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Introduction. Coronavirus disease 2019 (COVID-19) is an emerging pandemic disease caused by the severe acute respiratory syndrome. Back pain could be triggered by the COVID-19 virus either directly or indirectly. However, to our knowledge, limited studies are addressing the development of chronic back pain or new-onset of back pain among COVID-19 survivors. Therefore, the purpose of this study was to investigate the potential health consequences of COVID-19 on back pain among the COVID-19 survivors. Methods. We used an electronically distributed survey targeting adult Jordanian citizens and residents who recovered from COVID-19 virus infection. The questionnaire composed questions regarding demographic characteristics, COVID-19 and spine injury-related information. Results. 27.2% of participants reported increase of back pain with COVID during the episode and 16% of participants reported increased back pain after COVID with pain lasted less than 3 months in 16.6% and more than 3 months in 17.8% of them. There was significant increase on back pain level during COVID compared to pre COVID. At the same time there was significant increase on back pain post COVID compared to pre COVID. Conclusion. The COVID-19 virus resulted in a significant increase in spine pain among COVID-19 survivors and it lead to chronic back pain among patients with risk factors and some develop new spine pain.

The yeast cyclic AMP-dependent protein kinase A (PKA) is a ubiquitous serine–threonine kinase, encompassing three catalytic (Tpk1–3) and one regulatory (Bcy1) subunits. Evidence suggests PKA involvement in DNA damage checkpoint response, but how DNA repair pathways are regulated by PKA subunits remains inconclusive. Here, we report that deleting the tpk1 catalytic subunit reduces non-homologous end joining (NHEJ) efficiency, whereas tpk2-3 and bcy1 deletion does not. Epistatic analyses revealed that tpk1, as well as the DNA damage checkpoint kinase (dun1) and NHEJ factor (nej1), co-function in the same pathway, and parallel to the NHEJ factor yku80. Chromatin immunoprecipitation and resection data suggest that tpk1 deletion influences repair protein recruitments and DNA resection. Further, we show that Tpk1 phosphorylation of Nej1 at S298 (a Dun1 phosphosite) is indispensable for NHEJ repair and nuclear targeting of Nej1 and its binding partner Lif1. In mammalian cells, loss of PRKACB (human homolog of Tpk1) also reduced NHEJ efficiency, and similarly, PRKACB was found to phosphorylate XLF (a Nej1 human homolog) at S263, a corresponding residue of the yeast Nej1 S298. Together, our results uncover a new and conserved mechanism for Tpk1 and PRKACB in phosphorylating Nej1 (or XLF), which is critically required for NHEJ repair., Graphical Abstract Graphical AbstractCellular model of yeast Tpk1 role with Nej1 and PRKACBwith XLF on NHEJ, as well as the loss of tpk1 in DNA resection and MMEJ.

Este livro busca refletir a intr��nseca rela����o existente entre a Arte e a Ecologia Humana abordando diferentes processos criativos em espa��os formais e n��o formais de educa����o e de produ����o art��stica. Em diferentes tempos e ambientes a sensibilidade e a criatividade se firmam como caracter��sticas fundamentais da natureza humana em sua percep����o e di��logo com o ambiente natural e social envolvente. Comp��e esta obra reflex��es e experi��ncias emp��ricas capazes de retratar a ponte que articula conhecimentos herdados e acad��micos no pensar e fazer art��stico. Se a ecologia humana trata da rela����o do ser humano com o meio que o sensibiliza e desperta, a arte �� express��o decorrente de est��mulos ambientais. A ��gua e as cores, a terra e as texturas, o vento e a m��sica, o fogo e a cria����o s��o linhas que se cruzam na passagem do ser humano pelo planeta que o acolhe e habita., Malty, L., Pires, I. (coords.) (2021). Arte e ecologia humana. Lisboa: CICS.NOVA ��� Centro Interdisciplinar de Ci��ncias Sociais e Humanas, Faculdade de Ci��ncias Sociais e Humanas, Universidade NOVA de Lisboa. ISBN: 978-989-97344-7-0

Summary Mitochondrial protein (MP) assemblies undergo alterations during neurogenesis, a complex process vital in brain homeostasis and disease. Yet which MP assemblies remodel during differentiation remains unclear. Here, using mass spectrometry-based co-fractionation profiles and phosphoproteomics, we generated mitochondrial interaction maps of human pluripotent embryonal carcinoma stem cells and differentiated neuronal-like cells, which presented as two discrete cell populations by single-cell RNA sequencing. The resulting networks, encompassing 6,442 high-quality associations among 600 MPs, revealed widespread changes in mitochondrial interactions and site-specific phosphorylation during neuronal differentiation. By leveraging the networks, we show the orphan C20orf24 as a respirasome assembly factor whose disruption markedly reduces respiratory chain activity in patients deficient in complex IV. We also find that a heme-containing neurotrophic factor, neuron-derived neurotrophic factor [NENF], couples with Parkinson disease-related proteins to promote neurotrophic activity. Our results provide insights into the dynamic reorganization of mitochondrial networks during neuronal differentiation and highlights mechanisms for MPs in respirasome, neuronal function, and mitochondrial diseases., Graphical Abstract, Highlights • Rewiring of mitochondrial (mt) protein interaction network in distinct cell states • Dramatic changes in site-specific phosphorylation during neuronal differentiation • C20orf24 is a respirasome assembly factor depleted in patients deficient in CIV • NENF binding with DJ-1/PINK1 promotes neurotrophic activity and neuronal survival, Biological Sciences; Developmental Neuroscience; Developmental Biology; Proteomics

Drawing on Foucault’s notion of ‘regime of truth’ this chapter examines the spaces of contention through which watchdog organizations such as Airwars make counter-claims to the US-led Coalition air-strike campaign against IS in Syria and Iraq. The Coalition’s sanctioning of information, its discursive embracement of watchdog criticism, as well as its adoption of specialist technologies, worked to reinforce a regime of truth in which remote warfare is portrayed as ‘precise’ and ‘caring’: a form of perfect war which saves lives, not just of ‘our own’ but also of ‘civilian others’. We outline how this regime of truth about remote wars promotes violence rather than limits it. This prompts us to look at the ontology of remote war, and its capacity to ward off political questions on how it has transformative effects and (re)produces and sustains regimes of power.

BackgroundClinical trials establish the standard of care for cancer and other diseases. While social network analysis has been applied to basic sciences, the social component of clinical trial research is not well characterized. We examined the social network of cancer clinical trialists and its dynamic development over more than 70 years, including the roles of subspecialization and gender in relation to traditional and network-based metrics of productivity.MethodsWe conducted a social network analysis of authors publishing chemotherapy-based prospective trials from 1946-2018, based on the curated knowledge base HemOnc.org, examining: 1) network density; 2) modularity; 3) assortativity; 4) betweenness centrality; 5) PageRank; and 6) the proportion of co-authors sharing the same primary cancer subspecialty designation. Individual author impact and productive period were analyzed as a function of gender and subspecialty.FindingsFrom 1946-2018, the network grew to 29,197 authors and 697,084 co-authors. While 99.4% of authors were directly or indirectly connected as of 2018, the network had very few connections and was very siloed by cancer subspecialty. Small numbers of individuals were highly connected and had disproportionate impact (scale-free effects). Women were under-represented and likelier to have lower impact, shorter productive periods (PInterpretationThe network of cancer clinical trialists is best characterized as a strategic or “mixed-motive” network, with cooperative and competitive elements influencing its appearance.Network effects e.g., low centrality, which may limit access to high-profile individuals, likely contribute to ongoing disparities.FundingVanderbilt Initiative for Interdisciplinary Research; National Institutes of Health; National Science FoundationResearch in contextEvidence before this studyWe reviewed the literature on social networks from the 1800’s to 2018. Additionally, MEDLINE was searched for (“Social Networking”[Mesh] OR “Social Network Analysis”) AND (“Clinical Trials as Topic”[Mesh] OR “Hematology”[Mesh] OR “Medical Oncology”[Mesh]) without date restriction. The MEDLINE search yielded 43 results, of which 8 were relevant; none considered gender nor temporality in their analyses. To our knowledge, there has not been any similar study of the dynamic social network of clinical trialists from the inception of the fields of medical oncology and hematology to the present.Added value of this studyThis is the first dynamic social network analysis of cancer clinical trialists. We found that the network was sparse and siloed with a small number of authors having disproportionate impact and influence as measured by network metrics such as PageRank; these metrics have become more disproportionate over time. Women were under-represented and likelier to have lower impact, shorter productive periods, less network centrality, and a greater proportion of co-authors in their same cancer subspecialty.Implications of all the available evidenceWhile gender disparities have been demonstrated in many fields including hematology/oncology, our analysis is the first to show that network factors themselves are significantly implicated in gender disparity. The increasing coalescence of the network by traditional cancer type and around a small number of high-impact individuals implies challenges when the field pivots from traditionally disease-oriented subspecialties to a precision oncology paradigm. New mechanisms are needed to ensure diversity of clinical trialists.

Receptor tyrosine kinases (RTKs) and protein phosphatases comprise protein families that play crucial roles in cell signaling. We used two protein-protein interaction (PPI) approaches, the Membrane Yeast Two-Hybrid (MYTH) and the Mammalian Membrane Two-Hybrid (MaMTH), to map the PPIs between human RTKs and phosphatases. The resulting RTK-phosphatase interactome reveals a considerable number of previously unidentified interactions and suggests specific roles for different phosphatase families. Additionally, the differential PPIs of some protein tyrosine phosphatases (PTPs) and their mutants suggest diverse mechanisms of these PTPs in the regulation of RTK signaling. We further found that PTPRH and PTPRB directly dephosphorylate EGFR and repress its downstream signaling. By contrast, PTPRA plays a dual role in EGFR signaling: besides facilitating EGFR dephosphorylation, it enhances downstream ERK signaling by activating SRC. This comprehensive RTK-phosphatase interactome study provides a broad and deep view of RTK signaling.

Mitochondria (mt) are double-membraned, dynamic organelles that play an essential role in a large number of cellular processes, and impairments in mt function have emerged as a causative factor for a growing number of human disorders. Given that most biological functions are driven by physical associations between proteins, the first step towards understanding mt dysfunction is to map its protein-protein interaction (PPI) network in a comprehensive and systematic fashion. While mass-spectrometry (MS) based approaches possess the high sensitivity ideal for such an endeavor, it also requires stringent biochemical purification of bait proteins to avoid detecting spurious, non-specific PPIs. Here, we outline a tagging-based affinity purification coupled with mass spectrometry (AP-MS) workflow for discovering new mt protein associations and providing novel insights into their role in mt biology and human physiology/pathology. Because AP-MS relies on the creation of proteins fused with affinity tags, we employ a versatile-affinity (VA) tag, consisting of 3× FLAG, 6 × His, and Strep III epitopes. For efficient delivery of affinity-tagged open reading frames (ORF) into mammalian cells, the VA-tag is cloned onto a specific ORF using Gateway recombinant cloning, and the resulting expression vector is stably introduced in target cells using lentiviral transduction. In this chapter, we show a functional workflow for mapping the mt interactome that includes tagging, stable transduction, selection and expansion of mammalian cell lines, mt extraction, identification of interacting protein partners by AP-MS, and lastly, computational assessment of protein complexes/PPI networks.

Mitochondria (mt) are double-membraned, dynamic organelles that play an essential role in a large number of cellular processes, and impairments in mt function have emerged as a causative factor for a growing number of human disorders. Given that most biological functions are driven by physical associations between proteins, the first step towards understanding mt dysfunction is to map its protein-protein interaction (PPI) network in a comprehensive and systematic fashion. While mass-spectrometry (MS) based approaches possess the high sensitivity ideal for such an endeavor, it also requires stringent biochemical purification of bait proteins to avoid detecting spurious, non-specific PPIs. Here, we outline a tagging-based affinity purification coupled with mass spectrometry (AP-MS) workflow for discovering new mt protein associations and providing novel insights into their role in mt biology and human physiology/pathology. Because AP-MS relies on the creation of proteins fused with affinity tags, we employ a versatile-affinity (VA) tag, consisting of 3× FLAG, 6 × His, and Strep III epitopes. For efficient delivery of affinity-tagged open reading frames (ORF) into mammalian cells, the VA-tag is cloned onto a specific ORF using Gateway recombinant cloning, and the resulting expression vector is stably introduced in target cells using lentiviral transduction. In this chapter, we show a functional workflow for mapping the mt interactome that includes tagging, stable transduction, selection and expansion of mammalian cell lines, mt extraction, identification of interacting protein partners by AP-MS, and lastly, computational assessment of protein complexes/PPI networks.

Our analysis examines the conservation of multiprotein complexes among metazoa through use of high resolution biochemical fractionation and precision mass spectrometry applied to soluble cell extracts from 5 representative model organisms Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Strongylocentrotus purpuratus, and Homo sapiens. The interaction network obtained from the data was validated globally in 4 distant species (Xenopus laevis, Nematostella vectensis, Dictyostelium discoideum, Saccharomyces cerevisiae) and locally by targeted affinity-purification experiments. Here we provide details of our massive set of supporting biochemical fractionation data available via ProteomeXchange (http://www.ebi.ac.uk/pride/archive/projects/PXD002319-http://www.ebi.ac.uk/pride/archive/projects/PXD002328), PPIs via BioGRID (185267); and interaction network projections via (http://metazoa.med.utoronto.ca) made fully accessible to allow further exploration. The datasets here are related to the research article on metazoan macromolecular complexes in Nature [1]. Keywords: Proteomics, Metazoa, Protein complexes, Biochemical, Fractionation

Introduction: Granular cancer patient treatment data collection, and subsequent mapping to standard regimen definitions, are vital next steps in advancement of observational studies in oncology. However, the identification of regimen details, including dose and schedule, is a prerequisite for both collection and mapping. At the patient level, claims databases are a useful but limited resource. Most cancer registries, such as the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) program and the Commission on Cancer National Cancer Database, capture only a simplification of actual treatments, such as binary exposure to chemotherapy (yes/no) or a list of individual chemotherapeutic agent names. Moreover, even with optimal patient data collection, a “gold standard” database of expected chemotherapy doses and schedules as part of standard-of-care (SOC) does not currently exist. Methods: We extracted and normalized the semistructured dosing and timing data from HemOnc.org, the largest publicly available website of SOC chemotherapy drugs and regimens. To that end, we undertook two broad and parallel approaches: 1) standardization of prescribing instructions within a given cycle (SIGs) or pertaining to cycle timing (“cycleSIGs”); and 2) parsing of resultant content into structured variables. This effort was carried out iteratively with the goal of creating standard “canonical” forms for intermittent intravenous (IV), continuous IV (CIV), other routes (e.g., oral), and radiation SIGs. All SIGs were bound to regimen and treatment context (e.g., cyclophosphamide dosing differs in R-CHOP versus R-CVP, and number of cycles often differs between adjuvant and metastatic contexts). Results: There are currently 14,569 regimen-context-SIG-cycleSIG quartets in the database (October 2019). Parsing of SIGs into structured variables resulted in 7,792 canonical IV, 675 canonical CIV, 3,762 canonical other, 510 canonical radiation, and 2,249 noncanonical results. Some SIGs are multipart and were broken into steps. For example, “6 mg/m2 IV once on day 1, then 3 mg/m2 IV once on day 8” constitutes two separate steps. There were 948 unique cycleSIGs (e.g., “21-day cycle for 4 cycles”), which were also parsed into components. Discussion: This effort has produced a large dataset of granular drug and cycle SIG information that reflects SOC dosing parameters in hematology/oncology. This dataset can be used to understand discrepancies between real-world outcomes and clinical trial results, e.g., by elucidating the effect of dose reductions and treatment delays on treatment outcomes. The Observational Health Data Sciences and Informatics (OHDSI) oncology workgroup is arranging to add this new information to the portion of the HemOnc vocabulary that is available through the OHDSI terminology management tool. Ongoing efforts also include translating the maximum possible number of noncanonical sigs into canonical forms, which can further enhance simplicity and usability of the dataset. Citation Format: Zachary H. Moldwin, Andrew Malty, Donna R. Rivera, Anastasios Siapos, Christian G. Reich, Michael J. Gurley, Rimma Belenkaya, Dmitry Dymshyts, Jeremy L. Warner. Getting granular: A structured database of doses and schedules in hematology/oncology [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 27.

Connectivity webs mediate the unique biology of the mammalian brain. Yet, while cell circuit maps are increasingly available, knowledge of their underlying molecular networks remains limited. Here, we applied multi-dimensional biochemical fractionation with mass spectrometry and machine learning to survey endogenous macromolecules across the adult mouse brain. We defined a global "interactome" comprising over one thousand multi-protein complexes. These include hundreds of brain-selective assemblies that have distinct physical and functional attributes, show regional and cell-type specificity, and have links to core neurological processes and disorders. Using reciprocal pull-downs and a transgenic model, we validated a putative 28-member RNA-binding protein complex associated with amyotrophic lateral sclerosis, suggesting a coordinated function in alternative splicing in disease progression. This brain interaction map (BraInMap) resource facilitates mechanistic exploration of the unique molecular machinery driving core cellular processes of the central nervous system. It is publicly available and can be explored here https://www.bu.edu/dbin/cnsb/mousebrain/.

Purpose The systemic treatment of cancer is primarily through the administration of complex chemotherapy protocols. To date, this knowledge has not been systematized, because of the lack of a consistent nomenclature and the variation in which regimens are documented. For example, recording of treatment events in electronic health record notes is often through shorthand and acronyms, limiting secondary use. A standardized hierarchic ontology of cancer treatments, mapped to standard nomenclatures, would be valuable to a variety of end users. Methods We leveraged the knowledge contained in a large wiki of hematology/oncology drugs and treatment regimens, HemOnc.org. Through algorithmic parsing, we created a hierarchic ontology of treatment concepts in the World Wide Web Consortium Web Ontology Language. We also mapped drug names to RxNorm codes and created optional filters to restrict the ontology by disease and/or drug class. Results As of December 2017, the main ontology includes 30,526 axioms (eg, doxorubicin is an anthracycline), 1,196 classes (eg, regimens used in the neoadjuvant treatment of human epidermal growth factor receptor 2–positive breast cancer, nitrogen mustards), and 1,728 individual entities. More than 13,000 of the axioms are annotations including RxNorm codes, drug synonyms, literature references, and direct links to published articles. Conclusion This approach represents, to our knowledge, the largest effort to date to systematically categorize and relate hematology/oncology drugs and regimens. The ontology can be used to reason individual components from regimens mentioned in electronic health records (eg, R-CHOP maps to rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) and also to probabilistically reconstruct regimens from individual drug components. These capabilities may be particularly valuable in the implementation of rapid-learning health systems on the basis of real-world evidence. The derived Web Ontology Language ontology is freely available for noncommercial use through the Creative Commons 4.0 Attribution-NonCommercial-ShareAlike license.

Connectivity webs mediate the unique biology of the mammalian brain. Yet while cell circuit maps are increasingly available, knowledge of the underlying molecular networks remains limited. Here, we applied multi-dimensional biochemical fractionation with precision mass spectrometry and machine learning to survey endogenous macromolecules in adult mouse brain. We defined a global ‘interactome’ of multi-protein complexes, most never reported before. These brain-selective assemblies have distinct physical and functional attributes and show regional- and cell-type specificity. A striking number are also linked to neurological disorders and disease variants with broad pathophysiological relevance. Using reciprocal pulldowns and a transgenic model, we validated a putative 15-member RNA-binding protein complex associated with amyotrophic lateral sclerosis, establishing regulatory functions in alternative splicing and disease progression. This Brain Interaction Map – or BraInMap – resource facilitates mechanistic exploration of the molecular machinery driving core processes and diseases of the central nervous system.

SummaryThe prevalence of autism spectrum disorders (ASDs) is rapidly growing, yet its molecular basis is poorly understood. Here, we sought to gain a systems-level understanding of ASD candidate genes by mapping them onto ubiquitous human protein complexes and characterizing the resulting complexes. These studies revealed the role of histone deacetylases (HDAC1/2) in regulating the expression of ASD orthologs in the embryonic mouse brain. Next, proteome-wide screens for subunits co-complexed with HDAC1 and six other key ASD proteins in human neuronal cells revealed a protein interaction network that displayed preferential expression in fetal brain development, exhibited increased deleterious mutations in ASD cases, and encompassed genes strongly regulated by FMRP and MECP2, mutations that are causal for fragile X and Rett syndromes, respectively. Overall, our study reveals molecular components in ASD, suggests a shared mechanism between the syndromic and idiopathic forms of ASDs, and provides a groundwork for analyzing complex human diseases.