Cryo-EM structures of complex I from mouse heart mitochondria in two biochemically defined states.

Complex I (NADH:ubiquinone oxidoreductase) uses the reducing potential of NADH to drive protons across the energy-transducing inner membrane and power oxidative phosphorylation in mammalian mitochondria. Recent cryo-EM analyses have produced near-complete models of all 45 subunits in the bovine, ovine and porcine complexes and have identified two states relevant to complex I in ischemia-reperfusion injury. Here, we describe the 3.3-Å structure of complex I from mouse heart mitochondria, a biomedically relevant model system, in the 'active' state. We reveal a nucleotide bound in subunit NDUFA10, a nucleoside kinase homolog, and define mechanistically critical elements in the mammalian enzyme. By comparisons with a 3.9-Å structure of the 'deactive' state and with known bacterial structures, we identify differences in helical geometry in the membrane domain that occur upon activation or that alter the positions of catalytically important charged residues. Our results demonstrate the capability of cryo-EM analyses to challenge and develop mechanistic models for mammalian complex I

Increased Pace of Aging in COVID-Related Mortality

Identifying prognostic biomarkers and risk stratification for COVID-19 patients is a challenging necessity. One of the core survival factors is patient age. However, chronological age is often severely biased due to dormant conditions and existing comorbidities. In this retrospective cohort study, we analyzed the data from 5315 COVID-19 patients (1689 lethal cases) admitted to 11 public hospitals in New York City from 1 March 2020 to 1 December. We calculated patients’ pace of aging with BloodAge—a deep learning aging clock trained on clinical blood tests. We further constructed survival models to explore the prognostic value of biological age compared to that of chronological age. A COVID-19 score was developed to support a practical patient stratification in a clinical setting. Lethal COVID-19 cases had higher predicted age, compared to non-lethal cases (Δ = 0.8–1.6 years). Increased pace of aging was a significant risk factor of COVID-related mortality (hazard ratio = 1.026 per year, 95% CI = 1.001–1.052). According to our logistic regression model, the pace of aging had a greater impact (adjusted odds ratio = 1.09 ± 0.00, per year) than chronological age (1.04 ± 0.00, per year) on the lethal infection outcome. Our results show that a biological age measure, derived from routine clinical blood tests, adds predictive power to COVID-19 survival models

Higher blood biochemistry-based biological age developed by advanced deep learning techniques is associated with frailty in geriatric rehabilitation inpatients: RESORT

Background: Accelerated biological ageing is a major underlying mechanism of frailty development. This study aimed to investigate if the biological age measured by a blood biochemistry-based ageing clock is associated with frailty in geriatric rehabilitation inpatients. Methods: Within the REStORing health of acutely unwell adulTs (RESORT) cohort, patients' biological age was measured by an ageing clock based on completed data of 30 routine blood test variables measured at rehabilitation admission. The delta of biological age minus chronological age (years) was calculated. Ordinal logistic regression and multinomial logistic regression were performed to evaluate the association of the delta of ages with frailty assessed by the Clinical Frailty Scale. Effect modification of Cumulative Illness Rating Scale (CIRS) score was tested. Results: A total of 1187 geriatric rehabilitation patients were included (median age: 83.4 years, IQR: 77.7–88.5; 57.4 % female). The biochemistry-based biological age was strongly correlated with chronological age (Spearman r = 0.883). After adjustment for age, sex and primary reasons for acute admission, higher biological age (per 1 year higher in delta of ages) was associated with more severe frailty at admission (OR: 1.053, 95 % CI:1.012–1.096) in patients who had a CIRS score of 12. The delta of ages was not associated with frailty change from admission to discharge. The specific frailty manifestations as cardiac, hematological, respiratory, renal, and endocrine conditions were associated with higher biological age. Conclusion: Higher biological age was associated with severe frailty in geriatric rehabilitation inpatients with less comorbidity burden

Facile Chemical Access to Biologically Active Norcantharidin Derivatives from Biomass

Reductive amination of 2,5-diformylfuran (DFF) was used to implement the transition from bio-derived 5-hydroxymethylfurfural (HMF) to pharmaceuticals. The synthesized bis(aminomethyl)furans were utilized as building blocks for the construction of new derivatives with structural cores of naturally occurring biologically active compounds. Using the one-pot procedure, which included the Diels–Alder reaction followed by hydrogenation of the double bond, bio-derived analogues of the anticancer drug norcantharidin were obtained. The cyclization process was diastereoselective, and resulted in the formation of tricyclic products with the endo configuration. Analysis of cytotoxycity for the resulting tricyclic amine-containing compounds showed an increase of anticancer activity as compared with the unsubstituted norcantharimide

COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity

Coronavirus disease 2019 (COVID-19) is an acute infection of the respiratory tract that emerged in December 2019 in Wuhan, China. It was quickly established that both the symptoms and the disease severity may vary from one case to another and several strains of SARS-CoV-2 have been identified. To gain a better understanding of the wide variety of SARS-CoV-2 strains and their associated symptoms, thousands of SARS-CoV-2 genomes have been sequenced in dozens of countries. In this article, we introduce COVIDomic, a multi-omics online platform designed to facilitate the analysis and interpretation of the large amount of health data collected from patients with COVID-19. The COVIDomic platform provides a comprehensive set of bioinformatic tools for the multi-modal metatranscriptomic data analysis of COVID-19 patients to determine the origin of the coronavirus strain and the expected severity of the disease. An integrative analytical workflow, which includes microbial pathogens community analysis, COVID-19 genetic epidemiology and patient stratification, allows to analyze the presence of the most common microbial organisms, their antibiotic resistance, the severity of the infection and the set of the most probable geographical locations from which the studied strain could have originated. The online platform integrates a user friendly interface which allows easy visualization of the results. We envision this tool will not only have immediate implications for management of the ongoing COVID-19 pandemic, but will also improve our readiness to respond to other infectious outbreaks.</p

COVIDomic:A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity

Coronavirus disease 2019 (COVID-19) is an acute infection of the respiratory tract that emerged in December 2019 in Wuhan, China. It was quickly established that both the symptoms and the disease severity may vary from one case to another and several strains of SARS-CoV-2 have been identified. To gain a better understanding of the wide variety of SARS-CoV-2 strains and their associated symptoms, thousands of SARS-CoV-2 genomes have been sequenced in dozens of countries. In this article, we introduce COVIDomic, a multi-omics online platform designed to facilitate the analysis and interpretation of the large amount of health data collected from patients with COVID-19. The COVIDomic platform provides a comprehensive set of bioinformatic tools for the multi-modal metatranscriptomic data analysis of COVID-19 patients to determine the origin of the coronavirus strain and the expected severity of the disease. An integrative analytical workflow, which includes microbial pathogens community analysis, COVID-19 genetic epidemiology and patient stratification, allows to analyze the presence of the most common microbial organisms, their antibiotic resistance, the severity of the infection and the set of the most probable geographical locations from which the studied strain could have originated. The online platform integrates a user friendly interface which allows easy visualization of the results. We envision this tool will not only have immediate implications for management of the ongoing COVID-19 pandemic, but will also improve our readiness to respond to other infectious outbreaks