Search

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes Coronavirus Disease 2019 (COVID-19), which, since 2019 in China, has rapidly become a worldwide pandemic. The aggressiveness and global spread were enhanced by the many SARS-CoV-2 variants that have been isolated up to now. These mutations affect mostly the viral glycoprotein Spike (S), the capsid protein mainly involved in the early stages of viral entry processes, through the recognition of specific receptors on the host cell surface. In particular, the subunit S1 of the Spike glycoprotein contains the Receptor Binding Domain (RBD) and it is responsible for the interaction with the angiotensin-converting enzyme 2 (ACE2). Although ACE2 is the primary Spike host receptor currently studied, it has been demonstrated that SARS-CoV-2 is also able to infect cells expressing low levels of ACE2, indicating that the virus may have alternative receptors on the host cells. The identification of the alternative receptors can better elucidate the pathogenicity and the tropism of SARS-CoV-2. Therefore, we investigated the Spike S1 interactomes, starting from host membrane proteins of non-pulmonary cell lines, such as human kidney (HK-2), normal colon (NCM460D), and colorectal adenocarcinoma (Caco-2). We employed an affinity purification-mass spectrometry (AP-MS) to pull down, from the membrane protein extracts of all cell lines, the protein partners of the recombinant form of the Spike S1 domain. The purified interactors were identified by a shotgun proteomics approach. The lists of S1 potential interacting proteins were then clusterized according to cellular localization, biological processes, and pathways, highlighting new possible S1 intracellular functions, crucial not only for the entrance mechanisms but also for viral replication and propagation processes.

International audience; Persistent infection with some mucosal a-genus human papillomaviruses (HPVs; the most prevalent one being HPV16) can induce cervical carcinoma, anogenital cancers, and a subset of head and neck squamous cell carcinoma (HNSCC). Cutaneous b-genus HPVs (such as HPV5 and HPV8) associate with skin lesions that can progress into squamous cell carcinoma with sun exposure in Epidermodysplasia verruciformis patients and immunosuppressed patients. Here, we analyzed mechanisms used by E6 proteins from the aand b-genus to inhibit the interferon-b (IFNB1) response. HPV16 E6 mediates this effect by a strong direct interaction with interferon regulatory factor 3 (IRF3). The binding site of E6 was localized within a flexible linker between the DNAbinding domain and the IRF-activation domain of IRF3 containing an LxxLL motif. The crystallographic structure of the complex between HPV16 E6 and the LxxLL motif of IRF3 was solved and compared with the structure of HPV16 E6 interacting with the LxxLL motif of the ubiquitin ligase E6AP. In contrast, cutaneous HPV5 and HPV8 E6 proteins bind to the IRF3-binding domain (IBiD) of the CREB-binding protein (CBP), a key transcriptional coactivator in IRF3-mediated IFN-b expression. IMPORTANCE Persistent HPV infections can be associated with the development of several cancers. The ability to persist depends on the ability of the virus to escape the host immune system. The type I interferon (IFN) system is the first-line antiviral defense strategy. HPVs carry early proteins that can block the activation of IFN-I. Among mucosal a-genus HPV types, the HPV16 E6 protein has a remarkable property to strongly interact with the transcription factor IRF3. Instead, cutaneous HPV5 and HPV8 E6 proteins bind to the IRF3 cofactor CBP. These results highlight the versatility of E6 proteins to interact with different cellular targets. The interaction between the HPV16 E6 protein and IRF3 might contribute to the higher prevalence of HPV16 than that of other high-risk mucosal HPV types in HPV-associated cancers.

Graphical abstract, Neuropsychiatric disorders (NPDs) such as bipolar disorder (BD), schizophrenia (SZ) and mood disorder (MD) are hard to manage due to overlapping symptoms and lack of biomarkers. Risk alleles of BD/SZ/MD are emerging, with evidence suggesting mitochondrial (mt) dysfunction as a critical factor for disease onset and progression. Mood stabilizing treatments for these disorders are scarce, revealing the need for biomarker discovery and artificial intelligence approaches to design synthetically accessible novel therapeutics. Here, we show mt involvement in NPDs by associating 245 mt proteins to BD/SZ/MD, with 7 common players in these disease categories. Analysis of over 650 publications suggests that 245 NPD-linked mt proteins are associated with 800 other mt proteins, with mt impairment likely to rewire these interactions. High dosage of mood stabilizers is known to alleviate manic episodes, but which compounds target mt pathways is another gap in the field that we address through mood stabilizer-gene interaction analysis of 37 prescriptions and over-the-counter psychotropic treatments, which we have refined to 15 mood-stabilizing agents. We show 26 of the 245 NPD-linked mt proteins are uniquely or commonly targeted by one or more of these mood stabilizers. Further, induced pluripotent stem cell-derived patient neurons and three-dimensional human brain organoids as reliable BD/SZ/MD models are outlined, along with multiomics methods and machine learning-based decision making tools for biomarker discovery, which remains a bottleneck for precision psychiatry medicine.

The highly conserved zinc finger CCCTC-binding factor (CTCF) regulates genomic imprinting and gene expression by acting as a transcriptional activator or repressor of promoters and insulator of enhancers. The multiple functions of CTCF are accomplished by co-association with other protein partners and are dependent on genomic context and tissue specificity. Despite the critical role of CTCF in the organization of genome structure, to date, only a subset of CTCF interaction partners have been identified. Here we present a large-scale identification of CTCF binding partners using affinity purification and high-resolution LC-MS/MS analysis. In addition to functional enrichment of specific protein families such as the ribosomal proteins and the DEAD box helicases, we identified novel high-confidence CTCF interactors that provide a still unexplored biochemical context for CTCF's multiple functions. One of the newly validated CTCF interactors is BRG1, the major ATPase subunit of the chromatin remodeling complex SWI/SNF, establishing a relationship between two master regulators of genome organization. This work significantly expands the current knowledge of the human CTCF interactome and represents an important resource to direct future studies aimed at uncovering molecular mechanisms modulating CTCF pleiotropic functions throughout the genome. The highly conserved zinc finger CCCTC-binding factor (CTCF) regulates genomic imprinting and gene expression by acting as a transcriptional activator or repressor of promoters and insulator of enhancers. The multiple functions of CTCF are accomplished by co-association with other protein partners and are dependent on genomic context and tissue specificity. Despite the critical role of CTCF in the organization of genome structure, to date, only a subset of CTCF interaction partners have been identified. Here we present a large-scale identification of CTCF-binding partners using affinity purification and high-resolution LC-MS/MS analysis. In addition to functional enrichment of specific protein families such as the ribosomal proteins and the DEAD box helicases, we identified novel high-confidence CTCF interactors that provide a still unexplored biochemical context for CTCF’s multiple functions. One of the newly validated CTCF interactors is BRG1, the major ATPase subunit of the chromatin remodeling complex SWI/SNF, establishing a relationship between two master regulators of genome organization. This work significantly expands the current knowledge of the human CTCF interactome and represents an important resource to direct future studies aimed at uncovering molecular mechanisms modulating CTCF pleiotropic functions throughout the genome.

The current outbreak of COVID-19 has generated an unprecedented scientific response worldwide, with the generation of vast amounts of publicly available epidemiological, biological and clinical data. Bioinformatics scientists have quickly produced online methods to provide non-computational users with the opportunity of analyzing such data. In this review, we report the results of this effort, by cataloguing the currently most popular web tools for COVID-19 research and analysis. Our focus was driven on tools drawing data from the fields of epidemiology, genomics, interactomics and pharmacology, in order to provide a meaningful depiction of the current state of the art of COVID-19 online resources.

International audience; Protein ubiquitination and its reverse reaction, deubiquitination, regulate protein stability, protein binding activity, and their subcellular localization. These reactions are catalyzed by the enzymes E1, E2, and E3 ubiquitin (Ub) ligases and deubiquitinases (DUBs). The Ub-proteasome system (UPS) is targeted by viruses for the sake of their replication and to escape host immune response. To identify novel partners of human papillomavirus 16 (HPV16) E6 and E7 proteins, we assembled and screened a library of 590 cDNAs related to the UPS by using the Gaussia princeps luciferase protein complementation assay. HPV16 E6 was found to bind to the homology to E6AP C terminus-type Ub ligase (E6AP), three really interesting new gene (RING)-type Ub ligases (MGRN1, LNX3, LNX4), and the DUB Ub-specific protease 15 (USP15). Except for E6AP, the binding of UPS factors did not require the LxxLL-binding pocket of HPV16 E6. LNX3 bound preferentially to all high-risk mucosal HPV E6 tested, whereas LNX4 bound specifically to HPV16 E6. HPV16 E7 was found to bind to several broad-complex tramtrack and bric-a-brac domain-containing proteins (such as TNFAIP1/KCTD13) that are potential substrate adaptors of Cullin 3-RING Ub ligases, to RING-type Ub ligases implicated in innate immunity (RNF135, TRIM32, TRAF2, TRAF5), to the substrate adaptor DCAF15 of Cullin 4-RING Ub ligase and to some DUBs (USP29, USP33). The binding to UPS factors did not require the LxCxE motif but rather the C-terminal region of HPV16 E7 protein. The identified UPS factors interacted with most of E7 proteins across different HPV types. This study establishes a strategy for the rapid identification of interactions between host or pathogen proteins and the human ubiquitination system.

In order to investigate interactomic networks, in fact, a simple but powerful idea, as proved by Google usefulness and consequential success, is to investigate the ranking of each actor relationships to each other, being such actor either an internet page, as in the original Page algorithm, or a gene - or codified protein - in our case. It is worth noticing, as pointed out in a seminal review by Vidyiasagar, that the recent randomized approach introduced by Tempo and coworkers could be the technological key to drastically reduce, at the cheap price of a limited loss of precision, the over-helming computational complexity that would prevent to apply Page ranking to the analysis of every significant interactomic network, besides the almost-toy sub-networks already investigated, as for instance by Zaki In this paper, we would like to investigate a possible complementary approach, recently proposed by Landi & Piccardi, to our interactomic regulation network. As a public available benchmark, the data used in the reported work by Zaki are also used, in order to investigate which features of our proposed approach are possibly useful as a complement to even improve the already powerful Page approach on one side, or eventually able to surrogate it in a less costly way, even taking into account the recalled randomized economy.

Proteomics is acquiring a pivotal role in the comprehensive understanding of human biology. Biochemical processes involved in complex diseases, such as neurodegenerative diseases, diabetes and cancer, can be identified by combining proteomics analysis and bioinformatics tools. In the last ten years, the main output of differential proteomics investigations evolved from long lists of proteins to the generation of new hypotheses and their functional verification. The Journal of Proteomics participated to this progress, reporting more and more biologically-oriented papers with functional interpretation of proteomics data. This change in the field was due to both technological development and novel strategies in exploiting the deep characterization of proteomes. In this review, we explore several approaches that allow proteomics to turn functional. In particular, systems biology tools for data analysis are now routinely used to interpret results, thus defining the biological meaning of differentially abundant proteins. Moreover, by considering the importance of protein-protein interactions and the composition of macromolecular complexes, interactomics is complementing the information given by differential quantitative proteomics. Eventually, terminomics is unveiling new functions for cleaved proteoforms, by analyzing the effect of proteolysis globally. SIGNIFICANCE: Proteomics is rapidly evolving not only technologically but also strategically. The correct interpretation of proteomics data can reveal new functions of proteins in several biological backgrounds. Systems biology tools allow researchers to formulate new hypotheses to be further functionally tested. Interactomics is shedding new light on protein complexes truly involved in biochemical pathways and how their alteration can lead to dysfunctionality (in disease pathogenesis, for example). Terminomics is revealing the function of new discovered proteoforms and attributing a novel role to proteolysis. This review would provide the biologist important insights into current applications of several proteomic approaches that could offer new strategies to investigate biological systems.

un portafoglio di metodi per l'analisi interattomica volta a disvelare le complesse interazioni epigenetiche tra geni e proteine e' presentato con alcune applicazioni recenti in diagnostica differenziale, gerarchizzazione dei fattori prognostici, evidenziazione di cammini biochimici non ancora consolidati nel sapere diffuso

Deciphering omics data : on the importance of quality control. Application to ovarian cancer. Over the past 10 years, the size and complexity of biological data have exploded, and quality control is critical to interpret them correctly. Indeed, omics data (high- hroughput genomic and post-genomic data) are often incomplete and contain bias and errors that can easily be misinterpreted as biologically interesting findings. In this work, we show that literature-curated and high-throughput protein-protein interaction data, usually considered independent, are in fact significantly correlated. We examine the yeast interactome from a new perspective by taking into account how thoroughly proteins have been studied, and our results show that this bias can be corrected for by focusing on well- studied proteins. We thus propose a simple and reliable method to estimate the size of an interactome, combining literature-curated data involving well-studied proteins with high- hroughput data. It yields an estimate of at least 37,600 direct physical protein-protein interactions in S.cerevisiae, a significant increase over previous estimates. We then focus on next-generation DNA sequencing data. An analysis of the bias existing between short- eads aligned on each strand of the genome allows us to highlight numerous systematic errors. Furthermore, we observe many positions that exhibit between 20 and 40% of reads carrying the variant allele : these cannot be genotyped correctly.We then propose a method to overcome these biases and reliably call genotypes from NGS data. Finally, we apply our method to exome-seq data produced by the TCGA for tumor and matched normal samples from 520 ovarian cancer patients. We detect on average 30,632 germline variants per patient. Though an integrative approach, we then identify those which are likely to increase cancer risk : in particular, we focused on variants inducing a loss of function of the encoded protein, and selected those that are significantly more present in the patients than in the general population. We find 44 SNVs per patient on average, impacting 334 genes overall in the cohort. Among these genes, 42 have been previously reported as involved in carcinogenesis, confirming that our list is highly enriched in ovarian cancer susceptibility genes. In particular, our results confirm the tumor suppressor role of the MAP3K8 protein, recently identified in other types of cancer., Décrypter les données omiques : importance du contrôle qualité. Application au cancer de l’ovaire Au cours des dix dernières années, la taille et la complexité des données biologiques ont littéralement explosé, et une attention particulière doit être portée au contrôle qualité. En effet, certaines données omiques (données génomiques et post-génomiques obtenues à haut débit) sont très incomplètes et/ou contiennent de nombreux biais et erreurs qu’il est facile de confondre avec de l’information biologiquement intéressante. Dans cette thèse, nous montrons que les interactions protéine-protéine issues de curation de la littérature et les interactions identifiées à haut débit sont beaucoup plus corrélées que ce qui est communément admis. Nous examinons l’interactome de la levure d’un point de vue original, en prenant en compte le degré d’étude des protéines par la communauté scientifique et nos résultats indiquent que cette corrélation s’estompe lorsqu’on se restreint aux protéines très étudiées. Ces observations nous permettent de proposer une méthode simple et fiable pour estimer la taille d’un interactome. Notre méthode conduit à une estimation d’au moins 37 600 interactions physiques directes chez S. cerevisiae, et montre que les évaluations précédentes sont trop faibles. Par ailleurs, nous étudions des données de séquençage nouvelle génération de l’ADN. Par une analyse des biais existant entre les short-reads alignés sur un brin ou sur l’autre du génome, nous mettons en évidence de nombreuses erreurs systématiques. De plus, nous observons de multiples positions présentant entre 20 et 40% de short-reads portant l’allèle variant : celles-ci ne peuvent pas être génotypées correctement. Nous proposons une méthode fiable pour appeler les génotypes à partir des données NGS qui permet de s’affranchir de ses difficultés. Enfin, nous appliquons cette méthode sur des données massives de séquençage d’exome de cellules saines et tumorales de 520 patientes atteintes du cancer de l’ovaire, produites par le consortium TCGA. Nous détectons en moyenne 30 632 variants germinaux par patiente. Parmi ces variants, nous identifions ceux les plus enclins à conférer un risque accru de développer la maladie : nous nous restreignons notamment aux variants induisant une perte de fonction de la protéine encodée et significativement plus présents chez les patientes que dans la population générale. Cela conduit à 44 SNVs par patiente en moyenne, répartis sur 334 gènes dans l’ensemble de la cohorte. Parmi ces 334 gènes, 42 ont été reportés comme impliqués dans la cancerogénèse, confirmant que la liste de candidats identifiés est fortement enrichie en gènes de susceptibilité au cancer de l’ovaire. En particulier, nos travaux confirment le rôle de suppresseur de tumeur de la protéine MAP3K8, très récemment proposée comme jouant un rôle clé dans d’autres cancers.