Miracle-dz/UniFAR-FTUs huggingface dataset:
Contrasting segregation patterns among endogenous retroviruses across the koala population

Koalas (Phascolarctos cinereus) are subject to retroviral infections by the koala retrovirus (KoRV), resulting in accumulation of heritable endogenous KoRV in the koala germline. Like other vertebrates, koalas have experienced a history of retroviral epidemics leaving marks as endogenous retroviruses (ERVs) in their genomes; another recently identified ERV lineage, named phaCin-β, shows a pattern of recent, possibly current, activity with high insertional polymorphism in the koala population that predates the establishment of endogenous KoRV. We investigate geographic patterns of retrovirus activity, focusing on three distinct ERV lineages of increasing estimated ages from KoRV to phaCin-β and to phaCin-β-like, using a whole-genome sequencing dataset of 430 koalas produced by the Koala Genome Survey. We find thousands of ERV loci across the koala population and identify contrasting patterns of polymorphism. In the youngest established ERV-lineage (KoRV), we identify thousands of integrations among northern individuals. In the phaCin-β ERV lineage, which is slightly older yet with overlapping time of activity with KoRV, we observe hundreds of integrations in northern individuals. In southern koalas, however, phaCin-β loci are found at higher frequencies possibly reflecting a bottleneck effect. Overall, the findings suggest high ERV burden in koalas that represent historic retrovirus-host interactions. Importantly, the ERV catalogue supplies improved markers for population conservation genetics of this endangered species.
Learning to segment self-generated from externally caused optic flow through sensorimotor mismatch circuits

Efficient sensory detection requires the capacity to ignore task-irrelevant information, for example when optic flow patterns created by egomotion need to be disentangled from object perception. To investigate how this is achieved in the visual system, predictive coding with sensorimotor mismatch detection is an attractive starting point. Indeed, experimental evidence for sensorimotor mismatch signals in early visual areas exists, but it is not understood how they are integrated into cortical networks that perform input segmentation and categorization. Our model advances a biologically plausible solution by extending predictive coding models with the ability to distinguish self-generated from externally caused optic flow. We first show that a simple three neuron circuit produces experience-dependent sensorimotor mismatch responses, in agreement with calcium imaging data from mice. This microcircuit is then integrated into a neural network with two generative streams. The motor-to-visual stream consists of parallel microcircuits between motor and visual areas and learns to spatially predict optic flow resulting from self-motion. The second stream bidirectionally connects a motion-selective higher visual area (mHVA) to V1, assigning a crucial role to the abundant feedback connections: the maintenance of a generative model of externally caused optic flow. In the model, area mHVA learns to segment moving objects from the background, and facilitates object categorization. Based on shared neurocomputational principles across species, the model also maps onto primate vision. Our work extends the Hebbian predictive coding to sensorimotor settings, in which the agent actively moves - and learns to predict the consequences of its own movements. Significance statement This research addresses a fundamental challenge in sensory perception: how the brain distinguishes between self-generated and externally caused visual motion. Using a computational model inspired by predictive coding and sensorimotor mismatch detection, the study proposes a biologically plausible solution. The model incorporates a neural microcircuit that generates sensorimotor mismatch responses, aligning with experimental data from mice. This microcircuit is integrated into a neural network with two streams: one predicting self-motion-induced optic flow and another maintaining a generative model for externally caused optic flow. The research advances our understanding of how the brain segments visual input into object and background, shedding light on the neural mechanisms underlying perception and categorization not only in rodents, but also in primates.
Molybdate delays sulphide formation in the sediment and transfer to the bulk liquid in a model shrimp pond

Shrimp are commonly cultured in earthen aquaculture ponds where organic-rich uneaten feed and faeces accumulate on and in the sediment to form anaerobic zones. Since the pond water is rich in sulphate, these anaerobic conditions eventually lead to the production of sulphide. Sulphides are toxic and even lethal to the shrimp that live on the pond sediment, but physicochemical and microbial reactions that occur during the accumulation of organic waste and the subsequent formation of sulphide in shrimp pond sediments remain unclear. Molybdate treatment is a promising strategy to inhibit sulphate reduction, thus, preventing sulphide accumulation. We used an experimental shrimp pond model to simulate the organic waste accumulation and sulphide formation during the final 61 days of a full shrimp growth cycle. Sodium molybdate (5 and 25 mg/L Na2MoO4.2H2O) was applied as a preventive strategy to control sulphide production before oxygen depletion. Molybdate addition partially mitigated H2S production in the sediment, and delayed its transfer to the bulk liquid by pushing the higher sulphide concentration zone towards deeper sediment layers. Molybdate treatment at 25 mg/L significantly impacted the overall microbial community composition and treated samples (5 and 25 mg/L molybdate) had about 50% higher relative abundance of sulphate reducing bacteria than the control (no molybdate) treatment. In conclusion, molybdate has the potential to work as mitigation strategy against sulphide accumulation in the sediment during shrimp growth by directly steering the microbial community in a shrimp pond system.
Live-cell imaging reveals the trade-off between target search flexibility and efficiency for Cas9 and Cas12a

CRISPR-Cas systems have widely been adopted as genome editing tools, with two frequently employed Cas nucleases being SpyCas9 and LbCas12a. Although both nucleases use RNA guides to find and cleave target DNA sites, the two enzymes differ in terms of protospacer-adjacent motif (PAM) requirements, guide architecture and cleavage mechanism. In the last years, rational engineering led to the creation of PAM-relaxed variants SpRYCas9 and impLbCas12a to broaden the targetable DNA space. By employing their catalytically inactive variants (dCas9/dCas12a), we quantified how the protein-specific characteristics impact the target search process. To allow quantification, we fused these nucleases to the photoactivatable fluorescent protein PAmCherry2.1 and performed single-particle tracking in cells of Escherichia coli. From our tracking analysis, we derived kinetic parameters for each nuclease with a non-targeting RNA guide, strongly suggesting that interrogation of DNA by LbdCas12a variants proceeds faster than that of SpydCas9. In the presence of a targeting RNA guide, both simulations and imaging of cells confirmed that LbdCas12a variants are faster and more efficient in finding a specific target site. Our work demonstrates the trade-off of relaxing PAM requirements in SpydCas9 and LbdCas12a using a powerful framework, which can be applied to other nucleases to quantify their DNA target search.
Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls

Functional magnetic resonance imaging (fMRI) studies often estimate brain intrinsic connectivity networks (ICNs) from temporal relationships between hemodynamic signals using approaches such as independent component analysis (ICA). While ICNs are thought to represent functional sources that play important roles in various psychological phenomena, current approaches have been tailored to identify ICNs that mainly reflect linear statistical relationships. However, the elements comprising neural systems often exhibit remarkably complex nonlinear interactions that may be involved in cognitive operations and altered in psychiatric conditions such as schizophrenia. Consequently, there is a need to develop methods capable of effectively capturing ICNs from measures that are sensitive to nonlinear relationships. Here, we advance a novel approach to estimate ICNs from explicitly nonlinear whole-brain functional connectivity (ENL-wFC) by transforming resting-state fMRI (rsfMRI) data into the connectivity domain, allowing us to capture unique information from distance correlation patterns that would be missed by linear whole-brain functional connectivity (LIN-wFC) analysis. Our findings provide evidence that ICNs commonly extracted from linear (LIN) relationships are also reflected in explicitly nonlinear (ENL) connectivity patterns. ENL ICN estimates exhibit higher reliability and stability, highlighting our approach’s ability to effectively quantify ICNs from rsfMRI data. Additionally, we observed a consistent spatial gradient pattern between LIN and ENL ICNs with higher ENL weight in core ICN regions, suggesting that ICN function may be subserved by nonlinear processes concentrated within network centers. We also found that a uniquely identified ENL ICN distinguished individuals with schizophrenia from healthy controls while a uniquely identified LIN ICN did not, emphasizing the valuable complementary information that can be gained by incorporating measures that are sensitive to nonlinearity in future analyses. Moreover, the ENL estimates of ICNs associated with auditory, linguistic, sensorimotor, and self-referential processes exhibit heightened sensitivity towards differentiating between individuals with schizophrenia and controls compared to LIN counterparts, demonstrating the translational value of our approach and of the ENL estimates of ICNs that are frequently reported as disrupted in schizophrenia. In summary, our findings underscore the tremendous potential of connectivity domain ICA and nonlinear information in resolving complex brain phenomena and revolutionizing the landscape of clinical FC analysis.
Lesions to different regions of frontal cortex have dissociable effects on voluntary persistence

Deciding how long to keep waiting for uncertain future rewards is a complex problem. Previous research has shown that choosing to stop waiting results from an evaluative process that weighs the subjective value of the awaited reward against the opportunity cost of waiting. In functional neuroimaging data, activity in ventromedial prefrontal cortex (vmPFC) tracks the dynamics of this evaluation, while activation in the dorsomedial prefrontal cortex (dmPFC) and anterior insula (AI) ramps up before a decision to quit is made. Here, we provide causal evidence of the necessity of these brain regions for successful performance in a willingness-to-wait task. 28 participants with frontal lobe lesions were tested on their ability to adaptively calibrate how long they waited for monetary rewards. We grouped the participants based on the location of their lesions, which were primarily in ventromedial, dorsomedial, or lateral parts of their prefrontal cortex (vmPFC, dmPFC, and lPFC, respectively), or in the anterior insula. We compared the performance of each subset of lesion participants to behavior in a control group without lesions (n=18). Finally, we fit a newly developed computational model to the data to glean a more mechanistic understanding of how lesions affect the cognitive processes underlying choice. We found that participants with lesions to the vmPFC waited less overall, while participants with lesions to the dmPFC and anterior insula were specifically impaired at calibrating their level of persistence to the environment. These behavioral effects were accounted for by systematic differences in parameter estimates from a computational model of task performance: while the vmPFC group showed reduced initial willingness to wait, lesions to the dmPFC/anterior insula were associated with slower learning from negative feedback. These findings corroborate the notion that failures of persistence can be driven by sophisticated cost-benefit analyses rather than lapses in self-control. They also support the functional specialization of different parts of the prefrontal cortex in service of voluntary persistence.
The similarities in microbial and chemical patterns of fermentation in two open environments were promoted by using 150-year-old Nukadoko as starters

Background Nukadoko, a fermented rice bran employed in traditional Japanese pickling, uses lactic acid bacteria to ferment vegetables. Here, we report the microbiome and chemical data of a mixture of matured 150-year-old nukadoko and commercially available rice bran placed in two open environments. Results Across the two environments, Loigolactobacillus was identified as the dominant microbial genera in the later stages of fermentation in nukadoko. The period of increase in the relative abundance of Loigolactobacillus correlated with a decrease in pH and ORP values. While the two environments showed a difference in the rate of change in microbial diversity, they shared the common process through which Loigolactobacillus outcompeted adventitious bacteria in nukadoko, as indicated by the alpha and beta diversity index. Conclusion Thus, the similarities in microbial and chemical data across two open environments during fermentation using starters indicate that starters contribute to the stability of fermentation in open environments.
Identification of avaC from infant gut microbial isolates that convert 5AVA to 2-piperidone with high efficiency

2-Piperidone has been identified as a biomarker for various human diseases, but its origin in vivo remains poorly understood. Furthermore, 2-piperidone is a crucial industrial raw material, and thus the discovery of novel efficient 2-piperidone synthases may have an important application in its biosynthesis. In this study, we aimed to identify the bacterial source and metabolic mechanism of 2-piperidone from a previously generated infant gut microbial culture collection. We found that 2-piperidone could be produced from 5AVA by four bacterial strains, including Collinsella aerofaciens LFYP39, Collinsella intestinalis LFYP54, Clostridium bolteae LFYP116, and Clostridium hathewayi LFYP18 from 51 bacterial strains. Furthermore, 2-piperidone could be synthesized from proline by cross-feeding between Clostridium difficile LFYP43 and Collinsella intestinalis LFYP54. We employed a gain-of-function library to identify the gene avaC (5-aminovaleric acid cyclase) which can catalyze 5AVA to produce 2-piperidone in C. intestinalis LFYP54. Homologous genes of avaC were also identified and validated in the other three bacterial strains. Gene avaC exhibits a broad distribution in the natural environmental bacteria. Overall, our research identified the gut bacterial strains and the genes that are responsible for the production of 2-piperidone. This study may facilitate the prediction of 2-piperidone-related disease risks through the analysis of gut bacterial community composition, and enhance the efficiency of 2-piperidone in its biosynthesis in industry. Importance In recent decades, it has become a consensus that gut microbiota can affect host health through producing metabolites. However, the complexity of gut flora composition makes finding the sources of the particular metabolite challenging. 2-piperidone is a biomarker for various diseases and a highly valued raw material for nylons. In this study, we identified the gut bacterial strains that can transform 5AVA to 2-piperidone. A novel 2-piperidone synthase gene, avaC, was also identified and characterized. These findings provided new insights into the potential connection between 2-piperidone related diseases and the intestinal flora, as well as a possible novel approach for more efficient biosynthesis of 2-piperidone in industry.
Flow-field inference from neural data using deep recurrent networks

Computations involved in processes such as decision-making, working memory, and motor control are thought to emerge from the dynamics governing the collective activity of neurons in large populations. But the estimation of these dynamics remains a significant challenge. Here we introduce Flow-field Inference from Neural Data using deep Recurrent networks (FINDR), an unsupervised deep learning method that can infer low-dimensional nonlinear stochastic dynamics underlying neural population activity. Using population spike train data from frontal brain regions of rats performing an auditory decision-making task, we demonstrate that FINDR outperforms existing methods in capturing the heterogeneous responses of individual neurons. We further show that FINDR can discover interpretable low-dimensional dynamics when it is trained to disentangle task-relevant and irrelevant components of the neural population activity. Importantly, the low-dimensional nature of the learned dynamics allows for explicit visualization of flow fields and attractor structures. We suggest FINDR as a powerful method for revealing the low-dimensional task-relevant dynamics of neural populations and their associated computations.
De novo lipogenesis pathway is a vulnerability in IDH1 mutant glioma

Histone deacetylases (HDACs) have a wide range of targets and can rewire both the chromatin and lipidome of cancer cells. In this study, we show that valproic acid (VPA), a brain penetrant anti-epileptic and histone deacetylase inhibitor, inhibits the growth of IDH1 mutant tumors in vivo and in vitro, with at least some selectivity over IDH1 wild type tumors. Surprisingly, genes upregulated by VPA showed no change in chromatin accessibility at the promoter, but there was a correlation between VPA downregulated genes and diminished promoter chromatin accessibility. VPA inhibited the transcription of lipogenic genes and these lipogenic genes showed significant decrease in promoter chromatin accessibility only in the IDH1 MT glioma cell lines tested. VPA targeted a key lipogenic gene, fatty acid synthase (FASN), via inhibition of the mTOR pathway and both VPA and a selective FASN inhibitor TVB-2640 rewired the lipidome and promoted apoptosis in an IDH1 MT but not in an IDH1 WT glioma cell line. We further find HDACs are involved in the regulation of lipogenic genes and in particular HDAC6 is important for regulation of FASN in IDH1 MT glioma. Finally, we show that FASN knockdown alone and VPA in combination with FASN knockdown significantly improved the survival of mice in a IDH1 MT primary orthotopic xenograft model in vivo. We conclude that targeting fatty acid metabolism through HDAC inhibition and/or FASN inhibition may be a novel therapeutic option in IDH1 mutant gliomas.
The meditative brain: State and trait changes in harmonic complexity for long-term mindfulness meditators

Meditation is an ancient practice that is shown to yield benefits for cognition, emotion regulation and human flourishing. In the last two decades, there has been a surge of interest in extracting the neural correlates of meditation, in particular of mindfulness meditation. Yet, these efforts have been mostly limited to the analysis of certain regions or networks of interest and a clear understanding of meditation-induced changes in the whole-brain dynamics has been lacking. Here, we investigate meditation-induced changes in brain dynamics using a novel connectome-specific harmonic decomposition method. Specifically, utilising the connectome harmonics as brain states - elementary building blocks of complex brain dynamics - we study the immediate (state) and long-term (trait) effects of mindfulness meditation in terms of the energy, power and complexity of the repertoire of these harmonic brain states. Our results reveal increased power, energy and complexity of the connectome harmonic repertoire and demonstrate that meditation alters brain dynamics in a frequency selective manner. Remarkably, the frequency-specific alterations observed in meditation are reversed in resting state in group-wise comparison revealing for the first time the long-term (trait) changes induced by meditation. These findings also provide evidence for the entropic brain hypothesis in meditation and provide a novel understanding of state and trait changes in brain dynamics induced by mindfulness meditation revealing the unique connectome harmonic signatures of the meditative brain.
The sleep quality- and myopia-linked PDE11A-Y727C variant impacts neural physiology by reducing catalytic activity and altering subcellular compartmentalization of the enzyme

Recently, a Y727C variant in the dual-specific 3’,5’-cyclic nucleotide phosphodiesterase 11A (PDE11A-Y727C) was linked to increased sleep quality and reduced myopia risk in humans. Given the well-established role that the PDE11 substrates cAMP and cGMP play in eye physiology and sleep, we determined if 1) PDE11A protein is expressed in the retina or other eye segments in mouse, 2) PDE11A-Y7272C affects catalytic activity and/or subcellular compartmentalization more so than the nearby suicide-associated PDE11A-M878V variant, and 3) Pde11a deletion alters eye growth or sleep quality in male and female mice. Western blots show distinct protein expression of PDE11A4, but not PDE11A1-3, in eyes of Pde11a WT—but not KO mice—that vary by eye segment and age. In HT22 and COS-1 cells, PDE11A4-Y727C reduces PDE11A4 catalytic activity far more than PDE11A4-M878V, with both variants reducing PDE11A4-cAMP more so than PDE11A4-cGMP activity. Despite this, Pde11a deletion does not alter age-related changes in retinal or lens thickness, axial length, nor vitreous or anterior chamber depth. Further, Pde11a deletion only minimally changes refractive error and sleep quality. That said, both variants also dramatically alter the subcellular compartmentalization of human and mouse PDE11A4, an effect occurring independently of dephosphorylating PDE11A4-S117/S124 or phosphorylating PDE11A4-S162. Rather, re-compartmentalization of PDE11A4-Y727C is due to the loss of the tyrosine changing how PDE11A4 is packaged/repackaged via the trans-Golgi network. Therefore, the protective impact of the Y727C variant may reflect a gain-of-function (e.g., PDE11A4 displacing another PDE) that warrants further investigation in the context of reversing/preventing sleep disturbances or myopia.