNA adducts via an Mre11-dependent nucleolytic reaction. Next, MRN loads exonuclease 1 (Exo1) onto the free DNA ends to initiate DNA resection. In the presence of replication protein A (RPA), MRN acts as a processivity factor for Exo1, retaining the exonuclease on DNA for long-range resection. Our results provide a mechanism for how MRN promotes homologous recombination on nucleosome-coated DNA.

MYBPC3-c.772G>A mutation results in haploinsufficiency and altered myosin cycling kinetics in a patient induced stem cell derived cardiomyocyte model of hypertrophic cardiomyopathy.

Approximately 40% of hypertrophic cardiomyopathy (HCM) mutations are linked to the sarcomere protein cardiac myosin binding protein-C (cMyBP-C). These mutations are either classified as missense mutations or truncation mutations. One mutation whose nature has been inconsistently reported in the literature is the MYBPC3-c.772G > A mutation. Using patient-derived human induced pluripotent stem cells differentiated to cardiomyocytes (hiPSC-CMs), we have performed a mechanistic study of the structure-function relationship for this MYBPC3-c.772G > A mutation versus a mutation corrected, isogenic cell line. Our results confirm that this mutation leads to exon skipping and mRNA truncation that ultimately suggests ∼20% less cMyBP-C protein (i.e., haploinsufficiency). This, in turn, results in increased myosin recruitment and accelerated myofibril cycling kinetics. Our mechanistic studies suggest that faster ADP release from myosin is a primary cause of accelerated myofibril cross-bridge cycling due to this mutation. Additionally, the reduction in force generating heads expected from faster ADP release during isometric contractions is outweighed by a cMyBP-C phosphorylation mediated increase in myosin recruitment that leads to a net increase of myofibril force, primarily at submaximal calcium activations. These results match well with our previous report on contractile properties from myectomy samples of the patients from whom the hiPSC-CMs were generated, demonstrating that these cell lines are a good model to study this pathological mutation and extends our understanding of the mechanisms of altered contractile properties of this HCM MYBPC3-c.772G > A mutation.

A reduced proteomic signature in critically ill Covid-19 patients determined with plasma antibody micro-array and machine learning.

BACKGROUND: COVID-19 is a complex, multi-system disease with varying severity and symptoms. Identifying changes in critically ill COVID-19 patients' proteomes enables a better understanding of markers associated with susceptibility, symptoms, and treatment. We performed plasma antibody microarray and machine learning analyses to identify novel proteins of COVID-19. METHODS: A case-control study comparing the concentration of 2000 plasma proteins in age- and sex-matched COVID-19 inpatients, non-COVID-19 sepsis controls, and healthy control subjects. Machine learning was used to identify a unique proteome signature in COVID-19 patients. Protein expression was correlated with clinically relevant variables and analyzed for temporal changes over hospitalization days 1, 3, 7, and 10. Expert-curated protein expression information was analyzed with Natural language processing (NLP) to determine organ- and cell-specific expression. RESULTS: Machine learning identified a 28-protein model that accurately differentiated COVID-19 patients from ICU non-COVID-19 patients (accuracy = 0.89, AUC = 1.00, F1 = 0.89) and healthy controls (accuracy = 0.89, AUC = 1.00, F1 = 0.88). An optimal nine-protein model (PF4V1, NUCB1, CrkL, SerpinD1, Fen1, GATA-4, ProSAAS, PARK7, and NET1) maintained high classification ability. Specific proteins correlated with hemoglobin, coagulation factors, hypertension, and high-flow nasal cannula intervention (P < 0.01). Time-course analysis of the 28 leading proteins demonstrated no significant temporal changes within the COVID-19 cohort. NLP analysis identified multi-system expression of the key proteins, with the digestive and nervous systems being the leading systems. CONCLUSIONS: The plasma proteome of critically ill COVID-19 patients was distinguishable from that of non-COVID-19 sepsis controls and healthy control subjects. The leading 28 proteins and their subset of 9 proteins yielded accurate classification models and are expressed in multiple organ systems. The identified COVID-19 proteomic signature helps elucidate COVID-19 pathophysiology and may guide future COVID-19 treatment development.

Prediction of Pathologic Complete Response for Rectal Cancer Based on Pretreatment Factors Using Machine Learning.

BACKGROUND: Pathologic complete response after neoadjuvant therapy is an important prognostic indicator for locally advanced rectal cancer and may give insights into which patients might be treated nonoperatively in the future. Existing models for predicting pathologic complete response in the pretreatment setting are limited by small data sets and low accuracy. OBJECTIVE: We sought to use machine learning to develop a more generalizable predictive model for pathologic complete response for locally advanced rectal cancer. DESIGN: Patients with locally advanced rectal cancer who underwent neoadjuvant therapy followed by surgical resection were identified in the National Cancer Database from years 2010 to 2019 and were split into training, validation, and test sets. Machine learning techniques included random forest, gradient boosting, and artificial neural network. A logistic regression model was also created. Model performance was assessed using an area under the receiver operating characteristic curve. SETTINGS: This study used a national, multicenter data set. PATIENTS: Patients with locally advanced rectal cancer who underwent neoadjuvant therapy and proctectomy. MAIN OUTCOME MEASURES: Pathologic complete response defined as T0/xN0/x. RESULTS: The data set included 53,684 patients. Pathologic complete response was experienced by 22.9% of patients. Gradient boosting showed the best performance with an area under the receiver operating characteristic curve of 0.777 (95% CI, 0.773-0.781), compared with 0.684 (95% CI, 0.68-0.688) for logistic regression. The strongest predictors of pathologic complete response were no lymphovascular invasion, no perineural invasion, lower CEA, smaller size of tumor, and microsatellite stability. A concise model including the top 5 variables showed preserved performance. LIMITATIONS: The models were not externally validated. CONCLUSIONS: Machine learning techniques can be used to accurately predict pathologic complete response for locally advanced rectal cancer in the pretreatment setting. After fine-tuning a data set including patients treated nonoperatively, these models could help clinicians identify the appropriate candidates for a watch-and-wait strategy. See Video Abstract . EL CNCER DE RECTO BASADA EN FACTORES PREVIOS AL TRATAMIENTO MEDIANTE EL APRENDIZAJE AUTOMTICO: ANTECEDENTES:La respuesta patológica completa después de la terapia neoadyuvante es un indicador pronóstico importante para el cáncer de recto localmente avanzado y puede dar información sobre qué pacientes podrían ser tratados de forma no quirúrgica en el futuro. Los modelos existentes para predecir la respuesta patológica completa en el entorno previo al tratamiento están limitados por conjuntos de datos pequeños y baja precisión.OBJETIVO:Intentamos utilizar el aprendizaje automático para desarrollar un modelo predictivo más generalizable para la respuesta patológica completa para el cáncer de recto localmente avanzado.DISEÑO:Los pacientes con cáncer de recto localmente avanzado que se sometieron a terapia neoadyuvante seguida de resección quirúrgica se identificaron en la Base de Datos Nacional del Cáncer de los años 2010 a 2019 y se dividieron en conjuntos de capacitación, validación y prueba. Las técnicas de aprendizaje automático incluyeron bosque aleatorio, aumento de gradiente y red neuronal artificial. También se creó un modelo de regresión logística. El rendimiento del modelo se evaluó utilizando el área bajo la curva característica operativa del receptor.ÁMBITO:Este estudio utilizó un conjunto de datos nacional multicéntrico.PACIENTES:Pacientes con cáncer de recto localmente avanzado sometidos a terapia neoadyuvante y proctectomía.PRINCIPALES MEDIDAS DE VALORACIÓN:Respuesta patológica completa definida como T0/xN0/x.RESULTADOS:El conjunto de datos incluyó 53.684 pacientes. El 22,9% de los pacientes experimentaron una respuesta patológica completa. El refuerzo de gradiente mostró el mejor rendimiento con un área bajo la curva característica operativa del receptor de 0,777 (IC del 95%: 0,773 - 0,781), en comparación con 0,684 (IC del 95%: 0,68 - 0,688) para la regresión logística. Los predictores más fuertes de respuesta patológica completa fueron la ausencia de invasión linfovascular, la ausencia de invasión perineural, un CEA más bajo, un tamaño más pequeño del tumor y la estabilidad de los microsatélites. Un modelo conciso que incluye las cinco variables principales mostró un rendimiento preservado.LIMITACIONES:Los modelos no fueron validados externamente.CONCLUSIONES:Las técnicas de aprendizaje automático se pueden utilizar para predecir con precisión la respuesta patológica completa para el cáncer de recto localmente avanzado en el entorno previo al tratamiento. Después de realizar ajustes en un conjunto de datos que incluye pacientes tratados de forma no quirúrgica, estos modelos podrían ayudar a los médicos a identificar a los candidatos adecuados para una estrategia de observar y esperar. (Traducción-Dr. Ingrid Melo ).

Morphological and molecular data establishes Clinostomum dolichorchum n. sp. (Digenea: Clinostomidae) in the great blue heron Ardea herodias L. and American bullfrog Rana catesbeiana Shaw.

Clinostomum is a cosmopolitan genus of trematodes that infect piscivorous birds, freshwater molluscs, freshwater fish and amphibians. Herein, a novel species of Clinostomum is described based on morphological and molecular data from an adult in the oral cavity of the great blue heron Ardea herodias and metacercariae collected from the gills and skin of American bullfrog tadpoles Rana catesbeiana. The novel species shares similar qualitative and quantitative morphological features with a congener, Clinostomum marginatum, which has overlap in host and geographic distribution. The most notable morphological difference when compared to C. marginatum is the greater posterior testis length of the novel species. Molecular data resolved similarities with morphological comparisons to nominal species and supports the establishment of a novel species. Molecular data include partial small ribosomal subunit (18S rRNA gene), ribosomal internal transcribed spacer regions (ITS1, 5.8S rRNA gene, and ITS2), partial large ribosomal subunit (28S rRNA gene), cytochrome c oxidase subunit 1 gene (cox1), and nicotinamide adenine dinucleotide dehydrogenase subunit 1 gene (nad1) sequences. Phylogenetic analyses place the novel species in a sister clade to C. marginatum. Morphological and molecular data, combined with phylogenetic analyses support the establishment of Clinostomum dolichorchum n. sp.

Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning.

BACKGROUND: Survivors of acute COVID-19 often suffer prolonged, diffuse symptoms post-infection, referred to as "Long-COVID". A lack of Long-COVID biomarkers and pathophysiological mechanisms limits effective diagnosis, treatment and disease surveillance. We performed targeted proteomics and machine learning analyses to identify novel blood biomarkers of Long-COVID. METHODS: A case-control study comparing the expression of 2925 unique blood proteins in Long-COVID outpatients versus COVID-19 inpatients and healthy control subjects. Targeted proteomics was accomplished with proximity extension assays, and machine learning was used to identify the most important proteins for identifying Long-COVID patients. Organ system and cell type expression patterns were identified with Natural Language Processing (NLP) of the UniProt Knowledgebase. RESULTS: Machine learning analysis identified 119 relevant proteins for differentiating Long-COVID outpatients (Bonferonni corrected P < 0.01). Protein combinations were narrowed down to two optimal models, with nine and five proteins each, and with both having excellent sensitivity and specificity for Long-COVID status (AUC = 1.00, F1 = 1.00). NLP expression analysis highlighted the diffuse organ system involvement in Long-COVID, as well as the involved cell types, including leukocytes and platelets, as key components associated with Long-COVID. CONCLUSIONS: Proteomic analysis of plasma from Long-COVID patients identified 119 highly relevant proteins and two optimal models with nine and five proteins, respectively. The identified proteins reflected widespread organ and cell type expression. Optimal protein models, as well as individual proteins, hold the potential for accurate diagnosis of Long-COVID and targeted therapeutics.

Sleep fails to depotentiate amygdala-reactivity to negative emotional stimuli in youth with elevated symptoms of anxiety.

Sleep-related problems often precede escalating anxiety in early adolescence. Pushing beyond broad sleep-mental health associations and toward mechanistic theories of their interplay can inform etiological models of psychopathology. Recent studies suggest that sleep depotentiates neural (e.g., amygdala) reactivity during reexposure to negative emotional stimuli in adults. Persistent amygdala reactivity to negative experiences and poor sleep characterize anxiety, particularly at the transition to adolescence. We propose that sleep depotentiates amygdala reactivity in youth but fails to do so among youth with anxiety. Participants (n = 34; 18 males; age, mean [M] = 11.35, standard deviation [SD] = 2.00) recruited from the community and specialty anxiety clinics viewed valenced images (positive, negative, and neutral) across two fMRI sessions (Study, Test), separated by a 10-12-hour retention period of sleep or wake (randomized). Mixed linear models regressed basolateral amygdala (BLA) activation and BLA-medial prefrontal cortex (mPFC) functional connectivity to negative images on Time, Condition, and Anxiety Severity. There were greater reductions in BLA activations to negative target images from Study to Test in the Sleep Condition, which was blunted with higher anxiety (b = -0.065, z = -2.355, p = 0.019). No such sleep- or anxiety-related effects were observed for BLA-mPFC functional connectivity (ps > 0.05). Sleep supports depotentiation of amygdala reactivity to negative stimuli in youth, but this effect is blunted at higher levels of anxiety. Disruptions in sleep-related affective habituation may be a critical, modifiable driver of anxiety.

Plasma proteome of Long-COVID patients indicates HIF-mediated vasculo-proliferative disease with impact on brain and heart function.

AIMS: Long-COVID occurs after SARS-CoV-2 infection and results in diverse, prolonged symptoms. The present study aimed to unveil potential mechanisms, and to inform prognosis and treatment. METHODS: Plasma proteome from Long-COVID outpatients was analyzed in comparison to matched acutely ill COVID-19 (mild and severe) inpatients and healthy control subjects. The expression of 3072 protein biomarkers was determined with proximity extension assays and then deconvoluted with multiple bioinformatics tools into both cell types and signaling mechanisms, as well as organ specificity. RESULTS: Compared to age- and sex-matched acutely ill COVID-19 inpatients and healthy control subjects, Long-COVID outpatients showed natural killer cell redistribution with a dominant resting phenotype, as opposed to active, and neutrophils that formed extracellular traps. This potential resetting of cell phenotypes was reflected in prospective vascular events mediated by both angiopoietin-1 (ANGPT1) and vascular-endothelial growth factor-A (VEGFA). Several markers (ANGPT1, VEGFA, CCR7, CD56, citrullinated histone 3, elastase) were validated by serological methods in additional patient cohorts. Signaling of transforming growth factor-ß1 with probable connections to elevated EP/p300 suggested vascular inflammation and tumor necrosis factor-α driven pathways. In addition, a vascular proliferative state associated with hypoxia inducible factor 1 pathway suggested progression from acute COVID-19 to Long-COVID. The vasculo-proliferative process predicted in Long-COVID might contribute to changes in the organ-specific proteome reflective of neurologic and cardiometabolic dysfunction. CONCLUSIONS: Taken together, our findings point to a vasculo-proliferative process in Long-COVID that is likely initiated either prior hypoxia (localized or systemic) and/or stimulatory factors (i.e., cytokines, chemokines, growth factors, angiotensin, etc). Analyses of the plasma proteome, used as a surrogate for cellular signaling, unveiled potential organ-specific prognostic biomarkers and therapeutic targets.

Analysis of Specimen Mammography with Artificial Intelligence to Predict Margin Status.

BACKGROUND: Intraoperative specimen mammography is a valuable tool in breast cancer surgery, providing immediate assessment of margins for a resected tumor. However, the accuracy of specimen mammography in detecting microscopic margin positivity is low. We sought to develop an artificial intelligence model to predict the pathologic margin status of resected breast tumors using specimen mammography. METHODS: A dataset of specimen mammography images matched with pathologic margin status was collected from our institution from 2017 to 2020. The dataset was randomly split into training, validation, and test sets. Specimen mammography models pretrained on radiologic images were developed and compared with models pretrained on nonmedical images. Model performance was assessed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). RESULTS: The dataset included 821 images, and 53% had positive margins. For three out of four model architectures tested, models pretrained on radiologic images outperformed nonmedical models. The highest performing model, InceptionV3, showed sensitivity of 84%, specificity of 42%, and AUROC of 0.71. Model performance was better among patients with invasive cancers, less dense breasts, and non-white race. CONCLUSIONS: This study developed and internally validated artificial intelligence models that predict pathologic margins status for partial mastectomy from specimen mammograms. The models' accuracy compares favorably with published literature on surgeon and radiologist interpretation of specimen mammography. With further development, these models could more precisely guide the extent of resection, potentially improving cosmesis and reducing reoperations.

Application of machine learning to predict postoperative gastrointestinal bleed in bariatric surgery.

BACKGROUND: Postoperative gastrointestinal bleeding (GIB) is a rare but serious complication of bariatric surgery. The recent rise in extended venous thromboembolism regimens as well as outpatient bariatric surgery may increase the risk of postoperative GIB or lead to delay in diagnosis. This study seeks to use machine learning (ML) to create a model that predicts postoperative GIB to aid surgeon decision-making and improve patient counseling for postoperative bleeds. METHODS: The Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) database was used to train and validate three types of ML methods: random forest (RF), gradient boosting (XGB), and deep neural networks (NN), and compare them with logistic regression (LR) regarding postoperative GIB. The dataset was split using fivefold cross-validation into training and validation sets, in an 80/20 ratio. The performance of the models was assessed using area under the receiver operating characteristic curve (AUROC) and compared with the DeLong test. Variables with the strongest effect were identified using Shapley additive explanations (SHAP). RESULTS: The study included 159,959 patients. Postoperative GIB was identified in 632 (0.4%) patients. The three ML methods, RF (AUROC 0.764), XGB (AUROC 0.746), and NN (AUROC 0.741) all outperformed LR (AUROC 0.709). The best ML method, RF, was able to predict postoperative GIB with a specificity and sensitivity of 70.0% and 75.4%, respectively. Using DeLong testing, the difference between RF and LR was determined to be significant with p < 0.01. Type of bariatric surgery, pre-op hematocrit, age, duration of procedure, and pre-op creatinine were the 5 most important features identified by ML retrospectively. CONCLUSIONS: We have developed a ML model that outperformed LR in predicting postoperative GIB. Using ML models for risk prediction can be a helpful tool for both surgeons and patients undergoing bariatric procedures but more interpretable models are needed.

Phosphatidylinositol 3,5-bisphosphate regulates Ca2+ transport during yeast vacuolar fusion through the Ca2+ ATPase Pmc1.

The transport of Ca2+ across membranes precedes the fusion and fission of various lipid bilayers. Yeast vacuoles under hyperosmotic stress become fragmented through fission events that requires the release of Ca2+ stores through the TRP channel Yvc1. This requires the phosphorylation of phosphatidylinositol-3-phosphate (PI3P) by the PI3P-5-kinase Fab1 to produce transient PI(3,5)P2 pools. Ca2+ is also released during vacuole fusion upon trans-SNARE complex assembly, however, its role remains unclear. The effect of PI(3,5)P2 on Ca2+ flux during fusion was independent of Yvc1. Here, we show that while low levels of PI(3,5)P2 were required for Ca2+ uptake into the vacuole, increased concentrations abolished Ca2+ efflux. This was as shown by the addition of exogenous dioctanoyl PI(3,5)P2 or increased endogenous production of by the hyperactive fab1T2250A mutant. In contrast, the lack of PI(3,5)P2 on vacuoles from the kinase dead fab1EEE mutant showed delayed and decreased Ca2+ uptake. The effects of PI(3,5)P2 were linked to the Ca2+ pump Pmc1, as its deletion rendered vacuoles resistant to the effects of excess PI(3,5)P2 . Experiments with Verapamil inhibited Ca2+ uptake when added at the start of the assay, while adding it after Ca2+ had been taken up resulted in the rapid expulsion of Ca2+ . Vacuoles lacking both Pmc1 and the H+ /Ca2+ exchanger Vcx1 lacked the ability to take up Ca2+ and instead expelled it upon the addition of ATP. Together these data suggest that a balance of efflux and uptake compete during the fusion pathway and that the levels of PI(3,5)P2 can modulate which path predominates.

Elevated vascular transformation blood biomarkers in Long-COVID indicate angiogenesis as a key pathophysiological mechanism.

BACKGROUND: Long-COVID is characterized by prolonged, diffuse symptoms months after acute COVID-19. Accurate diagnosis and targeted therapies for Long-COVID are lacking. We investigated vascular transformation biomarkers in Long-COVID patients. METHODS: A case-control study utilizing Long-COVID patients, one to six months (median 98.5 days) post-infection, with multiplex immunoassay measurement of sixteen blood biomarkers of vascular transformation, including ANG-1, P-SEL, MMP-1, VE-Cad, Syn-1, Endoglin, PECAM-1, VEGF-A, ICAM-1, VLA-4, E-SEL, thrombomodulin, VEGF-R2, VEGF-R3, VCAM-1 and VEGF-D. RESULTS: Fourteen vasculature transformation blood biomarkers were significantly elevated in Long-COVID outpatients, versus acutely ill COVID-19 inpatients and healthy controls subjects (P < 0.05). A unique two biomarker profile consisting of ANG-1/P-SEL was developed with machine learning, providing a classification accuracy for Long-COVID status of 96%. Individually, ANG-1 and P-SEL had excellent sensitivity and specificity for Long-COVID status (AUC = 1.00, P < 0.0001; validated in a secondary cohort). Specific to Long-COVID, ANG-1 levels were associated with female sex and a lack of disease interventions at follow-up (P < 0.05). CONCLUSIONS: Long-COVID patients suffer prolonged, diffuse symptoms and poorer health. Vascular transformation blood biomarkers were significantly elevated in Long-COVID, with angiogenesis markers (ANG-1/P-SEL) providing classification accuracy of 96%. Vascular transformation blood biomarkers hold potential for diagnostics, and modulators of angiogenesis may have therapeutic efficacy.

Glucocorticoid toxicity reduction with mepolizumab using the Glucocorticoid Toxicity Index.

BACKGROUND: Reduction in glucocorticoid exposure is the primary benefit of new biologic treatments in severe asthma, but there is currently no evidence that reduction in glucocorticoid exposure corresponds to a proportionate reduction in associated toxicity. OBJECTIVES: To use the validated Glucocorticoid Toxicity Index (GTI) to assess change in glucocorticoid toxicity after 12 months treatment with mepolizumab, and compare toxicity change to glucocorticoid reduction and change in patient-reported outcome measures (PROMs). METHODS: A longitudinal, real-world prospective cohort of 101 consecutive patients with severe asthma commenced on mepolizumab in a specialist UK regional severe asthma clinic. GTI toxicity assessment, cumulative glucocorticoid exposure and PROMs were recorded on commencing mepolizumab (V1), and after 12 months treatment (V2). RESULTS: There was significant reduction in oral glucocorticoid exposure (V1 median 4280 mg prednisolone per year (interquartile range 3083-5475 mg) versus V2 2450 mg prednisolone per year (1243-3360 mg), p<0.001). Substantial improvements in individual toxicities were observed, but did not correlate with oral glucocorticoid reduction. Mean±sd GTI aggregate improvement score (AIS) was -35.7±57.8 with a wide range in toxicity change at individual patient level (AIS range -165 to +130); 70% (71 out of 101) had a reduction in toxicity (AIS <0); 3% (three out of 101) had no change (AIS=0); and 27% (27 out of 101) an increase in overall toxicity. 62% (62 out of 101) of patients met the AIS minimally clinically important difference of ≤-10, but AIS did not correlate with glucocorticoid reduction or change in PROMs. CONCLUSION: Mepolizumab resulted in substantial oral glucocorticoid reduction, but this did not correlate with reduction in oral glucocorticoid toxicity, which varies widely at the individual patient level. Oral glucocorticoid reduction is not a comprehensive measure of response to mepolizumab.

Novel plasma protein biomarkers from critically ill sepsis patients.

BACKGROUND: Despite the high morbidity and mortality associated with sepsis, the relationship between the plasma proteome and clinical outcome is poorly understood. In this study, we used targeted plasma proteomics to identify novel biomarkers of sepsis in critically ill patients. METHODS: Blood was obtained from 15 critically ill patients with suspected/confirmed sepsis (Sepsis-3.0 criteria) on intensive care unit (ICU) Day-1 and Day-3, as well as age- and sex-matched 15 healthy control subjects. A total of 1161 plasma proteins were measured with proximal extension assays. Promising sepsis biomarkers were narrowed with machine learning and then correlated with relevant clinical and laboratory variables. RESULTS: The median age for critically ill sepsis patients was 56 (IQR 51-61) years. The median MODS and SOFA values were 7 (IQR 5.0-8.0) and 7 (IQR 5.0-9.0) on ICU Day-1, and 4 (IQR 3.5-7.0) and 6 (IQR 3.5-7.0) on ICU Day-3, respectively. Targeted proteomics, together with feature selection, identified the leading proteins that distinguished sepsis patients from healthy control subjects with ≥ 90% classification accuracy; 25 proteins on ICU Day-1 and 26 proteins on ICU Day-3 (6 proteins overlapped both ICU days; PRTN3, UPAR, GDF8, NTRK3, WFDC2 and CXCL13). Only 7 of the leading proteins changed significantly between ICU Day-1 and Day-3 (IL10, CCL23, TGFα1, ST2, VSIG4, CNTN5, and ITGAV; P < 0.01). Significant correlations were observed between a variety of patient clinical/laboratory variables and the expression of 15 proteins on ICU Day-1 and 14 proteins on ICU Day-3 (P < 0.05). CONCLUSIONS: Targeted proteomics with feature selection identified proteins altered in critically ill sepsis patients relative to healthy control subjects. Correlations between protein expression and clinical/laboratory variables were identified, each providing pathophysiological insight. Our exploratory data provide a rationale for further hypothesis-driven sepsis research.

Escherichia coli recombinant expression of SARS-CoV-2 protein fragments.

We have developed a method for the inexpensive, high-level expression of antigenic protein fragments of SARS-CoV-2 proteins in Escherichia coli. Our approach uses the thermophilic family 9 carbohydrate-binding module (CBM9) as an N-terminal carrier protein and affinity tag. The CBM9 module was joined to SARS-CoV-2 protein fragments via a flexible proline-threonine linker, which proved to be resistant to E. coli proteases. Two CBM9-spike protein fragment fusion proteins and one CBM9-nucleocapsid fragment fusion protein largely resisted protease degradation, while most of the CBM9 fusion proteins were degraded at some site in the SARS-CoV-2 protein fragment. All of the fusion proteins were highly expressed in E. coli and the CBM9-ID-H1 fusion protein was shown to yield 122 mg/L of purified product. Three purified CBM9-SARS-CoV-2 fusion proteins were tested and found to bind antibodies directed to the appropriate SARS-CoV-2 antigenic regions. The largest intact CBM9 fusion protein, CBM9-ID-H1, incorporates spike protein amino acids 540-588, which is a conserved region overlapping and C-terminal to the receptor binding domain that is widely recognized by human convalescent sera and contains a putative protective epitope.