Robust, fast and accurate mapping of diffusional mean kurtosis.

Diffusional kurtosis imaging (DKI) is a methodology for measuring the extent of non-Gaussian diffusion in biological tissue, which has shown great promise in clinical diagnosis, treatment planning, and monitoring of many neurological diseases and disorders. However, robust, fast, and accurate estimation of kurtosis from clinically feasible data acquisitions remains a challenge. In this study, we first outline a new accurate approach of estimating mean kurtosis via the sub-diffusion mathematical framework. Crucially, this extension of the conventional DKI overcomes the limitation on the maximum b-value of the latter. Kurtosis and diffusivity can now be simply computed as functions of the sub-diffusion model parameters. Second, we propose a new fast and robust fitting procedure to estimate the sub-diffusion model parameters using two diffusion times without increasing acquisition time as for the conventional DKI. Third, our sub-diffusion-based kurtosis mapping method is evaluated using both simulations and the Connectome 1.0 human brain data. Exquisite tissue contrast is achieved even when the diffusion encoded data is collected in only minutes. In summary, our findings suggest robust, fast, and accurate estimation of mean kurtosis can be realised within a clinically feasible diffusion-weighted magnetic resonance imaging data acquisition time.

Potential applications of microbial genomics in nuclear non-proliferation.

As nuclear technology evolves in response to increased demand for diversification and decarbonization of the energy sector, new and innovative approaches are needed to effectively identify and deter the proliferation of nuclear arms, while ensuring safe development of global nuclear energy resources. Preventing the use of nuclear material and technology for unsanctioned development of nuclear weapons has been a long-standing challenge for the International Atomic Energy Agency and signatories of the Treaty on the Non-Proliferation of Nuclear Weapons. Environmental swipe sampling has proven to be an effective technique for characterizing clandestine proliferation activities within and around known locations of nuclear facilities and sites. However, limited tools and techniques exist for detecting nuclear proliferation in unknown locations beyond the boundaries of declared nuclear fuel cycle facilities, representing a critical gap in non-proliferation safeguards. Microbiomes, defined as "characteristic communities of microorganisms" found in specific habitats with distinct physical and chemical properties, can provide valuable information about the conditions and activities occurring in the surrounding environment. Microorganisms are known to inhabit radionuclide-contaminated sites, spent nuclear fuel storage pools, and cooling systems of water-cooled nuclear reactors, where they can cause radionuclide migration and corrosion of critical structures. Microbial transformation of radionuclides is a well-established process that has been documented in numerous field and laboratory studies. These studies helped to identify key bacterial taxa and microbially-mediated processes that directly and indirectly control the transformation, mobility, and fate of radionuclides in the environment. Expanding on this work, other studies have used microbial genomics integrated with machine learning models to successfully monitor and predict the occurrence of heavy metals, radionuclides, and other process wastes in the environment, indicating the potential role of nuclear activities in shaping microbial community structure and function. Results of this previous body of work suggest fundamental geochemical-microbial interactions occurring at nuclear fuel cycle facilities could give rise to microbiomes that are characteristic of nuclear activities. These microbiomes could provide valuable information for monitoring nuclear fuel cycle facilities, planning environmental sampling campaigns, and developing biosensor technology for the detection of undisclosed fuel cycle activities and proliferation concerns.

Minimal motifs for habituating systems.

Habituation-a phenomenon in which a dynamical system exhibits a diminishing response to repeated stimulations that eventually recovers when the stimulus is withheld-is universally observed in living systems from animals to unicellular organisms. Despite its prevalence, generic mechanisms for this fundamental form of learning remain poorly defined. Drawing inspiration from prior work on systems that respond adaptively to step inputs, we study habituation from a nonlinear dynamics perspective. This approach enables us to formalize classical hallmarks of habituation that have been experimentally identified in diverse organisms and stimulus scenarios. We use this framework to investigate distinct dynamical circuits capable of habituation. In particular, we show that driven linear dynamics of a memory variable with static nonlinearities acting at the input and output can implement numerous hallmarks in a mathematically interpretable manner. This work establishes a foundation for understanding the dynamical substrates of this primitive learning behavior and offers a blueprint for the identification of habituating circuits in biological systems.

Probing the orthogonality and robustness of the mammalian RNA-binding protein Musashi-1 in Escherichia coli.

RNA recognition motifs (RRMs) are widespread RNA-binding protein domains in eukaryotes, which represent promising synthetic biology tools due to their compact structure and efficient activity. Yet, their use in prokaryotes is limited and their functionality poorly characterized. Recently, we repurposed a mammalian Musashi protein containing two RRMs as a translation regulator in Escherichia coli. Here, employing high-throughput RNA sequencing, we explored the impact of Musashi expression on the transcriptomic and translatomic profiles of E. coli, revealing certain metabolic interference, induction of post-transcriptional regulatory processes, and spurious protein-RNA interactions. Engineered Musashi protein mutants displayed compromised regulatory activity, emphasizing the importance of both RRMs for specific and sensitive RNA binding. We found that a mutation known to impede allosteric regulation led to similar translation control activity. Evolutionary experiments disclosed a loss of function of the synthetic circuit in about 40 generations, with the gene coding for the Musashi protein showing a stability comparable to other heterologous genes. Overall, this work expands our understanding of RRMs for post-transcriptional regulation in prokaryotes and highlight their potential for biotechnological and biomedical applications.

Investigation of Human Milk as a Biological System in a Multicenter Mother-Infant Cohort: Protocol Design and Cohort Profile of the Phoenix Study.

Breastfeeding and human milk are the gold standard for infant feeding. Studying human milk with a systems biology approach in a large longitudinal cohort is needed to understand its complexity and health implications. The Phoenix study is a multicenter cohort study focusing on the interactions of maternal characteristics, human milk composition, infant feeding practices, and health outcomes of Chinese mothers and infants. A total of 779 mother-infant dyads were recruited from November 2021 to September 2022, and 769 mother-infant dyads were enrolled in the study. Scheduled home visits took place at 1, 4, 6, and 12 months postpartum, and 696 dyads (90.5% participants) completed the 12-month visit. At each visit, maternal and infant anthropometry was assessed. Questionnaires were administered to collect longitudinal information on maternal characteristics and lifestyle, infant feeding, and health. Digital diaries were used to record maternal dietary intake, infant feeding, and stool character. Human milk, maternal feces, infant feces, and infant saliva were collected. An external pharmaceutical-level quality assurance approach was implied to ensure the trial quality. Multi-omics techniques (including glycomics, lipidomics, proteomics, and microbiomics) and machine learning algorithms were integrated into the sample and data analysis. The protocol design of the Phoenix study provides a framework for prospective cohort studies of mother-infant dyads and will provide insights into the complex dynamics of human milk and its interplay with maternal and infant health outcomes in the Chinese population.

Integration of proteomic data with genome-scale metabolic models: A methodological overview.

The integration of proteomics data with constraint-based reconstruction and analysis (COBRA) models plays a pivotal role in understanding the relationship between genotype and phenotype and bridges the gap between genome-level phenomena and functional adaptations. Integrating a generic genome-scale model with information on proteins enables generation of a context-specific metabolic model which improves the accuracy of model prediction. This review explores methodologies for incorporating proteomics data into genome-scale models. Available methods are grouped into four distinct categories based on their approach to integrate proteomics data and their depth of modeling. Within each category section various methods are introduced in chronological order of publication demonstrating the progress of this field. Furthermore, challenges and potential solutions to further progress are outlined, including the limited availability of appropriate in vitro data, experimental enzyme turnover rates, and the trade-off between model accuracy, computational tractability, and data scarcity. In conclusion, methods employing simpler approaches demand fewer kinetic and omics data, consequently leading to a less complex mathematical problem and reduced computational expenses. On the other hand, approaches that delve deeper into cellular mechanisms and aim to create detailed mathematical models necessitate more extensive kinetic and omics data, resulting in a more complex and computationally demanding problem. However, in some cases, this increased cost can be justified by the potential for more precise predictions.

Recovering single-cell expression profiles from spatial transcriptomics with scResolve.

Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable with cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell-type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained through scResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.

Microbiome-induced Increases and Decreases in Bone Matrix Strength can be Initiated After Skeletal Maturity.

Recent studies in mice have indicated that the gut microbiome can regulate bone tissue strength. However, prior work involved modifications to the gut microbiome in growing animals and it is unclear if the same changes in the microbiome, applied later in life, would change matrix strength. Here we changed the composition of the gut microbiome before and/or after skeletal maturity (16 weeks of age) using oral antibiotics (ampicillin + neomycin). Male and female mice (n = 143 total, n = 12-17/group/sex) were allocated into five study groups:1) Unaltered, 2) Continuous (dosing 4-24 weeks of age), 3) Delayed (dosing only 16-24 weeks of age), 4) Initial (dosing 4-16 weeks of age, suspended at 16 weeks), and 5) Reconstituted (dosing from 4-16 weeks following by fecal microbiota transplant from Unaltered donors). Animals were euthanized at 24 weeks of age. In males, bone matrix strength in the femur was 25-35% less than expected by geometry in mice from the Continuous (P=.001), Delayed (P=.005), and Initial (P=.040) groups as compared to Unaltered. Reconstitution of the gut microbiota led to a bone matrix strength similar to Unaltered animals (P=.929). In females, microbiome-induced changes in bone matrix strength followed the same trend as males but were not significantly different, demonstrating a sex-dependent response of bone matrix to the gut microbiota. Minor differences in chemical composition of bone matrix were observed with Raman spectroscopy. Our findings indicate that microbiome-induced impairment of bone matrix in males can be initiated and/or reversed after skeletal maturity. The portion of the femoral cortical bone formed after skeletal maturity (16 weeks) was small; suggesting that microbiome-induced changes in bone matrix occurred without osteoblast/osteoclast turnover through a yet unidentified mechanism. These findings provide evidence that the mechanical properties of bone matrix can be altered in the adult skeleton.

This study looked at how changes in the gut microbiome affect bone strength in adult mice. The gut microbiome of male and female mice was altered either before or after skeletal maturity. In male mice, those with altered microbiomes had weaker bones (a 25-35% reduction). Alterations to the gut microbiome after skeletal maturity had the same effect as lifelong changes, and restoration of an altered gut microbiome after skeletal maturity reversed the effect. Female mice showed a similar trend, but the changes were not statistically significant. The study concluded that changes in the gut microbiome can weaken bone strength in adult male mice in as short as two months, but this effect can be reversed by restoring the microbiome. These changes seem to occur without removal and replacement of bone tissue using the common bone remodeling processes, suggesting an unknown mechanism. This research provides new evidence that gut bacteria can affect bone strength suggesting the possibility that the microbiome can influence bone fragility.

ImmCellTyper facilitates systematic mass cytometry data analysis for deep immune profiling.

Mass cytometry is a cutting-edge high-dimensional technology for profiling marker expression at the single-cell level, advancing clinical research in immune monitoring. Nevertheless, the vast data generated by cytometry by time-of-flight (CyTOF) poses a significant analytical challenge. To address this, we describe ImmCellTyper (https://github.com/JingAnyaSun/ImmCellTyper), a novel toolkit for CyTOF data analysis. This framework incorporates BinaryClust, an in-house developed semi-supervised clustering tool that automatically identifies main cell types. BinaryClust outperforms existing clustering tools in accuracy and speed, as shown in benchmarks with two datasets of approximately 4 million cells, matching the precision of manual gating by human experts. Furthermore, ImmCellTyper offers various visualisation and analytical tools, spanning from quality control to differential analysis, tailored to users' specific needs for a comprehensive CyTOF data analysis solution. The workflow includes five key steps: (1) batch effect evaluation and correction, (2) data quality control and pre-processing, (3) main cell lineage characterisation and quantification, (4) in-depth investigation of specific cell types; and (5) differential analysis of cell abundance and functional marker expression across study groups. Overall, ImmCellTyper combines expert biological knowledge in a semi-supervised approach to accurately deconvolute well-defined main cell lineages, while maintaining the potential of unsupervised methods to discover novel cell subsets, thus facilitating high-dimensional immune profiling.

Omics approaches to unravel insecticide resistance mechanism in Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae).

Bemisia tabaci (Gennadius) whitefly (BtWf) is an invasive pest that has already spread worldwide and caused major crop losses. Numerous strategies have been implemented to control their infestation, including the use of insecticides. However, prolonged insecticide exposures have evolved BtWf to resist these chemicals. Such resistance mechanism is known to be regulated at the molecular level and systems biology omics approaches could shed some light on understanding this regulation wholistically. In this review, we discuss the use of various omics techniques (genomics, transcriptomics, proteomics, and metabolomics) to unravel the mechanism of insecticide resistance in BtWf. We summarize key genes, enzymes, and metabolic regulation that are associated with the resistance mechanism and review their impact on BtWf resistance. Evidently, key enzymes involved in the detoxification system such as cytochrome P450 (CYP), glutathione S-transferases (GST), carboxylesterases (COE), UDP-glucuronosyltransferases (UGT), and ATP binding cassette transporters (ABC) family played key roles in the resistance. These genes/proteins can then serve as the foundation for other targeted techniques, such as gene silencing techniques using RNA interference and CRISPR. In the future, such techniques will be useful to knock down detoxifying genes and crucial neutralizing enzymes involved in the resistance mechanism, which could lead to solutions for coping against BtWf infestation.

A Systems Biology Approach to the Pathogenesis of Celiac Disease: Identification of Potential Protective and Promoting Mechanisms.

Background: Celiac disease (CeD) is an autoimmune enteropathy triggered by dietary gluten. Almost 90% of CeD patients have HLA-DQ2 or -DQ8 haplotypes. As a high proportion of first-degree relatives (FDRs) of CeD patients have the same haplotype, it is assumed that they are at a higher risk of disease development than the general population. Nevertheless, the prevalence of CeD among FDRs is considerably low (7.5%). Materials and Methods: In order to figure out this discrepancy, a microarray dataset of intestinal mucosal biopsies of CeD patients, FDRs, and control groups was reanalyzed, and a protein-protein interaction network was constructed. Results: Principal component analysis showed that CeD and FDR groups are far away in terms of gene expression. Comparing differentially expressed genes of both networks demonstrated inverse expression of some genes mainly related to cell cycle mechanisms. Moreover, analysis of the modular structures of up- and downregulated gene networks determined activation of protein degradation mechanisms and inhibition of ribosome-related protein synthesis in celiac patients with an upside-down pattern in FDRs. Conclusions: The top-down systems biology approach determined some regulatory pathways with inverse function in CeD and FDR groups. These genes and molecular mechanisms could be a matter of investigation as potential druggable targets or prognostic markers in CeD.

The Global Evolution and Impact of Systems Biology and Artificial Intelligence in Stem Cell Research and Therapeutics Development: A Scoping Review.

Advanced bioinformatics analysis, such as systems biology (SysBio) and artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL), is increasingly present in stem cell (SC) research. An approximate timeline on these developments and their global impact is still lacking. We conducted a scoping review on the contribution of SysBio and AI analysis to SC research and therapy development based on literature published in PubMed between 2000 and 2024. We identified an 8-10-fold increase in research output related to all three search terms between 2000 and 2021, with a 10-fold increase in AI-related production since 2010. Use of SysBio and AI still predominates in preclinical basic research with increasing use in clinically oriented translational medicine since 2010. SysBio- and AI-related research was found all over the globe, with SysBio output led by the United States (US, n=1487), United Kingdom (UK, n=1094), Germany (n=355), The Netherlands (n=339), Russia (n=215), and France (n=149), while for AI-related research the US (n=853) and UK (n=258) take a strong lead, followed by Switzerland (n=69), The Netherlands (n=37), and Germany (n=19). The US and UK are most active in SCs publications related to AI/ML and AI/DL. The prominent use of SysBio in ESC research was recently overtaken by prominent use of AI in iPSC and MSC research. This study reveals the global evolution and growing intersection between AI, SysBio, and SC research over the past two decades, with substantial growth in all three fields and exponential increases in AI-related research in the past decade.

White matter structural bases for phase accuracy during tapping synchronization.

We determined the intersubject association between the rhythmic entrainment abilities of human subjects during a synchronization-continuation tapping task (SCT) and the macro- and microstructural properties of their superficial (SWM) and deep (DWM) white matter. Diffusion-weighted images were obtained from 32 subjects who performed the SCT with auditory or visual metronomes and five tempos ranging from 550 to 950 ms. We developed a method to determine the density of short-range fibers that run underneath the cortical mantle, interconnecting nearby cortical regions (U-fibers). Notably, individual differences in the density of U-fibers in the right audiomotor system were correlated with the degree of phase accuracy between the stimuli and taps across subjects. These correlations were specific to the synchronization epoch with auditory metronomes and tempos around 1.5 Hz. In addition, a significant association was found between phase accuracy and the density and bundle diameter of the corpus callosum, forming an interval-selective map where short and long intervals were behaviorally correlated with the anterior and posterior portions of the corpus callosum. These findings suggest that the structural properties of the SWM and DWM in the audiomotor system support the tapping synchronization abilities of subjects, as cortical U-fiber density is linked to the preferred tapping tempo and the bundle properties of the corpus callosum define an interval-selective topography.

EnhancerNet: A predictive model of cell identity dynamics through enhancer selection.

Understanding how cell identity is encoded by the genome and acquired during differentiation is a central challenge in cell biology. We have developed a theoretical framework called EnhancerNet, which models the regulation of cell identity through the lens of transcription factor (TF)-enhancer interactions. We demonstrate that autoregulation in these interactions imposes a constraint on the model, resulting in simplified dynamics that can be parameterized from observed cell identities. Despite its simplicity, EnhancerNet recapitulates a broad range of experimental observations on cell identity dynamics, including enhancer selection, cell fate induction, hierarchical differentiation through multipotent progenitor states, and direct reprogramming by TF overexpression. The model makes specific quantitative predictions, reproducing known reprogramming recipes and the complex hematopoietic differentiation hierarchy without fitting unobserved parameters. EnhancerNet provides insights into how new cell types could evolve and highlights the functional importance of distal regulatory elements with dynamic chromatin in multicellular evolution.

Modeling tumors as complex ecosystems.

Many cancers resist therapeutic intervention. This is fundamentally related to intratumor heterogeneity: multiple cell populations, each with different phenotypic signatures, coexist within a tumor and its metastases. Like species in an ecosystem, cancer populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity or predict its consequences. Here, we propose that the generalized Lotka-Volterra model (GLV), a standard tool to describe species-rich ecological communities, provides a suitable framework to model the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties provide a new understanding of the disease. We discuss potential extensions of the model and their application to phenotypic plasticity, cancer-immune interactions, and metastatic growth. Our work outlines a set of questions and a road map for further research in cancer ecology.

Serum lipidome associates with neuroimaging features in patients with traumatic brain injury.

Acute traumatic brain injury (TBI) is associated with substantial abnormalities in lipid biology, including changes in the structural lipids that are present in the myelin in the brain. We investigated the relationship between traumatic microstructural changes in white matter from magnetic resonance imaging (MRI) and quantitative lipidomic changes from blood serum. The study cohort included 103 patients from the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) study. Diffusion tensor fitting generated fractional anisotropy (FA) and mean diffusivity (MD) maps for the MRI scans while ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry was applied to analyze the lipidome. Increasing severity of TBI was associated with higher MD and lower FA values, which scaled with different lipidomic signatures. There appears to be consistent patterns of lipid changes associating with the specific microstructure changes in the CNS white matter, but also regional specificity, suggesting that blood-based lipidomics may provide an insight into the underlying pathophysiology of TBI.

A three filament mechanistic model of musculotendon force and impedance.

The force developed by actively lengthened muscle depends on different structures across different scales of lengthening. For small perturbations, the active response of muscle is well captured by a linear-time-invariant (LTI) system: a stiff spring in parallel with a light damper. The force response of muscle to longer stretches is better represented by a compliant spring that can fix its end when activated. Experimental work has shown that the stiffness and damping (impedance) of muscle in response to small perturbations is of fundamental importance to motor learning and mechanical stability, while the huge forces developed during long active stretches are critical for simulating and predicting injury. Outside of motor learning and injury, muscle is actively lengthened as a part of nearly all terrestrial locomotion. Despite the functional importance of impedance and active lengthening, no single muscle model has all these mechanical properties. In this work, we present the viscoelastic-crossbridge active-titin (VEXAT) model that can replicate the response of muscle to length changes great and small. To evaluate the VEXAT model, we compare its response to biological muscle by simulating experiments that measure the impedance of muscle, and the forces developed during long active stretches. In addition, we have also compared the responses of the VEXAT model to a popular Hill-type muscle model. The VEXAT model more accurately captures the impedance of biological muscle and its responses to long active stretches than a Hill-type model and can still reproduce the force-velocity and force-length relations of muscle. While the comparison between the VEXAT model and biological muscle is favorable, there are some phenomena that can be improved: the low frequency phase response of the model, and a mechanism to support passive force enhancement.

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs.

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.

Mutations in an organism's DNA make the individual more likely to survive and reproduce in its environment, passing on its mutations to the next generation. Mutations can alter the proteins that a gene codes for in many ways. This leads to a situation where seemingly similar mutations ­ such as two mutations in the same gene ­ can have different effects. For example, two different mutations could affect the primary function of the encoded protein in the same way but have different side effects. One mutation might also cause the protein to interact with a new molecule or protein. Organisms possessing one or the other mutation will thus have similar odds of surviving and reproducing in some environments, but differences in environments where the new interaction is important. In microorganisms, mutations can lead to drug resistance. If drug-resistant mutations have different side effects, it can be challenging to treat microbial infections, as drug-resistant pathogens are often treated with sequential drug strategies. These strategies rely on mutations that cause resistance to the first drug all having susceptibility to the second drug. But if similar seeming mutations can have diverse side effects, predictions about how they will respond to a second drug are more complicated. To address this issue, Schmidlin, Apodaca et al. collected a diverse group of nearly a thousand mutant yeast strains that were resistant to a drug called fluconazole. Next, they asked to what extent the fitness ­ the ability to survive and reproduce ­ of these mutants responded similarly to environmental change. They used this information to cluster mutations into groups that likely have similar effects at the molecular level, finding at least six such groups with unique trade-offs across environments. For example, some groups resisted only low drug concentrations, and others were unique in that they resisted treatment with two single drugs but not their combination. These diverse types of fluconazole-resistant yeast lineages highlight the challenges of designing a simple sequential drug treatment that targets all drug-resistant mutants. However, the results also suggest some predictability in how drug-resistant infections can evolve and be treated.

Decoded cardiopoietic cell secretome linkage to heart repair biosignature.

Cardiopoiesis-primed human stem cells exert sustained benefit in treating heart failure despite limited retention following myocardial delivery. To assess potential paracrine contribution, the secretome of cardiopoiesis conditioned versus naïve human mesenchymal stromal cells was decoded by directed proteomics augmented with machine learning and systems interrogation. Cardiopoiesis doubled cellular protein output generating a distinct secretome that segregated the conditioned state. Altering the expression of 1035 secreted proteins, cardiopoiesis reshaped the secretome across functional classes. The resolved differential cardiopoietic secretome was enriched in mesoderm development and cardiac progenitor signaling processes, yielding a cardiovasculogenic profile bolstered by upregulated cardiogenic proteins. In tandem, cardiopoiesis enhanced the secretion of immunomodulatory proteins associated with cytokine signaling, leukocyte migration, and chemotaxis. Network analysis integrated the differential secretome within an interactome of 1745 molecules featuring prioritized regenerative processes. Secretome contribution to the repair signature of cardiopoietic cell-treated infarcted hearts was assessed in a murine coronary ligation model. Intramyocardial delivery of cardiopoietic cells improved the performance of failing hearts, with undirected proteomics revealing 50 myocardial proteins responsive to cell therapy. Pathway analysis linked the secretome to cardiac proteome remodeling, pinpointing 17 cardiopoiesis-upregulated secretome proteins directly upstream of 44% of the cell therapy-responsive cardiac proteome. Knockout, in silico, of this 22-protein secretome-dependent myocardial ensemble eliminated indices of the repair signature. Accordingly, in vivo, cell therapy rendered the secretome-dependent myocardial proteome of an infarcted heart indiscernible from healthy counterparts. Thus, the secretagogue effect of cardiopoiesis transforms the human stem cell secretome, endows regenerative competency, and upregulates candidate paracrine effectors of cell therapy-mediated molecular restitution.

Systems biology of electrogenic Pseudomonas putida - multi-omics insights and metabolic engineering for enhanced 2-ketogluconate production.

BACKGROUND: Pseudomonas putida KT2440 has emerged as a promising host for industrial bioproduction. However, its strictly aerobic nature limits the scope of applications. Remarkably, this microbe exhibits high bioconversion efficiency when cultured in an anoxic bio-electrochemical system (BES), where the anode serves as the terminal electron acceptor instead of oxygen. This environment facilitates the synthesis of commercially attractive chemicals, including 2-ketogluconate (2KG). To better understand this interesting electrogenic phenotype, we studied the BES-cultured strain on a systems level through multi-omics analysis. Inspired by our findings, we constructed novel mutants aimed at improving 2KG production. RESULTS: When incubated on glucose, P. putida KT2440 did not grow but produced significant amounts of 2KG, along with minor amounts of gluconate, acetate, pyruvate, succinate, and lactate. 13C tracer studies demonstrated that these products are partially derived from biomass carbon, involving proteins and lipids. Over time, the cells exhibited global changes on both the transcriptomic and proteomic levels, including the shutdown of translation and cell motility, likely to conserve energy. These adaptations enabled the cells to maintain significant metabolic activity for several weeks. Acetate formation was shown to contribute to energy supply. Mutants deficient in acetate production demonstrated superior 2KG production in terms of titer, yield, and productivity. The ∆aldBI ∆aldBII double deletion mutant performed best, accumulating 2KG at twice the rate of the wild type and with an increased yield (0.96 mol/mol). CONCLUSIONS: By integrating transcriptomic, proteomic, and metabolomic analyses, this work provides the first systems biology insight into the electrogenic phenotype of P. putida KT2440. Adaptation to anoxic-electrogenic conditions involved coordinated changes in energy metabolism, enabling cells to sustain metabolic activity for extended periods. The metabolically engineered mutants are promising for enhanced 2KG production under these conditions. The attenuation of acetate synthesis represents the first systems biology-informed metabolic engineering strategy for enhanced 2KG production in P. putida. This non-growth anoxic-electrogenic mode expands our understanding of the interplay between growth, glucose phosphorylation, and glucose oxidation into gluconate and 2KG in P. putida.