Coordinating brain-distributed network activities in memory resistant to extinction.

Certain memories resist extinction to continue invigorating maladaptive actions. The robustness of these memories could depend on their widely distributed implementation across populations of neurons in multiple brain regions. However, how dispersed neuronal activities are collectively organized to underpin a persistent memory-guided behavior remains unknown. To investigate this, we simultaneously monitored the prefrontal cortex, nucleus accumbens, amygdala, hippocampus, and ventral tegmental area (VTA) of the mouse brain from initial recall to post-extinction renewal of a memory involving cocaine experience. We uncover a higher-order pattern of short-lived beta-frequency (15-25 Hz) activities that are transiently coordinated across these networks during memory retrieval. The output of a divergent pathway from upstream VTA glutamatergic neurons, paced by a slower (4-Hz) oscillation, actuates this multi-network beta-band coactivation; its closed-loop phase-informed suppression prevents renewal of cocaine-biased behavior. Binding brain-distributed neural activities in this temporally structured manner may constitute an organizational principle of robust memory expression.

A Hippocampus-Accumbens Tripartite Neuronal Motif Guides Appetitive Memory in Space.

Retrieving and acting on memories of food-predicting environments are fundamental processes for animal survival. Hippocampal pyramidal cells (PYRs) of the mammalian brain provide mnemonic representations of space. Yet the substrates by which these hippocampal representations support memory-guided behavior remain unknown. Here, we uncover a direct connection from dorsal CA1 (dCA1) hippocampus to nucleus accumbens (NAc) that enables the behavioral manifestation of place-reward memories. By monitoring neuronal ensembles in mouse dCA1→NAc pathway, combined with cell-type selective optogenetic manipulations of input-defined postsynaptic neurons, we show that dCA1 PYRs drive NAc medium spiny neurons and orchestrate their spiking activity using feedforward inhibition mediated by dCA1-connected parvalbumin-expressing fast-spiking interneurons. This tripartite cross-circuit motif supports spatial appetitive memory and associated NAc assemblies, being independent of dorsal subiculum and dispensable for both spatial novelty detection and reward seeking. Our findings demonstrate that the dCA1→NAc pathway instantiates a limbic-motor interface for neuronal representations of space to promote effective appetitive behavior.

Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping.

Unsupervised learning, particularly clustering, plays a pivotal role in disease subtyping and patient stratification, especially with the abundance of large-scale multi-omics data. Deep learning models, such as variational autoencoders (VAEs), can enhance clustering algorithms by leveraging inter-individual heterogeneity. However, the impact of confounders-external factors unrelated to the condition, e.g. batch effect or age-on clustering is often overlooked, introducing bias and spurious biological conclusions. In this work, we introduce four novel VAE-based deconfounding frameworks tailored for clustering multi-omics data. These frameworks effectively mitigate confounding effects while preserving genuine biological patterns. The deconfounding strategies employed include (i) removal of latent features correlated with confounders, (ii) a conditional VAE, (iii) adversarial training, and (iv) adding a regularization term to the loss function. Using real-life multi-omics data from The Cancer Genome Atlas, we simulated various confounding effects (linear, nonlinear, categorical, mixed) and assessed model performance across 50 repetitions based on reconstruction error, clustering stability, and deconfounding efficacy. Our results demonstrate that our novel models, particularly the conditional multi-omics VAE (cXVAE), successfully handle simulated confounding effects and recover biologically driven clustering structures. cXVAE accurately identifies patient labels and unveils meaningful pathological associations among cancer types, validating deconfounded representations. Furthermore, our study suggests that some of the proposed strategies, such as adversarial training, prove insufficient in confounder removal. In summary, our study contributes by proposing innovative frameworks for simultaneous multi-omics data integration, dimensionality reduction, and deconfounding in clustering. Benchmarking on open-access data offers guidance to end-users, facilitating meaningful patient stratification for optimized precision medicine.

Making PBPK models more reproducible in practice.

Systems biology aims to understand living organisms through mathematically modeling their behaviors at different organizational levels, ranging from molecules to populations. Modeling involves several steps, from determining the model purpose to developing the mathematical model, implementing it computationally, simulating the model's behavior, evaluating, and refining the model. Importantly, model simulation results must be reproducible, ensuring that other researchers can obtain the same results after writing the code de novo and/or using different software tools. Guidelines to increase model reproducibility have been published. However, reproducibility remains a major challenge in this field. In this paper, we tackle this challenge for physiologically-based pharmacokinetic (PBPK) models, which represent the pharmacokinetics of chemicals following exposure in humans or animals. We summarize recommendations for PBPK model reporting that should apply during model development and implementation, in order to ensure model reproducibility and comprehensibility. We make a proposal aiming to harmonize abbreviations used in PBPK models. To illustrate these recommendations, we present an original and reproducible PBPK model code in MATLAB, alongside an example of MATLAB code converted to Systems Biology Markup Language format using MOCCASIN. As directions for future improvement, more tools to convert computational PBPK models from different software platforms into standard formats would increase the interoperability of these models. The application of other systems biology standards to PBPK models is encouraged. This work is the result of an interdisciplinary collaboration involving the ELIXIR systems biology community. More interdisciplinary collaborations like this would facilitate further harmonization and application of good modeling practices in different systems biology fields.

Metagenomics harvested genus-specific single-stranded DNA-annealing proteins improve and expand recombineering in Pseudomonas species.

The widespread Pseudomonas genus comprises a collection of related species with remarkable abilities to degrade plastics and polluted wastes and to produce a broad set of valuable compounds, ranging from bulk chemicals to pharmaceuticals. Pseudomonas possess characteristics of tolerance and stress resistance making them valuable hosts for industrial and environmental biotechnology. However, efficient and high-throughput genetic engineering tools have limited metabolic engineering efforts and applications. To improve their genome editing capabilities, we first employed a computational biology workflow to generate a genus-specific library of potential single-stranded DNA-annealing proteins (SSAPs). Assessment of the library was performed in different Pseudomonas using a high-throughput pooled recombinase screen followed by Oxford Nanopore NGS analysis. Among different active variants with variable levels of allelic replacement frequency (ARF), efficient SSAPs were found and characterized for mediating recombineering in the four tested species. New variants yielded higher ARFs than existing ones in Pseudomonas putida and Pseudomonas aeruginosa, and expanded the field of recombineering in Pseudomonas taiwanensisand Pseudomonas fluorescens. These findings will enhance the mutagenesis capabilities of these members of the Pseudomonas genus, increasing the possibilities for biotransformation and enhancing their potential for synthetic biology applications. .

Identification of genes associated with the high-temperature fermentation trait in the Saccharomyces cerevisiae natural isolate BCC39850.

The fermentative model yeast Saccharomyces cerevisiae has been extensively used to study the genetic basis of stress response and homeostasis. In this study, we performed quantitative trait loci (QTL) analysis of the high-temperature fermentation trait of the progeny from the mating of the S. cerevisiae natural isolate BCC39850 (haploid#17) and the laboratory strain CEN.PK2-1C. A single QTL on chromosome X was identified, encompassing six candidate genes (GEA1, PTK2, NTA1, NPA3, IRT1, and IML1). The functions of these candidates were tested by reverse genetic experiments. Deletion mutants of PTK2, NTA1, and IML1 showed growth defects at 42 °C. The PTK2 knock-out mutant also showed significantly reduced ethanol production and plasma membrane H+ ATPase activity and increased sensitivity to acetic acid, ethanol, amphotericin B (AMB), and ß-1,3-glucanase treatment. The CRISPR-Cas9 system was used to construct knock-in mutants by replacement of PTK2, NTA1, IML1, and NPA3 genes with BCC39850 alleles. The PTK2 and NTA1 knock-in mutants showed increased growth and ethanol production titers at 42 °C. These findings suggest an important role for the PTK2 serine/threonine protein kinase in regulating plasma membrane H+ ATPase activity and the NTA1 N-terminal amidase in protein degradation via the ubiquitin-proteasome system machinery, which affects tolerance to heat stress in S. cerevisiae.

Genes controlling hydrolysate toxin tolerance identified by QTL analysis of the natural Saccharomyces cerevisiae BCC39850.

Lignocellulosic material can be converted to valorized products such as fuels. Pretreatment is an essential step in conversion, which is needed to increase the digestibility of the raw material for microbial fermentation. However, pretreatment generates by-products (hydrolysate toxins) that are detrimental to microbial growth. In this study, natural Saccharomyces strains isolated from habitats in Thailand were screened for their tolerance to synthetic hydrolysate toxins (synHTs). The Saccharomyces cerevisiae natural strain BCC39850 (toxin-tolerant) was crossed with the laboratory strain CEN.PK2-1C (toxin-sensitive), and quantitative trait locus (QTL) analysis was performed on the segregants using phenotypic scores of growth (OD600) and glucose consumption. VMS1, DET1, KCS1, MRH1, YOS9, SYO1, and YDR042C were identified from QTLs as candidate genes associated with the tolerance trait. CEN.PK2-1C knockouts of the VMS1, YOS9, KCS1, and MRH1 genes exhibited significantly greater hydrolysate toxin sensitivity to growth, whereas CEN.PK2-1C knock-ins with replacement of VMS1 and MRH1 genes from the BCC39850 alleles showed significant increased ethanol production titers compared with the CEN.PK2-1C parental strain in the presence of synHTs. The discovery of VMS1, YOS9, MRH1, and KCS1 genes associated with hydrolysate toxin tolerance in S. cerevisiae indicates the roles of the endoplasmic-reticulum-associated protein degradation pathway, plasma membrane protein association, and the phosphatidylinositol signaling system in this trait. KEY POINTS: • QTL analysis was conducted using a hydrolysate toxin-tolerant S. cerevisiae natural strain • Deletion of VMS1, YOS9, MRH1, and KCS1 genes associated with hydrolysate toxin-sensitivity • Replacement of VMS1 and MRH1 with natural strain alleles increased ethanol production titers in the presence of hydrolysate toxins.

PortPred: Exploiting deep learning embeddings of amino acid sequences for the identification of transporter proteins and their substrates.

The physiology of every living cell is regulated at some level by transporter proteins which constitute a relevant portion of membrane-bound proteins and are involved in the movement of ions, small and macromolecules across bio-membranes. The importance of transporter proteins is unquestionable. The prediction and study of previously unknown transporters can lead to the discovery of new biological pathways, drugs and treatments. Here we present PortPred, a tool to accurately identify transporter proteins and their substrate starting from the protein amino acid sequence. PortPred successfully combines pre-trained deep learning-based protein embeddings and machine learning classification approaches and outperforms other state-of-the-art methods. In addition, we present a comparison of the most promising protein sequence embeddings (Unirep, SeqVec, ProteinBERT, ESM-1b) and their performances for this specific task.

Systemic immune activation profiles in streptococcal necrotizing soft tissue infections: A prospective multicenter study.

OBJECTIVE: Early stages with streptococcal necrotizing soft tissue infections (NSTIs) are often difficult to discern from cellulitis. Increased insight into inflammatory responses in streptococcal disease may guide correct interventions and discovery of novel diagnostic targets. METHODS: Plasma levels of 37 mediators, leucocytes and CRP from 102 patients with ß-hemolytic streptococcal NSTI derived from a prospective Scandinavian multicentre study were compared to those of 23 cases of streptococcal cellulitis. Hierarchical cluster analyses were also performed. RESULTS: Differences in mediator levels between NSTI and cellulitis cases were revealed, in particular for IL-1ß, TNFα and CXCL8 (AUC >0.90). Across streptococcal NSTI etiologies, eight biomarkers separated cases with septic shock from those without, and four mediators predicted a severe outcome. CONCLUSION: Several inflammatory mediators and wider profiles were identified as potential biomarkers of NSTI. Associations of biomarker levels to type of infection and outcomes may be utilized to improve patient care and outcomes.

Paving the way for synthetic C1 - Metabolism in Pseudomonas putida through the reductive glycine pathway.

One-carbon (C1) compounds such as methanol, formate, and CO2 are alternative, sustainable microbial feedstocks for the biobased production of chemicals and fuels. In this study, we engineered the carbon metabolism of the industrially important bacterium Pseudomonas putida to modularly assimilate these three substrates through the reductive glycine pathway. First, we demonstrated the functionality of the C1-assimilation module by coupling the growth of auxotrophic strains to formate assimilation. Next, we extended the module in the auxotrophic strains from formate to methanol-dependent growth using both NAD and PQQ-dependent methanol dehydrogenases. Finally, we demonstrated, for the first time, engineered CO2-dependent formation of part of the biomass through CO2 reduction to formate by the native formate dehydrogenase, which required short-term evolution to rebalance the cellular NADH/NAD + ratio. This research paves the way to further engineer P. putida towards full growth on formate, methanol, and CO2 as sole feedstocks, thereby substantially expanding its potential as a sustainable and versatile cell factory.

Metabolic engineering of Pseudomonas putida KT2440 for medium-chain-length fatty alcohol and ester production from fatty acids.

Medium-chain-length fatty alcohols have broad applications in the surfactant, lubricant, and cosmetic industries. Their acetate esters are widely used as flavoring and fragrance substances. Pseudomonas putida KT2440 is a promising chassis for fatty alcohol and ester production at the industrial scale due to its robustness, versatility, and high oxidative capacity. However, P. putida has also numerous native alcohol dehydrogenases, which lead to the degradation of these alcohols and thereby hinder its use as an effective biocatalyst. Therefore, to harness its capacity as a producer, we constructed two engineered strains (WTΔpedFΔadhP, GN346ΔadhP) incapable of growing on mcl-fatty alcohols by deleting either a cytochrome c oxidase PedF and a short-chain alcohol dehydrogenase AdhP in P. putida or AdhP in P. putida GN346. Carboxylic acid reductase, phosphopantetheinyl transferase, and alcohol acetyltransferase were expressed in the engineered P. putida strains to produce hexyl acetate. Overexpression of transporters further increased 1-hexanol and hexyl acetate production. The optimal strain G23E-MPAscTP produced 93.8 mg/L 1-hexanol and 160.5 mg/L hexyl acetate, with a yield of 63.1%. The engineered strain is applicable for C6-C10 fatty alcohols and their acetate ester production. This study lays a foundation for P. putida being used as a microbial cell factory for sustainable synthesis of a broad range of products based on medium-chain-length fatty alcohols.

A tunable metabolic valve for precise growth control and increased product formation in Pseudomonas putida.

Metabolic engineering of microorganisms aims to design strains capable of producing valuable compounds under relevant industrial conditions and in an economically competitive manner. From this perspective, and beyond the need for a catalyst, biomass is essentially a cost-intensive, abundant by-product of a microbial conversion. Yet, few broadly applicable strategies focus on the optimal balance between product and biomass formation. Here, we present a genetic control module that can be used to precisely modulate growth of the industrial bacterial chassis Pseudomonas putida KT2440. The strategy is based on the controllable expression of the key metabolic enzyme complex pyruvate dehydrogenase (PDH) which functions as a metabolic valve. By tuning the PDH activity, we accurately controlled biomass formation, resulting in six distinct growth rates with parallel overproduction of excess pyruvate. We deployed this strategy to identify optimal growth patterns that improved the production yield of 2-ketoisovalerate and lycopene by 2.5- and 1.38-fold, respectively. This ability to dynamically steer fluxes to balance growth and production substantially enhances the potential of this remarkable microbial chassis for a wide range of industrial applications.

SALARECON connects the Atlantic salmon genome to growth and feed efficiency.

Atlantic salmon (Salmo salar) is the most valuable farmed fish globally and there is much interest in optimizing its genetics and rearing conditions for growth and feed efficiency. Marine feed ingredients must be replaced to meet global demand, with challenges for fish health and sustainability. Metabolic models can address this by connecting genomes to metabolism, which converts nutrients in the feed to energy and biomass, but such models are currently not available for major aquaculture species such as salmon. We present SALARECON, a model focusing on energy, amino acid, and nucleotide metabolism that links the Atlantic salmon genome to metabolic fluxes and growth. It performs well in standardized tests and captures expected metabolic (in)capabilities. We show that it can explain observed hypoxic growth in terms of metabolic fluxes and apply it to aquaculture by simulating growth with commercial feed ingredients. Predicted limiting amino acids and feed efficiencies agree with data, and the model suggests that marine feed efficiency can be achieved by supplementing a few amino acids to plant- and insect-based feeds. SALARECON is a high-quality model that makes it possible to simulate Atlantic salmon metabolism and growth. It can be used to explain Atlantic salmon physiology and address key challenges in aquaculture such as development of sustainable feeds.

Context matters: On the road to responsible biosafety technologies in synthetic biology.

Biosafety is a major challenge for developing for synthetic organisms. An early focus on application and their context could assist with the design of appropriate genetic safeguards.

Increasing cellular fitness and product yields in Pseudomonas putida through an engineered phosphoketolase shunt.

BACKGROUND: Pseudomonas putida has received increasing interest as a cell factory due to its remarkable features such as fast growth, a versatile and robust metabolism, an extensive genetic toolbox and its high tolerance to oxidative stress and toxic compounds. This interest is driven by the need to improve microbial performance to a level that enables biologically possible processes to become economically feasible, thereby fostering the transition from an oil-based economy to a more sustainable bio-based one. To this end, one of the current strategies is to maximize the product-substrate yield of an aerobic biocatalyst such as P. putida during growth on glycolytic carbon sources, such as glycerol and xylose. We demonstrate that this can be achieved by implementing the phosphoketolase shunt, through which pyruvate decarboxylation is prevented, and thus carbon loss is minimized. RESULTS: In this study, we introduced the phosphoketolase shunt in the metabolism of P. putida KT2440. To maximize the effect of this pathway, we first tested and selected a phosphoketolase (Xfpk) enzyme with high activity in P. putida. Results of the enzymatic assays revealed that the most efficient Xfpk was the one isolated from Bifidobacterium breve. Using this enzyme, we improved the P. putida growth rate on glycerol and xylose by 44 and 167%, respectively, as well as the biomass yield quantified by OD600 by 50 and 30%, respectively. Finally, we demonstrated the impact on product formation and achieved a 38.5% increase in mevalonate and a 25.9% increase in flaviolin yield from glycerol. A similar effect was observed on the mevalonate-xylose and flaviolin-xylose yields, which increased by 48.7 and 49.4%, respectively. CONCLUSIONS: Pseudomonas putida with the implemented Xfpk shunt grew faster, reached a higher final OD600nm and provided better product-substrate yields than the wild type. By reducing the pyruvate decarboxylation flux, we significantly improved the performance of this important workhorse for industrial applications. This work encompasses the first steps towards full implementation of the non-oxidative glycolysis (NOG) or the glycolysis alternative high carbon yield cycle (GATCHYC), in which a substrate is converted into products without CO2 loss These enhanced properties of P. putida will be crucial for its subsequent use in a range of industrial processes.

Immunological mechanisms involved in macrophage activation and polarization in schistosomiasis.

Human schistosomiasis is caused by helminths of the genus Schistosoma. Macrophages play a crucial role in the immune regulation of this disease. These cells acquire different phenotypes depending on the type of stimulus they receive. M1 macrophages can be 'classically activated' and can display a proinflammatory phenotype. M2 or 'alternatively activated' macrophages are considered anti-inflammatory cells. Despite the relevance of macrophages in controlling infections, the role of the functional types of these cells in schistosomiasis is unclear. This review highlights different molecules and/or macrophage activation and polarization pathways during Schistosoma mansoni and Schistosoma japonicum infection. This review is based on original and review articles obtained through searches in major databases, including Scopus, Google Scholar, ACS, PubMed, Wiley, Scielo, Web of Science, LILACS and ScienceDirect. Our findings emphasize the importance of S. mansoni and S. japonicum antigens in macrophage polarization, as they exert immunomodulatory effects in different stages of the disease and are therefore important as therapeutic targets for schistosomiasis and in vaccine development. A combination of different antigens can provide greater protection, as it possibly stimulates an adequate immune response for an M1 or M2 profile and leads to host resistance; however, this warrants in vitro and in vivo studies.

ATOM Calibration Framework: Interaction and Visualization Functionalities.

Robotic systems are evolving to include a large number of sensors and diverse sensor modalities. In order to operate a system with multiple sensors, the geometric transformations between those sensors must be accurately estimated. The process by which these transformations are estimated is known as sensor calibration. Behind every sensor calibration approach is a formulation and a framework. The formulation is the method by which the transformations are estimated. The framework is the set of operations required to carry out the calibration procedure. This paper proposes a novel calibration framework that gives more flexibility, control and information to the user, enhancing the user interface and the user experience of calibrating a robotic system. The framework consists of several visualization and interaction functionalities useful for a calibration procedure, such as the estimation of the initial pose of the sensors, the data collection and labeling, the data review and correction and the visualization of the estimation of the extrinsic and intrinsic parameters. This framework is supported by the Atomic Transformations Optimization Method formulation, referred to as ATOM. Results show that this framework is applicable to various robotic systems with different configurations, number of sensors and sensor modalities. In addition to this, a survey comparing the frameworks of different calibration approaches shows that ATOM provides a very good user experience.

Recognition of Grasping Patterns Using Deep Learning for Human-Robot Collaboration.

Recent advances in the field of collaborative robotics aim to endow industrial robots with prediction and anticipation abilities. In many shared tasks, the robot's ability to accurately perceive and recognize the objects being manipulated by the human operator is crucial to make predictions about the operator's intentions. In this context, this paper proposes a novel learning-based framework to enable an assistive robot to recognize the object grasped by the human operator based on the pattern of the hand and finger joints. The framework combines the strengths of the commonly available software MediaPipe in detecting hand landmarks in an RGB image with a deep multi-class classifier that predicts the manipulated object from the extracted keypoints. This study focuses on the comparison between two deep architectures, a convolutional neural network and a transformer, in terms of prediction accuracy, precision, recall and F1-score. We test the performance of the recognition system on a new dataset collected with different users and in different sessions. The results demonstrate the effectiveness of the proposed methods, while providing valuable insights into the factors that limit the generalization ability of the models.

Domain Adaptation with Contrastive Simultaneous Multi-Loss Training for Hand Gesture Recognition.

Hand gesture recognition from images is a critical task with various real-world applications, particularly in the field of human-robot interaction. Industrial environments, where non-verbal communication is preferred, are significant areas of application for gesture recognition. However, these environments are often unstructured and noisy, with complex and dynamic backgrounds, making accurate hand segmentation a challenging task. Currently, most solutions employ heavy preprocessing to segment the hand, followed by the application of deep learning models to classify the gestures. To address this challenge and develop a more robust and generalizable classification model, we propose a new form of domain adaptation using multi-loss training and contrastive learning. Our approach is particularly relevant in industrial collaborative scenarios, where hand segmentation is difficult and context-dependent. In this paper, we present an innovative solution that further challenges the existing approach by testing the model on an entirely unrelated dataset with different users. We use a dataset for training and validation and demonstrate that contrastive learning techniques in simultaneous multi-loss functions provide superior performance in hand gesture recognition compared to conventional approaches in similar conditions.

Exploring the Impact of Tensions in Stakeholder Norms on Designing for Value Change: The Case of Biosafety in Industrial Biotechnology.

Synthetic biologists design and engineer organisms for a better and more sustainable future. While the manifold prospects are encouraging, concerns about the uncertain risks of genome editing affect public opinion as well as local regulations. As a consequence, biosafety and associated concepts, such as the Safe-by-design framework and genetic safeguard technologies, have gained notoriety and occupy a central position in the conversation about genetically modified organisms. Yet, as regulatory interest and academic research in genetic safeguard technologies advance, the implementation in industrial biotechnology, a sector that is already employing engineered microorganisms, lags behind. The main goal of this work is to explore the utilization of genetic safeguard technologies for designing biosafety in industrial biotechnology. Based on our results, we posit that biosafety is a case of a changing value, by means of further specification of how to realize biosafety. Our investigation is inspired by the Value Sensitive Design framework, to investigate scientific and technological choices in their appropriate social context. Our findings discuss stakeholder norms for biosafety, reasonings about genetic safeguards, and how these impact the practice of designing for biosafety. We show that tensions between stakeholders occur at the level of norms, and that prior stakeholder alignment is crucial for value specification to happen in practice. Finally, we elaborate in different reasonings about genetic safeguards for biosafety and conclude that, in absence of a common multi-stakeholder effort, the differences in informal biosafety norms and the disparity in biosafety thinking could end up leading to design requirements for compliance instead of for safety.