Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel evolution of phage resistance - virulence trade - offs during in vitro and nasal Pseudomonas aeruginosa phage treatment

With rising antibiotic resistance, there has been increasing interest in the treatment of pathogenic bacteria with bacteriophages (phage therapy). One limitation of phage therapy is the ease at which bacteria can evolve resistance. The negative effects of resistance may be partially mitigated when resistance results in reduced bacterial growth and virulence, or when phage coevolve to overcome resistance. Resistance evolution and its consequences are highly contingent on the particular combination of bacteria and phage and the ecological context they interact in, making therapeutic outcomes hard to predict. One solution might be to conduct “in vitro evolutionary simulations” using the bacteria-phage combinations specific to the therapeutic context. Here, we investigate parallels between in vitro experiments and in vivo dynamics in a human participant. Evolutionary dynamics were similar in vivo and in vitro, with high levels of de novo resistance evolving quickly with limited evidence of phage evolution. Moreover, resistant bacteria – evolved both in vitro and in vivo - had lower virulence when measured in an insect model. In vivo, this was linked to lower growth rates of resistant isolates, whereas in vitro isolates evolved greater biofilm production with phage resistance. Population sequencing suggests resistance was typically the result of selection on de novo mutations rather than sorting of existing variants in the population. These results highlight the speed at which resistance to phages can evolve in vivo, and that in vitro evolution may give useful insights for evolutionary outcomes in vivo.
Novel Insights on Obligate Symbiont Lifestyle and Adaptation to Chemosynthetic Environment as Revealed by the Giant Tubeworm Genome

The mutualism between the giant tubeworm Riftia pachyptila and its endosymbiont Candidatus Endoriftia persephone has been extensively researched over the past 40 years. However, the lack of the host whole genome information has impeded the full comprehension of the genotype/phenotype interface in Riftia. Here we described the high-quality draft genome of Riftia, its complete mitogenome, and tissue-specific transcriptomic data. The Riftia genome presents signs of reductive evolution, with gene family contractions exceeding expansions. Expanded gene families are related to sulphur metabolism, detoxification, anti-oxidative stress, oxygen transport, immune system, and lysosomal digestion, reflecting evolutionary adaptations to the vent environment and endosymbiosis. Despite the derived body plan, the developmental gene repertoire in the gutless tubeworm is extremely conserved with the presence of a near intact and complete Hox cluster. Gene expression analyses establishes that the trophosome is a multi-functional organ marked by intracellular digestion of endosymbionts, storage of excretory products and haematopoietic functions. Overall, the plume and gonad tissues both in contact to the environment harbour highly expressed genes involved with cell cycle, programmed cell death, and immunity indicating a high cell turnover and defence mechanisms against pathogens. We posit that the innate immune system plays a more prominent role into the establishment of the symbiosis during the infection in the larval stage, rather than maintaining the symbiostasis in the trophosome. This genome bridges four decades of physiological research in Riftia, whilst simultaneously provides new insights into the development, whole organism functions and evolution in the giant tubeworm.
Determinants of Directionality and Efficiency of the ATP Synthase Fo Motor at Atomic Resolution

Fo subcomplex of ATP synthase is an membraneembedded rotary motor that converts proton motive force into mechanical energy. Despite a rapid increase in the number of high-resolution structures, the mechanism of tight coupling between proton transport and motion of the rotary c-ring remains elusive. Here, using extensive all-atom free energy simulations, we show how the motor’s directionality naturally arises from the interplay between intra-protein interactions and energetics of protonation of the c-ring. Notably, our calculations reveal that the strictly conserved arginine in the a-subunit (R176) serves as a jack-of-all-trades: it dictates the direction of rotation, controls the protonation state of the proton-release site and separates the two proton-access half-channels. Therefore, arginine is necessary to avoid slippage between the proton flux and the mechanical output and guarantees highly efficient energy conversion. We also provide mechanistic explanations for the reported defective mutations of R176, reconciling the structural information on the Fo motor with previous functional and single-molecule data.
Can deep convolutional neural networks support relational reasoning in the same-different task?

Same-different visual reasoning is a basic skill central to abstract combinatorial thought. This fact has lead neural networks researchers to test same-different classification on deep convolutional neural networks (DCNNs), which has resulted in a controversy regarding whether this skill is within the capacity of these models. However, most tests of same-different classification rely on testing on images that come from the same pixel-level distribution as the testing images, yielding the results inconclusive. In this study we tested relational same-different reasoning DCNNs. In a series of simulations we show that models based on the ResNet-50 architecture are capable of visual same-different classification, but only when the test images are similar to the training images at the pixel-level. In contrast, even when there are only subtle differences between the testing and training images, the performance of DCNNs drops substantially. This is true even when DCNNs’ training regime is augmented with images from new versions of the same-different task or through multi-task learning on the test images. Furthermore, we show that the Relation Network, a deep learning architecture specifically designed to tackle visual relational reasoning problems, suffers the same kind of limitations than ResNet-50 classifiers.
Network modeling predicts personalized gene expression and drug responses in valve myofibroblasts cultured with patient sera

Significance A major contributor to heart valve disease is the excessive buildup of scar-like tissue in the valve, which can hinder the ability of the valve to open and close and can ultimately lead to heart failure. Controlling this scar tissue remodeling is very difficult due, in part, to a complex cellular regulation system and, in part, to large variabilities between different patients. We have built a computational model of the cell biochemical network that regulates valve remodeling, which enables virtual predictions of valve scarring given patient-specific biochemical levels. With this model, we ran personalized drug screens to predict each patient’s response to particular therapies, and follow-up cell culture experiments validated our predictions with over 80% accuracy. Aortic valve stenosis (AVS) patients experience pathogenic valve leaflet stiffening due to excessive extracellular matrix (ECM) remodeling. Numerous microenvironmental cues influence pathogenic expression of ECM remodeling genes in tissue-resident valvular myofibroblasts, and the regulation of complex myofibroblast signaling networks depends on patient-specific extracellular factors. Here, we combined a manually curated myofibroblast signaling network with a data-driven transcription factor network to predict patient-specific myofibroblast gene expression signatures and drug responses. Using transcriptomic data from myofibroblasts cultured with AVS patient sera, we produced a large-scale, logic-gated differential equation model in which 11 biochemical and biomechanical signals were transduced via a network of 334 signaling and transcription reactions to accurately predict the expression of 27 fibrosis-related genes. Correlations were found between personalized model-predicted gene expression and AVS patient echocardiography data, suggesting links between fibrosis-related signaling and patient-specific AVS severity. Further, global network perturbation analyses revealed signaling molecules with the most influence over network-wide activity, including endothelin 1 (ET1), interleukin 6 (IL6), and transforming growth factor β (TGFβ), along with downstream mediators c-Jun N-terminal kinase (JNK), signal transducer and activator of transcription (STAT), and reactive oxygen species (ROS). Lastly, we performed virtual drug screening to identify patient-specific drug responses, which were experimentally validated via fibrotic gene expression measurements in valvular interstitial cells cultured with AVS patient sera and treated with or without bosentan—a clinically approved ET1 receptor inhibitor. In sum, our work advances the ability of computational approaches to provide a mechanistic basis for clinical decisions including patient stratification and personalized drug screening.
Geometrical Study of Virus RNA Sequences

In this contribution, some applications of the earlier developed fast algorithm of calculating coordinates of single nucleotides and RNA fragments are considered to create multi-scale geometrical models of RNAs and their mutations. The algorithm allows to plot single nucleotides and RNA’s fragments on one figure and to track the RNA mutations of any level visually and numerically using interpolation formulas and point-to-point estimates of coordinates of ATG starting triplets and single nucleotides. The performed study of many samples of SARS CoV-2 viruses shows perturbations of ATG starting triplet coordinates in the vicinity of orf1ab gene end only.
Distinct inflammatory and transcriptomic profiles in dominant versus subordinate males in mouse social hierarchies

Social status is a critical factor determining health outcomes in human and nonhuman social species. In social hierarchies with reproductive skew, individuals compete to monopolize resources and increase mating opportunities. This can come at a significant energetic cost leading to trade-offs between different physiological systems. Particularly, changes in energetic investment in the immune system can have significant short and long-term effects on fitness and health. We have previously found that dominant alpha male mice living in social hierarchies have increased metabolic demands related to territorial defense. In this study, we tested the hypothesis that high-ranking male mice favor energetically inexpensive adaptive immunity, while subordinate mice show higher investment in innate immunity. We housed 12 groups of 10 outbred CD-1 male mice in a social housing system. All formed linear social hierarchies and subordinate mice had higher concentrations of plasma corticosterone (CORT) than alpha males. This difference was heightened in highly despotic hierarchies. Using flow cytometry, we found that dominant status was associated with a significant shift in immunophenotypes towards favoring adaptive versus innate immunity. Using Tag-Seq to profile hepatic and splenic transcriptomes of alpha and subordinate males, we identified genes that regulate metabolic and immune defense pathways that are associated with status and/or CORT concentration. In the liver, dominant animals showed an up-regulation of specific genes involved in major urinary production and catabolic processes, whereas subordinate animals showed an up-regulation of genes promoting biosynthetic processes, wound healing, and proinflammatory responses. In spleen, subordinate mice showed up-regulation of genes facilitating oxidative phosphorylation and DNA repair and CORT was negatively associated with genes involved in lymphocyte proliferation and activation. Together, our findings suggest that dominant and subordinate animals adaptively shift energy investment in immune functioning and gene expression to match their contextual energetic demands. Highlights Immunity is shaped by stress and energetic pressures associated with social status Dominant and subordinate mice favor adaptive and innate immunity, respectively Dominants increase expression of genes involved in energy production Wound healing and DNA repair genes are upregulated in subordinates Genes related to maintaining and signaling social status are upregulated in dominants
Optimization of closed-loop electrical stimulation enables robust cerebellar-directed seizure control

Additional treatment options for temporal lobe epilepsy are needed, and potential interventions targeting the cerebellum are of interest. Previous animal work has shown strong inhibition of hippocampal seizures through on-demand optogenetic manipulation of the cerebellum. However, decades of work examining electrical stimulation – a more immediately translatable approach – targeting the cerebellum has produced very mixed results. We were therefore interested in exploring the impact that stimulation parameters may have on seizure outcomes. Using a mouse model of temporal lobe epilepsy, we conducted on-demand electrical stimulation of the cerebellar cortex, and varied stimulation charge, frequency, and pulse width, resulting in over a thousand different potential combinations of settings. To explore this parameter space in an efficient, data-driven, manner, we utilized Bayesian optimization with Gaussian process regression, implemented in Matlab with an Expected Improvement Plus acquisition function. We examined two different fitting conditions and two different electrode orientations. Following the optimization process, we conducted additional on-demand experiments to test the effectiveness of selected settings. Across all animals, we found that Bayesian optimization allowed identification of effective intervention settings. Additionally, generally similar optimal settings were identified across animals, suggesting that personalized optimization may not always be necessary. While optimal settings were consistently effective, stimulation with settings predicted from the Gaussian process regression to be ineffective failed to provide seizure control. Taken together, our results provide a blueprint for exploration of a large parameter space for seizure control, and illustrate that robust inhibition of seizures can be achieved with electrical stimulation of the cerebellum, but only if the correct stimulation parameters are used.
Catalytic fast pyrolysis of biomass: superior selectivity of hierarchical zeolites to aromatics

The catalytic fast pyrolysis of oak over two zeolites (microporous and hierarchical) was investigated in a microfluidized bed reactor (MFBR) at 500 °C and as a function of the biomass-to-catalyst ratio. A hierarchical zeolite was produced by desilication with a NaOH solution of a conventional HZSM-5 zeolite. The outlet of the MFBR was connected to a single photoionization mass spectrometer (SPI-MS) for the on-line analysis of volatiles. This on-line analysis method allows studying the dynamics of volatile formation (in real time) and the deactivation of 2 zeolites upon stepwise injections of wood particles. Strikingly, the selectivity of targeted mono-aromatic compounds (quantified by gas chromatography) is doubled after desilication of the zeolite. The coked zeolites were characterized by TEM-EDX, digestion in fluoric acid, MALDI-TOF MS, etc. Three different types of coke are evidenced: (1) coke trapped inside micropores, (2) external coke formed on the outer surface of the crystals and (3) coke precursors deposited in the mesopores. The latter two cokes are much less toxic than the microporous coke. The open mesopores produced after desilication can be seen as “highways” where big molecules (such as levoglucosan) can diffuse to more accessible pore mouths. Bronsted acid sites present on microporous mouths can be active for the conversion of bigger molecules to small fragments. These fragments diffuse and form aromatics in the micropores (shape selectivity). The mesopores also promote the evacuation of catalytic products, thus enhancing the selectivity of mono-aromatic hydrocarbons. Desilicated zeolites present higher selectivity to mono-aromatics and stability upon coke deposition than microporous zeolites.
Temporal irreversibility of neural dynamics as a signature of consciousness

Even though the fundamental laws of physics are the same when the direction of time is inverted, dissipative systems evolve in the preferred temporal direction indicated by the thermodynamic arrow of time. The fundamental nature of this temporal asymmetry led us to hypothesize its presence in the neural activity evoked by conscious perception of the physical world, and thus its covariance with the level of conscious awareness. Inspired by recent developments in stochastic thermodynamics, we implemented a data-driven and model-free deep learning framework to decode the temporal inversion of electrocorticography signals acquired from non-human primates. Brain activity time series recorded during conscious wakefulness could be distinguished from their inverted counterparts with high accuracy, both using frequency and phase information. However, classification accuracy was reduced for data acquired during deep sleep and under ketamine-induced anesthesia; moreover, the predictions obtained from multiple independent neural networks were less consistent for sleep and anesthesia than for conscious wakefulness. Finally, the analysis of feature importance scores highlighted transitions between slow (≈20 Hz) and fast frequencies (> 40 Hz) as the main contributors to the temporal asymmetry observed during conscious wakefulness. Our results show that a preferred temporal direction is simultaneously manifest in the neural activity evoked by conscious mentation and in the phenomenology of the passage of time, establishing common ground to tackle the relationship between brain and subjective experience.
Movement-dependent electrical stimulation for volitional strengthening of cortical connections in behaving monkeys

Correlated activity of neurons can lead to long-term strengthening or weakening of the connections between them. In addition, the behavioral context, imparted by execution of physical movements or the presence of a reward, can modulate the plasticity induced by Hebbian mechanisms. In the present study, we have combined behavior and induced neuronal correlations to strengthen connections in the motor cortex of adult behaving monkeys. Correlated activity was induced using an electrical-conditioning protocol in which stimuli gated by voluntary movements were used to produce co-activation of neurons at motor-cortical sites involved in those movements. Delivery of movement-dependent stimulation resulted in small increases in the strength of associated cortical connections immediately after conditioning. Remarkably, when paired with further repetition of the movements that gated the conditioning stimuli, there were substantially larger gains in the strength of cortical connections, that occurred in a use-dependent manner, without delivery of additional conditioning stimulation. In the absence of such movements, little change was observed in the strength of motor-cortical connections. Performance of the motor behavior in the absence of conditioning also did not produce any changes in connectivity. Our results show that combining movement-gated stimulation with further natural use of the “conditioned” pathways after stimulation ends can produce use-dependent strengthening of connections in adult primates, highlighting an important role for behavior in cortical plasticity. Our data also provide strong support for combining movement-gated stimulation with use-dependent physical rehabilitation for strengthening connections weakened by a stroke or spinal-cord injury. Significance Statement We describe an electrical-conditioning protocol in adult behaving monkeys in which stimuli gated by voluntary movements were used to strengthen connections between motor-cortical neurons involved in those movements. Movement-gated stimulation created a plastic landscape in which repetition of the movements that gated conditioning stimuli produced strengthening of cortical connections, in a use-dependent manner, long after stimulation had ended, a finding that is both novel and unique. In the absence of such behavior, little change was observed in the strength of connections. Similarly, movements alone did not produce any changes in connectivity. Our data highlight a critical role for behavior in plasticity and provide strong support for combining movement-gated stimulation with use-dependent rehabilitation for strengthening connections weakened by injury or disease.
The Potyviral Protein 6K1 Reduces Plant Proteases Activity during Turnip mosaic virus Infection

Potyviral genomes encode just 11 major proteins and multifunctionality is associated to most of these proteins at different stages of virus life cycle. The potyviral protein 6K1 is required for potyvirus replication at the early stages of viral infection and may mediate cell-to-cell movement at later stages. Our study demonstrates that the 6K1 protein from Turnip mosaic virus (TuMV) reduces the abundance of transcripts related to jasmonic acid biosynthesis and transcripts that encode cysteine protease inhibitors when expressed in trans in Nicotiana benthamiana relative to controls. Furthermore, 6K1 stability increases when lipoxygenase and cysteine protease activity is inhibited chemically, linking a mechanism to the rapid turnover of 6K1 when expressed in trans. Using transient expression, we show 6K1 is degraded rapidly at early time points in the infection process, whereas at later stages of infection protease activity is reduced and 6K1 becomes more stable, resulting in higher TuMV accumulation in systemic leaves. There was no impact of 6K1 transient expression on TuMV accumulation in local leaves. Together, these results suggest a novel function for the TuMV 6K1 protein which has not been reported previously and enhances our understanding of the complex interactions occurring between plants and potyviruses.
Gazing into the Metaboverse: Automated exploration and contextualization of metabolic data

Metabolism and its component reactions are complex, each with variable inputs, outputs, and modifiers. The harmony between these factors consequently determines the health and stability of a cell or an organism. Perturbations to any reaction or set of reactions can have rippling downstream effects, which can be challenging to trace across the total reaction network, particularly when the effects occur between complex interconnected pathways. Researchers have primarily utilized reductionist approaches to understand metabolic reaction systems; however, these simplistic methods often limit the scope of the analysis. Even systems-centric omics approaches can be limited when only a handful of high magnitude signals in the data are prioritized for interpretation. To address these challenges, we developed Metaboverse, an interactive tool for the exploration and automated extraction of potential regulatory events, patterns, and trends from multi-omic data within the context of the metabolic network and other reaction networks. This framework will be foundational in increasing our ability to holistically understand static, temporal, and multi-condition metabolic events and perturbations as well as gene-metabolite intra-cooperativity. Metaboverse is freely available under a GPL-3.0 license at https://github.com/Metaboverse/.