Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating protein copy numbers with interaction networks to quantify stoichiometry in mammalian endocytosis

Proteins that drive processes like clathrin-mediated endocytosis (CME) are expressed at various copy numbers within a cell, from hundreds (e.g. auxilin) to millions (e.g. clathrin). Between cell types with identical genomes, copy numbers further vary significantly both in absolute and relative abundance. These variations contain essential information about each protein’s function, but how significant are these variations and how can they be quantified to infer useful functional behavior? Here, we address this by quantifying the stoichiometry of proteins involved in the CME network. We find robust trends across three cell types in proteins that are sub- vs super-stoichiometric in terms of protein function, network topology (e.g. hubs), and abundance. To perform this analysis, we first constructed the interface resolved network of 82 proteins involved in CME in mammals, plus lipid and cargo binding partners, totaling over 600 specific binding interactions. Our model solves for stoichiometric balance by optimizing each copy of a protein interface to match up to its partner interfaces, keeping the optimized copies as close as possible to observed copies. We find highly expressed, structure-forming proteins such as actin and clathrin do tend to be super-stoichiometric, or in excess of their partners, but they are not the most extreme cases. We test sensitivity of network stoichiometry to protein removal and find that hub proteins tend to be less sensitive to removal of any single partner, thus acting as buffers that compensate dosage changes. As expected, tightly coupled protein pairs (e.g. CAPZA2 and CAPZB) are strongly correlated. Unexpectedly, removal of functionally similar cargo adaptor proteins produces widely variable levels of disruption to the network stoichiometry. Our results predict that knockdown of the adaptor protein DAB2 will globally impact the stoichiometry of most other cargo adaptor proteins in Hela cells, with significantly less impact in fibroblast cells. This inexpensive analysis can be applied to any protein network, synthesizing disparate sources of biological data into a relatively simple and intuitive model of binding stoichiometry that can aid in dynamical modeling and experimental design.
A systematic analysis of the beta hairpin motif in the Protein Data Bank

The beta hairpin motif is a ubiquitous protein structural motif that can be found in molecules across the tree of life. This motif, which is also popular in synthetically designed proteins and peptides, is known for its stability and adaptability to broad functions. Here we systematically probe all 49,000 unique beta hairpin substructures contained within the Protein Data Bank (PDB) to uncover key characteristics correlated with stable beta hairpin structure, including amino acid biases and enriched inter-strand contacts. We also establish a set of broad design principles that can be applied to the generation of libraries encoding proteins or peptides containing beta hairpin structures. Importance The beta hairpin motif is a common protein structural motif that is known for its stability and varied activity in diverse proteins. Here we use nearly fifty thousand beta hairpin substructures from the Protein Data Bank to systematically analyze and identify key characteristics of the beta hairpin motif. Ultimately, we provide a set of design principles for the generation of synthetic libraries encoding proteins containing beta hairpin structures.
Diversity of ACE2 and its interaction with SARS-CoV-2 receptor binding domain

COVID-19, the clinical syndrome caused by the SARS-CoV-2 virus, has rapidly spread globally causing tens of millions of infections and over a million deaths. The potential animal reservoirs for SARS-CoV-2 are currently unknown, however sequence analysis has provided plausible potential candidate species. SARS-CoV-2 binds to the angiotensin I converting enzyme 2 (ACE2) to enable its entry into host cells and establish infection. We analyzed the binding surface of ACE2 from several important animal species to begin to understand the parameters for the ACE2 recognition by the SARS-CoV-2 spike protein receptor binding domain (RBD). We employed Shannon entropy analysis to determine the variability of ACE2 across its sequence and particularly in its RBD interacting region, and assessed differences between various species’ ACE2 and human ACE2. As cattle are a known reservoir for coronaviruses with previous human zoonotic transfer, and has a relatively divergent ACE2 sequence, we compared the binding kinetics of bovine and human ACE2 to SARS-CoV-2 RBD. This revealed a nanomolar binding affinity for bovine ACE2 but an approximate ten-fold reduction of binding compared to human ACE2. Since cows have been experimentally infected by SARS-CoV-2, this lower affinity sets a threshold for sequences with lower homology to human ACE2 to be able to serve as a productive viral receptor for SARS-CoV-2.
Natural cystatin C fragments inhibit GPR15-mediated HIV and SIV infection without interfering with GPR15L signaling

Significance G protein-coupled receptors (GPCRs) are involved in many physiological processes and important drug targets. However, most therapeutic agents targeting GPCRs, such as the coreceptors of HIV-1 CCR5 and CXCR4, also interfere with their signaling function. Here, we used primate lentiviruses as tools to discover novel endogenous ligands of GPR15, a coreceptor for HIV-2 and SIV proposed to play a role in mucosal immunity. We found that C-terminal fragments of cystatin C generated by immune-activated proteases inhibit GPR15-mediated HIV and SIV infection without interfering with GPR15L chemokine signaling. Thus, we identified an endogenous bioactive peptide that specifically prevents the detrimental activity of a GPCR (i.e. virus infection) without compromising its physiological signaling function. GPR15 is a G protein-coupled receptor (GPCR) proposed to play a role in mucosal immunity that also serves as a major entry cofactor for HIV-2 and simian immunodeficiency virus (SIV). To discover novel endogenous GPR15 ligands, we screened a hemofiltrate (HF)-derived peptide library for inhibitors of GPR15-mediated SIV infection. Our approach identified a C-terminal fragment of cystatin C (CysC95-146) that specifically inhibits GPR15-dependent HIV-1, HIV-2, and SIV infection. In contrast, GPR15L, the chemokine ligand of GPR15, failed to inhibit virus infection. We found that cystatin C fragments preventing GPR15-mediated viral entry do not interfere with GPR15L signaling and are generated by proteases activated at sites of inflammation. The antiretroviral activity of CysC95-146 was confirmed in primary CD4+ T cells and is conserved in simian hosts of SIV infection. Thus, we identified a potent endogenous inhibitor of GPR15-mediated HIV and SIV infection that does not interfere with the physiological function of this GPCR.
Intra-axonal translation of Khsrp mRNA slows axon regeneration by destabilizing localized mRNAs

Proteins generated by localized mRNA translation in axons support nerve regeneration through retrograde injury signaling and localized axon growth mechanisms. RNA binding proteins (RBP) are needed for this and other aspects of post-transcriptional control of localized mRNAs, but only a limited number of axonal RBPs have been reported. We used a targeted mass spectrometry approach to profile the axonal RBPs in naïve, injured and regenerating PNS axons. We detected 76 axonal proteins that are reported to have RNA binding activity, with the levels of several of these axonal RBPs changing with axonal injury and regeneration. These axonal RBPs with altered axoplasm levels include KHSRP that we previously reported decreases neurite outgrowth in developing CNS neurons. We show that KHSRP levels rapidly increase in sciatic nerve axons after crush injury and remain elevated increasing in levels out to 28 days post-sciatic nerve crush injury. Khsrp mRNA localizes into axons and the rapid increase in axonal KHSRP after axotomy is mediated by the local translation of its mRNA. KHSRP binds to mRNAs with a 3’UTR AU-rich element and targets those mRNAs to the cytoplasmic exosome for degradation. KHSRP knockout mice show increased axonal levels of defined KHSRP target mRNAs, Gap43 and Snap25 mRNAs, following sciatic nerve injury and accelerated nerve regeneration in vivo. These data indicate that axonal translation of Khsrp mRNA following nerve injury serves to destabilize other axonal mRNAs and slow axon regeneration.
Phylum barrier and Escherichia coli intra-species phylogeny drive the acquisition of resistome in E. coli

Escherichia coli is a ubiquitous bacterium that has widely been exposed to antibiotics over the last 70 years. It has adapted by acquiring different antibiotic resistance genes (ARG), which we aim at characterizing the census here. To do so, we analysed 70,301 E. coli genomes obtained from the EnteroBase database and detected 1,027,651 ARG using the AMRFinder, Mustard and ResfinderFG ARG databases. We observed a strong phylogroup/clonal lineage specific distribution of some ARG, arguing for epistasis between ARG and the strain genetic background. However, each phylogroup had ARG conferring a similar resistance pattern, indicating phenotypic convergence. The GC content or the type of ARG was not associated to the frequency of the ARG in the database. Besides, we identified ARG from anaerobic, non-Proteobacteria bacteria in four genomes of E. coli supporting that the transfer between anaerobic bacteria and E. coli can spontaneously occur but remain exceptional. In conclusion, we showed that phylum barrier and intra-species phylogenetic history are major drivers of the acquisition of resistome in E. coli.
Leveraging big data for classification of children who stutter from fluent peers

Large datasets, consisting of hundreds or thousands of subjects, are becoming the new data standard within the neuroimaging community. While big data creates numerous benefits, such as detecting smaller effects, many of these big datasets have focused on non-clinical populations. The heterogeneity of clinical populations makes creating datasets of equal size and quality more challenging. There is a need for methods to connect these robust large datasets with the carefully curated clinical datasets collected over the past decades. In this study, we use resting-state fMRI data from the Adolescent Brain Cognitive Development study and the Human Connectome Project to discover generalizable brain features for use in an out-of-sample predictive model to classify young (3-10yrs) children who stutter from fluent peers. We achieve up to 72% classification accuracy using 10-fold cross validation. This study suggests that big data has the potential to yield generalizable biomarkers that are clinically meaningful. Specifically, this is the first study to demonstrate that big data-derived brain features can help differentiate children who stutter from their fluent peers and provide novel information on brain networks relevant to stuttering pathophysiology. The results provide a significant expansion to previous understanding of the neural bases of stuttering. In addition to auditory, somatomotor, and subcortical networks, the big data-based models highlight the importance of considering large scale brain networks supporting error sensitivity, attention, cognitive control, and emotion regulation/self-inspection in the neural bases of stuttering.
LH LepR neurons directly drive sustained food seeking behavior after AgRP neuronal deactivation via neuromodulation of NPY

1.Agouti-related protein (AgRP) has been believed to be the main driver of feeding behaviors ever since its discovery. However, recent studies using fiber photometry and optogenetics proved that feeding behaviors are not directly driven by AgRP neurons (temporal discrepancy between neuronal activity and behavior). To resolve this paradox, we conducted novel multi-phase feeding experiments to scrutinize the dynamics of AgRP. Fiber photometry study showed that AgRP neurons start to deactivate even before the initiation of the food search phase. Using optogenetics, we could prove that the feeding behavior induced by AgRP neuron activation had substantial temporal delay and the feeding behavior was sustained for substantial time even after cessation of optogenetic activation. These results indicate that AgRP neurons are not the direct driver of feeding behavior and another downstream neuron is the driver of feeding behavior. Leptin receptor (LepR) neurons in the lateral hypothalamus (LH). LH LepR neurons were activated before voluntary food search behavior initiation and showed robust increase after food approach behavior. Artificial activation of LH LepR neurons drives food search and food approach behavior. In accordance, chemogenetic activation of LepR neurons increased food search and food approach behaviors. Lastly, slice calcium imaging results showed the possibility that NPY from the AgRP neurons could be the downstream neuromodulator of AgRP neuron, driving LH LepR neuron activation. Overall, our study shows that AgRP neurons are not the direct drivers of feeding behavior, whereas LH LepR neurons directly drive sustained food seeking behavior.
RL-118 and 11β-HSD1 target engagement through TAPS assay: behaviour and molecular analysis

Taking into consideration the convergence of ageing, stress and neurodegenerative diseases, such as AD, there is impaired GC signalling. Therefore, the study of GC-mediated stress response to chronic moderate stressful situations, as account in daily life, becomes of huge interest to design pharmacological strategies to prevent neurodegeneration. To address this issue, SAMP8 were exposed for 4 weeks to the CMS paradigm and treated with RL-118, an 11β-HSD1 inhibitor. In fact, several pieces of evidence link the inhibition of this enzyme with reduction of GC levels and cognitive improvement, while CMS exposure has been associated with reduced cognitive performance. The aim of this project was to assess whether RL-118 treatment could restore the deleterious effects of CMS on cognition and behavioural abilities, but also on molecular mechanisms that compromise healthy ageing in SAMP8 mice. On the one hand, we determined the target engagement between RL-118 and 11β-HSD1. Therefore all the beneficial effects previously described in SAMP8 treated with the drug can undoubtedly be attributed to the inhibition of this enzyme. Besides, herein we observed decreased DNA methylation, hydroxymethylation and histone phosphorylation induced by CMS but, on the contrary, increased after RL-118 treatment. In addition, CMS exposure produced ROS damage accumulation, and increments of pro-oxidant enzymes as well as pro-inflammatory mediators through NF-κB pathway and astrogliosis markers, like Gfap. Of note, those modifications were recovered by 11β-HSD1 inhibition. Remarkably, although CMS altered mTORC1 signalling, autophagy was increased in SAMP8 treated with RL-118 mice. Also, we found amyloidogenic APP processing pathway favoured and decreased synaptic plasticity and neuronal remodelling markers in mice under CMS, but changed after RL-118 treatment. In consequence, detrimental effects on behaviour and cognitive performance were detected in CMS exposed mice, but restored after concomitant 11β-HSD1 inhibition by RL-118. Overall, CMS is a feasible intervention to understand the influence of stress on epigenetic mechanisms underlying cognition and accelerating senescence. However and most important, 11β-HSD1 inhibition through RL-118 turned up to restore the majority of these detrimental effects caused by CMS, indicating that GC excess attenuation may become a potential therapeutic strategy for age-related cognitive decline and AD.
ViMIC: a database of human disease-related virus mutations, integration sites and cis-effects

Molecular mechanisms of virus-related diseases involve multiple factors, including viral mutation accumulation and integration of a viral genome into the host DNA. With increasing attention being paid to virus-mediated pathogenesis and the development of many useful technologies to identify virus mutations (VMs) and viral integration sites (VISs), abundant literatures on these topics are available in PubMed. However, knowledge of VMs and VISs is widely scattered in numerous published papers, and the association of VMs with VISs in the viral genome or the functional annotation of VISs still lacks integration and curation. To address these challenges, we built a database of human disease-related Virus Mutations, Integration sites and Cis-effects (ViMIC), which specialize in three features: virus mutation sites, viral integration sites and target genes. In total, the ViMIC provides information on 6,461 VMs, 79,089 VISs, and 15,056 viral target genes of 8 viruses in 65 human diseases obtained from literatures. Furthermore, in ViMIC, users are allowed to explore the cis-effects of virus-host interactions by surveying 78 histone modifications, binding of 1,358 transcription regulators, and chromatin accessibility on these VISs. We believe ViMIC will become a valuable resource for the virus research community. The database is available at http://bmtongji.cn/ViMIC/index.php.
HiC-ACT: Improved Detection of Chromatin Interactions from Hi-C Data via Aggregated Cauchy Test

Genome-wide chromatin conformation capture technologies such as Hi-C are commonly employed to study chromatin spatial organization. In particular, to identify statistically significant long-range chromatin interactions from Hi-C data, most existing methods such as Fit-Hi-C/FitHiC2 and HiCCUPS assume that all chromatin interactions are statistically independent. Such an independence assumption is reasonable at low resolution (e.g., 40Kb bin), but is invalid at high resolution (e.g., 5 or 10Kb bins) since spatial dependency of neighboring chromatin interactions is non-negligible at high resolution. Our previous hidden Markov random field based methods accommodate spatial dependency but are computationally intensive. It is urgent to develop approaches that can model spatial dependence, in a computationally efficient and scalable manner. Here, we develop HiC-ACT, an aggregated Cauchy test (ACT) based approach, to improve the detection of chromatin interactions by post-processing results from methods assuming independence. To benchmark the performance of HiC-ACT, we re-analyzed deeply sequenced Hi-C data from a human lymphoblastoid cell line GM12878 and mouse embryonic stem cell line (mESC). Our results demonstrate advantages of HiC-ACT in improving sensitivity with controlled type-I error. By leveraging information from neighboring chromatin interactions, HiC-ACT enhances the power to detect interactions with lower signal to noise ratio and similar (if not stronger) epigenetic signatures that suggest regulatory roles. We further demonstrate that HiC-ACT peaks show higher overlap with known enhancers than Fit-Hi-C/FitHiC2 peaks, in both GM12878 and mESC. HiC-ACT, effectively a summary statistic based approach, is computationally efficient (~6 minutes and ~2GB memory to process 25,000 pairwise interactions).
Semisupervised definition of hippocampal ripples

Hippocampal ripples are transient neuronal features observed in high-frequency oscillatory bands of local field potentials, and they occur primarily during periods of behavioral immobility and slow-wave sleep. Ripples have been defined based on mathematically engineered features, such as magnitudes, durations, and cycles per event. However, the “ripples” could vary from laboratory to laboratory because their definition is subject to human bias, including the arbitrary choice of parameters and thresholds. In addition, local field potentials are often influenced by myoelectric noise arising from animal movement, making it difficult to distinguish ripples from high-frequency noises. To overcome these problems, we extracted ripple candidates under few constraints and labeled them as binary or stochastic “true” or “false” ripples using Gaussian mixed model clustering and a deep convolutional neural network in a weakly supervised fashion. Our automatic method separated ripples and myoelectric noise and was able to detect ripples even when the animals were moving. Moreover, we confirmed that a convolutional neural network was able to detect ripples defined by our method. Leave-one-animal-out cross-validation estimated the area under the precision-recall curve for ripple detection to be 0.72. Finally, our model establishes an appropriate threshold for the ripple magnitude in the case of the conventional detection of ripples.
Sex-specific predictive values of biomarkers for immunotherapy efficacy in lung adenocarcinoma

It remains a challenge to accurately predict patient responses to tumor immunotherapy, although various biomarkers have been proposed to predict patient responses to anti-PD-1 therapy. Here by integrating genomic, transcriptomic, proteomic and clinical phenotype data from three immunotherapeutic cohorts and a multiple-dimensional dataset of The Cancer Genome Atlas (TCGA) project, we uncovered a profound effect of Sex on the predictive values of conventional biomarkers in lung adenocarcinoma (LUAD): only in females were nonsynonymous mutation burden (TMB), neoantigen burden, smoking signature, KRAS mutations (especially G12C and G12V) or tumor microenvironment robustly correlated with anti-PD-1 efficacy; the correlations in males were either absent or weaker. We propose that Sex be considered in conjunction with conventional biomarkers when predicting immunotherapy efficacy, and conversely, conventional biomarkers be carefully controlled for when attempting to dissect the impact of Sex on immunotherapy efficacy.
Biological insights from the whole genome analysis of human embryonic stem cells

There has not yet been a systematic analysis of hESC whole genomes at a single nucleotide resolution. We therefore performed whole genome sequencing (WGS) of 143 hESC lines and annotated their single nucleotide and structural genetic variants. We found that while a substantial fraction of hESC lines contained large deleterious structural variants, finer scale structural and single nucleotide variants (SNVs) that are ascertainable only through WGS analyses were present in hESCs genomes and human blood-derived genomes at similar frequencies. However, WGS did identify SNVs associated with cancer or other diseases that will likely alter cellular phenotypes and may compromise the safety of hESC-derived cellular products transplanted into humans. As a resource to enable reproducible hESC research and safer translation, we provide a user-friendly WGS data portal and a data-driven scheme for cell line maintenance and selection. GRAPHICAL ABSTRACT IN BRIEF Merkle and Ghosh et al. describe insights from the whole genome sequences of commonly used human embryonic stem cell (hESC) lines. Analyses of these sequences show that while hESC genomes had more large structural variants than humans do from genetic inheritance, hESCs did not have an observable excess of finer-scale variants. However, many hESC lines contained rare loss-of-function variants and combinations of common variants that may profoundly shape their biological phenotypes. Thus, genome sequencing data can be valuable to those selecting cell lines for a given biological or clinical application, and the sequences and analysis reported here should facilitate such choices. HIGHLIGHTS One third of hESCs we analysed are siblings, and almost all are of European ancestry Large structural variants are common in hESCs, but finer-scale variation is similar to that human populations Many strong-effect loss-of-function mutations and cancer-associated mutations are present in specific hESC lines We provide user-friendly resources for rational hESC line selection based on genome sequence