Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural networks to learn protein sequence–function relationships from deep mutational scanning data

Significance Understanding the relationship between protein sequence and function is necessary to design new and useful proteins with applications in bioenergy, medicine, and agriculture. The mapping from sequence to function is tremendously complex because it involves thousands of molecular interactions that are coupled over multiple lengths and timescales. We show that neural networks can learn the sequence–function mapping from large protein datasets. Neural networks are appealing for this task because they can learn complicated relationships from data, make few assumptions about the nature of the sequence–function relationship, and can learn general rules that apply across the length of the protein sequence. We demonstrate that learned models can be applied to design new proteins with properties that exceed natural sequences. The mapping from protein sequence to function is highly complex, making it challenging to predict how sequence changes will affect a protein’s behavior and properties. We present a supervised deep learning framework to learn the sequence–function mapping from deep mutational scanning data and make predictions for new, uncharacterized sequence variants. We test multiple neural network architectures, including a graph convolutional network that incorporates protein structure, to explore how a network’s internal representation affects its ability to learn the sequence–function mapping. Our supervised learning approach displays superior performance over physics-based and unsupervised prediction methods. We find that networks that capture nonlinear interactions and share parameters across sequence positions are important for learning the relationship between sequence and function. Further analysis of the trained models reveals the networks’ ability to learn biologically meaningful information about protein structure and mechanism. Finally, we demonstrate the models’ ability to navigate sequence space and design new proteins beyond the training set. We applied the protein G B1 domain (GB1) models to design a sequence that binds to immunoglobulin G with substantially higher affinity than wild-type GB1.
Synergistic Combination of Cytotoxic Chemotherapy and Cyclin Dependent Kinase 4/6 Inhibitors in Biliary Tract Cancers

Biliary tract cancers (BTCs) are uncommon but highly lethal gastrointestinal malignancies. Gemcitabine/cisplatin is a standard-of-care (SOC) systemic therapy, but has a modest impact on survival and harbor toxicities including myelosuppression, nephropathy, neuropathy and ototoxicity. While BTCs are characterized by aberrations activating the cyclinD1-CDK4/6-CDKN2A-RB pathway, clinical use of CDK4/6 inhibitors as monotherapy is limited by lack of validated biomarkers, diffident pre-clinical efficacy and development of acquired drug resistance. Emerging studies have explored therapeutic strategies to enhance the anti-tumor efficacy of CDK4/6 inhibitors by combination with chemotherapy-regimens but their mechanism of action remains elusive. Here, we report in vitro and in vivo synergy in BTC models, showing enhanced efficacy, reduced toxicity and better survival with a combination comprising gemcitabine/cisplatin and CDK4/6 inhibitors. Furthermore, we demonstrated that abemaciclib monotherapy had only modest efficacy due to autophagy induced resistance. Notably, triplettherapy was able to potentiate efficacy through elimination of the autophagic flux. Correspondingly, abemaciclib potentiated RRM1 reduction, resulting in sensitization to gemcitabine. Conclusions: As such, these data provide robust pre-clinical mechanistic evidence of synergy between gemcitabine/cisplatin and CDK4/6 inhibitors, and delineate a path forward for translation of these findings to preliminary clinical studies in advanced BTC patients.
Pallidal deep brain stimulation alters cortico-striatal synaptic communication in dystonic hamsters

Background Deep brain stimulation (DBS) of the globus pallidus internus (GPi) is considered to be the most relevant therapeutic option for patients with severe dystonias, which are thought to arise from a disturbance in striatal control of the GPi, possibly resulting in thalamic disinhibition. The mechanisms of GPi-DBS are far from understood. Hypotheses range from an overall silencing of target nuclei (due to e.g. depolarisation block), via differential alterations in thalamic firing, to disruption of oscillatory activity in the β-range. Although a disturbance of striatal function is thought to play a key role in dystonia, the effects of DBS on cortico-striatal function are unknown. Objective We hypothesised that DBS, via axonal backfiring, or indirectly via thalamic and cortical coupling, alters striatal network function. We aimed to test this hypothesis in the dtsz-hamster, an animal model of inherited generalised, paroxysmal dystonia. Methods Hamsters (dtsz-dystonic and non-dystonic controls) were bilaterally implanted with stimulation electrodes targeting the entopeduncular nucleus (EPN, equivalent of human GPi). DBS (130 Hz), and sham DBS, were performed in unanaesthetised animals for 3 hours. Synaptic cortico-striatal field potential responses, as well as miniature excitatory postsynaptic currents (mEPSC) and firing properties of medium spiny striatal neurons were subsequently recorded in brain slice preparations obtained from these animals immediately after EPN-DBS, to gauge synaptic responsiveness of cortico-striatal projections, their inhibitory control, and striatal neuronal excitability. Results DBS increased cortico-striatal responses in slices from control, but not dystonic animals. Inhibitory control of these responses, in turn, was differentially affected: DBS increased inhibitory control in dystonic, and decreased it in healthy tissue. A modulation of presynaptic mechanisms is likely involved, as mEPSC frequency was reduced strongly in dystonic, and less prominently in healthy tissues, while cellular properties of medium-spiny neurons remained unchanged. Conclusion DBS leads to dampening of cortico-striatal communication with restored inhibitory tone.
Transsynaptic modulation of presynaptic short-term plasticity induction in hippocampal mossy fiber synapses

The hippocampal mossy fiber synapse is a key synapse of the trisynaptic circuit of the hippocampus. Post-tetanic potentiation (PTP) is the most powerful form of plasticity at this synaptic connection. It is widely believed that mossy fiber PTP is an entirely presynaptic phenomenon, implying that PTP induction is input-specific, and requires neither activity of multiple inputs nor stimulation of postsynaptic neurons for induction. Thus, mossy fiber PTP appears to lack cooperativity and associativity that characterize other forms of plasticity. To directly test these predictions, we made paired recordings between single mossy fiber terminals and postsynaptic CA3 pyramidal neurons in rat brain slices. By stimulating parallel but non-overlapping mossy fiber bouton (MFB) inputs converging onto single CA3 neurons, we confirmed that PTP was inputspecific and non-cooperative. Unexpectedly, mossy fiber PTP showed anti-associative induction properties. Mossy fiber excitatory postsynaptic currents (EPSCs) showed only minimal PTP after combined pre- and postsynaptic high-frequency stimulation (HFS) with intact postsynaptic Ca2+ signaling (0.1 mM EGTA), but marked PTP in the absence of postsynaptic spiking and after suppression of postsynaptic Ca2+ signaling (10 mM EGTA). PTP was rescued by blocking Ca2+ entry via voltage-gated R-type and to a smaller extent L-type Ca2+channels. PTP was also recovered by extracellular application of group II metabotropic glutamate receptor (mGluR) antagonists and vacuolar-type (v-type) H+-ATPase inhibitors, suggesting the involvement of retrograde vesicular glutamate signaling. Transsynaptic regulation of PTP induction may increase the computational power of mossy fiber synapses, and implement a break on hippocampal mossy fiber detonation.
Microbial Quality and the Occurrence of Aflatoxins In Plantain/Yam And Wheat Flours In Ado-Odo Ota

Flours made from various foods including plantain, yam and wheat are a major part of daily diet for millions of people in Nigerian. If these food crops are not dried rapidly and thoroughly prior to milling, fungal growth and mycotoxin production can occur. Aflatoxins, a type of mycotoxin produced by Aspergillus species have been classified as Group 1 human carcinogens hence should be monitored in routinely consumed foods as the populace maybe potentially exposed to doses of aflatoxins in their daily diet. This study sought to determine the microbial quality and the occurrence of fungi and aflatoxins in plantain, yam and wheat purchased from four markets (Oja-ota, Sango, Atan and Owode markets) in Ado Odo Local Government Area. The mean microbial count for each sample was determined by plating each sample on nutrient agar and fungi was isolated by plating on Potato Dextrose Agar. The total aflatoxin content of the food samples was determined using the Agra Quant® competitive enzyme linked immunosorbent assay (ELISA) kit. The highest mean microbial count (9.30 × 1013 cfu/g) was observed in a plantain flour bought from Sango market while the lowest (1.16 × 1012 cfu/g), was observed in wheat flour from Oja-Ota market. Aspergillus flavus was the predominant (31%) aflatoxigenic fungi isolated compared to A. niger (21%). The other fungi isolated include Rhizopus spp, Geotrichium spp, Yeast, Penicillium spp and Paecilomyces spp. Aflatoxin was detected in all the food samples tested in this study at concentrations ranging from 0.2 ppb to 5.9 ppb which were all within the CODEX Alimentarius Commission (CAC) aflatoxin acceptable limit of 15 ppb.
Acoustic stimulation increases implicit adaptation in sensorimotor adaptation

Sensorimotor adaptation is an important part of our ability to perform novel motor tasks (i.e., learning of motor skills). Efforts to improve adaptation in healthy and clinical patients using non-invasive brain stimulation methods have been hindered by interindividual and intra-individual variability in brain susceptibility to stimulation. Here, we explore unpredictable loud acoustic stimulation as an alternative method of modulating brain excitability to improve sensorimotor adaptation. In two experiments, participants moved a cursor towards targets, and adapted to a 30° rotation of cursor feedback, either with or without unpredictable acoustic stimulation. Acoustic stimulation improved initial adaptation to sensory prediction errors in Study 1, and improved overnight retention of adaptation in Study 2. Unpredictable loud acoustic stimulation might thus be a potent method of modulating sensorimotor adaptation in healthy adults.
Artificial intelligence learning and entrepreneurial performance among university students: evidence from malaysian higher educational institutions

Artificial intelligence learning at higher educational institutions is one of the emerging concepts having vital importance to promote entrepreneurship activities among the university students. However, Malaysian Universities are lacking with the artificial intelligence learning activities. The objective of the study is to examine the role of artificial intelligence learning to promote entrepreneurship performance with the help of entrepreneurial orientation and strategic entrepreneurship. Moreover, the moderating role of government funding and attitude towards entrepreneurship is also examined. To achieve the objective of this study, a survey was carried out among the Malaysian universities. 500 questionnaires were distributed among the universities and data were collected from the teaching staff. After collection of data, it was analysed with the help of Partial Least Square (PLS)-Structural Equation Modeling (SEM). It is concluded that artificial intelligence learning is most significant to promote entrepreneurial performance among university students. Entrepreneurial orientation and strategic entrepreneurship play a key role to transfer the positive effect of artificial intelligence learning on entrepreneurial performance. Additionally, government funding and attitude towards entrepreneurship also has significant role.
HEXOKINASE1 interferes with cytokinin synthesis and strigolactone perception during sugar-induced shoot branching

- Plant architecture is controlled by several endogenous signals including hormones and sugars. However, only little is known about the nature and roles of the sugar signalling pathways in this process. Here we test whether the sugar pathway mediated by HEXOKINASE1 (HXK1) is involved in the control of shoot branching. - To test the involvement of HXK1 in the control of shoot architecture we modulated the HXK1 pathway using physiological and genetic approaches in diverse plants, rose, arabidopsis and pea and evaluated impacts of hormonal pathways. - We show that triggering a hexokinase-dependent pathway was able to promote bud outgrowth in pea and rose. In arabidopsis, both HXK1 deficiency and defoliation led to decreased shoot branching and conferred hypersensitivity to auxin. HXK1 expression was positively correlated with sugar availability. HXK1-deficient plants displayed decreased cytokinin levels and increased expression of MAX2 which is required for strigolactone signalling. The branching phenotype of HXK1-deficient plants could be partly restored by cytokinin treatment and strigolactone deficiency could override the negative impact of HXK1 deficiency on shoot branching. - Our observations demonstrate that a HXK1-dependent pathway contributes to the regulation of shoot branching and interact with hormones to modulate plant architecture.
Compression-based Network Interpretability Schemes

Deep learning methods have achieved state-of-the-art performance in many domains of artificial intelligence, but are typically hard to interpret. Network interpretation is important for multiple reasons, including knowledge discovery, hypothesis generation, fairness and establishing trust. Model transformations provide a general approach to interpreting a trained network post-hoc: the network is approximated by a model, which is typically compressed, whose structure can be more easily interpreted in some way (we call such approaches interpretability schemes). However, the relationship between compression and interpretation has not been fully explored: How much should a network be compressed for optimal extraction of interpretable information? Should compression be combined with other criteria when selecting model transformations? We investigate these issues using two different compression-based schemes, which aim to extract orthogonal kinds of information, pertaining to feature and data instance-based groupings respectively. The first (rank projection trees) uses a structured sparsification method such that nested groups of features can be extracted having potential joint interactions. The second (cascaded network decomposition) splits a network into a cascade of simpler networks, allowing groups of training instances with similar characteristics to be extracted at each stage of the cascade. We use predictive tasks in cancer and psychiatric genomics to assess the ability of these approaches to extract informative feature and data-point groupings from trained networks. We show that the generalization error of a network provides an indicator of the quality of the information extracted; further we derive PAC-Bayes generalization bounds for both schemes, which we show can be used as proxy indicators, and can thus provide a criterion for selecting the optimal compression. Finally, we show that the PAC-Bayes framework can be naturally modified to incorporate additional criteria alongside compression, such as prior knowledge based on previous models, which can enhance interpretable model selection.
Poxviruses capture host genes by LINE-1 retrotransposition

Horizontal gene transfer (HGT) provides a major source of genetic variation. Many viruses, including poxviruses, encode genes with crucial functions directly gained by gene transfer from hosts. The mechanism of transfer to poxvirus genomes is unknown. Using genome analysis and experimental screens of infected cells, we discovered a central role for Long Interspersed Nuclear Element-1 (LINE-1) retrotransposition in HGT to virus genomes. The process recapitulates processed pseudogene generation, but with host messenger RNA directed into virus genomes. Intriguingly, hallmark features of retrotransposition appear to favor virus adaption through rapid duplication of captured host genes on arrival. Our study reveals a previously unrecognized conduit of genetic traffic with fundamental implications for the evolution of many virus classes and their hosts. Summary Active selfish genetic elements in infected cells aid virus adaptation by catalyzing transfer of host genes to virus genomes.
The Arabidopsis E3 ubiquitin ligase PUB4 regulates BIK1 homeostasis and is targeted by a bacterial type-III effector

Plant immunity is tightly controlled by a complex and dynamic regulatory network, which ensures optimal activation upon detection of potential pathogens. Accordingly, each component of this network is a potential target for manipulation by pathogens. Here, we report that RipAC, a type III-secreted effector from the bacterial pathogen Ralstonia solanacearum, targets the plant E3 ubiquitin ligase PUB4 to inhibit pattern-triggered immunity (PTI). PUB4 plays a positive role in PTI by regulating the homeostasis of the central immune kinase BIK1. Before PAMP perception, PUB4 promotes the degradation of non-activated BIK1, while, after PAMP perception, PUB4 contributes to the accumulation of activated BIK1. RipAC leads to BIK1 degradation, which correlates with its PTI-inhibitory activity. RipAC causes a reduction in pathogen-associated molecular pattern (PAMP)-induced PUB4 accumulation and phosphorylation. Our results shed light on the role played by PUB4 in immune regulation, and illustrate an indirect targeting of the immune signalling hub BIK1 by a bacterial effector.
Characterisation of the biosurfactants from phyllosphere colonising Pseudomonads and their effect on plant colonisation and diesel degradation

Biosurfactant production is a common trait in leaf surface colonising bacteria that has been associated with increased survival and movement on leaves. At the same time the ability to degrade aliphatics is common in biosurfactant-producing leaf colonisers. Pseudomonads are common leaf colonisers and have been recognised for their ability to produce biosurfactants and degrade aliphatic compounds. In this study, we have investigated the role of biosurfactants in four non-plant plant pathogenic Pseudomonas strains by performing a series of experiments to characterise the surfactant properties, and their role during leaf colonisation and diesel degradation. The produced biosurfactants were identified using mass-spectrometry. Two strains produced viscosin-like biosurfactants and the other two produced Massetolide A-like biosurfactants which aligned with the phylogenetic relatedness between the strains. To further investigate the role of surfactant production, random Tn5 transposon mutagenesis was performed to generate knockout mutants. The knockout mutants were compared to their respective wildtypes in their ability to colonise gnotobiotic Arabidopsis thaliana and to degrade diesel. It was not possible to detect negative effects during plant colonisation in direct competition or individual colonisation experiments. When grown on diesel, knockout mutants grew significantly slower compared to their respective wildtypes. By adding isolated wildtype biosurfactants it was possible to complement the growth of the knockout mutants. Importance Many leaf colonising bacteria produce surfactants and are able to degrade aliphatic compounds, however, if surfactant production provides a competitive advantage during leaf colonisation is unclear. Furthermore, it is unclear if leaf colonisers take advantage of the aliphatic compounds that constitute the leaf cuticle and cuticular waxes. Here we test the effect of surfactant production on leaf colonisation and demonstrate that the lack of surfactant production decreases the ability to degrade aliphatic compounds. This indicates that leaf surface dwelling, surfactant producing bacteria contribute to degradation of environmental hydrocarbons and may be able to utilise leaf surface waxes. This has implications for plant-microbe interactions and future studies.
Infant functional connectivity fingerprints predict long-term language and pre-literacy outcomes

Functional connectivity analyses can delineate brain organization in infancy, enabling the characterization of early brain characteristics associated with subsequent behavioral outcomes. Previous studies have identified specific functional networks of infant brains that underlie cognitive abilities and pathophysiology subsequently observed in toddlers. However, it is unknown whether and how functional networks emerging in infancy contribute to the development of higher-order complex functions of language and literacy. Employing a five-year longitudinal imaging project starting in infancy, the current study utilizes resting-state functional connectivity MRI to examine prospective associations between infant functional connectivity fingerprints (FCF) and subsequent school-age language and foundational literacy skills. Utilizing multivariate pattern analyses, we demonstrate that FCF patterns associated with language-related brain regions established during the first year of life are prospectively associated with oral language and phonological skills at a mean age of 6.5. Moreover, these longitudinal associations are preserved when key environmental influences are controlled for and are independent of emergent language abilities in infancy, suggesting early development of functional network characteristics in supporting the acquisition of high-order language and pre-literacy skills. Altogether, the current results highlight the importance of functional organization established in infancy as a neural scaffold underlying the protracted development of complex cognitive functions. Significance statement Functional brain networks undergo extensive development within the first few years of life. Although functional connectivity has been linked to cognitive abilities in toddlerhood, little is known regarding the long-term relevance of functional connections established in infancy for the protracted development of higher-order abilities such as language. Employing a five-year infant longitudinal imaging project, we demonstrate, for the first time, prospective associations between functional connectivity patterns in infancy and language and pre-literacy abilities after the onset of formal schooling. These results provide insights into the far-reaching developmental significance of early neural network mechanisms on the learning process of higher-order cognitive functions.