Miracle-dz/UniFAR-FTUs huggingface dataset:
Negative DNA supercoiling tunes protein-mediated looping

Protein-mediated DNA looping is fundamental to gene regulation. Such loops occur stochastically in purified systems, but additional proteins and/or DNA supercoiling might tune looping for definitive responses. In lac repressor-mediated looping, the probabilities of loops in individual molecules ranged 0-100, and individual molecules exhibited representative behavior only in observations lasting an hour or more. Titrating with HU protein progressively compacted the DNA and increased the average probability of looping without narrowing the 0-100 distribution. Increased negative supercoiling also raised the average looping probability for the ensemble of molecules as well, but individual molecules more closely resembled the average. Furthermore, in only twelve minutes of observation, well within the shortest doubling time of the bacterium, most molecules exhibited the looping probability of the ensemble. DNA supercoiling, an inherent feature of all genomes, appears to be a fundamental determinant of time-constrained, emergent behavior in otherwise random molecular activity.
Aerobic growth physiology of Saccharomyces cerevisiae on sucrose is strain-dependent

Present knowledge on the quantitative aerobic physiology of the yeast Saccharomyces cerevisiae during growth on sucrose as sole carbon and energy source is limited to either adapted cells or to the model laboratory strain CEN.PK113-7D. To broaden our understanding of this matter and open novel opportunities for sucrose-based biotechnological processes, we characterized three strains, with distinct backgrounds, during aerobic batch bioreactor cultivations. Our results reveal that sucrose metabolism in S. cerevisiae is a strain-specific trait. Each strain displayed a distinct extracellular hexose concentration and invertase activity profiles. Especially, the inferior maximum specific growth rate (0.21 h−1) of the CEN.PK113-7D strain, with respect to that of strains UFMG-CM-Y259 (0.37 h−1) and JP1 (0.32 h−1), could be associated to its low invertase activity (0.04 to 0.09 U mgDM−1). Moreover, comparative experiments with glucose or fructose alone, or in combination, suggest mixed mechanisms of sucrose utilization by the industrial strain JP1, and points out the remarkable ability of the wild isolate UFMG-CM-259 to grow faster on sucrose than on glucose in a well-controlled cultivation system. This work hints to a series of metabolic traits that can be exploited to increase sucrose catabolic rates and bioprocess efficiency.
Replicating Light-Off Startle Responses in Drosophila melanogaster

We present a highly reproducible method for investigating the startle flight responses of wild type Drosophila melanogaster to light-off stimuli, using the automated Zantiks MWP unit. The built-in, live video-tracking of the Zantiks unit measured distance travelled between frames for 24 flies after light-off stimuli, whilst providing video-recordings of each startle. Using light-off stimuli which elicited peak startling, we found evidence for habituation of the startle response after only a few consecutive trials. Distance travelled on startle trials was reduced when a prepulse stimulus of shorter duration was introduced before the light-off stimulus, providing behavioural evidence for prepulse inhibition (PPI). Deficits in habituation and PPI are linked to various psychiatric disorders and our method holds great potential for use alongside genetic and pharmacological manipulations. Here, we demonstrate the capability of this highly automated, high throughput technology to streamline behavioural research on Drosophila, using a replicable, controlled environment.
More comprehensive proprioceptive stimulation of the hand amplifies its cortical processing

Corticokinematic coherence (CKC) quantifies the phase coupling between limb kinematics and cortical neurophysiological signals reflecting proprioceptive feedback to the primary sensorimotor (SM1) cortex. We studied CKC to proprioceptive stimulation (i.e. movement-actuator-evoked movements) of right-hand digits (index, middle, ring and little) performed simultaneously or separately. CKC was computed between magnetoencephalography (MEG) and finger acceleration signals. The strongest CKC was obtained by stimulating the fingers simultaneously at fixed 3-Hz frequency, and can, therefore, be recommended as design for fast functional localization of the hand area in the primary sensorimotor (SM1) cortex using MEG. The peaks of CKC sources were concentrated in the hand region of the SM1 cortex, but did not follow consistent somatotopic order. This result suggests that spatial specificity of MEG is not sufficient to separate proprioceptive finger representations of the same hand adequately or that their representations are overlapping.
Modulating the voltage sensor of a cardiac potassium channel shows antiarrhythmic effects

Significance C28, a chemical compound identified by computational screening, selectively facilitates voltage-dependent activation of a cardiac potassium ion channel, IKs. This compound reverses drug-induced prolongation of the electric signals across the cardiac cell membrane known as action potentials (APs) but minimally affects the normal AP at the same dosage. This outcome supports a computational prediction that enhancing voltage-dependent activation of IKs could be a potential therapy for AP prolongation. This therapy would increase the safety and expand the therapeutic efficacy of many currently approved drugs that induce AP prolongation, which can trigger life-threatening cardiac arrhythmias. Cardiac arrhythmias are the most common cause of sudden cardiac death worldwide. Lengthening the ventricular action potential duration (APD), either congenitally or via pathologic or pharmacologic means, predisposes to a life-threatening ventricular arrhythmia, Torsade de Pointes. IKs (KCNQ1+KCNE1), a slowly activating K+ current, plays a role in action potential repolarization. In this study, we screened a chemical library in silico by docking compounds to the voltage-sensing domain (VSD) of the IKs channel. Here, we show that C28 specifically shifted IKs VSD activation in ventricle to more negative voltages and reversed the drug-induced lengthening of APD. At the same dosage, C28 did not cause significant changes of the normal APD in either ventricle or atrium. This study provides evidence in support of a computational prediction of IKs VSD activation as a potential therapeutic approach for all forms of APD prolongation. This outcome could expand the therapeutic efficacy of a myriad of currently approved drugs that may trigger arrhythmias.
A triple threat: the Parastagonospora nodorum SnTox267 effector exploits three distinct host genetic factors to cause disease in wheat

Parastagonospora nodorum is a fungal pathogen of wheat. As a necrotrophic specialist, it deploys a suite of effector proteins that target dominant host susceptibility genes to elicit programmed cell death (PCD). Nine effector – host susceptibility gene interactions have been reported in this pathosystem, presumed to be governed by unique pathogen effectors. This study presents the characterization of the SnTox267 necrotrophic effector that hijacks two separate host pathways to cause necrosis. An association mapping approach identified SnTox267 and the generation of gene-disrupted mutants and gain-of-function transformants confirmed its role in Snn2-, Snn6-, and Snn7-mediated necrosis. The Snn2 and Snn6 host susceptibility genes were complementary, and together they functioned cooperatively to elicit SnTox267-induced necrosis in the same light-dependent PCD pathway. Additionally, we showed that SnTox267 targeted Snn7, resulting in light-independent necrosis. Therefore, SnTox267 co-opts two distinct host pathways to elicit PCD. SnTox267 sequence comparison among a natural population of 197 North American P. nodorum isolates revealed 20 protein isoforms conferring variable levels of virulence, indicating continuing selection pressure on this gene. Protein isoform prevalence among discrete populations indicated that SnTox267 has likely evolved in response to local selection pressures and has diversified more rapidly in the Upper Midwest. Deletion of SnTox267 resulted in the upregulation of the unrelated effector genes SnToxA, SnTox1, and SnTox3, providing evidence for a complex genetic compensation mechanism. These results illustrate a novel evolutionary path by which a necrotrophic fungal pathogen uses a single proteinaceous effector to hijack two host pathways to induce cell death.
Odor Blocking Of Stress

Scents have been employed for millennia to allay fear and stress, but whether they do so is poorly understood. In response to fear and stress, hypothalamic corticotropin releasing hormone neurons (CRHNs) induce increases in blood stress hormones. Here, we find that certain structurally and perceptually dissimilar odorants can block mouse stress hormone responses to three potent stressors: physical restraint, predator odor, and male-male social confrontation. Both odorants activate GABAergic inhibitory neurons presynaptic to CRHNs in the hypothalamic ventromedial nucleus (VMH). Stimulation of those neurons inhibits restraint-induced activation of CRHNs and stress hormone increase, mimicking a blocking odorant. Conversely, their silencing prevents odorant blocking of both responses. Notably, we also observed odor blocking of stressor activation in neurons presynaptic to CRHNs in the bed nucleus of the stria terminalis (BNST). Together, these findings indicate that selected odorants can indeed block stress responses, and that odor blocking can occur via two routes: a direct route in which blocking odor signals directly inhibit CRHNs and an indirect route in which they inhibit stressor activation of neurons presynaptic to CRHNs and prevent them from transmitting stress signals to CRHNs.
SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data

Mass spectrometry (MS)-based phosphoproteomics enables the quantification of proteome-wide phosphorylation in cells and tissues. A major challenge in MS-based phosphoproteomics lies in identifying the substrates of kinases, as currently only a small fraction of substrates identified can be confidently linked with a known kinase. By leveraging large-scale phosphoproteomics data, machine learning has become an increasingly popular approach for computationally predicting substrates of kinases. However, the small number of high-quality experimentally validated kinase substrates (true positive) and the high data noise in many phosphoproteomics datasets together impact the performance of existing approaches. Here, we aim to develop advanced kinase-substrate prediction methods to address these challenges. Using a collection of seven large phosphoproteomics datasets, including six published datasets and a new muscle differentiation dataset, and both traditional and deep learning models, we first demonstrate that a ‘pseudo-positive’ learning strategy for alleviating small sample size is effective at improving model predictive performance. We next show that a data re-sampling based ensemble learning strategy is useful for improving model stability while further enhancing prediction. Lastly, we introduce an ensemble deep learning model (‘SnapKin’) incorporating the above two learning strategies into a ‘snapshot’ ensemble learning algorithm. We demonstrate that the SnapKin model achieves overall the best performance in kinase-substrate prediction. Together, we propose SnapKin as a promising approach for predicting substrates of kinases from large-scale phosphoproteomics data. SnapKin is freely available at https://github.com/PYangLab/SnapKin.
Targeted Drug Repurposing Against the SARS-CoV-2 E channel Identifies Blockers With in vitro Antiviral Activity

It is difficult to overstate the impact that COVID-19 had on humankind. The pandemic’s etiological agent, SARS-CoV-2, is a member of the Coronaviridae, and as such, is an enveloped virus with ion channels in its membrane. Therefore, in an attempt to provide an option to curb the viral spread, we searched for blockers of its E protein viro-porin. Using three bacteria-based assays, we identified eight compounds that exhibited activity after screening a library of ca. 3000 approved-for-human-use drugs. Reassuringly, analysis of viral replication in tissue culture indicated that most of the compounds could reduce infectivity to varying extents. In conclusion, targeting a particular channel in the virus for drug repurposing may increase our arsenal of treatment options to combat COVID-19 virulence. Significance Statement The goal of our study was to expand the treatment arsenal against COVID-19. To that end, we have decided to focus on drug therapy, and as a target - the E protein, an ion channel in the virus. Ion channels as a family are excellent drug targets, but viral channels have been underexploited for pharmaceutical point intervention. To hasten future regulatory requirements and focus the chemical search space, we screened a library of ca. 3000 approved-for-human-use drugs using three independent bacteria-based assays. Our results yielded eight compounds, which were subsequently tested for antiviral activity in tissue culture. Gratifyingly, most compounds were able to reduce viral replication, and as such, both validate our approach and potentially augment our anti-COVID tool kit.
Discovering differential genome sequence activity with interpretable and efficient deep learning

Discovering sequence features that differentially direct cells to alternate fates is key to understanding both cellular development and the consequences of disease related mutations. We introduce Expected Pattern Effect and Differential Expected Pattern Effect, two black-box methods that can interpret genome regulatory sequences for cell type-specific or condition specific patterns. We show that these methods identify relevant transcription factor motifs and spacings that are predictive of cell state-specific chromatin accessibility. Finally, we integrate these methods into framework that is readily accessible to non-experts and available for download as a binary or installed via PyPI or bioconda at https://cgs.csail. mit.edu/deepaccess-package/. Author Summary Within the genome are the instructions to build all the cell types that make up the human body. However, understanding these instructions and how and when these instructions go wrong in cancer or genetically inherited disease is an open problem. Deep neural networks provide powerful models to learn the relationship between DNA sequence and functional consequence across many different cell types, such as whether a particular stretch of DNA is accessible and genes in that region can be expressed or is inaccessible and therefore genes are inactive. Despite these advances, a major setback in deep learning is that it is challenging to understand what patterns of DNA sequences a deep learning model has learned to associate with a particular genomic function, whether these patterns are significant, and how to determine whether these patterns are specific to a particular cell type or are general “housekeeping” patterns that function across many cell types. We introduce Expected Pattern Effect and Differential Expected Pattern Effect, two methods which allow us to evaluate the significance of particular patterns of DNA sequence features on models trained to predict function across multiple cell types, and apply this to problems of transcription factor binding and DNA accessibility across multiple cell types.
The impact of invader number on whole community invasions in biomethane-producing communities

Microbes can invade as whole communities, but the ecology of whole community invasions are poorly understood. Here, we investigate how invader frequency affects the composition and function of invaded laboratory methanogenic communities. An invading community was equally successful at establishing itself in a resident community regardless of initial invader frequency, which varied between 0.01 and 10%. Invasion resulted in enhanced biogas production (to the level of the pure invading community), but only when invader frequency was 1% or greater. This inconsistency between invasion success and changes in function can be explained by a lower number of invading taxa (but not individuals) at lower initial invader frequencies, and an important functional role of the taxa that were absent. Our results highlight that whole community invasion ecology cannot simply be extrapolated from our understanding of single species invasions. Moreover, we show that methane production can be enhanced by invading poorly performing reactors with a better performing community at levels that may be practical in industrial settings.
Catching a Liar Through Facial Expression of Fear

The leakage theory in the field of deception detection predicted that liars could not repress the leaked felt emotions (e.g., the fear or delight); and people who were lying would feel fear (to be discovered), especially under the high-stake situations. Therefore, we assumed that the aim of revealing deceits could be reached via analyzing the facial expression of fear. Detecting and analyzing the subtle leaked fear facial expressions is a challenging task for laypeople. It is, however, a relatively easy job for computer vision and machine learning. To test the hypothesis, we analyzed video clips from a game show “The moment of truth” by using OpenFace (for outputting the Action Units of fear and face landmarks) and WEKA (for classifying the video clips in which the players was lying or telling the truth). The results showed that some algorithms could achieve an accuracy of greater than 80% merely using AUs of fear. Besides, the total durations of AU 20 of fear were found to be shorter under the lying condition than under the truth-telling condition. Further analysis found the cause why durations of fear were shorter was that the duration from peak to offset of AU20 under the lying condition was less than that under the truth-telling condition. The results also showed that the facial movements around the eyes were more asymmetrical while people telling lies. All the results suggested that there do exist facial clues to deception, and fear could be a cue for distinguishing liars from truth-tellers.
Binocular vs. monocular recovery experience differentially promote recovery from visual deficits in a mouse model of amblyopia

Altered visual experience during monocular deprivation (MD) profoundly changes in ocular dominance (OD) in the developing primary visual cortex (V1). MD-driven changes in OD are an experimental model of amblyopia, where early-life alterations in vision lead visual disruption in adulthood. Current treatments for amblyopia include patching of the dominant eye, and more recently-developed binocular therapies. However, the relative impact of monocular vs. binocular recovery experiences on recovery of function in V1 is not well understood. Using single-unit recording, we compared how binocular recovery [BR] or reverse occlusion [RO] of identical duration and content affects OD and visual response recovery in mouse binocular V1 after a period of MD. We also tested how BR and RO affected MD-driven alterations of parvalbumin expression, and visually-driven expression of cFos in parvalbumin-positive and negative neurons. Finally, we tested how BR and RO affected recovery of normal visual acuity for the two eyes in the context of visually-driven behavior. We find that BR is quantitatively superior with respect to normalization of V1 neurons’ OD, visually-driven cFos expression, and visual acuity for the two eyes. However, MD-driven changes in the firing rate and response properties of V1 principal neuron and fast-spiking interneuron populations do not recover fully after either BR or RO. Binocular matching of orientation preference also remains disrupted in V1 neurons after both forms of recovery experience. Thus BR and RO, analogs of differing treatment regimens for amblyopia, differentially impact various aspects of visual recovery in a mouse model for amblyopia. Significance Statement Amblyopia resulting from altered childhood eye function is a leading cause of lifelong vision loss. Treatment typically involves patching of the dominant eye (forcing monocular visual experience), and produces only partial recovery of vision. Using a well-established mouse model of amblyopia, we directly compared how two types of visual experiences influence recovery of visual function. Our findings suggest that binocular vs. monocular visual experience differentially effect restoration of normal visual responses in cortical neurons, visually-driven neuronal gene expression, and visual acuity. Understanding how the quality of recovery experience impacts visual system recovery in amblyopia should provide critical insights for clinical strategies for its treatment.
Development of a label-free microfluidic impedimetric immunoassay for anti-SPAG16 antibody

A recently found biomarker regarding multiple sclerosis, namely the anti-SPAG16 antibody (Ab), could be a potential way for early detection, prognosis and diagnosis of said autoimmune disease. Merging electrochemical analysis with a microfluidic system is a novel approach, which avoids the use of labelling steps as seen in traditional ELISA immunoassays. In this study, aluminium interdigitated electrodes on polystyrene-coated PET foils were implemented in a microfluidic flow cell to bind and detect SPAG16 Abs by impedimetric measurements. The coated PET foils showed a clear affinity for the fusion protein SPAG16-THIO and thioredoxin (THIO). Determining sensitivity and specificity of antibody-antigen binding using a microfluidic ELISA immunoassay has revealed the test to be unreliable by showing no linear pattern of a dilution series of the standard and producing skewed inconsistent results. The impedimetric analysis showed opposite results of what one would expect. The systems efficiency is in need to be revised and optimised before undergoing actual diagnostic tests. Further advancement could be done by reducing leakage, securing a more stable entrance for injection and circumventing the occurence of air bubbles in the wells. SAMENVATTING Multiple sclerosis (MS) is een auto-immune ziekte, gekenmerkt door inflammatie van het centraal zenuwstelsel en demyelinisatie van axonen. Een recent gevonden proteïne, namelijk het anti-SPAG16 antilichaam, is bewezen een biomerker te zijn voor MS. Met behulp van een elektrochemisch analytisch systeem gecombineerd met microfluidica zou men anti-SPAG16 antilichamen kunnen opsporen voor vroegtijdige detectie, prognose en diagnose. Het grote voordeel tegenover traditionele ELISA testen is het elimineren van labelling stappen waardoor de immunoassay goedkoper en gebruiksvriendelijk wordt. In deze studie werden overlappende aluminium elektrodes op polystyreen-gecoate PET-plaatjes geïmplementeerd in een microfluidische flow cell voor binding en detectie van anti-SPAG16 antilichamen door middel van impedimetrische metingen. De gecoate PET-plaatjes vertonen een duidelijke affiniteit voor 0,1 μg/ml SPAG16 en THIO. Het bepalen van sensitiviteit en specificiteit van antilichaam-antigen binding, gebruikmakende van een ELISA immunoassays, gaf aan dat de test onbetrouwbaar is doordat geen duidelijk patroon voor standaardoplossingen zichtbaar was en resultaten vertekend en inconsistent bleken te zijn. De impedimetrische analyse vertoonde tevens onbetrouwbare resultaten, waarbij een omgekeerd effect werd geobserveerd van wat er in theorie zou moeten gebeuren. De efficiëntie van het systeem moet herzien en geoptimaliseerd worden voordat men het voor diagnostische testen kan gebruiken. Verdere vorderingen zouden gerealiseerd kunnen worden door lekkages te reduceren, steviger bevestigen van de PDMS-ingang en de ontwikkeling van luchtbellen in de wells te vermijden.