Miracle-dz/UniFAR-FTUs huggingface dataset:
The Central Importance of Hub Proteins in a Disease-Gene Network Model: A New Paradigm of Chronic Myeloid Leukemia Disease Study

Background The network biology of disease-gene association provides a holistic framework to decipher the intrinsic complexity of disease signaling pathways into cellular communication level. Different types of studies including large-scale genome-wide association, multifactor dimensional reduction analysis, whole genome, or exome-based sequencing strategies of diseases are striving to connect genes to diseases. Indeed, these approaches have had some accomplishments, but the cellular communication level needs a more streamlining outcome to understand the mechanistic impact of context. The higher-order combination of disease-gene interaction has a great potential to decipher the intricateness of diseases. The molecular interaction pattern of diseases at the genomic and proteomic level offers a revolutionized platform not only to understand the complexity of particular disease modules and pathways but also leading towards design novel therapeutics. Results The enrichment and topology analysis was performed by JEPETTO a plugin of Cytoscape software. We identified the chronic myeloid leukemia (CML) disease signaling pathways that appeared first in the ranking order based on XD-score among the bone, breast, and colon genes set and second at kidney and liver. This result validates the highest proximity between CML and five cancerous tissue gene set clusters. The topology analysis also supports the results while (p<0.0001) is considered to be extremely significant between CML and fives cancerous tissues genes set. Enrichment analysis identified that abl-gene acts as an overlapping node which is the major gene for inducing various mutations in CML. Amazingly, we identified 56 common path expansion/added genes among these five cancerous tissues which can be considered the direct cofactors of CML disease. By relative node degree, resolution, possible ligand, stoichiometry, Q-mean, and Z-score analysis we found 11 hubs proteins like SMAD3, GRB2, TP53, SMAD4, RB1, HDAC1, RAF1, ABL1, SHC1, TGFBR1, RELA which can be regarded for further drug target identification. Conclusions Our proposed network analysis reflects on the gene set interaction pattern of disease signaling pathways of humans. The integrated multidrug computational and experimental approaches boost up to improve the novel drug target approach. Besides, such a trove can yield unprecedented insights to lead to an enhanced understanding of potential application both in drug target optimization and for drug dislodging.
Identifying Brain Regions Related to Word Prediction During Listening to Japanese Speech by Combining a LSTM Language Model and MEG

Recently, a neuroscientific approach has revealed that humans understand language while subconsciously predicting the next word from the preceding context. Most studies on human word prediction have investigated the correlations between brain activity while reading or listening to sentences on functional magnetic resonance imaging (fMRI) and the predictive difficulty of each word in a sentence calculated by the N-gram language model. However, because of its low temporal resolution, fMRI is not optimal for identifying the changes in brain activity that accompany language comprehension. In addition, the N-gram language model is a simple computational structure that does not account for the structure of the human brain. Furthermore, it is necessary for humans to retain information prior to the N-1 word in order to form a contextual understanding of a presented story. Therefore, in the present study, we measured brain activity using magnetoencephalography (MEG), which has a higher temporal resolution than fMRI, and calculated the prediction difficulty of words using a long short-term memory language model (LSTMLM), which is based on a neural network inspired by the structure of the human brain and has longer information retention than the N-gram language model. We then identified the brain regions involved in language prediction during Japanese-language speech listening using encoding and decoding analyses. In addition to surprisal-related regions revealed in previous studies, such as the superior temporal gyrus, fusiform gyrus, and temporal pole, we also found relationships between surprisal and brain activity in other regions, including the insula, superior temporal sulcus, and middle temporal gyrus, which are believed to be involved in longer-term, sentence-level cognitive processing.
The oxygen sensor prolyl hydroxylase domain 2 regulates the in vivo suppressive capacity of regulatory T cells

The oxygen sensor PHD2 (prolyl hydroxylase domain 2) plays an important role in cell hypoxia adaptation by regulating the stability of HIF proteins (HIF1α and HIF2α) in numerous cell types including T lymphocytes. The role of oxygen sensor on immune cells, in particular on regulatory T cell (Treg) function, has not been fully elucidated. The purpose of our study was to evaluate the role of PHD2 in the regulation of Treg phenotype and function. We demonstrate herein that selective ablation of PHD2 expression in Treg (PHD2ΔTreg mice) leads to a spontaneous systemic inflammatory syndrome, as evidenced by weight loss, development of a rectal prolapse, splenomegaly, shortening of the colon and elevated expression of IFN-γ in the mesenteric lymph nodes, intestine and spleen. PHD2 deficiency in Tregs led to an increased number of activated CD4 conventional T cells expressing a Th1-like effector phenotype. Concomitantly, the expression of innate-type cytokines such as Il1b, Il12a, Il12b and Tnfa was found to be elevated in peripheral (gut) tissues and spleen. PHD2ΔTreg mice also displayed an enhanced sensitivity to DSS-induced colitis and to toxoplasmosis, suggesting that PHD2-deficient Tregs did not efficiently control inflammatory response in vivo, in particular those characterized by IFN-γ production. Further analysis revealed that Treg dysregulation was largely prevented in PHD2-HIF2α (PHD2-HIF2αΔTreg mice), but not in PHD2-HIF1α (PHD2-HIF1αΔTreg mice) double KOs, suggesting an important and possibly selective role of the PHD2-HIF2α axis in the control of Treg function. Finally, the transcriptomic analysis of PHD2-deficient Tregs identified the STAT1 pathway as a target of the PHD2-HIF2α axis in regulatory T cell phenotype and in vivo function.
FECAL MICROBIOME OF CHILDREN WITH NOROVIRUS GASTROENTERITIS

The human fecal microbiome is composed of endogenous bacteria, eukaryotic viruses, bacteriophages and retroviruses. Several pathological conditions, including gastroenteritis, may be characterized by imbalance of gastrointestinal functions, with alteration in the diversity and composition of the fecal microbiota. Were analyzed twenty-seven fecal microbiome in children hospitalized with gastroenteritis (norovirus positive) from northern region of Brazil. After sequencing, was verified the presence of the domains Bacteria (95%) and Eukaryota (3.1%), the viruses represented 1.9%. Among the pathogenic viruses were found in addition to noroviruses the picornaviruses, enterovirus and parechovirus. The bacteriophages detected were of Caudovirales order, families Siphoviridae, Podoviridae and Myoviridae. In 22.2% (6/27) of the samples was observed co-infection between norovirus, enterovirus B and echovirus. As for the others components of the microbiome, we can highlight the presence of the taxonomic groups: Terrabacteria (50.2%), composed mainly of Actinobacteria and Firmicutes; Proteobacteria (34.5%) represented by the Enterobacteriaceae family; and FCB group (22%) whose most abundant microorganisms were those of the phylum Bacterioidetes. We performed a metagenomic approach to analyze the fecal microbiota of children with viral gastroenteritis, it was observed that the bacterias (Enterobacteriaceae) deserve attention in a possible association with noroviruses, as they were found in large quantities in infections. In addition, other enteric viruses were observed, such as enteroviruses.
Full scale structural, mechanical and dynamical properties of HIV-1 liposomes

Enveloped viruses are enclosed by a lipid membrane inside of which are all of the components necessary for the virus life cycle; viral proteins, the viral genome and metabolites. Viral envelopes are lipid bilayers that adopt morphologies ranging from spheres to tubes. The envelope is derived from the host cell during viral replication. Thus, the composition of the bilayer depends on the complex constitution of lipids from the host-cell’s organelle(s) where assembly and/or budding of the viral particle occurs. Here, molecular dynamics (MD) simulations of authentic, asymmetric HIV-1 liposomes are used to derive a unique level of resolution of its full-scale structure, mechanics and dynamics. Analysis of the structural properties reveal the distribution of thicknesses of the bilayers over the entire liposome as well as its global fluctuations. Moreover, full-scale mechanical analyses are employed to derive the global bending rigidity of HIV-1 liposomes. Finally, dynamical properties of the lipid molecules reveal important relationships between their 3D diffusion, the location of lipid-rafts and the asymmetrical composition of the envelope. Overall, our simulations reveal complex relationships between the rich lipid composition of the HIV-1 liposome and its structural, mechanical and dynamical properties with critical consequences to different stages of HIV-1’s life cycle.
Seasonal dynamics of phytoplankton and bacterioplankton at the ultra-oligotrophic southeastern Mediterranean Sea

The Eastern Mediterranean Sea (EMS) is an ultra-oligotrophic, enclosed basin strongly impacted by regional and global anthropogenic stressors. Here, we describe the annual (2018-19) dynamics of phyto- and bacterioplankton (abundances, pigments and productivity) in relation to the physical and chemical conditions in the photic water column at the offshore EMS water (Station THEMO-2, ∼1,500m depth, 50km offshore). Annual patterns in phytoplankton biomass (as chlorophyll a), primary and bacterial productivity differed between the mixed winter (January-April) and the thermally stratified (May-December) periods. Prochlorococcus and Synechococcus numerically dominated the picophytoplankton populations, with each clade revealing different temporal and depth patterns, while picoeukaryotes (primarily haptophytes) were less abundant, yet likely contributed significant biomass. Integrated primary productivity (∼32 gC m-2 y-1) was lower compared with other well-studied oligotrophic locations, including the north Atlantic and Pacific (HOT and BATS observatories), the western Mediterranean (DYFAMED observatory) and the Red Sea, and was on-par with the ultra-oligotrophic South Pacific Gyre. In contrast, integrated bacterial production (∼11 gC m-2 y-1) was similar to other oligotrophic locations. Phytoplankton seasonal dynamics were reminiscent of those at BATS and the Red Sea, suggesting an observable effect of winter mixing in this ultra-oligotrophic location. These results highlight the ultra-oligotrophic conditions in the EMS and provide, for the first time in this region, a full-year baseline and context to ocean observatories in the region.Bacterioplankton dynamics were assessed monthly in the Eastern Mediterranean SeaSmall-sized picophytoplankton numerically dominated the phytoplankton communitySeasonal phytoplankton dynamics are similar to BATS and Red Sea, but not ALOHAAnnual primary productivity is among the lowest in the world’s oceansBacterial to primary production ratio is higher than most oligotrophic seas
The N400 ERP component reflects a learning signal during language comprehension

The functional significance of the N400 ERP component is still actively debated. Based on neural network modeling it was recently proposed that the N400 component can be interpreted as the change in a probabilistic representation corresponding to an internal temporal-difference prediction error at the level of meaning that drives adaptation in language processing. These computational modeling results imply that increased N400 amplitudes should correspond to greater adaptation. To investigate this model derived hypothesis, the current study manipulated expectancy in a sentence reading task, which influenced N400 amplitudes, and critically also later implicit memory for the manipulated word: reaction times in a perceptual identification task were significantly faster for previously unexpected words. Additionally, it could be demonstrated that this adaptation seems to specifically depend on the process underlying N400 amplitudes, as participants with larger N400 differences also exhibited a larger implicit memory benefit. These findings support the interpretation of the N400 as an implicit learning signal in language processing.
Germ plasm anchors at tight junctions in the early zebrafish embryo

The zebrafish germline is specified during early embryogenesis by inherited maternal RNAs and proteins collectively called germ plasm. Only the cells containing germ plasm will become part of the germline, whereas other cells will commit to somatic cell fates. Therefore, proper localization of germ plasm is key for germ cell specification and its removal is critical for the development of soma. The molecular mechanism underlying this process in vertebrates is largely unknown. Here we show that germ plasm localization in zebrafish is similar to Xenopus and amniotes but distinct from Drosophila. We identified non muscle myosin II (NMII) and tight junction (TJ) components as interaction candidates of Bucky ball (Buc), which is the germ plasm organizer in zebrafish. Remarkably, we also found that TJ protein ZO1 colocalizes with germ plasm and electron microscopy (EM) of zebrafish embryos uncovered TJ like structures at early cleavage furrows. In addition, injection of the TJ-receptor Claudin-d (Cldn-d) produced extra germ plasm aggregates. Our findings discover for the first time a role of TJs in germ plasm localization.
Application of an integrated computational antibody engineering platform to design SARS-CoV-2 neutralizers

As the COVID-19 pandemic continues to spread, hundreds of new initiatives including studies on existing medicines are running to fight the disease. To deliver a potentially immediate and lasting treatment to current and emerging SARS-CoV-2 variants, new collaborations and ways of sharing are required to create as many paths forward as possible. Here we leverage our expertise in computational antibody engineering to rationally design/optimize three previously reported SARS-CoV neutralizing antibodies and share our proposal towards anti-SARS-CoV-2 biologics therapeutics. SARS-CoV neutralizing antibodies, m396, 80R, and CR-3022 were chosen as templates due to their diversified epitopes and confirmed neutralization potency against SARS. Structures of variable fragment (Fv) in complex with receptor binding domain (RBD) from SARS-CoV or SARS-CoV2 were subjected to our established in silico antibody engineering platform to improve their binding affinity to SARS-CoV2 and developability profiles. The selected top mutations were ensembled into a focused library for each antibody for further screening. In addition, we convert the selected binders with different epitopes into the trispecific format, aiming to increase potency and to prevent mutational escape. Lastly, to avoid antibody induced virus activation or enhancement, we applied NNAS and DQ mutations to the Fc region to eliminate effector functions and extend half-life.
Thriving of hyperthermophilic microbial communities from a deep-sea sulfidic hydrothermal chimney under electrolithoautotrophic conditions with nitrate as electron acceptor

Recent studies have shown the presence of an abiotic electrical current across the walls of deep-sea hydrothermal chimneys, allowing the growth of electroautotrophic microbial communities. To understand the role of the different phylogenetic groups and metabolisms involved, this study focused on an electrotrophic enrichment, with nitrate as electron acceptor. The biofilm density, the community composition, the organic products released, and the electrical consumption were monitored by FISH confocal microscopy, qPCR, Metabarcoding, MNR and potentiostat measurements. A statistic analysis by PCA showed the correlation between the different parameters in 3 distinct temporal phases. The Archaeoglobales have been shown to play a key role in the development of the community, as first colonizers and producing pyruvate, therefor used as organic source for heterotrophs. Some Thermococcales showed the ability to perform electrofermentation of this pyruvate into acetate and H2. Finally, through subcultures of the community, we showed the development of a larger biodiversity over time. This observed phenomenon could explain the biodiversity development in hydrothermal context where energy sources are transient and unstable.
ENNAVIA is an innovative new method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides

Viruses represent one of the greatest threats to human health, necessitating the development of new antiviral drug candidates. Antiviral peptides often possess excellent biological activity and a favourable toxicity profile, and therefore represent a promising field of novel antiviral drugs. As the quantity of sequencing data grows annually, the development of an accurate in silico method for the prediction of peptide antiviral activities is important. This study leverages advances in deep learning and cheminformatics to produce a novel sequence-based deep neural network classifier for the prediction of antiviral peptide activity. The method out-performs the existent best-in-class, with an external test accuracy of 93.9%, Matthews correlation coefficient of 0.87 and an Area Under the Curve of 0.93 on the dataset of experimentally validated peptide activities. This cutting-edge classifier is available as an online web server at https://research.timmons. eu/ennavia, facilitating in silico screening and design of peptide antiviral drugs by the wider research community.
Integrated population genomic analysis and numerical simulation to estimate larval dispersal of Acanthaster cf. solaris between Ogasawara and other Japanese regions

The estimation of larval dispersal of marine species occurring on an ecological timescale is significant for conservation. In 2018, a semi-population outbreak of crown of thorns starfish, Acanthaster cf. solaris was observed on a relatively isolated oceanic island, Ogasawara. The aim of this study was to assess whether this population outbreak was caused by large-scale larval recruitment (termed secondary outbreak) from the Kuroshio region. We estimated larval dispersal of the coral predator A. cf. solaris between the Kuroshio and Ogasawara regions using both population genomic analysis and oceanographic dispersal simulation. Population genomic analysis revealed overall genetically homogenized patterns among Ogasawara and other Japanese populations, suggesting that the origin of the populations in the two regions is the same. In contrast, a simulation of 26-year oceanographic dispersal indicated that larvae are mostly self-seeded in Ogasawara populations and have difficulty reaching Ogasawara from the Kuroshio region within one generation. However, a connectivity matrix produced by the larval dispersal simulation assuming a Markov chain indicated gradual larval dispersal migration from the Kuroshio region to Ogasawara in a stepping-stone manner over multiple years. These results suggest that, while large-scale larval dispersal from an outbreak of the Kuroshio population spreading to the Ogasawara population within one generation is unlikely. This study also highlighted the importance of using both genomic and oceanographic methods to estimate larval dispersal, which provides significant insight into larval dispersal that occurs on ecological and evolutionary timescales.
Mechanical influences on in silico tumor evolution

Experimental insight and conceptual understanding of tumor growth are steadily growing and leading to new therapeutic interventions. Experiments and clinical studies are able to link single-cell properties to macroscopic tumor attributes. The development of cellular subpopulations in heterogeneous tumors can be understood as an evolutionary system with different cell types competing over both space and nutrients. However, to predict the growth trajectory and development of a tumor, fitness and trade-offs of cell properties in the context of the surroundings are required and often inaccessible. The optimum of the evolutionary trajectory provides a target for intervention, but can mostly not be identified. We propose that the optimal value of cellular properties is influenced by the tumor surrounding. Computational multiscale-modeling of tissue enables the observation of the trajectory of each cell while modeling the tumor surrounding. We model a 3D spheroid tumor and the fitness of individual cells and the evolutionary behavior of the tumor are quantified and linked to global parameters. Cell–cell adhesion and cell motility are two important mechanical properties for cell development and used as free parameters. Mechanical properties alone are able to drive the tumor towards low adhesion.We implement a dynamically changing nutrient surrounding representing the fluctuating blood-supply through blood vessel collapse and angiogenesis. We find that the evolutionary speed depends on the frequency of the fluctuations. We identify a frequency domain in which the evolutionary speed is significantly increased over a tumor with constant nutrient supply. The findings suggest that mechanically-induced fluctuations can accelerate tumor evolution. Author summary Limited space and nutrients together with competing cell types drive an evolutionary process inside tumors. This process selects for the fittest cell types and optimizes the growing behavior for its local surroundings. An expanding tumor exerts mechanical forces on its cells and its surroundings, leading to a fluctuating nutrient supply through collapsing blood vessels. Here, we observe the influence of a dynamically changing surrounding on the evolutionary behavior of heterogeneous tumors in a high-resolution computational model. We find that the evolutionary speed depends on the frequency of the fluctuations and a fitness advantage of low-adhesion cells.
A Differential Equation for Mutation Rates in Environmental Coevolution

In their paper Natural selection for least action (Kaila and Annila 2008) they depict evolution as a process conforming to the Principle of Least Action (PLA). From this concept, together with the Coevolution model of Lewontin, an equation of motion for environmental coevolution is derived which shows that it is the time rate (frequency) of evolutionary change of the organism (mutations) that responds to changes in the environment. It is not possible to compare the theory with viral or bacterial mutation rates, as these are not measured on a time base. There is positive evidence from population level avian studies where the coefficient of additive evolvability (Cav) and its square (IA) change with environmental favourability in agreement with this model. Further analysis shows that the time rate of change of the coefficient of additive evolvability (Cav) and its square (IA) are linear with environmental favourability, which could help in defining the Lagrangian of the environmental effects.