Development of NMR and thermal shift assays for the evaluation of Mycobacterium tuberculosis isocitrate lyase inhibitors.

The enzymes isocitrate lyase (ICL) isoforms 1 and 2 are essential for Mycobacterium tuberculosis survival within macrophages during latent tuberculosis (TB). As such, ICLs are attractive therapeutic targets for the treatment of tuberculosis. However, there are few biophysical assays that are available for accurate kinetic and inhibition studies of ICL in vitro. Herein we report the development of a combined NMR spectroscopy and thermal shift assay to study ICL inhibitors for both screening and inhibition constant (IC50) measurement. Operating this new assay in tandem with virtual high-throughput screening has led to the discovery of several new ICL1 inhibitors

Facile Stereoselective Reduction of Prochiral Ketones by using an F ₄₂₀-dependent alcohol dehydrogenase

Effective procedures for the synthesis of optically pure alcohols are highly valuable. A commonly employed method involves the biocatalytic reduction of prochiral ketones. This is typically achieved by using nicotinamide cofactor-dependent reductases. In this work, we demonstrate that a rather unexplored class of enzymes can also be used for this. We used an F420-dependent alcohol dehydrogenase (ADF) from Methanoculleus thermophilicus that was found to reduce various ketones to enantiopure alcohols. The respective (S) alcohols were obtained in excellent enantiopurity (>99 % ee). Furthermore, we discovered that the deazaflavoenzyme can be used as a self-sufficient system by merely using a sacrificial cosubstrate (isopropanol) and a catalytic amount of cofactor F420 or the unnatural cofactor FOP to achieve full conversion. This study reveals that deazaflavoenzymes complement the biocatalytic toolbox for enantioselective ketone reductions

NK cells armed with chimeric antigen receptors (CAR): roadblocks to successful development

In recent years, cell-based immunotherapies have demonstrated promising results in the treatment of cancer. Chimeric antigen receptors (CARs) arm effector cells with a weapon for targeting tumor antigens, licensing engineered cells to recognize and kill cancer cells. The quality of the CAR-antigen interaction strongly depends on the selected tumor antigen and its expression density on cancer cells. CD19 CAR-engineered T cells approved by the Food and Drug Administration have been most frequently applied in the treatment of hematological malignancies. Clinical challenges in their application primarily include cytokine release syndrome, neurological symptoms, severe inflammatory responses, and/or other off-target effects most likely mediated by cytotoxic T cells. As a consequence, there remains a significant medical need for more potent technology platforms leveraging cell-based approaches with enhanced safety profiles. A promising population that has been advanced is the natural killer (NK) cell, which can also be engineered with CARs. NK cells which belong to the innate arm of the immune system recognize and kill virally infected cells as well as (stressed) cancer cells in a major histocompatibility complex I independent manner. NK cells play an important role in the host’s immune defense against cancer due to their specialized lytic mechanisms which include death receptor (i.e., Fas)/death receptor ligand (i.e., Fas ligand) and granzyme B/perforin-mediated apoptosis, and antibody-dependent cellular cytotoxicity, as well as their immunoregulatory potential via cytokine/chemokine release. To develop and implement a highly effective CAR NK cell-based therapy with low side effects, the following three principles which are specifically addressed in this review have to be considered: unique target selection, well-designed CAR, and optimized gene delivery

Acetyl-CoA-mediated activation of Mycobacterium tuberculosis isocitrate lyase 2

Isocitrate lyase is important for lipid utilisation by Mycobacterium tuberculosis but its ICL2 isoform is poorly understood. Here we report that binding of the lipid metabolites acetyl-CoA or propionyl-CoA to ICL2 induces a striking structural rearrangement, substantially increasing isocitrate lyase and methylisocitrate lyase activities. Thus, ICL2 plays a pivotal role regulating carbon flux between the tricarboxylic acid (TCA) cycle, glyoxylate shunt and methylcitrate cycle at high lipid concentrations, a mechanism essential for bacterial growth and virulence

Metabolic Engineering of Cofactor F420 Production in Mycobacterium smegmatis

Cofactor F420 is a unique electron carrier in a number of microorganisms including Archaea and Mycobacteria. It has been shown that F420 has a direct and important role in archaeal energy metabolism whereas the role of F420 in mycobacterial metabolism has only begun to be uncovered in the last few years. It has been suggested that cofactor F420 has a role in the pathogenesis of M. tuberculosis, the causative agent of tuberculosis. In the absence of a commercial source for F420, M. smegmatis has previously been used to provide this cofactor for studies of the F420-dependent proteins from mycobacterial species. Three proteins have been shown to be involved in the F420 biosynthesis in Mycobacteria and three other proteins have been demonstrated to be involved in F420 metabolism. Here we report the over-expression of all of these proteins in M. smegmatis and testing of their importance for F420 production. The results indicate that co–expression of the F420 biosynthetic proteins can give rise to a much higher F420 production level. This was achieved by designing and preparing a new T7 promoter–based co-expression shuttle vector. A combination of co–expression of the F420 biosynthetic proteins and fine-tuning of the culture media has enabled us to achieve F420 production levels of up to 10 times higher compared with the wild type M. smegmatis strain. The high levels of the F420 produced in this study provide a suitable source of this cofactor for studies of F420-dependent proteins from other microorganisms and for possible biotechnological applications

F420H2-Dependent Degradation of Aflatoxin and other Furanocoumarins Is Widespread throughout the Actinomycetales

Two classes of F420-dependent reductases (FDR-A and FDR-B) that can reduce aflatoxins and thereby degrade them have previously been isolated from Mycobacterium smegmatis. One class, the FDR-A enzymes, has up to 100 times more activity than the other. F420 is a cofactor with a low reduction potential that is largely confined to the Actinomycetales and some Archaea and Proteobacteria. We have heterologously expressed ten FDR-A enzymes from diverse Actinomycetales, finding that nine can also use F420H2 to reduce aflatoxin. Thus FDR-As may be responsible for the previously observed degradation of aflatoxin in other Actinomycetales. The one FDR-A enzyme that we found not to reduce aflatoxin belonged to a distinct clade (herein denoted FDR-AA), and our subsequent expression and analysis of seven other FDR-AAs from M. smegmatis found that none could reduce aflatoxin. Certain FDR-A and FDR-B enzymes that could reduce aflatoxin also showed activity with coumarin and three furanocoumarins (angelicin, 8-methoxysporalen and imperatorin), but none of the FDR-AAs tested showed any of these activities. The shared feature of the compounds that were substrates was an α,β-unsaturated lactone moiety. This moiety occurs in a wide variety of otherwise recalcitrant xenobiotics and antibiotics, so the FDR-As and FDR-Bs may have evolved to harness the reducing power of F420 to metabolise such compounds. Mass spectrometry on the products of the FDR-catalyzed reduction of coumarin and the other furanocoumarins shows their spontaneous hydrolysis to multiple products

Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different model on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the Sensorium 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with the behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 competition track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at www.sensorium-competition.net

Epidemiology of nausea and vomiting of pregnancy: prevalence, severity, determinants, and the importance of race/ethnicity

<p>Abstract</p> <p>Background</p> <p>Studies that contributed to the epidemiology of nausea and vomiting of pregnancy have reported conflicting findings, and often failed to account for all possible co-variables necessary to evaluate the multidimensional associations. The objectives of this study were to: 1) Estimate the prevalence and the severity of nausea and vomiting of pregnancy during the 1^stand the 2^ndtrimester of pregnancy, and 2) Identify determinants of presence and severity of nausea and vomiting of pregnancy during the 1^stand 2^ndtrimesters separately, with a special emphasis on the impact of race/ethnicity.</p> <p>Methods</p> <p>A prospective study including pregnant women attending the Centre Hospitalier Universitaire (CHU) Sainte-Justine or René-Laennec clinics for their prenatal care was conducted from 2004 to 2006. Women were eligible if they were ≥ 18 years of age, and ≤ 16 weeks of gestation. Women were asked to fill out a 1^sttrimester self-administered questionnaire and were interviewed over the telephone during their 2^ndtrimester of pregnancy. Presence of nausea and vomiting of pregnancy was based on the reporting of pregnant women (yes/no); severity of symptoms was measured by the validated modified-PUQE index.</p> <p>Results</p> <p>Of the 367 women included in the study, 81.2% were Caucasians, 10.1% Blacks, 4.6% Hispanics, and 4.1% Asians. Multivariate analyses showed that race/ethnicity was significantly associated with a decreased likelihood of reporting nausea and vomiting of pregnancy (Asians vs. Caucasians OR: 0.13; 95%CI 0.02–0.73; and Blacks vs. Caucasians OR: 0.29; 95%CI 0.09–0.99).</p> <p>Conclusion</p> <p>Our study showed that race/ethnicity was associated with the reporting of nausea and vomiting of pregnancy in the 1^sttrimester of pregnancy.</p

Intelligent negotiation model for ubiquitous group decision scenarios

Supporting group decision-making in ubiquitous contexts is a complex task that must deal with a large amount of factors to succeed. Here we propose an approach for an intelligent negotiation model to support the group decision-making process specially designed for ubiquitous contexts. Our approach can be used by researchers that intend to include arguments, complex algorithms and agents' modelling in a negotiation model. It uses a social networking logic due to the type of communication employed by the agents and it intends to support the ubiquitous group decision-making process in a similar way to the real process, which simultaneously preserves the amount and quality of intelligence generated in face-to-face meetings. We propose a new look into this problematic by considering and defining strategies to deal with important points such as the type of attributes in the multicriteria problems, agents' reasoning and intelligent dialogues.This work has been supported by COMPETE Programme (operational programme for competitiveness) within project POCI-01-0145-FEDER-007043, by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within the Projects UID/CEC/00319/2013, UID/EEA/00760/2013, and the João Carneiro PhD grant with the reference SFRH/BD/89697/2012 and by Project MANTIS - Cyber Physical System Based Proactive Collaborative Maintenance (ECSEL JU Grant nr. 662189).info:eu-repo/semantics/publishedVersio

Global, regional, and national burden of meningitis and its aetiologies, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: Although meningitis is largely preventable, it still causes hundreds of thousands of deaths globally each year. WHO set ambitious goals to reduce meningitis cases by 2030, and assessing trends in the global meningitis burden can help track progress and identify gaps in achieving these goals. Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we aimed to assess incident cases and deaths due to acute infectious meningitis by aetiology and age from 1990 to 2019, for 204 countries and territories. Methods: We modelled meningitis mortality using vital registration, verbal autopsy, sample-based vital registration, and mortality surveillance data. Meningitis morbidity was modelled with a Bayesian compartmental model, using data from the published literature identified by a systematic review, as well as surveillance data, inpatient hospital admissions, health insurance claims, and cause-specific meningitis mortality estimates. For aetiology estimation, data from multiple causes of death, vital registration, hospital discharge, microbial laboratory, and literature studies were analysed by use of a network analysis model to estimate the proportion of meningitis deaths and cases attributable to the following aetiologies: Neisseria meningitidis, Streptococcus pneumoniae, Haemophilus influenzae, group B Streptococcus, Escherichia coli, Klebsiella pneumoniae, Listeria monocytogenes, Staphylococcus aureus, viruses, and a residual other pathogen category. Findings: In 2019, there were an estimated 236 000 deaths (95% uncertainty interval [UI] 204 000–277 000) and 2·51 million (2·11–2·99) incident cases due to meningitis globally. The burden was greatest in children younger than 5 years, with 112 000 deaths (87 400–145 000) and 1·28 million incident cases (0·947–1·71) in 2019. Age-standardised mortality rates decreased from 7·5 (6·6–8·4) per 100 000 population in 1990 to 3·3 (2·8–3·9) per 100 000 population in 2019. The highest proportion of total all-age meningitis deaths in 2019 was attributable to S pneumoniae (18·1% [17·1–19·2]), followed by N meningitidis (13·6% [12·7–14·4]) and K pneumoniae (12·2% [10·2–14·3]). Between 1990 and 2019, H influenzae showed the largest reduction in the number of deaths among children younger than 5 years (76·5% [69·5–81·8]), followed by N meningitidis (72·3% [64·4–78·5]) and viruses (58·2% [47·1–67·3]). Interpretation: Substantial progress has been made in reducing meningitis mortality over the past three decades. However, more meningitis-related deaths might be prevented by quickly scaling up immunisation and expanding access to health services. Further reduction in the global meningitis burden should be possible through low-cost multivalent vaccines, increased access to accurate and rapid diagnostic assays, enhanced surveillance, and early treatment. Funding: Bill & Melinda Gates Foundation