bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep learning of Protein Sequence Design of Protein-protein Interactions
Authors: Syrlybaeva Raulia , Strauch Eva-Maria 
Published: February 2022

AbstractMotivationAs more data of experimentally determined protein structures is becoming available, data-driven models to describe protein sequence-structure relationship become more feasible. Within this space, the amino acid sequence design of protein-protein interactions has still been a rather challenging sub-problem with very low success rates - yet it is central for the most biological processes.ResultsWe developed an attention-based deep learning model inspired by algorithms used for image-caption assignments for sequence design of peptides or protein fragments. These interaction fragments are derived from and represent core parts of protein-protein interfaces. Our trained model allows the one-sided design of a given protein fragment which can be applicable for the redesign of protein-interfaces or the de novo design of new interactions fragments. Here we demonstrate its potential by recapitulating naturally occurring protein-protein interactions including antibody-antigen complexes. The designed interfaces capture essential native interactions with high prediction accuracy and have native-like binding affinities. It further does not need precise backbone location, making it an attractive tool for working with de novo design of protein-protein interactions.AvailabilityThe source code of the method is available at https://github.com/strauchlab/iNNterfaceDesignSupplementary informationSupplementary data are available at Bioinformatics online.
Neural correlates of perceived emotions in human insula and amygdala
Authors: Zhang Yang , Zhou Wenjing , Huang Juan , Hong Bo , Wang Xiaoqin 
Published: February 2022

AbstractThe emotional status of a speaker is an important non-linguistic cue carried by human voice and can be perceived by a listener in vocal communication. Understanding the neural circuits involved in processing emotions carried by human voice is crucial for understanding the neural basis of social interaction. Previous studies have shown that human insula and amygdala responded more selectively to emotional sounds than non-emotional sounds. However, it is not clear whether the neural selectivity to emotional sounds in these brain structures is determined by the emotion presented by a speaker or by the emotion perceived by a listener. In this study, we recorded intracranial electroencephalography (iEEG) responses to emotional human voices while subjects performed emotion recognition tasks. We found that the iEEG responses of Heschl’s gyrus (HG) and posterior insula were determined by the presented emotion, whereas the iEEG responses of anterior insula and amygdala were driven by the perceived emotion. These results suggest that the anterior insula and amygdala play a crucial role in representing subjectively perceived emotion.
Differences between Omicron SARS-CoV-2 RBD and other variants in their ability to interact with cell receptors and monoclonal antibodies
Authors: Giron Carolina Corrêa , Laaksonen Aatto , da Silva Fernando Luís Barroso 
Published: February 2022

AbstractSARS-CoV-2 has caused immeasurable damage worldwide and available treatments with high efficacy are still scarce. With the continuous emergence of new variants of the virus, such as Omicron, Alpha, Beta, Gamma, and Delta - the so-called variants of concern, the available therapeutic and prevention strategies had to return to the experimental trial to verify their effectiveness against them. This work aims to expand the knowledge about the SARS-CoV-2 receptor-binding domain (RBD) interactions with cell receptors and monoclonal antibodies (mAbs). Special attention is given to the Omicron variant and its comparison with the others, including its sublineage BA.2 and two new ones (B.1.640.1 and B.1.640.2/IHU) recently found in France. By using constant-pH Monte Carlo simulations, the free energy of interactions between the SARS-CoV-2 receptor-binding domain (RBD) from different variants and several partners (Angiotensin-Converting Enzyme-2 (ACE2) polymorphisms and several mAbs) were calculated. It was evaluated both the impact of mutations for the RBD-ACE2 and how strongly each of mAb can bind to the virus RBD, which can indicate their therapeutic potential for neutralization. RBD-ACE2-binding affinities were higher for two ACE2 polymorphisms typically found in Europeans (rs142984500 and rs4646116), indicating that these types of polymorphisms may be related to genetic susceptibility to COVID-19. The antibody landscape was computationally investigated with the largest set of mAbs so far in the literature. From the 33 studied binders, groups of mAbs were identified with weak (e.g. S110 and Ab3b4), medium (e.g. CR3022), and strong binding affinities (e.g. P01’’’, S2K146 and S230). All the mAbs with strong binding capacity could also bind to the RBD from SARS-CoV-1, SARS-CoV-2 wt, and all studied variants. These mAbs and especially their combination are amenable to experimentation and clinical trials because of their high binding affinities and neutralization potential for current known virus mutations and a universal coronavirus.
The spatial periodic computation of hippocampus-entorhinal circuit in navigation
Authors: Zhang Bo , Liu Jia 
Published: February 2022

SummaryTo achieve the computational goal of navigating in both physical and mental spaces, human brain employs a cognitive map constructed by the global metrics of the entorhinal cortex and the local locations of the hippocampus. However, how these two regions work collaboratively in navigation remains unclear. Here, we designed an object-matching task where human participants unknowingly manipulated object variants arranged in a ring-like structure around a central prototype. Functional MRI revealed a 3-fold spatial periodicity of hippocampal activity, which tracked the navigation path from the original object variants to the central prototype in the object space. Importantly, this spatial periodicity of the hippocampus was phase-locked with the well-documented 6-fold periodicity of the entorhinal cortex, suggesting a periodic scaffold connecting these two regions. In addition, a 3-fold periodicity was found embedded in human behavior, which fluctuated as a function of the navigation path and phase-locked with hippocampal activity. Finally, we proposed an E-H PhaseSync model to illustrate that the spatial periodicity originated from the population activity of entorhinal grid cells may serve as a scaffold in the hippocampal- entorhinal network, where hippocampal vector fields emerge as the neural basis for utilizing the cognitive map in navigation.
An intranasal lentiviral booster broadens immune recognition of SARS-CoV-2 variants and reinforces the waning mRNA vaccine-induced immunity that it targets to lung mucosa
Authors: Vesin Benjamin , Lopez Jodie , Noirat Amandine , Authié Pierre , Fert Ingrid , Le Chevalier Fabien , Moncoq Fanny , Nemirov Kirill , Blanc Catherine , Planchais Cyril , Mouquet Hugo , Guinet Françoise , Hardy David , Gerke Christiane , Anna François , Bourgine Maryline , Majiessi Laleh , Charneau Pierre 
Published: February 2022

AbstractAs the COVID-19 pandemic continues and new SARS-CoV-2 variants of concern emerge, the adaptive immunity initially induced by the first-generation COVID-19 vaccines wains and needs to be strengthened and broadened in specificity. Vaccination by the nasal route induces mucosal humoral and cellular immunity at the entry point of SARS-CoV-2 into the host organism and has been shown to be the most effective for reducing viral transmission. The lentiviral vaccination vector (LV) is particularly suitable for this route of immunization because it is non-cytopathic, non-replicative and scarcely inflammatory. Here, to set up an optimized cross-protective intranasal booster against COVID-19, we generated an LV encoding stabilized Spike of SARS-CoV-2 Beta variant (LV::SBeta-2P). mRNA vaccine–primed and -boosted mice, with waning primary humoral immunity at 4 months post-vaccination, were boosted intranasally with LV::SBeta-2P. Strong boost effect was detected on cross-sero-neutralizing activity and systemic T-cell immunity. In addition, mucosal anti-Spike IgG and IgA, lung resident B cells, and effector memory and resident T cells were efficiently induced, correlating with complete pulmonary protection against the SARS-CoV-2 Delta variant, demonstrating the suitability of the LV::SBeta-2P vaccine candidate as an intranasal booster against COVID-19.
Vamos en bici: Study protocol of an investigation of cognitive and neural changes following language training, physical exercise training, or a combination of both
Authors: Wenger Elisabeth , Düzel Sandra , Polk Sarah E. , Bodammer Nils C. , Misgeld Carola , Porst Johanna , Wolfarth Bernd , Kühn Simone , Lindenberger Ulman 
Published: February 2022

AbstractAs the relative number of elderly people as well as the average life expectancy increases, identifying potential means to alter the ongoing trajectory of aging and specifically the trajectory of cognitive performance is of great importance. Some modifiable lifestyle factors, such as physical and cognitive activity, have shown positive effects on cognition and brain structure, and the combination of the two might even show a boosted, interactive effect. In this study protocol, we describe in detail our data which was acquired to explore how cognitive stimulation in the form of acquiring a new language, physical exercise on stationary bikes or the combination of the two interventions affect brain structure, cognitive performance, and psychosocial functioning. One-hundred and forty-two older healthy adults (63–78 years) were randomly assigned to one of four six-month intervention programs, comprising (a) foreign language learning, (b) physical exercise training on a bicycle ergometer, (c) a combination of language learning and physical exercise, or (d) a book club (serving as an active control condition). We collected a rich neuroimaging data set, comprising T1-weighted structural, resting state functional, high resolution hippocampal, myelin water fraction, diffusion-weighted, arterial spin labeling, and multi-parameter images. Using a cognitive battery, we collected data from the domains of episodic memory, working memory, perceptual speed, and fluid intelligence. We performed comprehensive physical assessments including cardiopulmonary exercise testing, and additionally collected data on psychosocial functioning (e.g., well-being, perceived stress, control beliefs). We assume that physical activity boosts brain plasticity per se by inducing structural and neurochemical changes in brain regions that are important for learning and memory and therefore may facilitate the effects of cognitive training. (269 words)
Constructing Causal Cytokine Networks for Autoimmune and Inflammatory Diseases Target Identification
Authors: Chen Yanqing 
Published: February 2022

AbstractCytokines are cell signaling proteins that act as inducers of physiological responses including the activation and differentiation of innate and adaptive immune cell types. In some cases, this mediates the initiation or progression of inflammatory and auto-immune diseases such as septic shock, rheumatoid arthritis, psoriasis, and inflammatory bowel disease. Many cytokines are pleiotropic, meaning that they can have effects on multiple cell types and play several distinct functions. On the other hand, multiple cytokines might have similar function and be redundant or degenerate. Some cytokines also regulate the expression and functions of other cytokines. Such complex cause-effect relationships can be modeled computationally by building “cytokine networks”. Those networks describe intricate interactions between different cytokines in their associated tissue environment and cell types. Here we propose a computational approach to find sensitive and “causal” nodes within a cytokine interaction network by identifying whether persistent positive feedback (self-loop) exists on that cytokine in its mRNA transcription network. Based upon two microarray gene expression datasets from immune stimulation experiments using up to 15 different cytokines, we identified TNFα as a “causal” cytokine in the colonic epithelial cell line HCT8, and IL-23, IL-1β and TGFβ as “causal” cytokines in primary myofibroblasts. We hypothesize that a cytokine with a persistent positive feedback loop that amplifies its transcription and secretion could be identified as “causal” for the transcriptional regulation of other cytokines and potentially, downstream disease phenotypes. The existence of such positive feedback loop may determine whether targeting suppression of such cytokine will result in clinical efficacy.
AI-assisted Discovery of an Ethnicity-influenced Driver of Cell Transformation in Esophageal and Gastroesophageal Junction Adenocarcinomas
Authors: Ghosh Pradipta , Campos Vinicius J. , Vo Daniella T. , Guccione Caitlin , Goheen-Holland Vanae , Tindle Courtney , Mazzini Guilherme S. , He Yudou , Alexandrov Ludmil , Lippman Scott M. , Gurski Richard R. , Das Soumita , Yadlapati Rena , Curtius Kit , Sahoo Debashis 
Published: February 2022

ABSTRACTAlthough Barrett’s metaplasia of the esophagus (BE) is the only known precursor lesion to esophageal adenocarcinomas (EACs), drivers of the metaplasia→dysplasia→neoplasia cascade in the esophagus remains incompletely understood. Using an AI-guided network transcriptomics approach, in which EAC initiation and progression is modeled as networks to simplify complex multi-cellular processes, we first predict cellular continuum states and disease driving processes with an unprecedented degree of precision. Key AI-guided predictions are subsequently validated in a human organoid model and patient-derived biopsies of BE, a case-control study of genomics of BE progression, and in a cross-sectional study of 113 patients with BE and EACs. We find that all EACs must originate from BE, pinpoint a CXCL8/IL8↔neutrophil immune microenvironment as a driver of cellular transformation in both EACs and gastroesophageal junction-ACs. This driver is prominent in Caucasians (Cau), but notably absent in African Americans (AAs). Network-derived gene signatures, independent signatures of neutrophil processes, CXCL8/IL8, and an absolute neutrophil count (ANC) are associated with risk of progression. SNPs associated with ethnic changes in ANC modify that risk. Thus, findings define a racially influenced immunological basis for cell transformation and suggest that benign ethnic neutropenia in AAs may serve as a deterrent to BE→EAC progression.BRIEF SUMMARYEsophageal adenocarcinoma (EAC) is a highly lethal cancer among Caucasians, while African Americans are somewhat protected; what factors drive transformation with racial disparity remain unknown. AI-enabled creation of the first computational map of neoplastic progression in the esophagus built and validated using transcriptomic datasets from diverse cohorts of human samples pinpointed CXCL8↔neutrophil tumor immune-microenvironment as a racially influenced driver of EACs and GEJ-ACs. Computational tools pinpoint a racially influenced driver of cell transformation during BE→EAC progression; in doing so, it reveals new novel biology, informs disease modeling, therapeutic strategies, and biomarkers.LAY SUMMARYBy modeling diseases as networks, this work unravels a fundamental race-influenced immunologic driver of cell transformation in adenocarcinomas of the esophagus and the gastroesophageal junction.
Exploring the thermal limits of malaria transmission in the western Himalaya
Authors: Mozaffer Farhina , Menon Gautam I. , Ishtiaq Farah 
Published: February 2022

AbstractEnvironmental temperature is a key driver of malaria transmission dynamics. Using detailed temperature records from four sites (1800-3200m) in the western Himalaya, we model how temperature regulates parasite development rate (the inverse of the extrinsic incubation period, EIP) in the wild. Using a Briére parametrization of the EIP, combined with Bayesian parameter inference, we study the thermal limits of transmission for avian (P. relictum) and human Plasmodium parasites (P. vivax and P. falciparum) as well as for two malaria-like avian parasites, Haemoproteus and Leucocytozoon. We demonstrate that temperature conditions can substantially alter the incubation period of parasites at high elevation sites (2600-3200m) leading to restricted parasite development or long transmission windows. We then compare estimates of EIP based on measures of mean temperature versus hourly temperatures to show that EIP days vary in cold versus warm environments. We found that human Plasmodium parasites experience a limited transmission window at 2600m. In contrast, for avian Plasmodium transmission was not possible between September to March at 2600m. In addition, temperature conditions suitable for both Haemoproteus and Leucocytozoon transmission were obtained from June to August and in April, at 2600m. Finally, we use temperature projections from a suite of climate models to predict that by 2040, high elevation sites (~ 2600 m) will have a temperature range conducive for malaria transmission, albeit with a limited transmission window. Our study highlights the importance of accounting for fine-scale thermal effects in the expansion of the range of the malaria parasite with global climate change.
Neural network for the prediction of treatment response in Triple Negative Breast Cancer *
Authors: Naylor Peter , Lazard Tristan , Bataillon Guillaume , Lae Marick , Vincent-Salomon Anne , Hamy Anne-Sophie , Reyal Fabien , Walter Thomas 
Published: February 2022

AbstractThe automatic analysis of stained histological sections is becoming increasingly popular. Deep Learning is today the method of choice for the computational analysis of such data, and has shown spectacular results for large datasets for a large variety of cancer types and prediction tasks. On the other hand, many scientific questions relate to small, highly specific cohorts. Such cohorts pose serious challenges for Deep Learning, typically trained on large datasets.In this article, we propose a modification of the standard nested cross-validation procedure for hyper-parameter tuning and model selection, dedicated to the analysis of small cohorts. We also propose a new architecture for the particularly challenging question of treatment prediction, and apply this workflow to the prediction of response to neoadjuvant chemotherapy for Triple Negative Breast Cancer.
Constraint-based modeling identifies metabolic vulnerabilities during the epithelial to mesenchymal transition
Authors: Campit Scott , Keshamouni Venkateshwar G. , Chandrasekaran Sriram 
Published: February 2022

AbstractEpithelial-to-mesenchymal transition (EMT) is a developmentally conserved cellular process critical for tumor metastasis. EMT enables malignant epithelial cells to acquire mesenchymal-like migratory and invasive phenotype. During EMT cancer cells undergo extensive metabolic reprogramming that correlates with the suppression of proliferation, and stimulation of the energy-intensive migratory behavior. However, the causal relationship between metabolic changes and coordinated physiological phenotypes that occur during EMT is still unclear. We used bulk time-course transcriptomics and proteomics, and single-cell transcriptomics from five independent EMT studies in A549 lung adenocarcinoma cells to simulate metabolic network activity using constraint-based modeling. Model predictions were validated using literature mining, experimental studies and CRISPR-Cas9 essentiality screens.We uncovered temporal metabolic dependencies in glycolysis and glutamine metabolism reactions, and experimentally validated isoform-specific dependency on Enolase3 for cell survival during EMT. Together, our approach uncovered temporally regulated cell-state-specific metabolic dependencies in cells undergoing EMT.
Discovery of novel therapeutic targets in cancer using patient-specific gene regulatory networks
Authors: Forbes Andre Neil , Xu Duo , Cohen Sandra , Pancholi Priya , Khurana Ekta 
Published: February 2022

AbstractMost cancer types lack effective targeted therapeutic options and in cancers where first-line targeted therapies are available, treatment resistance is a huge challenge. Recent technological advances enable the use of ATAC-seq and RNA-seq on patient biopsies in a high-throughput manner. Here we present a computational approach that leverages these datasets to identify novel drug targets based on tumor lineage. We constructed patient-specific gene regulatory networks for 371 patients of 22 cancer types using machine learning approaches trained using three-dimensional genomic data for enhancer to promoter contacts. Next, we identify the key transcription factors (TFs) in these networks, which are used to identify therapeutic vulnerabilities either by direct targeting of TFs or proteins that they co-operate with. We validate four novel candidates identified for neuroendocrine, liver and renal cancers, which have a dismal prognosis with current therapeutic options. We present a novel approach to use the increasing amounts of functional genomics data from patient biospecimens for identification of novel drug targets.
Amyloid fibrils in frontotemporal lobar degeneration with TDP-43 inclusions are composed of TMEM106B, rather than TDP-43
Authors: Jiang Yi Xiao , Cao Qin , Sawaya Michael R. , Abskharon Romany , Ge Peng , DeTure Michael , Dickson Dennis W. , Fu Janine Y. , Ogorzalek Loo Rachel R. , Loo Joseph A. , Eisenberg David S. 
Published: February 2022

AbstractFTLD is the third most common neurodegenerative condition, following only Alzheimer’s and Parkinson’s diseases. FTLD typically presents in 45-64-year-olds with behavioral changes or progressive decline of language skills. The subtype FTLD-TDP is characterized by certain clinical symptoms and pathological neuronal inclusions detected by TDP-43 immunoreactivity. Here, we extracted amyloid fibrils from brains of four patients, representing four out of five FTLD-TDP subclasses and determined their near-atomic resolution structures by cryo-EM. Unexpectedly, all amyloid fibrils examined are composed of a 135-residue C-terminal fragment of TMEM106B, a lysosomal membrane protein previously implicated as a genetic risk factor for FTLD-TDP. In addition to TMEM106B fibrils, abundant non-fibrillar aggregated TDP-43 is present, as revealed by immunogold labeling. Our observations confirm that FTLD-TDP is an amyloid-involved disease and suggest that amyloid involvement in FTLD-TDP is of protein TMEM106B, rather than of TDP-43.