Miracle-dz/UniFAR-FTUs huggingface dataset:
A neural circuit for competing approach and avoidance underlying prey capture

Predators must frequently balance competing approach and avoidance behaviors elicited by a moving and potentially dangerous prey. Several brain circuits supporting predation have recently been localized. However, the mechanisms by which these circuits balance the conflict between approach and avoidance responses remain unknown. Laboratory mice initially show alternating approach and avoidance responses toward cockroaches, a natural prey, but with repeated exposure become avid hunters. Here we used in vivo neural activity recording and cell-type specific manipulations in hunting mice to identify neurons in the lateral hypothalamus and periaqueductal grey that encode and control predatory approach and avoidance. We found a subset of GABAergic neurons in lateral hypothalamus that specifically encoded hunting behaviors and whose stimulation triggered predation, but not feeding. This population projects to the periaqueductal grey and stimulation of these projections promoted predation. Neurons in periaqueductal grey encoded both approach and avoidance behaviors, but only initially when the mouse showed high levels of fear of the prey. Our findings allow us to propose that GABAergic neurons in lateral hypothalamus facilitate predation in part by suppressing defensive responses to prey encoded in the periaqueductal grey. Our findings reveal a neural circuit mechanism for controlling the balance between conflicting approach and avoidance behaviors elicited by the same stimulus.
The Bromodomains of the mammalian SWI/SNF (mSWI/SNF) ATPases Brahma (BRM) and Brahma Related Gene 1 (BRG1) promote chromatin interaction and are critical for skeletal muscle differentiation

Skeletal muscle differentiation induces changes in the epigenome of myoblasts as they proceed towards a myogenic phenotype. mSWI/SNF chromatin remodeling enzymes coordinate with lineage-determining transcription factors and are key regulators of differentiation. Three mSWI/SNF proteins, the mutually exclusive ATPases, BRG1 and BRM, and the BAF180 protein (Polybromo1, PBRM1) contain bromodomains belonging to the same structural subfamily. Bromodomains bind to acetylated lysines on histone N-terminal tails and on other proteins. Pharmacological inhibition of mSWI/SNF bromodomain function using the selective inhibitor PFI-3 reduced differentiation, decreased expression of myogenic genes, and increased the expression of cell cycle-related genes, and the number of cells that remained in the cell cycle. Knockdown of BAF180 had no effect on differentiation, suggesting that only the BRG1 and BRM bromodomains contributed to differentiation. Comparison with existing gene expression data from myoblasts subjected to knockdown of BRG1 or BRM showed that bromodomain function was required for a subset of BRG1- and BRM-dependent gene expression. ChIP analysis revealed decreased BRG1 and BRM binding to target gene promoters, indicating that the BRG1 and BRM bromodomains promote chromatin binding. Thus mSWI/SNF ATPase bromodomains contribute to cell cycle exit, to skeletal muscle-specific gene expression, and to stable promoter binding by the mSWI/SNF ATPases.
Response of gut microbiota to feed-borne bacteria depends on fish growth rate: a snapshot survey of farmed juvenile Takifugu obscurus

Understanding the ecological processes in controlling the assemblage of gut microbiota becomes an essential prerequisite for a more sustainable aquaculture. Here we used 16S rRNA amplicon sequencing to characterize the hindgut microbiota from cultured obscure puffer Takifugu obscurus. The gut microbiota is featured with lower alpha-diversity, greater beta-dispersion and higher average 16S rRNA copy numbers comparing to water and sediment, but far less so to feed. SourceTracker predicted a notable source signature from feed in gut microbiota. Furthermore, effect of varying degrees of feed-associated bacteria on compositional, functional and phylogenetic diversity of gut microbiota were revealed. Coincidently, considerable increase of species richness and feed source proportions both were observed in slow growth fugu, implying a reduced stability in gut microbiota upon bacterial disturbance from feed. Moreover, quantitative ecological analytic framework was applied and the ecological processes underlying such community shift were determined. In the context of lower degree of feed disturbance, homogeneous selection and dispersal limitation largely contribute to the community stability and partial variations among hosts. Whilst with the degree of feed disturbance increased, variable selection leads to an augmented interaction within gut microbiota, entailing community unstability and shift. Altogether, our findings illustrated a clear diversity-function relationships in fugu gut microbiota, and it has implicated in a strong correlation between feed-borne bacteria and host growth rate. These results provide a new insight into aquaculture of fugu and other economically important fishes, as well as a better understanding of host-microbe interactions in the vertebrate gastrointestinal tract. IMPORTANCE Environmental bacteria has a great impact on fish gut microbiota, yet little is known as to where fish acquire their gut symbionts, and how gut microbiota response to environmental bacteria. Through the integrative analysis by community profiling and source tracking, we show that feed-associated bacteria can impose a strong disturbance upon fugu gut microbiota. As a result, marked alterations in the composition and function of gut microbiota in slow growth fugu were observed, which is potentially correlated with the host physiological condition such as gastric evacuation rate. Our findings emphasized the intricate linkage between feed and gut microbiota, and highlighted the importance of resolving the feed source signal before the conclusion of comparative analysis of microbiota can be drawn. Our results provide a deeper insight into aquaculture of fugu and other economically important fishes, and have further implications for an improved understanding of host-microbe interactions in the vertebrate gastrointestinal tract.
Phenotype prediction from genome-wide genotyping data: a crowdsourcing experiment

Background The increasing statistical power of genome-wide association studies is fostering the development of precision medicine through genomic predictions of complex traits. Nevertheless, it has been shown that the results remain relatively modest. A reason might be the nature of the methods typically used to construct genomic predictions. Recent machine learning techniques have properties that could help to capture the architecture of complex traits better and improve genomic prediction accuracy. Methods We relied on crowd-sourcing to efficiently compare multiple genomic prediction methods. This represents an innovative approach in the genomic field because of the privacy concerns linked to human genetic data. There are two crowd-sourcing elements building our study. First, we constructed a dataset from openSNP (opensnp.org), an open repository where people voluntarily share their genotyping data and phenotypic information in an effort to participate in open science. To leverage this resource we release the ‘openSNP Cohort Maker’, a tool that builds a homogeneous and up-to-date cohort based on the data available on opensnp.org. Second, we organized an open online challenge on the CrowdAI platform (crowdai.org) aiming at predicting height from genome-wide genotyping data. Results The ‘openSNP Height Prediction’ challenge lasted for three months. A total of 138 challengers contributed to 1275 submissions. The winner computed a polygenic risk score using the publicly available summary statistics of the GIANT study to achieve the best result (r2 = 0.53 versus r2 = 0.49 for the second-best). Conclusion We report here the first crowd-sourced challenge on publicly available genome-wide genotyping data. We also deliver the ‘openSNP Cohort Maker’ that will allow people to make use of the data available on opensnp.org.
Dynamic Hormone Control of Stress and Fertility

Neuroendocrine axes display a remarkable diversity of dynamic signalling processes relaying information between the brain, endocrine glands, and peripheral target tissues. These dynamic processes include oscillations, elastic responses to perturbations, and plastic long term changes observed from the cellular to the systems level. While small transient dynamic changes can be considered physiological, larger and longer disruptions are common in pathological scenarios involving more than one neuroendocrine axes, suggesting that a robust control of hormone dynamics would require the coordination of multiple neuroendocrine clocks. The idea of apparently different axes being in fact exquisitely intertwined through neuroendocrine signals can be investigated in the regulation of stress and fertility. The stress response and the reproductive cycle are controlled by the Hypothalamic-Pituitary-Adrenal (HPA) axis and the Hypothalamic-Pituitary-Gonadal (HPG) axis, respectively. Despite the evidence surrounding the effects of stress on fertility, as well as of the reproductive cycle on stress hormone dynamics, there is a limited understanding on how perturbations in one neuroendocrine axis propagate to the other. We hypothesize that the links between stress and fertility can be better understood by considering the HPA and HPG axes as coupled systems. In this manuscript, we investigate neuroendocrine rhythms associated to the stress response and reproduction by mathematically modelling the HPA and HPG axes as a network of interlocked oscillators. We postulate a network architecture based on physiological data and use the model to predict responses to stress perturbations under different hormonal contexts: normal physiological, gonadectomy, hormone replacement with estradiol or corticosterone (CORT), and high excess CORT (hiCORT) similar to hypercortisolism in humans. We validate our model predictions against experiments in rodents, and show how the dynamic responses of these endocrine axes are consistent with our postulated network architecture. Importantly, our model also predicts the conditions that ensure robustness of fertility to stress perturbations, and how chronodisruptions in glucocorticoid hormones can affect the reproductive axis’ ability to withstand stress. This insight is key to understand how chronodisruption leads to disease, and to design interventions to restore normal rhythmicity and health.
piRNA-mediated gene regulation and adaptation to sex-specific transposon expression in D. melanogaster male germline

Small non-coding piRNAs act as sequence-specific guides to repress complementary targets in Metazoa. Prior studies in Drosophila ovaries have demonstrated the function of piRNA pathway in transposon silencing and therefore genome defense. However, the ability of piRNA program to respond to different transposon landscape and the role of piRNAs in regulating host gene expression remain poorly understood. Here, we comprehensively analyzed piRNA expression and defined the repertoire of their targets in Drosophila melanogaster testes. Comparison of piRNA programs between sexes revealed sexual dimorphism in piRNA programs that parallel sex-specific transposon expression. Using a novel bioinformatic pipeline, we identified new piRNA clusters and established complex satellites as dual-strand piRNA clusters. While sharing most piRNA clusters, two sexes employ them differentially to combat sex-specific transposon landscape. We found several host genes targeted by piRNAs in testis, including CG12717/pita, a SUMO protease gene. piRNAs encoded on Y chromosome silence pita, but not its paralog, to exert sex- and paralog-specific gene regulation. Interestingly, pita is targeted by endogenous siRNAs in a sibling species, Drosophila mauritiana, suggesting distinct but related silencing strategies invented in recent evolution to regulate a conserved protein-encoding gene.
ENERGY OPTIMIZATION DURING WALKING CAN BE A PRIMARILY IMPLICIT PROCESS

Gait adaptations, in response to novel environments, devices or changes to the body, can be driven by the continuous optimization of energy expenditure. However, whether energy optimization is primarily an implicit process—occurring automatically and with minimal cognitive attention—or an explicit process—occurring as a result of a conscious, attention-demanding, strategy—remains unclear. Here, we use a dual-task paradigm to test whether energy optimization during walking is primarily an implicit or explicit process. To create our primary energy optimization task, we used lower-limb exoskeletons to shift people’s energetically optimal step frequency to frequencies lower than normally preferred. Our secondary task, designed to draw explicit attention from the optimization task, was an auditory tone discrimination task. We found that adding this secondary task did not disrupt energy optimization during walking; participants in our dual-task experiment adapted their step frequency toward the optima by an amount similar to participants in our previous single-task experiment. We also found that performance on the tone discrimination task did not worsen when participants were optimizing for energetic cost; accuracy scores and reaction times remained unchanged when the exoskeleton altered the energy optimal gaits. Survey responses suggest that dual-task participants were largely unaware of the changes they made to their gait to optimize energy, whereas single-task participants were more aware of their gait changes yet did not leverage this explicit awareness to improve gait optimization. Collectively, our results suggest that energy optimization is primarily an implicit process, allowing attentional resources to be directed toward other cognitive and motor objectives during walking. Summary statement People can adapt to energy optimal walking patterns without being consciously aware they are doing so. This allows people to discover energetically efficient gaits while preserving attentional resources for other tasks.
A Novel Lossless Encoding Algorithm for Data Compression - Genomics Data as an Exemplar

Data compression is a challenging and increasingly important problem. As the amount of data generated daily continues to increase, efficient transmission and storage has never been more critical. In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and associated characteristics. The proposed algorithm follows a divide-and-conquer approach by scanning the whole genome, classifying subsequences based on similarity patterns, and binning similar subsequences together. The data are then compressed in each bin independently. This approach is different than the currently known approaches: entropy, dictionary, predictive, or transform based methods. Proof-of-concept performance was evaluated using a benchmark dataset with seventeen genomes ranging in size from kilobytes to gigabytes. The results showed considerable improvement in the compression of each genome, preserving several megabytes compared with state-of-art tools. Moreover, the algorithm can be applied to the compression of other data types include mainly text, numbers, images, audio, and video which are being generated daily and unprecedentedly in massive volumes.
Genomic diversity generated by a transposable element burst in a rice recombinant inbred population

Significance Transposable elements (TEs) represent the largest component of the genomes of higher eukaryotes. Among this component are some TEs that have attained very high copy numbers, with hundreds, even thousands, of elements. By documenting the spread of mPing elements throughout the genomes of a rice population, we demonstrate that such bursts of amplification generate functionally relevant genomic variations upon which selection can act. Specifically, continued mPing amplification increases the number of tightly linked elements that, in turn, increases the frequency of structural variations that appear to be derived from aberrant transposition events. The significance of this finding is that it provides a TE-mediated mechanism that may generate much of the structural variation represented by pan-genomes in plants and other organisms. Genomes of all characterized higher eukaryotes harbor examples of transposable element (TE) bursts—the rapid amplification of TE copies throughout a genome. Despite their prevalence, understanding how bursts diversify genomes requires the characterization of actively transposing TEs before insertion sites and structural rearrangements have been obscured by selection acting over evolutionary time. In this study, rice recombinant inbred lines (RILs), generated by crossing a bursting accession and the reference Nipponbare accession, were exploited to characterize the spread of the very active Ping/mPing family through a small population and the resulting impact on genome diversity. Comparative sequence analysis of 272 individuals led to the identification of over 14,000 new insertions of the mPing miniature inverted-repeat transposable element (MITE), with no evidence for silencing of the transposase-encoding Ping element. In addition to new insertions, Ping-encoded transposase was found to preferentially catalyze the excision of mPing loci tightly linked to a second mPing insertion. Similarly, structural variations, including deletion of rice exons or regulatory regions, were enriched for those with break points at one or both ends of linked mPing elements. Taken together, these results indicate that structural variations are generated during a TE burst as transposase catalyzes both the high copy numbers needed to distribute linked elements throughout the genome and the DNA cuts at the TE ends known to dramatically increase the frequency of recombination.
Group I p21-activated kinases in leukemia cell adhesion to fibronectin

P21-activated kinases (PAK) regulate many processes associated with cytoskeleton dynamics, including cell adhesion, migration, and apoptosis. PAK function is frequently altered in cancer, and PAK were proposed as therapy targets both in solid tumors and in hematological malignancies. However, current knowledge about PAK function in cell adhesion is mainly based on adherent cell models. Moreover, existing functional differences among the individual PAK family members are unsufficiently characterized. We measured expression of PAK group I members in leukemia cell lines and in primary leukemia cells, both on protein and mRNA levels. In functional assays, we analyzed the effect of two PAK inhibitors with different mechanisms of action, IPA-3 and FRAX597. Changes in cell interaction with fibronectin were monitored through impedance measurement and by interference reflection microscopy. Cytotoxic effects of inhibitors were assessed by Annexin V/propidium iodide test. PAK intracellular localization was analyzed by confocal microscopy. PAK2 transcript was dominant in cell lines, whereas primary leukemia cells also expressed comparable amount of PAK1, which was detected as two transcription isoforms: PAK1-full and PAK1Δ15. PAK1Δ15 and PAK2 transcript levels correlated with surface density of integrins β1 and αVβ3. PAK1-full, but not PAK2, was present in membrane protrusions. The inhibitors had partly opposed effects: IPA-3, which prevents PAK activation, induced cell contraction in semi-adherent HEL cells only. FRAX597, which inhibits PAK kinase activity, increased cell-surface contact area in all leukemia cells. Both inhibitors reduced the stability of cell attachment and induced cell death. Although many cells accumulated high FRAX597 amounts, low doses were sufficient to kill sensitive cells. FRAX597-induced cell death was fast in the MV4-11 cell line and in primary AML cells. Although PAK group I seem to be essential for leukemia cell adhesion and survival, and might thus serve as therapy targets, many PAK functions still remain to be attributed to individual isoforms and to their functional domains.
Genome-wide methylation prediction of biological age using reproducing kernel Hilbert spaces and Bayesian ridge regressions

The use of DNA methylation signatures to predict chronological age and the aging rate is of interest in many fields, including disease prevention and treatment, forensics, and anti-aging medicine. Although a large number of methylation markers have been found to be significantly associated with age, most age-prediction methods use a small number of markers selected based on either previously published studies or datasets containing methylation information. Here, we implemented reproducing kernel Hilbert spaces (RKHS) regression and ridge regression model in a Bayesian framework that utilized phenotypic and methylation profiles simultaneously to predict chronological age. We used over 450,000 CpG sites from the whole blood of a large cohort of 4,409 human individuals with a range of 10-101 years of age. Models were fitted using adjusted and un-adjusted methylation measurements for cell heterogeneity. Non-adjusted methylation scores delivered a significantly higher prediction accuracy than adjusted methylation data, with a correlation between age and predicted age of 0.98 and a root-mean-square error (RMSE) of 3.54 years in non-adjusted data, 0.90 (correlation) and 7.16 (RMSE) years in adjusted data. Reducing the number of predictors through subset selection improved predictive power with a correlation of 0.98 and an RMSE of 2.98 years in the RKHS model. We found distinct global methylation patterns, with significant hypermethylation in CpG islands and hypomethylation in other CpG types including CpG shore, shelf, and open sea (p < 5e-06). Epigenetic drift seemed to be a widespread phenomenon as more than 97% of the age-associated methylation sites had heteroscedasticity. Apparent methylomic aging rate (AMAR) had a sex-specific pattern, with an increase in AMAR in females with age compared to males.
The case for using Mapped Exonic Non-Duplicate (MEND) read counts in RNA-Seq experiments: examples from pediatric cancer datasets

Background The accuracy of gene expression as measured by RNA sequencing (RNA-Seq) is dependent on the amount of sequencing performed. However, some types of reads are not informative for determining this accuracy. Unmapped and non-exonic reads do not contribute to gene expression quantification. Duplicate reads can be the product of high gene expression or technical errors. Findings We surveyed bulk RNA-Seq datasets from 2179 tumors in 48 cohorts to determine the fractions of uninformative reads. Total sequence depth was 0.2-668 million reads (median (med.) 61 million; interquartile range (IQR) 53 million). Unmapped reads constitute 1-77% of all reads (med. 3%; IQR 3%); duplicate reads constitute 3-100% of mapped reads (med. 27%; IQR 30%); and non-exonic reads constitute 4-97% of mapped, non-duplicate reads (med. 25%; IQR 21%). Informative reads--Mapped, Exonic, Non-duplicate (MEND) reads--constitute 0-79% of total reads (med. 50%; IQR 31%). Further, we find that MEND read counts have a 0.22 Pearson correlation to the number of genes expressed above 1 Transcript Per Million, while total reads have a correlation of −0.05. Conclusions Since the fraction of uninformative reads vary, we propose using only definitively informative reads, MEND reads, for the purposes of asserting the accuracy of gene expression measured in a bulk RNA-Seq experiment. We provide a Docker image containing 1) the existing required tools (RSeQC, sambamba and samblaster) and 2) a custom script. We recommend that all results, sensitivity studies and depth recommendations use MEND units.