Dietary Influence on Drug Efficacy: A Comprehensive Review of Ketogenic Diet-Pharmacotherapy Interactions.

It is widely acknowledged that the ketogenic diet (KD) has positive physiological effects as well as therapeutic benefits, particularly in the treatment of chronic diseases. Maintaining nutritional ketosis is of utmost importance in the KD, as it provides numerous health advantages such as an enhanced lipid profile, heightened insulin sensitivity, decreased blood glucose levels, and the modulation of diverse neurotransmitters. Nevertheless, the integration of the KD with pharmacotherapeutic regimens necessitates careful consideration. Due to changes in their absorption, distribution, metabolism, or elimination, the KD can impact the pharmacokinetics of various medications, including anti-diabetic, anti-epileptic, and cardiovascular drugs. Furthermore, the KD, which is characterised by the intake of meals rich in fats, has the potential to impact the pharmacokinetics of specific medications with high lipophilicity, hence enhancing their absorption and bioavailability. However, the pharmacodynamic aspects of the KD, in conjunction with various pharmaceutical interventions, can provide either advantageous or detrimental synergistic outcomes. Therefore, it is important to consider the pharmacokinetic and pharmacodynamic interactions that may arise between the KD and various drugs. This assessment is essential not only for ensuring patients' compliance with treatment but also for optimising the overall therapeutic outcome, particularly by mitigating adverse reactions. This highlights the significance and necessity of tailoring pharmacological and dietetic therapies in order to enhance the effectiveness and safety of this comprehensive approach to managing chronic diseases.

IDEATION AND ASSOCIATED FACTORS AMONG UNIVERSITY STUDENTS IN THE SOUTH OF MATO GROSSO / Ideação suicida e fatores associados entre universitários do sul de Mato Grosso

Suicidal ideation among university students is increasing and concerning, making it necessary to understand the factors associated with this phenomenon. In the present study, three hundred and thirty-three (n=333) students, regularly enrolled and over 18 years old, were evaluated, randomly selected from the total population of 5,148 university students enrolled in 2021 at a public university in the southern state of Mato Grosso. An observational cross-sectional approach with descriptive and analytical components was used, employing a structured questionnaire containing information on sociodemographic, economic, student status, and health aspects. Data analysis involved descriptive measures, chi-square test, and multiple Poisson model. In the studied population, suicidal ideation was higher among females (70.6%) and those who did not have a religion (77.5%). There was an association between suicidal ideation and factors such as non-heterosexuality (82.9%), not entering the desired course (79.6%), unsatisfactory relationship with academic peers (83.8%), and previous diagnosis of psychiatric illness (82.4%). The findings highlight the impact of the university environment on the mental health of students, which in turn affects social relationships and the future outlook of this population. This context emphasizes the need for broader discussions on the topic associated with effective actions during academic training aimed at suicide prevention and promotion of mental health.

A ideação suicida entre universitários é crescente e preocupante, fazendo-se necessário compreender os fatores associados a esse fenômeno. No presente estudo, foram avaliados trezentos e trinta e três (n=333) estudantes, regularmente matriculados e maiores de 18 anos, selecionados aleatoriamente entre a população total de 5.148 universitários matriculados em 2021 numa universidade pública do sul do estado de Mato Grosso. Foi utilizada abordagem observacional de corte transversal com componentes de análise descritivo e analítico, empregando questionário estruturado contendo informações sobre aspectos sociodemográficos, econômicos, situação estudantil e saúde. A análise dos dados envolveu medidas descritivas, teste de qui-quadrado e modelo de Poisson múltiplo. Na população estudada, observou-se que a ideação suicida foi maior no sexo feminino (70,6%) e naqueles que não possuíam religião (77,5%). Houve associação entre ideação suicida e fatores como não heterossexualidade (82,9%), não ingresso no curso desejado (79,6%), relacionamento insatisfatório com acadêmicos do curso (83,8%) e diagnóstico prévio de doença psiquiátrica (82,4%). Os achados evidenciam o impacto do ambiente universitário sobre a saúde mental dos estudantes que, por sua vez, afeta as relações sociais e a perspectiva de futuro dessa população. Esse contexto desperta a necessidade de discussões mais amplas quanto ao tema associadas a ações efetivas durante a formação acadêmica visando a prevenção do suicídio e a promoção da saúde mental.

Evaluation of Neuro-Hormonal Dynamics after the Administration of Probiotic Microbial Strains in a Murine Model of Hyperthyroidism.

The microbiota-gut-brain axis has received increasing attention in recent years through its bidirectional communication system, governed by the ability of gut microorganisms to generate and regulate a wide range of neurotransmitters in the host body. In this research, we delve into the intricate area of microbial endocrinology by exploring the dynamic oscillations in neurotransmitter levels within plasma and brain samples. Our experimental model involved inducing hyperthyroidism in mice after a "probiotic load" timeframe using two strains of probiotics (Lactobacillus acidophilus, Saccharomyces boulardii, and their combination). These probiotic interventions continued throughout the experiment and were intended to uncover potential modulatory effects on neurotransmitter levels and discern if certain probiotic strains exhibit any protection from hyperthyroidism. Moreover, we aimed to outline the eventual connections between the gut microbiota and the hypothalamus-pituitary-thyroid axis. As our study reveals, there are significant fluctuations in crucial neurotransmitters within the hyperthyroidism model, related to the specific probiotic strain or combination. These findings could support future therapeutic approaches, help healthcare professionals choose between different probiotic therapies, and also allow us proceed with caution when administering such treatments, depending on the health status of hyperthyroid patients.

The Phenomenon of Antiretroviral Drug Resistance in the Context of Human Immunodeficiency Virus Treatment: Dynamic and Ever Evolving Subject Matter.

Human immunodeficiency virus (HIV) is a significant global health issue that affects a substantial number of individuals across the globe, with a total of 39 million individuals living with HIV/AIDS. ART has resulted in a reduction in HIV-related mortality. Nevertheless, the issue of medication resistance is a significant obstacle in the management of HIV/AIDS. The unique genetic composition of HIV enables it to undergo rapid mutations and adapt, leading to the emergence of drug-resistant forms. The development of drug resistance can be attributed to various circumstances, including noncompliance with treatment regimens, insufficient dosage, interactions between drugs, viral mutations, preexposure prophylactics, and transmission from mother to child. It is therefore essential to comprehend the molecular components of HIV and the mechanisms of antiretroviral medications to devise efficacious treatment options for HIV/AIDS.

Opportunities and challenges in design and optimization of protein function.

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.

Computational design of soluble functional analogues of integral membrane proteins.

De novo design of complex protein folds using solely computational means remains a significant challenge. Here, we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from GPCRs, are not found in the soluble proteome and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses reveal high thermal stability of the designs and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, standing as a proof-of-concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.

An atlas of protein homo-oligomerization across domains of life.

Protein structures are essential to understanding cellular processes in molecular detail. While advances in artificial intelligence revealed the tertiary structure of proteins at scale, their quaternary structure remains mostly unknown. We devise a scalable strategy based on AlphaFold2 to predict homo-oligomeric assemblies across four proteomes spanning the tree of life. Our results suggest that approximately 45% of an archaeal proteome and a bacterial proteome and 20% of two eukaryotic proteomes form homomers. Our predictions accurately capture protein homo-oligomerization, recapitulate megadalton complexes, and unveil hundreds of homo-oligomer types, including three confirmed experimentally by structure determination. Integrating these datasets with omics information suggests that a majority of known protein complexes are symmetric. Finally, these datasets provide a structural context for interpreting disease mutations and reveal coiled-coil regions as major enablers of quaternary structure evolution in human. Our strategy is applicable to any organism and provides a comprehensive view of homo-oligomerization in proteomes.

Antinociceptive Effect of the Combination of a Novel α4ß2* Agonist with Donepezil in a Chronic Pain Model.

Chronic pain presents a major challenge in contemporary medicine, given the limited effectiveness and numerous adverse effects linked to available treatments. Recognizing the potential of the cholinergic pathway as a therapeutic target, the present work evaluates the antinociceptive activity of a combination of Cris-104, a novel α4ß2* receptor agonist, and donepezil, a central anticholinesterase agent. Isobolographic analysis revealed that equimolar combination was approximately 10 times more potent than theoretically calculated equipotent additive dose. Administration of Cris-104 and donepezil combination (3 µmol/kg) successfully reversed hyperalgesia and mechanical allodynia observed in rats subjected to spinal nerve ligation (SNL). The combination also modulated neuroinflammation by reducing astrocyte activation, evident in the decreased expression of glial fibrillary acidic protein (GFAP) in the spinal cord. The observed synergism in combining a nicotinic receptor agonist with an anticholinesterase agent underscores its potential for treating chronic pain. This alternative therapeutic distinct advantage, including dose reduction and high selectivity for the receptor, contribute to a more favorable profile with minimized adverse effects.

Protein-based bandpass filters for controlling cellular signaling with chemical inputs.

Biological signal processing is vital for cellular function. Similar to electronic circuits, cells process signals via integrated mechanisms. In electronics, bandpass filters transmit frequencies with defined ranges, but protein-based counterparts for controlled responses are lacking in engineered biological systems. Here, we rationally design protein-based, chemically responsive bandpass filters (CBPs) showing OFF-ON-OFF patterns that respond to chemical concentrations within a specific range and reject concentrations outside that range. Employing structure-based strategies, we designed a heterodimeric construct that dimerizes in response to low concentrations of a small molecule (ON), and dissociates at high concentrations of the same molecule (OFF). The CBPs have a multidomain architecture in which we used known drug receptors, a computationally designed protein binder and small-molecule inhibitors. This modular system allows fine-tuning for optimal performance in terms of bandwidth, response, cutoff and fold changes. The CBPs were used to regulate cell surface receptor signaling pathways to control cellular activities in engineered cells.

Rules and mechanisms governing G protein coupling selectivity of GPCRs.

G protein-coupled receptors (GPCRs) convert extracellular stimuli into intracellular signaling by coupling to heterotrimeric G proteins of four classes: Gi/o, Gq, Gs, and G12/13. However, our understanding of the G protein selectivity of GPCRs is incomplete. Here, we quantitatively measure the enzymatic activity of GPCRs in living cells and reveal the G protein selectivity of 124 GPCRs with the exact rank order of their G protein preference. Using this information, we establish a classification of GPCRs by functional selectivity, discover the existence of a G12/13-coupled receptor, G15-coupled receptors, and a variety of subclasses for Gi/o-, Gq-, and Gs-coupled receptors, culminating in development of the predictive algorithm of G protein selectivity. We further identify the structural determinants of G protein selectivity, allowing us to synthesize non-existent GPCRs with de novo G protein selectivity and efficiently identify putative pathogenic variants.

Rational design of small-molecule responsive protein switches.

Small-molecule responsive protein switches are powerful tools for controlling cellular processes. These switches are designed to respond rapidly and specifically to their inducer. They have been used in numerous applications, including the regulation of gene expression, post-translational protein modification, and signal transduction. Typically, small-molecule responsive protein switches consist of two proteins that interact with each other in the presence or absence of a small molecule. Recent advances in computational protein design already contributed to the development of protein switches with an expanded range of small-molecule inducers and increasingly sophisticated switch mechanisms. Further progress in the engineering of small-molecule responsive switches is fueled by cutting-edge computational design approaches, which will enable more complex and precise control over cellular processes and advance synthetic biology applications in biotechnology and medicine. Here, we discuss recent milestones and how technological advances are impacting the development of chemical switches.

Integrating multiple regulations on enzyme activity: the case of phosphoenolpyruvate carboxykinases.

Data on protein post-translational modifications (PTMs) increased exponentially in the last years due to the refinement of mass spectrometry techniques and the development of databases to store and share datasets. Nevertheless, these data per se do not create comprehensive biochemical knowledge. Complementary studies on protein biochemistry are necessary to fully understand the function of these PTMs at the molecular level and beyond, for example, designing rational metabolic engineering strategies to improve crops. Phosphoenolpyruvate carboxykinases (PEPCKs) are critical enzymes for plant metabolism with diverse roles in plant development and growth. Multiple lines of evidence showed the complex regulation of PEPCKs, including PTMs. Herein, we present PEPCKs as an example of the integration of combined mechanisms modulating enzyme activity and metabolic pathways. PEPCK studies strongly advanced after the production of the recombinant enzyme and the establishment of standardized biochemical assays. Finally, we discuss emerging open questions for future research and the challenges in integrating all available data into functional biochemical models.

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study.

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

Plant traits poorly predict winner and loser shrub species in a warming tundra biome.

Climate change is leading to species redistributions. In the tundra biome, shrubs are generally expanding, but not all tundra shrub species will benefit from warming. Winner and loser species, and the characteristics that may determine success or failure, have not yet been fully identified. Here, we investigate whether past abundance changes, current range sizes and projected range shifts derived from species distribution models are related to plant trait values and intraspecific trait variation. We combined 17,921 trait records with observed past and modelled future distributions from 62 tundra shrub species across three continents. We found that species with greater variation in seed mass and specific leaf area had larger projected range shifts, and projected winner species had greater seed mass values. However, trait values and variation were not consistently related to current and projected ranges, nor to past abundance change. Overall, our findings indicate that abundance change and range shifts will not lead to directional modifications in shrub trait composition, since winner and loser species share relatively similar trait spaces.

De novo protein design by inversion of the AlphaFold structure prediction network.

De novo protein design enhances our understanding of the principles that govern protein folding and interactions, and has the potential to revolutionize biotechnology through the engineering of novel protein functionalities. Despite recent progress in computational design strategies, de novo design of protein structures remains challenging, given the vast size of the sequence-structure space. AlphaFold2 (AF2), a state-of-the-art neural network architecture, achieved remarkable accuracy in predicting protein structures from amino acid sequences. This raises the question whether AF2 has learned the principles of protein folding sufficiently for de novo design. Here, we sought to answer this question by inverting the AF2 network, using the prediction weight set and a loss function to bias the generated sequences to adopt a target fold. Initial design trials resulted in de novo designs with an overrepresentation of hydrophobic residues on the protein surface compared to their natural protein family, requiring additional surface optimization. In silico validation of the designs showed protein structures with the correct fold, a hydrophilic surface and a densely packed hydrophobic core. In vitro validation showed that 7 out of 39 designs were folded and stable in solution with high melting temperatures. In summary, our design workflow solely based on AF2 does not seem to fully capture basic principles of de novo protein design, as observed in the protein surface's hydrophobic vs. hydrophilic patterning. However, with minimal post-design intervention, these pipelines generated viable sequences as assessed experimental characterization. Thus, such pipelines show the potential to contribute to solving outstanding challenges in de novo protein design.

Rational Design of Chemically Controlled Antibodies and Protein Therapeutics.

Protein-based therapeutics, such as monoclonal antibodies and cytokines, are important therapies for various pathophysiological conditions such as oncology, autoimmune disorders, and viral infections. However, the wide application of such protein therapeutics is often hindered by dose-limiting toxicities and adverse effects, namely, cytokine storm syndrome, organ failure, and others. Therefore, spatiotemporal control of the activities of these proteins is crucial to further expand their application. Here, we report the design and application of small-molecule-controlled switchable protein therapeutics by taking advantage of a previously engineered OFF-switch system. We used the Rosetta modeling suite to computationally optimize the affinity between B-cell lymphoma 2 (Bcl-2) protein and a previously developed computationally designed protein partner (LD3) to obtain a fast and efficient heterodimer disruption upon the addition of a competing drug (Venetoclax). The incorporation of the engineered OFF-switch system into anti-CTLA4, anti-HER2 antibodies, or an Fc-fused IL-15 cytokine demonstrated an efficient disruption in vitro, as well as fast clearance in vivo upon the addition of the competing drug Venetoclax. These results provide a proof-of-concept for the rational design of controllable biologics by introducing a drug-induced OFF-switch into existing protein-based therapeutics.

The involvement of allosteric effectors and post-translational modifications in the control of plant central carbon metabolism.

Plant metabolism is finely orchestrated to allow the occurrence of complementary and sometimes opposite metabolic pathways. In part this is achieved by the allosteric regulation of enzymes, which has been a cornerstone of plant research for many decades. The completion of the Arabidopsis genome and the development of the associated toolkits for Arabidopsis research moved the focus of many researchers to other fields. This is reflected by the increasing number of high-throughput proteomic studies, mainly focused on post-translational modifications. However, follow-up 'classical' biochemical studies to assess the functions and upstream signaling pathways responsible for such modifications have been scarce. In this work, we review the basic concepts of allosteric regulation of enzymes involved in plant carbon metabolism, comprising photosynthesis and photorespiration, starch and sucrose synthesis, glycolysis and gluconeogenesis, the oxidative pentose phosphate pathway and the tricarboxylic acid cycle. Additionally, we revisit the latest results on the allosteric control of the enzymes involved in these pathways. To conclude, we elaborate on the current methods for studying protein-metabolite interactions, which we consider will become crucial for discoveries in the future.

De novo design of protein interactions with learned surface fingerprints.

Physical interactions between proteins are essential for most biological processes governing life1. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications2-9. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein-protein interactions10. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

Global beta-diversity of angiosperm trees is shaped by Quaternary climate change.

As Earth's climate has varied strongly through geological time, studying the impacts of past climate change on biodiversity helps to understand the risks from future climate change. However, it remains unclear how paleoclimate shapes spatial variation in biodiversity. Here, we assessed the influence of Quaternary climate change on spatial dissimilarity in taxonomic, phylogenetic, and functional composition among neighboring 200-kilometer cells (beta-diversity) for angiosperm trees worldwide. We found that larger glacial-interglacial temperature change was strongly associated with lower spatial turnover (species replacements) and higher nestedness (richness changes) components of beta-diversity across all three biodiversity facets. Moreover, phylogenetic and functional turnover was lower and nestedness higher than random expectations based on taxonomic beta-diversity in regions that experienced large temperature change, reflecting phylogenetically and functionally selective processes in species replacement, extinction, and colonization during glacial-interglacial oscillations. Our results suggest that future human-driven climate change could cause local homogenization and reduction in taxonomic, phylogenetic, and functional diversity of angiosperm trees worldwide.