bluuebunny/crossref_metadata_2025_split huggingface dataset:
COInr and mkCOInr: Building and customizing a non-redundant barcoding reference database from BOLD and NCBI using a lightweight pipeline
Authors: Meglécz Emese 
Published: May 2022

AbstractThe taxonomic assignment of metabarcoding data strongly depends on the taxonomic coverage of the reference database. Therefore, it is fundamental to access and pool data from the two major sources of COI sequences, the BOLD and the NCBI nucleotide databases, and enrich them with custom COI data, when available.The COInr database is a freely available, easy-to-access database of COI reference sequences extracted from the BOLD and NCBI nucleotide databases. It is a comprehensive database: not limited to a taxon, a gene region, or a taxonomic resolution; therefore, it is a good starting point for creating custom databases. Sequences are dereplicated between databases and within taxa. Each taxon has a unique taxonomic Identifier (taxID), fundamental to avoid ambiguous associations of homonyms and synonyms in the source database. TaxIDs form a coherent hierarchical system fully compatible with the NCBI taxIDs allowing to create their full or ranked linages.The mkCOInr tool is a series of Perl scripts necessary to download sequences from BOLD and NCBI, build the COInr database and customize it according to the users’ needs. It is possible to select or eliminate sequences for a list of taxa, select a specific gene region, select for minimum taxonomic resolution, add new custom sequences, and format the database for BLAST, QIIME, RDP classifier.The COInr database can be downloaded from https://doi.org/10.5281/zenodo.6555985 and mkCOInr and the full documentation is available at https://github.com/meglecz/mkCOInr.
Interplay between Polycomb PCGF protein interactomes revealed by screening under endogenous conditions
Authors: Munawar Nayla , Wynne Kieran , Oliviero Giorgio 
Published: May 2022

ABSTRACTThe six PCGF proteins (PCGF1-6) define the biochemical identity of Polycomb Repressor Complex 1 (PRC1) subcomplexes. While structural and functional studies of PRC1 subcomplexes have revealed specialized roles in distinct aspects of epigenetic regulation, our understanding of variation in protein interaction networks between the PCGF subunits is incomplete. We carried out an affinity purification mass spectrometry (AP-MS) screen of subunits PCGF1 (NSPC1), PCGF2 (MEL18), and PCGF4 (BMI1), using an immunoprecipitation approach that replicated endogenous cellular conditions in a cell line capable of differentiation programs. Over 200 interactions were found, including 83 that had not been described previously. Bioinformatic analysis found that these interacting proteins covered a range of functional pathways, often focused on cell biology and chromatin regulation. We found evidence of mutual regulation (at mRNA and protein level) between distinct PCGF subunits. Furthermore, we confirmed that disruption of each subunit using shRNA results in reduced proliferation ability. Overall, our work adds to understanding of the role of PCGF proteins within the wider cellular network.
Comprehensive analysis of pathways in Coronavirus 2019 (COVID-19) using an unsupervised machine learning method
Authors: Taheri Golnaz , Habibi Mahnaz 
Published: May 2022

AbstractThe World Health Organization (WHO) introduced “Coronavirus disease 19” or “COVID-19” as a novel coronavirus in March 2020. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide crisis. Artificial intelligence and bioinformatics analysis pipelines can assist with finding biomarkers, explanations, and cures. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data. On the other hand, pathway enrichment analysis, as a dominant tool, could help researchers discover potential key targets present in biological pathways of host cells that are targeted by SARS-CoV-2. In this work, we propose a two-stage machine learning approach for pathway analysis. During the first stage, four informative gene sets that can represent important COVID-19 related pathways are selected. These “representative genes” are associated with the COVID-19 pathology. Then, two distinctive networks were constructed for COVID-19 related signaling and disease pathways. In the second stage, the pathways of each network are ranked with respect to some unsupervised scorning method based on our defined informative features. Finally, we present a comprehensive analysis of the top important pathways in both networks. Materials and implementations are available at: https://github.com/MahnazHabibi/Pathway.
Using unsupervised learning algorithms to identify essential genes associated with SARS-CoV-2 as potential therapeutic targets for COVID-19
Authors: Taheri Golnaz , Habibi Mahnaz 
Published: May 2022

AbstractMotivationSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide concern. Several genes associated with the SARS-CoV-2, which are essential for its functionality, pathogenesis, and survival, have been identified. These genes, which play crucial roles in SARS-CoV-2 infection, are considered potential therapeutic targets. Developing drugs against these essential genes to inhibit their regular functions could be a good approach for COVID-19 treatment. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data and can assist in finding fast explanations and cures.ResultsWe propose a method to highlight the essential genes that play crucial roles in SARS-CoV-2 pathogenesis. For this purpose, we define eleven informative topological and biological features for the biological and PPI networks constructed on gene sets that correspond to COVID-19. Then, we use three different unsupervised learning algorithms with different approaches to rank the important genes with respect to our defined informative features. Finally, we present a set of 18 important genes related to COVID-19.AvailabilityMaterials and implementations are available at: https://github.com/MahnazHabibi/Gene_analysis.Contactm_habibi@qiau.ac.irSupplementary informationSupplementary data are available at Bioinformatics online.
An evolutionary paradigm favoring crosstalk between bacterial two-component signaling systems
Authors: Vemparala Bharadwaj , Parambathu Arjun Valiya , Saini Deepak Kumar , Dixit Narendra M 
Published: May 2022

ABSTRACTThe prevalent paradigm governing bacterial two-component signaling systems (TCSs) is specificity, wherein the histidine kinase (HK) of a TCS exclusively activates its cognate response regulator (RR). Crosstalk, where HKs activate noncognate RRs, is considered evolutionarily disadvantageous because it can compromise adaptive responses by leaking signals. Yet, crosstalk is observed in several bacteria. Here, to resolve this paradox, we propose an alternative paradigm where crosstalk can be advantageous. We envisioned ‘programmed’ environments, wherein signals appear in predefined sequences. In such environments, crosstalk that primes bacteria to upcoming signals may improve adaptive responses and confer evolutionary benefits. To test this hypothesis, we employed mathematical modeling of TCS signaling networks and stochastic evolutionary dynamics simulations. We considered the comprehensive set of bacterial phenotypes, comprising thousands of distinct crosstalk patterns, competing in varied signaling environments. Our simulations predicted that in programmed environments phenotypes with crosstalk facilitating priming would outcompete phenotypes without crosstalk. In environments where signals appear randomly, bacteria without crosstalk would dominate, explaining the specificity widely seen. Additionally, a testable prediction was that the phenotypes selected in programmed environments would display ‘one-way’ crosstalk, ensuring priming to ‘future’ signals. Interestingly, the crosstalk networks we deduced from available data on TCSs of Mycobacterium tuberculosis all displayed one-way crosstalk, offering strong support to our predictions. Our study thus identifies potential evolutionary underpinnings of crosstalk in bacterial TCSs, suggests a reconciliation of specificity and crosstalk, makes testable predictions of the nature of crosstalk patterns selected, and has implications for understanding bacterial adaptation and the response to interventions.IMPORTANCEBacteria use two-component signaling systems (TCSs) to sense and respond to environmental changes. The prevalent paradigm governing TCSs is specificity, where signal flow through TCSs is insulated; leakage to other TCSs is considered evolutionarily disadvantageous. Yet, crosstalk between TCSs is observed in many bacteria. Here, we present a potential resolution of this paradox. We envision programmed environments, wherein stimuli appear in predefined sequences. Crosstalk that primes bacteria to upcoming stimuli could then confer evolutionary benefits. We demonstrate this benefit using mathematical modeling and evolutionary simulations. Interestingly, we found signatures of predicted crosstalk patterns in Mycobacterium tuberculosis. Furthermore, specificity was selected in environments where stimuli occurred randomly, thus reconciling specificity and crosstalk. Implications follow for understanding bacterial evolution and for interventions.
Soluble cyclase-mediated nuclear cAMP synthesis is sufficient for cell proliferation
Authors: Pizzoni Alejandro , Zhang Xuefeng , Naim Nyla , Altschuler Daniel L 
Published: May 2022

ABSTRACTcAMP is a key player in many physiological processes. Classically considered to originate solely from the plasma membrane, this view was recently challenged by observations showing that GPCRs can sustain cAMP signaling from intracellular compartments associated with nuclear PKA translocation and activation of transcriptional events. In this report we show that neither PKA translocation nor cAMP diffusion, but rather nuclear sAC activation represents the only source of nuclear cAMP accumulation, PKA activation, and CREB phosphorylation. Both pharmacological and genetic sAC inhibition, that did not affect the cytosolic cAMP levels, completed blunted nuclear cAMP accumulation, PKA activation and proliferation, while an increase in sAC nuclear expression significantly enhanced cell proliferation. Moreover, utilizing novel compartment-specific optogenetic actuators we showed that light-dependent nuclear cAMP synthesis can stimulate PKA, CREB and trigger cell proliferation. Thus, our results show that sAC-mediated nuclear accumulation is not only necessary but sufficient and rate-limiting for cAMP-dependent proliferation.
Cellular senescence in malignant cells promotes tumor progression in mouse and patient Glioblastoma
Authors: Salam Rana , Saliou Alexa , Bielle Franck , Bertrand Mathilde , Antoniewski Christophe , Carpentier Catherine , Alentorn Agusti , Capelle Laurent , Sanson Marc , Huillard Emmanuelle , Bellenger Léa , Guégan Justine , Le Roux Isabelle 
Published: May 2022

AbstractGlioblastoma (GBM) is the most common primary malignant brain tumor in adults, yet it remains refractory to systemic therapy. Elimination of senescent cells has emerged as a promising new treatment approach against cancer. Here, we investigated the contribution of senescent cells to GBM progression. Senescent cells were identified in patient and mouse GBMs. Partial removal of p16Ink4a-expressing malignant senescent cells, which make up less than 7 % of the tumor, modified the tumor ecosystem and improved the survival of GBM-bearing mice. By combining single cell and bulk RNA sequencing, immunohistochemistry and genetic knockdowns, we identified the NRF2 transcription factor as a determinant of the senescent phenotype. Remarkably, our mouse senescent transcriptional signature and underlying mechanisms of senescence are conserved in patient GBMs, in whom higher senescence scores correlate with shorter survival times. These findings suggest that senolytic drug therapy may be a beneficial adjuvant therapy for patients with GBM.
Sparse RNNs can support high-capacity classification
Authors: Turcu Denis , Abbott L. F. 
Published: May 2022

AbstractFeedforward network models performing classification tasks rely on highly convergent output units that collect the information passed on by preceding layers. Although convergent output-unit like neurons may exist in some biological neural circuits, notably the cerebellar cortex, neocortical circuits do not exhibit any obvious candidates for this role; instead they are highly recurrent. We investigate whether a sparsely connected recurrent neural network (RNN) can perform classification in a distributed manner without ever bringing all of the relevant information to a single convergence site. Our model is based on a sparse RNN that performs classification dynamically. Specifically, the interconnections of the RNN are trained to resonantly amplify the magnitude of responses to some external inputs but not others. The amplified and non-amplified responses then form the basis for binary classification. Furthermore, the network acts as an evidence accumulator and maintains its decision even after the input is turned off. Despite highly sparse connectivity, learned recurrent connections allow input information to flow to every neuron of the RNN, providing the basis for distributed computation. In this arrangement, the minimum number of synapses per neuron required to reach maximum memory capacity scales only logarithmically with network size. The model is robust to various types of noise, works with different activation and loss functions and with both backpropagation- and Hebbian-based learning rules. The RNN can also be constructed with a split excitation-inhibition architecture with little reduction in performance.
Neural learning rules for generating flexible predictions and computing the successor representation
Authors: Fang Ching , Aronov Dmitriy , Abbott L.F. , Mackevicius Emily 
Published: May 2022

AbstractThe predictive nature of the hippocampus is thought to be useful for memory-guided cognitive behaviors. Inspired by the reinforcement learning literature, this notion has been formalized as a predictive map called the successor representation (SR). The SR captures a number of observations about hippocampal activity. However, the algorithm does not provide a neural mechanism for how such representations arise. Here, we show the dynamics of a recurrent neural network naturally calculate the SR when the synaptic weights match the transition probability matrix. Interestingly, the predictive horizon can be flexibly modulated simply by changing the network gain. We derive simple, biologically plausible learning rules to learn the SR in a recurrent network. We test our model with realistic inputs and match hippocampal data recorded during random foraging. Taken together, our results suggest that the SR is more accessible in neural circuits than previously thought and can support a broad range of cognitive functions.
Characterizing metabolism from bulk and single-cell RNA-seq data using METAFlux
Authors: Huang Yuefan , Mohanty Vakul , Dede Merve , Daher May , Li Li , Rezvani Katayoun , Chen Ken 
Published: May 2022

AbstractCells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the TME (Tumor Microenvironment) is of emerging importance in ongoing cancer research and therapy development. Recent developments in mass spectrometry (MS)-based technologies allow simultaneous characterization of metabolic features of tumor, stroma, and immune cells in the TME. However, they only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and have recently been extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable the results are, particularly in the context of tissue TME characterization. To investigate this question and fill the analytical gaps, we developed a computational program METAFlux (METAbolic Flux balance analysis), which extends the FBA framework to infer metabolic fluxes from either bulk or single-cell transcriptomic TME data. We benchmarked the prediction accuracy of METAFlux using the exometabolomics data generated on the NCI-60 cell lines and observed significant improvement over existing approaches. We tested METAFlux in bulk RNA-seq data obtained from various tumor types including those in the TCGA. We validated previous knowledge, e.g., lung squamous cell carcinoma (LUSC) has higher glucose uptake than lung adenocarcinoma (LUAD). We also found a novel subset of LUAD samples with unique metabolic profiles and distinct survival outcome. We further examined METAFlux on scRNA-seq data obtained from coculturing tumor cells with CAR-NK cells and observed high consistency between the predicted and the experimental (i.e., Seahorse extracellular) flux measurements. Throughout our investigation, we discovered various modes of metabolic cooperation and competition between various cell-types in TMEs, which could lead to further target discovery and development.
Cell-to-cell variability of dynamic CXCL12-CXCR4 signaling and morphological processes in chemotaxis
Authors: Ho Kenneth K.Y. , Srivastava Siddhartha , Kinnunen Patrick C. , Garikipati Krishna , Luker Gary D. , Luker Kathryn E. 
Published: May 2022

AbstractChemotaxis drives critical processes in cancer metastasis. While commonly studied at the population scale, metastasis arises from small numbers of cells that successfully disseminate, underscoring the need to analyze chemotaxis at single-cell resolution. Here we focus on chemotaxis driven by the CXCL12-CXCR4 pathway, a signaling network that promotes metastasis in more than 20 different human cancers. CXCL12-CXCR4 activates ERK and Akt, kinases known to promote chemotaxis, but how cells couple signaling to chemotaxis remain poorly defined. To address this challenge, we implemented single-cell analysis of MDA-MB-231 breast cancer cells migrating in a chemotaxis device towards chemokine CXCL12. We integrated live, single-cell imaging with advanced computational analysis methods to discover processes defining subsets of cells that move efficiently toward a CXCL12 gradient. We identified dynamic oscillations in ERK and Akt signaling and associated morphological transitions as key determinants of successful chemotaxis. Cells with effective chemotaxis toward CXCL12 exhibit faster and more persistent movement than non-migrating cells, but both cell populations show similar random motion. Migrating cells exhibit higher amplitude fluctuations in ERK and Akt signaling and greater frequencies of generating lateral cell membrane protrusions. Interestingly, computational analysis reveals less correlated network coupling of signaling and morphological changes in migrating cells. These data reveal processing events that enable cells to convert a signaling input to chemotaxis and highlight how cells in a uniform environment produce heterogeneous responses.
Self-generation of goal-directed choices in a distributed dopaminergic and prefrontal circuit
Authors: Bousseyrol E , Didienne S , Takillah S , Solié C , Come M , Yahia T Ahmed , Mondoloni S , Vicq E , Tricoire L , Mourot A , Naudé J , Faure Ph 
Published: May 2022

SummaryGoal-directed choices that are not triggered by external cues arise from internal representations of the outcomes. The use of a stimulus to specify when to act, which option to take, or whether to explore, has led to consider the reward circuit as a feedforward set of modules carrying independent computations. Here, we develop an uncued task in which mice self-determine the initiation, direction, vigor and pace of their actions based on their knowledge of the outcomes. Using electrophysiological recordings, pharmacology and optogenetics, we identify a sequence of oscillations and firing in the ventral tegmental area (VTA), orbitofrontal (OFC) and prefrontal cortices (PFC) that co-encodes and co-determines self-initiation and choices. This sequence appeared with learning as an unguided realignment of spontaneous dynamics. The interactions between the structures depended on the reward context, in particular regarding the uncertainty associated with the different options. We suggest that self-generated choices arise from a distributed circuit based on an OFC-VTA core setting whether to wait or to initiate actions, while the PFC is specifically engaged by reward uncertainty to participate in both the selection and pace of actions.HighlightsSelf-paced actions arise from contextual reorganization of mesocortical dynamics.VTA, PFC and OFC complementarily encode predictions and errors about outcomes.Distributed firing-then-oscillations dynamics set the goal, initiation and pace of actions.VTA and PFC antagonistically promote and inhibit motivation by reward uncertainty.
Human intracranial pulsatility during the cardiac cycle: a computational modelling framework
Authors: Causemann Marius , Vinje Vegard , Rognes Marie E. 
Published: May 2022

ABSTRACTBackgroundToday’s availability of medical imaging and computational resources set the scene for high-fidelity computational modelling of brain biomechanics. The brain and its environment feature a dynamic and complex interplay between the tissue, blood, cerebrospinal fluid (CSF) and interstitial fluid (ISF). Here, we design a computational platform for modelling and simulation of intracranial dynamics, and assess the models’ validity in terms of clinically relevant indicators of brain pulsatility.MethodsWe develop finite element models of fully coupled cardiac-induced pulsatile CSF flow and tissue motion in the human brain environment. The three-dimensional model geometry is derived from magnetic resonance images (MRI), features a high level of detail including the brain tissue, the ventricular system, and the cranial subarachnoid space (SAS). We model the brain parenchyma at the organ-scale as an elastic medium permeated by an extracellular fluid network and describe flow of CSF in the SAS and ventricles as viscous fluid movement. Representing vascular expansion during the cardiac cycle, a pulsatile net blood flow distributed over the brain parenchyma acts as the driver of motion. Additionally, we investigate the effect of model variations on a set of clinically relevant quantities of interest.ResultsOur model predicts a complex interplay between the CSF-filled spaces and poroelastic parenchyma in terms of ICP, CSF flow, and parenchymal displacements. Variations in the ICP are dominated by their temporal amplitude, but with small spatial variations in both the CSF-filled spaces and the parenchyma. Induced by ICP differences, we find substantial ventricular and cranial-spinal CSF flow, some flow in the cranial SAS, and small pulsatile ISF velocities in the brain parenchyma. Moreover, the model predicts a funnel-shaped deformation of parenchymal tissue in dorsal direction at the beginning of the cardiac cycle.ConclusionsOur model accurately depicts the complex interplay of ICP, CSF flow and brain tissue movement and is well-aligned with clinical observations. It offers a qualitative and quantitative platform for detailed investigation of coupled intracranial dynamics and interplay, both under physiological and pathophysiological conditions.