Miracle-dz/UniFAR-FTUs huggingface dataset:
Ligand effects on phase separation of multivalent macromolecules

Significance Phase transitions of multivalent macromolecules known as scaffolds help drive the formation of functional biomolecular condensates in cells. The formation and dissolution of condensates is tightly regulated, as aberrant phase behavior is associated with disease. Here, we show that distinct types of ligands can exert control over the formation and dissolution of condensates by binding to distinct sites on scaffold molecules. We further show that the extent and direction of regulation can be inferred through direct measurements of how ligands impact scaffold phase boundaries. Our findings have broad implications for understanding and modeling ligand-mediated regulation of condensates in cells and for designing novel molecules that exert regulatory control over condensates. Biomolecular condensates enable spatial and temporal control over cellular processes by concentrating biomolecules into nonstoichiometric assemblies. Many condensates form via reversible phase transitions of condensate-specific multivalent macromolecules known as scaffolds. Phase transitions of scaffolds can be regulated by changing the concentrations of ligands, which are defined as nonscaffold molecules that bind to specific sites on scaffolds. Here, we use theory and computation to uncover rules that underlie ligand-mediated control over scaffold phase behavior. We use the stickers-and-spacers model wherein reversible noncovalent cross-links among stickers drive phase transitions of scaffolds, and spacers modulate the driving forces for phase transitions. We find that the modulatory effects of ligands are governed by the valence of ligands, whether they bind directly to stickers versus spacers, and the relative affinities of ligand–scaffold versus scaffold–scaffold interactions. In general, all ligands have a diluting effect on the concentration of scaffolds within condensates. Whereas monovalent ligands destabilize condensates, multivalent ligands can stabilize condensates by binding directly to spacers or destabilize condensates by binding directly to stickers. Bipartite ligands that bind to stickers and spacers can alter the structural organization of scaffold molecules within condensates even when they have a null effect on condensate stability. Our work highlights the importance of measuring dilute phase concentrations of scaffolds as a function of ligand concentration in cells. This can reveal whether ligands modulate scaffold phase behavior by enabling or suppressing phase separation at endogenous levels, thereby regulating the formation and dissolution of condensates in vivo.
Etude de l'activité antimicrobienne des souches de lactobacilles thermophiles utilisées dans l'industrie laitière.

Cette etude a ete inspiree des problemes rencontres dans l'industrie laitiere et illustre l'importance antimicrobienne des lactobacilles vis-avis des germes d'alteration. Dix souches de lactobacilles thermophiles appartenant aux especes : Lactobacillus acidophilus, Lactobacillus helveticus, Lactobacillus lactis, Lactobacillus bulgaricus) ont ete isolees sur milieu MRS a partir du lait cru et d'un ferment lactique commercial pour l'etude de leur activite antimicrobienne contre des germes Gram positifs et Gram (.)
Puffaligner: An Efficient and Accurate Aligner Based on the Pufferfish Index

Motivation Sequence alignment is one of the first steps in many modern genomic analyses, such as variant detection, transcript abundance estimation and metagenomic profiling. Unfortunately, it is often a computationally expensive procedure. As the quantity of data and wealth of different assays and applications continue to grow, the need for accurate and fast alignment tools persists. Results In this paper, we introduce PuffAligner, a fast, accurate and versatile aligner built on top of the Pufferfish index. PuffAligner is able to produce highly-sensitive alignments, similar to those of Bowtie2, but much more quickly. While exhibiting similar speed to the ultrafast STAR aligner, PuffAligner requires considerably less memory to construct its index and align reads. PuffAligner strikes a desirable balance with respect to the time, space, and accuracy tradeoffs made by different alignment tools, and provides a promising foundation on which to test new alignment ideas over large collections of sequences. Availability PuffAligner is a free and open-source software. It is implemented in C++14 and can be obtained from https://github.com/COMBINE-lab/pufferfish/tree/cigar-strings
Shedding light on the underlying characteristics of genomes using Kronecker model families of codon evolution

The mechanistic models of codon evolution rely on some simplistic assumptions in order to reduce the computational complexity of estimating the high number of parameters of the models. This paper is an attempt to investigate how much these simplistic assumptions are misleading when they violate the nature of the biological dataset in hand. We particularly focus on three simplistic assumptions made by most of the current mechanistic codon models including: 1) only single substitutions between nucleotides within codons in the codon transition rate matrix are allowed. 2) mutation is homogenous across nucleotides within a codon. 3) assuming HKY nucleotide model is good enough at the nucleotide level. For this purpose, we developed a framework of mechanistic codon models, each model in the framework hold or relax some of the mentioned simplifying assumptions. Holding or relaxing the three simplistic assumptions results in total to eight different mechanistic models in the framework. Through several experiments on biological datasets and simulations we show that the three simplistic assumptions are unrealistic for most of the biological datasets and relaxing these assumptions lead to accurate estimation of evolutionary parameters such as selection pressure.
Src-dependent NM2A tyrosine-phosphorylation regulates actomyosin dynamics

Non-muscle myosin 2A (NM2A) is a key cytoskeletal enzyme that along with actin assembles into actomyosin filaments inside cells. NM2A is fundamental in cellular processes requiring force generation such as cell adhesion, motility and cell division, and plays important functions in different stages of development and during the progression of viral and bacterial infections. We previously identified a novel tyrosine phosphorylation on residue 158 (pTyr158) in the motor domain of NM2A. This phosphorylation is dependent on Src kinase and is promoted by Listeria monocytogenes infection of epithelial cells, however its role is unknown. Here we show that Listeriolysin O (LLO), the pore-forming toxin (PFT) secreted by L. monocytogenes, is sufficient to trigger NM2A pTyr158 by activating Src, an upstream regulator of actomyosin remodeling. We further address the role of NM2A pTyr158 on the organization and dynamics of the actomyosin cytoskeleton and find that, by controlling the activation of the NM2A, the status of the pTyr158 alters cytoskeletal organization, dynamics of focal adhesions and cell motility. In vitro, we observe that non-phosphorylatable and phospho-mimetic versions of NM2A at Tyr158 display motor and ATPase activities similar to the wild-type NM2A, which indicates that the phenotype of these mutants in cells is independent of their ability to translocate actin filaments. Importantly, we find the regulation of this phosphorylation site to be of physiological relevance in Caenorhabditis elegans, in particular in response to intoxication by a PFT and to heat shock. We conclude that the control of the phosphorylation status at NM2A Tyr158 is a conserved trait that contributes to the regulation of actomyosin dynamics and the ability of cells to respond to bacterial infection. We propose Src-dependent NM2A pTyr158 as a novel layer of regulation of the actomyosin cytoskeleton.
Standing on elevated platform changes postural responses during arm movement

This study investigated the muscle activity during the preparatory (anticipatory postural adjustment, APA), execution (online postural adjustments, OPA), and compensatory (compensatory postural adjustment, CPA) phases during standing with eyes opened or closed on an elevated platform. Eight healthy young women stood in the upright position, with eyes opened or closed, and did as-fast-as-they-could shoulder flexions on the ground and on 1-m-height-portable-elevated-platform. The surface EMG of trunk (lumbar extensor, and rectus abdominis) and lower limb (rectus femoris, biceps femoris, tibialis anterior and gastrocnemius lateralis) muscles during this task were recorded (1 kHz sampling frequency) and compared during these three phases. Analysis of variance was applied to compare the effects of height (floor and elevated platform), vision (open and closed), and postural adjustment (APA, OPA and CPA) into the activity of each muscle. These muscles were more active during OPA (p<0.0001) and less active during APA. On the elevated platform, these postural muscles presented more activty during APA (p<0.001). During the most stable condition (on the ground with eyes opened), muscle activity during APA and OPA was negatively correlated, and not correlated between OPA and CPA. Our results suggest postural control adapts to sensory, motor, and cognitive conditions. Therefore, the increased demand for postural control, generated due to the height of the support base, provokes the need for greater flexibility of postural synergies and causes a change in muscle activity. Summary Statement We discuss how postural muscle activity behaves before and after a fast upper arms movement when someone stands on a elevated platform or on the ground.
NetCoMi: network construction and comparison for microbiome data in R

Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization, and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g. between healthy individuals and patients, identifying network differences between groups is often an integral secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analyzing, and comparing microbial association networks from high-throughput sequencing data. Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analyzing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa, or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMi’s wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from children’s rooms between samples from two study centers (Ulm and Munich). Availability A script with R code used for producing the examples shown in this manuscript are provided as Supplementary data. The NetCoMi package, together with a tutorial, is available at https://github.com/stefpeschel/NetCoMi.
Emotion Profile Refinery for Speech Emotion Classification

Human emotions are inherently ambiguous and impure. When designing systems to anticipate human emotions based on speech, the lack of emotional purity must be considered. However, most of the current methods for speech emotion classification rest on the consensus, e.g., one single hard label for an utterance. This labeling principle imposes challenges for system performance considering emotional impurity. In this paper, we recommend the use of emotional profiles (EPs), which provides a time series of segment-level soft labels to capture the subtle blends of emotional cues present across a specific speech utterance. We further propose the emotion profile refinery (EPR), an iterative procedure to update EPs. The EPR method produces soft, dynamically-generated, multiple probabilistic class labels during successive stages of refinement, which results in significant improvements in the model accuracy. Experiments on three well-known emotion corpora show noticeable gain using the proposed method.
P2X receptors in Aplysia californica: Chemosensory systems, bio-energetic and development

ATP and its ionotropic P2X receptors are components of one of the most ancient signaling systems. However, little is known about the distribution and function of purinergic transmission in invertebrates. Here, we cloned, expressed, and pharmacologically characterized P2X receptors in the sea slug Aplysia californica – a prominent model in cellular and system neuroscience. We showed that ATP and P2X receptors are essential signaling components within the unique bioenergetic center located in the CNS of Aplysia, also known as the cerebral F-cluster of insulin-containing neurons. Functional P2X receptors were successfully expressed in Xenopus oocytes to characterize their ATP-dependence (EC50=306μM), two-phased kinetics, ion selectivity (Na+-dependence), sensitivity to the ATP analog Bz-ATP (~20% compare to ATP) and antagonists (with PPADS as a more potent inhibitor compared to suramin). Next, using RNA-seq, we characterized the expression of P2X receptors across more than a dozen Aplysia peripheral tissues and developmental stages. We showed that P2X receptors are predominantly expressed in chemosensory structures and during early cleavage stages. The localization and pharmacology of P2X receptors in Aplysia highlight the evolutionary conservation of bioenergetic sensors and chemosensory purinergic transmission across animals. This study also provides a foundation to decipher homeostatic mechanisms in development and neuroendocrine systems. Graphical Abstract We show that ATP and its ligand-gated P2X receptors are essential signaling components within both the chemosensory systems and the unique bioenergetic center, present in the CNS of the sea slug Aplysia californica – a prominent model in neuroscience. Expression and pharmacology of P2X receptors in Aplysia confirms the preservation of evolutionary conserved bioenergetic sensors across animals and provide new tools to decipher homeostatic mechanisms in neuro-endocrine systems in general.
Enhanced synaptic properties of the prefrontal cortex and hippocampus after learning a spatial working memory task in adult male mice

Working memory (WM) is the ability to hold on-line and manipulate information. The prefrontal cortex (PFC) is a key brain region involved in WM, while the hippocampus is also involved, particularly, in spatial WM. Although several studies have investigated the neuronal substrates of WM in trained animals, the effects and the mechanisms underlying learning WM tasks have not been explored. In our study, we investigated the effects of learning WM tasks in mice on the function of PFC and hippocampus, by training mice in the delayed alternation task for 9 days (adaptive group). This group was compared to naïve mice that stayed in their homecage (naïve) and mice trained in the alternation procedure only (non-adaptive). Following training, a cohort of mice (Experiment A) was tested in the left-right discrimination task and the reversal learning task, while another cohort (Experiment B) was tested in the attention set- shifting task (AST). The adaptive group performed significantly better in the reversal learning task (Experiment A) and AST (Experiment B), compared to non-adaptive and naïve groups. At the end of the behavioral experiments in Experiment A, field excitatory post-synaptic potential (fEPSP) recordings were performed in PFC and hippocampal brain slices. The adaptive group had enhanced the long-term potentiation (LTP) in the PFC, compared to the other groups. In the hippocampus, both the adaptive and the non-adaptive groups exhibited increased fEPSP compared to the naive group, but no differences in LTP. In Experiment B, the dendritic spine density was measured, which, in the PFC, was found increased in the adaptive group, compared to the non-adaptive and naive groups. In the hippocampus, there was an increase in mature dendritic spine density in the adaptive group, compared to the other two groups. Our results indicate a role for long-term potentiation and dendritic spine density in learning WM tasks. Significance statement Working memory (WM) allows for transient storage and manipulation of information and has a central role in cognition. While a great number of research studies have investigated the mechanisms underlying the ‘memory’ part of WM in well-trained animals, the mechanisms that underlie learning WM tasks are not known. Studies have indicated that learning a WM tasks alters and enhances neuronal firing during the delay period, suggesting that long-term plasticity mechanisms could be involved. Our results in this study suggest that learning a working memory task primarily increases long-term potentiation and dendritic spine density in the prefrontal cortex, providing evidence for a role of long-term plasticity processes in learning working memory tasks. Furthermore, learning working memory tasks enhances cognitive flexibility.
Multi-speaker Text-to-speech Synthesis Using Deep Gaussian Processes

Multi-speaker speech synthesis is a technique for modeling multiple speakers' voices with a single model. Although many approaches using deep neural networks (DNNs) have been proposed, DNNs are prone to overfitting when the amount of training data is limited. We propose a framework for multi-speaker speech synthesis using deep Gaussian processes (DGPs); a DGP is a deep architecture of Bayesian kernel regressions and thus robust to overfitting. In this framework, speaker information is fed to duration/acoustic models using speaker codes. We also examine the use of deep Gaussian process latent variable models (DGPLVMs). In this approach, the representation of each speaker is learned simultaneously with other model parameters, and therefore the similarity or dissimilarity of speakers is considered efficiently. We experimentally evaluated two situations to investigate the effectiveness of the proposed methods. In one situation, the amount of data from each speaker is balanced (speaker-balanced), and in the other, the data from certain speakers are limited (speaker-imbalanced). Subjective and objective evaluation results showed that both the DGP and DGPLVM synthesize multi-speaker speech more effective than a DNN in the speaker-balanced situation. We also found that the DGPLVM outperforms the DGP significantly in the speaker-imbalanced situation.
The Contributions of the Allopolyploid Parents of the Mesopolyploid Brassiceae are Evolutionarily Distinct but Functionally Compatible

The members of the tribe Brassiceae share an ancient whole genome triplication (WGT), and plants in this tribe display extraordinarily high within-species morphological diversity. One proposed model for the formation of these hexaploid Brassiceae is that they result from a “two-step” pair of hybridizations. However, direct evidence supporting this model of formation has been lacking; meanwhile, the evolutionary and functional constraints that drove evolution after the hexaploidy are even less understood. Here we report a new genome sequence of Crambe hispanica, a species sister to most sequenced Brassiceae. After adding this new genome to three others that are also descended from the ancient hexaploidy, we traced the history of gene loss after the WGT using a phylogenomic pipeline called POInT (the Polyploidy Orthology Inference Tool). This approach allowed us to confirm the two-step model of hexaploidy formation and to assign statistical confidence to our parental “subgenome” assignments for >90,000 individual genes. We show that each subgenome has a statistically distinguishable rate of homeolog losses. Moreover, our modeling allowed us to infer that there was a significant temporal gap between the two allopolyploidizations, with about one third of the total shared gene losses between the four analyzed Brassiceae species in the first two subgenomes prior to the arrival of the third subgenome. There is little indication of functional distinction between the three subgenomes: the individual subgenomes show no patterns of functional enrichment, no excess of shared protein-protein or metabolic interactions between their members, and no biases in their likelihood of having experienced a recent selective sweep. We propose a “mix and match” model of allopolyploidy, where subgenome origin drives homeolog loss propensities but where genes from different subgenomes function together without difficulty.
Cell-specific imputation of drug connectivity mapping with incomplete data

Drug repositioning allows expedited discovery of new applications for existing compounds, but re-screening vast compound libraries is often prohibitively expensive. “Connectivity mapping” is a process that links drugs to diseases by identifying compounds whose impact on expression in a collection of cells reverses the disease’s impact on expression in disease-relevant tissues. The LINCS project has expanded the universe of compounds and cells for which data are available, but even with this effort, many clinically useful combinations are missing. To evaluate the possibility of repurposing drugs despite missing data, we compared collaborative filtering using either neighborhood-based or SVD imputation methods to two naive approaches via cross-validation. Methods were evaluated for their ability to predict drug connectivity despite missing data. Predictions improved when cell type was taken into account. Neighborhood collaborative filtering was the most successful method, with the best improvements in non-immortalized primary cells. We also explored which classes of compounds are most and least reliant on cell type for accurate imputation. We conclude that even for cells in which drug responses have not been fully characterized, it is possible to identify unassayed drugs that reverse in those cells the expression signatures observed in disease.
CoRhythMo: A Computational Framework for Modeling Biobehavioral Rhythms from Mobile and Wearable Data Streams

This paper presents CoRhythMo, the first computational framework for modeling biobehavioral rhythms - the repeating cycles of physiological, psychological, social, and environmental events - from mobile and wearable data streams. The framework incorporates four main components: mobile data processing, rhythm discovery, rhythm modeling, and machine learning. We use a dataset of smartphone and Fitbit data collected from 138 college students over a semester to evaluate the framework’s ability to 1) model biobehavioral rhythms of students, 2) measure the stability of their rhythms over the course of the semester, 3) model differences between rhythms of students with different health status, and 4) predict the mental health status in students using the model of their biobehavioral rhythms. Our evaluation provides evidence for the feasibility of using CoRhythMo for modeling and discovering human rhythms and using them to assess and predict different life and health outcomes.