Lung Tissue Multi-Layer Network Analysis Uncovers the Molecular Heterogeneity of COPD.

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous condition. We hypothesized that the unbiased integration of different COPD lung omics using a novel multi-layer approach may unravel mechanisms associated with clinical characteristics. METHODS: We profiled mRNA, miRNA and methylome in lung tissue samples from 135 former smokers with COPD. For each omic (layer) we built a patient network based on molecular similarity. The three networks were used to build a multi-layer network, and optimization of multiplex-modularity was employed to identify patient communities across the three distinct layers. Uncovered communities were related to clinical features. RESULTS: We identified five patient communities in the multi-layer network which were molecularly distinct and related to clinical characteristics, such as FEV1 and blood eosinophils. Two communities (C#3 and C#4) had both similarly low FEV1 values and emphysema, but were molecularly different: C#3, but not C#4, presented B and T cell signatures and a downregulation of secretory (SCGB1A1/SCGB3A1) and ciliated cells. A machine learning model was set up to discriminate C#3 and C#4 in our cohort, and to validate them in an independent cohort. Finally, using spatial transcriptomics we characterized the small airway differences between C#3 and C#4, identifying an upregulation of T/B cell homing chemokines, and bacterial response genes in C#3. CONCLUSIONS: A novel multi-layer network analysis is able to identify clinically relevant COPD patient communities. Patients with similarly low FEV1 and emphysema can have molecularly distinct small airways and immune response patterns, indicating that different endotypes can lead to similar clinical presentation.

Prevalence and differences in the co-administration of drugs known to interact: an analysis of three distinct and large populations.

BACKGROUND: The co-administration of drugs known to interact greatly impacts morbidity, mortality, and health economics. This study aims to examine the drug-drug interaction (DDI) phenomenon with a large-scale longitudinal analysis of age and gender differences found in drug administration data from three distinct healthcare systems. METHODS: This study analyzes drug administrations from population-wide electronic health records in Blumenau (Brazil; 133 K individuals), Catalonia (Spain; 5.5 M individuals), and Indianapolis (USA; 264 K individuals). The stratified prevalences of DDI for multiple severity levels per patient gender and age at the time of administration are computed, and null models are used to estimate the expected impact of polypharmacy on DDI prevalence. Finally, to study actionable strategies to reduce DDI prevalence, alternative polypharmacy regimens using drugs with fewer known interactions are simulated. RESULTS: A large prevalence of co-administration of drugs known to interact is found in all populations, affecting 12.51%, 12.12%, and 10.06% of individuals in Blumenau, Indianapolis, and Catalonia, respectively. Despite very different healthcare systems and drug availability, the increasing prevalence of DDI as patients age is very similar across all three populations and is not explained solely by higher co-administration rates in the elderly. In general, the prevalence of DDI is significantly higher in women - with the exception of men over 50 years old in Indianapolis. Finally, we show that using proton pump inhibitor alternatives to omeprazole (the drug involved in more co-administrations in Catalonia and Blumenau), the proportion of patients that are administered known DDI can be reduced by up to 21% in both Blumenau and Catalonia and 2% in Indianapolis. CONCLUSIONS: DDI administration has a high incidence in society, regardless of geographic, population, and healthcare management differences. Although DDI prevalence increases with age, our analysis points to a complex phenomenon that is much more prevalent than expected, suggesting comorbidities as key drivers of the increase. Furthermore, the gender differences observed in most age groups across populations are concerning in regard to gender equity in healthcare. Finally, our study exemplifies how electronic health records' analysis can lead to actionable interventions that significantly reduce the administration of known DDI and its associated human and economic costs.

Immunization with V987H-stabilized Spike glycoprotein protects K18-hACE2 mice and golden Syrian hamsters upon SARS-CoV-2 infection.

Safe and effective severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines are crucial to fight against the coronavirus disease 2019 pandemic. Most vaccines are based on a mutated version of the Spike glycoprotein [K986P/V987P (S-2P)] with improved stability, yield and immunogenicity. However, S-2P is still produced at low levels. Here, we describe the V987H mutation that increases by two-fold the production of the recombinant Spike and the exposure of the receptor binding domain (RBD). S-V987H immunogenicity is similar to S-2P in mice and golden Syrian hamsters (GSH), and superior to a monomeric RBD. S-V987H immunization confer full protection against severe disease in K18-hACE2 mice and GSH upon SARS-CoV-2 challenge (D614G or B.1.351 variants). Furthermore, S-V987H immunized K18-hACE2 mice show a faster tissue viral clearance than RBD- or S-2P-vaccinated animals challenged with D614G, B.1.351 or Omicron BQ1.1 variants. Thus, S-V987H protein might be considered for future SARS-CoV-2 vaccines development.

Virus-like particle-mediated delivery of structure-selected neoantigens demonstrates immunogenicity and antitumoral activity in mice.

BACKGROUND: Neoantigens are patient- and tumor-specific peptides that arise from somatic mutations. They stand as promising targets for personalized therapeutic cancer vaccines. The identification process for neoantigens has evolved with the use of next-generation sequencing technologies and bioinformatic tools in tumor genomics. However, in-silico strategies for selecting immunogenic neoantigens still have very low accuracy rates, since they mainly focus on predicting peptide binding to Major Histocompatibility Complex (MHC) molecules, which is key but not the sole determinant for immunogenicity. Moreover, the therapeutic potential of neoantigen-based vaccines may be enhanced using an optimal delivery platform that elicits robust de novo immune responses. METHODS: We developed a novel neoantigen selection pipeline based on existing software combined with a novel prediction method, the Neoantigen Optimization Algorithm (NOAH), which takes into account structural features of the peptide/MHC-I interaction, as well as the interaction between the peptide/MHC-I complex and the TCR, in its prediction strategy. Moreover, to maximize neoantigens' therapeutic potential, neoantigen-based vaccines should be manufactured in an optimal delivery platform that elicits robust de novo immune responses and bypasses central and peripheral tolerance. RESULTS: We generated a highly immunogenic vaccine platform based on engineered HIV-1 Gag-based Virus-Like Particles (VLPs) expressing a high copy number of each in silico selected neoantigen. We tested different neoantigen-loaded VLPs (neoVLPs) in a B16-F10 melanoma mouse model to evaluate their capability to generate new immunogenic specificities. NeoVLPs were used in in vivo immunogenicity and tumor challenge experiments. CONCLUSIONS: Our results indicate the relevance of incorporating other immunogenic determinants beyond the binding of neoantigens to MHC-I. Thus, neoVLPs loaded with neoantigens enhancing the interaction with the TCR can promote the generation of de novo antitumor-specific immune responses, resulting in a delay in tumor growth. Vaccination with the neoVLP platform is a robust alternative to current therapeutic vaccine approaches and a promising candidate for future personalized immunotherapy.

Novel Spike-stabilized trimers with improved production protect K18-hACE2 mice and golden Syrian hamsters from the highly pathogenic SARS-CoV-2 Beta variant.

Most COVID-19 vaccines are based on the SARS-CoV-2 Spike glycoprotein (S) or their subunits. However, S shows some structural instability that limits its immunogenicity and production, hampering the development of recombinant S-based vaccines. The introduction of the K986P and V987P (S-2P) mutations increases the production and immunogenicity of the recombinant S trimer, suggesting that these two parameters are related. Nevertheless, S-2P still shows some molecular instability and it is produced with low yield. Here we described a novel set of mutations identified by molecular modeling and located in the S2 region of the S-2P that increase its production up to five-fold. Besides their immunogenicity, the efficacy of two representative S-2P-based mutants, S-29 and S-21, protecting from a heterologous SARS-CoV-2 Beta variant challenge was assayed in K18-hACE2 mice (an animal model of severe SARS-CoV-2 disease) and golden Syrian hamsters (GSH) (a moderate disease model). S-21 induced higher level of WH1 and Delta variants neutralizing antibodies than S-2P in K18-hACE2 mice three days after challenge. Viral load in nasal turbinate and oropharyngeal samples were reduced in S-21 and S-29 vaccinated mice. Despite that, only the S-29 protein protected 100% of K18-hACE2 mice from severe disease. When GSH were analyzed, all immunized animals were protected from disease development irrespectively of the immunogen they received. Therefore, the higher yield of S-29, as well as its improved immunogenicity and efficacy protecting from the highly pathogenic SARS-CoV-2 Beta variant, pinpoint the S-29 mutant as an alternative to the S-2P protein for future SARS-CoV-2 vaccine development.

Epitope-engineered human hematopoietic stem cells are shielded from CD123-targeted immunotherapy.

Targeted eradication of transformed or otherwise dysregulated cells using monoclonal antibodies (mAb), antibody-drug conjugates (ADC), T cell engagers (TCE), or chimeric antigen receptor (CAR) cells is very effective for hematologic diseases. Unlike the breakthrough progress achieved for B cell malignancies, there is a pressing need to find suitable antigens for myeloid malignancies. CD123, the interleukin-3 (IL-3) receptor alpha-chain, is highly expressed in various hematological malignancies, including acute myeloid leukemia (AML). However, shared CD123 expression on healthy hematopoietic stem and progenitor cells (HSPCs) bears the risk for myelotoxicity. We demonstrate that epitope-engineered HSPCs were shielded from CD123-targeted immunotherapy but remained functional, while CD123-deficient HSPCs displayed a competitive disadvantage. Transplantation of genome-edited HSPCs could enable tumor-selective targeted immunotherapy while rebuilding a fully functional hematopoietic system. We envision that this approach is broadly applicable to other targets and cells, could render hitherto undruggable targets accessible to immunotherapy, and will allow continued posttransplant therapy, for instance, to treat minimal residual disease (MRD).

Analysis of electronic health records from three distinct and large populations reveals high prevalence and biases in the co-administration of drugs known to interact.

The co-administration of drugs known to interact has a high impact on morbidity, mortality, and health economics. We study the drug-drug interaction (DDI) phenomenon by analyzing drug administrations from population-wide Electronic Health Records (EHR) in Blumenau (Brazil), Catalonia (Spain), and Indianapolis (USA). Despite very different health care systems and drug availability, we find a common large risk of DDI administration that affected 13 to 20% of patients in these populations. In addition, the increasing risk of DDI as patients age is very similar across all three populations but is not explained solely by higher co-administration rates in the elderly. We also find that women are at higher risk of DDI overall- except for men over 50 years old in Indianapolis. Finally, we show that PPI alternatives to Omeprazole can reduce the number of patients affected by known DDIs by up to 21% in both Blumenau and Catalonia, and 2% in Indianapolis, exemplifying how analysis of EHR data can lead to a significant reduction of DDI and its associated human and economic costs. Although the risk of DDIs increases with age, administration patterns point to a complex phenomenon that cannot be solely explained by polypharmacy and multimorbidity. The lack of safer drug alternatives, particularly for chronic conditions, further overburdens health systems, thus highlighting the need for disruptive drug research.

Heterogeneous Infectivity and Pathogenesis of SARS-CoV-2 Variants Beta, Delta and Omicron in Transgenic K18-hACE2 and Wildtype Mice.

The emerging SARS-CoV-2 variants of concern (VOCs) may display enhanced transmissibility, more severity and/or immune evasion; however, the pathogenesis of these new VOCs in experimental SARS-CoV-2 models or the potential infection of other animal species is not completely understood. Here we infected K18-hACE2 transgenic mice with B.1, B.1.351/Beta, B.1.617.2/Delta and BA.1.1/Omicron isolates and demonstrated heterogeneous infectivity and pathogenesis. B.1.351/Beta variant was the most pathogenic, while BA.1.1/Omicron led to lower viral RNA in the absence of major visible clinical signs. In parallel, we infected wildtype (WT) mice and confirmed that, contrary to B.1 and B.1.617.2/Delta, B.1.351/Beta and BA.1.1/Omicron can infect them. Infection in WT mice coursed without major clinical signs and viral RNA was transient and undetectable in the lungs by day 7 post-infection. In silico modeling supported these findings by predicting B.1.351/Beta receptor binding domain (RBD) mutations result in an increased affinity for both human and murine ACE2 receptors, while BA.1/Omicron RBD mutations only show increased affinity for murine ACE2.

Assessing the accuracy of contact and distance predictions in CASP14.

We present the results of the assessment of the intramolecular residue-residue contact and distance predictions from groups participating in the 14th round of the CASP experiment. The performance of contact prediction methods was evaluated with the measures used in previous CASPs, while distance predictions were assessed based on a new protocol, which considers individual distance pairs as well as the whole predicted distance matrix, using a graph-based framework. The results of the evaluation indicate that predictions by the tFold framework, TripletRes and DeepPotential were the most accurate in both categories. With regards to progress in method performance, the results of the assessment in contact prediction did not reveal any discernible difference when compared to CASP13. Arguably, this could be due to CASP14 FM targets being more challenging than ever before.

Quantitative Description of Surface Complementarity of Antibody-Antigen Interfaces.

Antibodies have the remarkable ability to recognise their cognate antigens with extraordinary affinity and specificity. Discerning the rules that define antibody-antigen recognition is a fundamental step in the rational design and engineering of functional antibodies with desired properties. In this study we apply the 3D Zernike formalism to the analysis of the surface properties of the antibody complementary determining regions (CDRs). Our results show that shape and electrostatic 3DZD descriptors of the surface of the CDRs are predictive of antigen specificity, with classification accuracy of 81% and area under the receiver operating characteristic curve (AUC) of 0.85. Additionally, while in terms of surface size, solvent accessibility and amino acid composition, antibody epitopes are typically not distinguishable from non-epitope, solvent-exposed regions of the antigen, the 3DZD descriptors detect significantly higher surface complementarity to the paratope, and are able to predict correct paratope-epitope interaction with an AUC = 0.75.

Target highlights in CASP14: Analysis of models by structure providers.

The biological and functional significance of selected Critical Assessment of Techniques for Protein Structure Prediction 14 (CASP14) targets are described by the authors of the structures. The authors highlight the most relevant features of the target proteins and discuss how well these features were reproduced in the respective submitted predictions. The overall ability to predict three-dimensional structures of proteins has improved remarkably in CASP14, and many difficult targets were modeled with impressive accuracy. For the first time in the history of CASP, the experimentalists not only highlighted that computational models can accurately reproduce the most critical structural features observed in their targets, but also envisaged that models could serve as a guidance for further studies of biologically-relevant properties of proteins.

Previous SARS-CoV-2 Infection Increases B.1.1.7 Cross-Neutralization by Vaccinated Individuals.

With the spread of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is a need to assess the protection conferred by both previous infections and current vaccination. Here we tested the neutralizing activity of infected and/or vaccinated individuals against pseudoviruses expressing the spike of the original SARS-CoV-2 isolate Wuhan-Hu-1 (WH1), the D614G mutant and the B.1.1.7 variant. Our data show that parameters of natural infection (time from infection and nature of the infecting variant) determined cross-neutralization. Uninfected vaccinees showed a small reduction in neutralization against the B.1.1.7 variant compared to both the WH1 strain and the D614G mutant. Interestingly, upon vaccination, previously infected individuals developed more robust neutralizing responses against B.1.1.7, suggesting that vaccines can boost the neutralization breadth conferred by natural infection.

DGLinker: flexible knowledge-graph prediction of disease-gene associations.

As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration.

Unraveling the molecular basis of host cell receptor usage in SARS-CoV-2 and other human pathogenic ß-CoVs.

The recent emergence of the novel SARS-CoV-2 in China and its rapid spread in the human population has led to a public health crisis worldwide. Like in SARS-CoV, horseshoe bats currently represent the most likely candidate animal source for SARS-CoV-2. Yet, the specific mechanisms of cross-species transmission and adaptation to the human host remain unknown. Here we show that the unsupervised analysis of conservation patterns across the ß-CoV spike protein family, using sequence information alone, can provide valuable insights on the molecular basis of the specificity of ß-CoVs to different host cell receptors. More precisely, our results indicate that host cell receptor usage is encoded in the amino acid sequences of different CoV spike proteins in the form of a set of specificity determining positions (SDPs). Furthermore, by integrating structural data, in silico mutagenesis and coevolution analysis we could elucidate the role of SDPs in mediating ACE2 binding across the Sarbecovirus lineage, either by engaging the receptor through direct intermolecular interactions or by affecting the local environment of the receptor binding motif. Finally, by the analysis of coevolving mutations across a paired MSA we were able to identify key intermolecular contacts occurring at the spike-ACE2 interface. These results show that effective mining of the evolutionary records held in the sequence of the spike protein family can help tracing the molecular mechanisms behind the evolution and host-receptor adaptation of circulating and future novel ß-CoVs.

Target highlights in CASP13: Experimental target structures through the eyes of their authors.

The functional and biological significance of selected CASP13 targets are described by the authors of the structures. The structural biologists discuss the most interesting structural features of the target proteins and assess whether these features were correctly reproduced in the predictions submitted to the CASP13 experiment.

Insights on protein thermal stability: a graph representation of molecular interactions.

MOTIVATION: Understanding the molecular mechanisms of thermal stability is a challenge in protein biology. Indeed, knowing the temperature at which proteins are stable has important theoretical implications, which are intimately linked with properties of the native fold, and a wide range of potential applications from drug design to the optimization of enzyme activity. RESULTS: Here, we present a novel graph-theoretical framework to assess thermal stability based on the structure without any a priori information. In this approach we describe proteins as energy-weighted graphs and compare them using ensembles of interaction networks. Investigating the position of specific interactions within the 3D native structure, we developed a parameter-free network descriptor that permits to distinguish thermostable and mesostable proteins with an accuracy of 76% and area under the receiver operating characteristic curve of 78%. AVAILABILITY AND IMPLEMENTATION: Code is available upon request to edoardo.milanetti@uniroma1.it. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modeling of Protein Tertiary and Quaternary Structures Based on Evolutionary Information.

Proteins are subject to evolutionary forces that shape their three-dimensional structure to meet specific functional demands. The knowledge of the structure of a protein is therefore instrumental to gain information about the molecular basis of its function. However, experimental structure determination is inherently time consuming and expensive, making it impossible to follow the explosion of sequence data deriving from genome-scale projects. As a consequence, computational structural modeling techniques have received much attention and established themselves as a valuable complement to experimental structural biology efforts. Among these, comparative modeling remains the method of choice to model the three-dimensional structure of a protein when homology to a protein of known structure can be detected.The general strategy consists of using experimentally determined structures of proteins as templates for the generation of three-dimensional models of related family members (targets) of which the structure is unknown. This chapter provides a description of the individual steps needed to obtain a comparative model using SWISS-MODEL, one of the most widely used automated servers for protein structure homology modeling.

Genome-wide RNA-seq analysis indicates that the DAG1 transcription factor promotes hypocotyl elongation acting on ABA, ethylene and auxin signaling.

Hypocotyl elongation is influenced by light and hormones, but the molecular mechanisms underlying this process are not yet fully elucidated. We had previously suggested that the Arabidopsis DOF transcription factor DAG1 may be a negative component of the mechanism of light-mediated inhibition of hypocotyl elongation, as light-grown dag1 knock-out mutant seedlings show significant shorter hypocotyls than the wild type. By using high-throughput RNA-seq, we compared the transcriptome profile of dag1 and wild type hypocotyls and seedlings. We identified more than 250 genes differentially expressed in dag1 hypocotyls, and their analysis suggests that DAG1 is involved in the promotion of hypocotyl elongation through the control of ABA, ethylene and auxin signaling. Consistently, ChIP-qPCR results show that DAG1 directly binds to the promoters of WRKY18 encoding a transcription factor involved in ABA signaling, of the ethylene- induced gene ETHYLENE RESPONSE FACTOR (ERF2), and of the SMALL AUXIN UP RNA 67 (SAUR67), an auxin-responding gene encoding a protein promoting hypocotyl cell expansion.

SWISS-MODEL: homology modelling of protein structures and complexes.

Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.