pathDIP 4: an extended pathway annotations and enrichment analysis resource for human, model organisms and domesticated species

Cecal microbiota and mammary gland microRNA signatures are related and modifiable by dietary flaxseed with implications for breast cancer risk

IMPORTANCE

Breast cancer is a leading cause of cancer mortality worldwide. There is a growing interest in using dietary approaches, including flaxseed (FS) and its oil and lignan components, to mitigate breast cancer risk. Importantly, there is recognition that pubertal processes and lifestyle, including diet, are important for breast health throughout life. Mechanisms remain incompletely understood. Our research uncovers a link between mammary gland miRNA expression and the gut microbiota in young female mice. We found that this relationship is modifiable via a dietary intervention. Using data from The Cancer Genome Atlas, we also show that the expression of miRNAs involved in these relationships is altered in breast cancer in humans. These findings highlight a role for the gut microbiome as a modulator, and thus a target, of interventions aiming at reducing breast cancer risk. They also provide foundational knowledge to explore the effects of early life interventions and mechanisms programming breast health.

Tumor Cell-Intrinsic c-Myb Upregulation Stimulates Antitumor Immunity in a Murine Colorectal Cancer Model

The transcription factor c-Myb is overexpressed in many different types of solid tumors, including colorectal cancer. However, its exact role in tumorigenesis is unclear. In this study, we show that tumor-intrinsic c-Myb expression in mouse models of colon cancer and melanoma suppresses tumor growth. Although no differences in proliferation, apoptosis, and angiogenesis of tumors were evident in tumors with distinct levels of c-Myb expression, we observed changes in intratumoral immune cell infiltrates. MC38 tumors with upregulated c-Myb expression showed increased numbers of CD103+ dendritic cells and eosinophils, but decreased tumor-associated macrophages (TAM). Concomitantly, an increase in the number of activated cytotoxic CD8+ T cells upon c-Myb upregulation was observed, which correlated with a pro-inflammatory tumor microenvironment and increased numbers of M1 polarized TAMs. Mechanistically, c-Myb upregulation in immunogenic MC38 colon cancer cells resulted in enhanced expression of immunomodulatory genes, including those encoding β2-microglobulin and IFNβ, and decreased expression of the gene encoding the chemokine receptor CCR2. The increased numbers of activated cytotoxic CD8+ T cells contributed to tumor growth attenuation. In poorly immunogenic CT26, LLC, and B16-BL6 tumor cells, c-Myb upregulation did not affect the immunomodulatory gene expression. Despite this, c-Myb upregulation led to reduced B16-BL6 tumor growth but it did not affect tumor growth of CT26 and LLC tumors. Altogether, we postulate that c-Myb functions as a tumor suppressor in a tumor cell-type specific manner and modulates antitumor immunity.

USNAP: fast unique dense region detection and its application to lung cancer

MOTIVATION

Many real-world problems can be modeled as annotated graphs. Scalable graph algorithms that extract actionable information from such data are in demand since these graphs are large, varying in topology, and have diverse node/edge annotations. When these graphs change over time they create dynamic graphs, and open the possibility to find patterns across different time points. In this article, we introduce a scalable algorithm that finds unique dense regions across time points in dynamic graphs. Such algorithms have applications in many different areas, including the biological, financial, and social domains.

RESULTS

There are three important contributions to this manuscript. First, we designed a scalable algorithm, USNAP, to effectively identify dense subgraphs that are unique to a time stamp given a dynamic graph. Importantly, USNAP provides a lower bound of the density measure in each step of the greedy algorithm. Second, insights and understanding obtained from validating USNAP on real data show its effectiveness. While USNAP is domain independent, we applied it to four non-small cell lung cancer gene expression datasets. Stages in non-small cell lung cancer were modeled as dynamic graphs, and input to USNAP. Pathway enrichment analyses and comprehensive interpretations from literature show that USNAP identified biologically relevant mechanisms for different stages of cancer progression. Third, USNAP is scalable, and has a time complexity of O(m+mc log nc+nc log nc), where m is the number of edges, and n is the number of vertices in the dynamic graph; mc is the number of edges, and nc is the number of vertices in the collapsed graph.

AVAILABILITY AND IMPLEMENTATION

The code of USNAP is available at https://www.cs.utoronto.ca/~juris/data/USNAP22.

Traumatic MicroRNAs: Deconvolving the Signal After Severe Traumatic Brain Injury

History of traumatic brain injury (TBI) represents a significant risk factor for development of dementia and neurodegenerative disorders in later life. While histopathological sequelae and neurological diagnostics of TBI are well defined, the molecular events linking the post-TBI signaling and neurodegenerative cascades remain unknown. It is not only due to the brain's inaccessibility to direct molecular analysis but also due to the lack of well-defined and highly informative peripheral biomarkers. MicroRNAs (miRNAs) in blood are promising candidates to address this gap. Using integrative bioinformatics pipeline including miRNA:target identification, pathway enrichment, and protein-protein interactions analysis we identified set of genes, interacting proteins, and pathways that are connected to previously reported peripheral miRNAs, deregulated following severe traumatic brain injury (sTBI) in humans. This meta-analysis revealed a spectrum of genes closely related to critical biological processes, such as neuroregeneration including axon guidance and neurite outgrowth, neurotransmission, inflammation, proliferation, apoptosis, cell adhesion, and response to DNA damage. More importantly, we have identified molecular pathways associated with neurodegenerative conditions, including Alzheimer's and Parkinson's diseases, based on purely peripheral markers. The pathway signature after acute sTBI is similar to the one observed in chronic neurodegenerative conditions, which implicates a link between the post-sTBI signaling and neurodegeneration. Identified key hub interacting proteins represent a group of novel candidates for potential therapeutic targets or biomarkers.

Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning

Cancer is one of the leading causes of death worldwide and can be caused by environmental aspects (for example, exposure to asbestos), by human behavior (such as smoking), or by genetic factors. To understand which genes might be involved in patients’ survival, researchers have invented prognostic genetic signatures: lists of genes that can be used in scientific analyses to predict if a patient will survive or not. In this study, we joined together five different prognostic signatures, each of them related to a specific cancer type, to generate a unique pan-cancer prognostic signature, that contains 207 unique probesets related to 187 unique gene symbols, with one particular probeset present in two cancer type-specific signatures (203072_at related to the MYO1E gene). We applied our proposed pan-cancer signature with the Random Forests machine learning method to 57 microarray gene expression datasets of 12 different cancer types, and analyzed the results. We also compared the performance of our pan-cancer signature with the performances of two alternative prognostic signatures, and with the performances of each cancer type-specific signature on their corresponding cancer type-specific datasets. Our results confirmed the effectiveness of our prognostic pan-cancer signature. Moreover, we performed a pathway enrichment analysis, which indicated an association between the signature genes and a protein-protein interaction analysis, that highlighted PIK3R2 and FN1 as key genes having a fundamental relevance in our signature, suggesting an important role in pan-cancer prognosis for both of them.

A Python Clustering Analysis Protocol of Genes Expression Data Sets

Gene expression and SNPs data hold great potential for a new understanding of disease prognosis, drug sensitivity, and toxicity evaluations. Cluster analysis is used to analyze data that do not contain any specific subgroups. The goal is to use the data itself to recognize meaningful and informative subgroups. In addition, cluster investigation helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. Cluster analysis could also be used to identify bio-markers and yield computational predictive models. The methods used to analyze microarrays data can profoundly influence the interpretation of the results. Therefore, a basic understanding of these computational tools is necessary for optimal experimental design and meaningful data analysis. This manuscript provides an analysis protocol to effectively analyze gene expression data sets through the K-means and DBSCAN algorithms. The general protocol enables analyzing omics data to identify subsets of features with low redundancy and high robustness, speeding up the identification of new bio-markers through pathway enrichment analysis. In addition, to demonstrate the effectiveness of our clustering analysis protocol, we analyze a real data set from the GEO database. Finally, the manuscript provides some best practice and tips to overcome some issues in the analysis of omics data sets through unsupervised learning.

Comprehensive analysis of pathways in Coronavirus 2019 (COVID-19) using an unsupervised machine learning method

The World Health Organization (WHO) introduced “Coronavirus disease 19” or “COVID-19” as a novel coronavirus in March 2020. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide crisis. Artificial intelligence and bioinformatics analysis pipelines can assist with finding biomarkers, explanations, and cures. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data. On the other hand, pathway enrichment analysis, as a dominant tool, could help researchers discover potential key targets present in biological pathways of host cells that are targeted by SARS-CoV-2. In this work, we propose a two-stage machine learning approach for pathway analysis. During the first stage, four informative gene sets that can represent important COVID-19 related pathways are selected. These “representative genes” are associated with the COVID-19 pathology. Then, two distinctive networks were constructed for COVID-19 related signaling and disease pathways. In the second stage, the pathways of each network are ranked with respect to some unsupervised scorning method based on our defined informative features. Finally, we present a comprehensive analysis of the top important pathways in both networks. Materials and implementations are available at: https://github.com/MahnazHabibi/Pathway.

Dysregulated autoantibodies targeting vaso- and immunoregulatory receptors in Post COVID Syndrome correlate with symptom severity

Most patients with Post COVID Syndrome (PCS) present with a plethora of symptoms without clear evidence of organ dysfunction. A subset of them fulfills diagnostic criteria of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Symptom severity of ME/CFS correlates with natural regulatory autoantibody (AAB) levels targeting several G-protein coupled receptors (GPCR). In this exploratory study, we analyzed serum AAB levels against vaso- and immunoregulatory receptors, mostly GPCRs, in 80 PCS patients following mild-to-moderate COVID-19, with 40 of them fulfilling diagnostic criteria of ME/CFS. Healthy seronegative (n=38) and asymptomatic post COVID-19 controls (n=40) were also included in the study as control groups. We found lower levels for various AABs in PCS compared to at least one control group, accompanied by alterations in the correlations among AABs. Classification using random forest indicated AABs targeting ADRB2, STAB1, and ADRA2A as the strongest classifiers (AABs stratifying patients according to disease outcomes) of post COVID-19 outcomes. Several AABs correlated with symptom severity in PCS groups. Remarkably, severity of fatigue and vasomotor symptoms were associated with ADRB2 AAB levels in PCS/ME/CFS patients. Our study identified dysregulation of AAB against various receptors involved in the autonomous nervous system (ANS), vaso-, and immunoregulation and their correlation with symptom severity, pointing to their role in the pathogenesis of PCS.

Quantitative proteomic analytic approaches to identify metabolic changes in the medial prefrontal cortex of rats exposed to space radiation

NASA’s planned mission to Mars will result in astronauts being exposed to ∼350 mSv/yr of Galactic Cosmic Radiation (GCR). A growing body of data from ground-based experiments indicates that exposure to space radiation doses (approximating those that astronauts will be exposed to on a mission to Mars) impairs a variety of cognitive processes, including cognitive flexibility tasks. Some studies report that 33% of individuals may experience severe cognitive impairment. Translating the results from ground-based rodent studies into tangible risk estimates for astronauts is an enormous challenge, but it would be germane for NASA to use the vast body of data from the rodent studies to start developing appropriate countermeasures, in the expectation that some level of space radiation (SR) -induced cognitive impairment could occur in astronauts. While some targeted studies have reported radiation-induced changes in the neurotransmission properties and/or increased neuroinflammation within space radiation exposed brains, there remains little information that can be used to start the development of a mechanism-based countermeasure strategy. In this study, we have employed a robust label-free mass spectrometry (MS) -based untargeted quantitative proteomic profiling approach to characterize the composition of the medial prefrontal cortex (mPFC) proteome in rats that have been exposed to 15 cGy of 600 MeV/n28Si ions. A variety of analytical techniques were used to mine the generated expression data, which in such studies is typically hampered by low and variable sample size. We have identified several pathways and proteins whose expression alters as a result of space radiation exposure, including decreased mitochondrial function, and a further subset of proteins differs in rats that have a high level of cognitive performance after SR exposure in comparison with those that have low performance levels. While this study has provided further insight into how SR impacts upon neurophysiology, and what adaptive responses can be invoked to prevent the emergence of SR-induced cognitive impairment, the main objective of this paper is to outline strategies that can be used by others to analyze sub-optimal data sets and to identify new information.

Autoantibodies targeting GPCRs and RAS-related molecules associate with COVID-19 severity

COVID-19 shares the feature of autoantibody production with systemic autoimmune diseases. In order to understand the role of these immune globulins in the pathogenesis of the disease, it is important to explore the autoantibody spectra. Here we show, by a cross-sectional study of 246 individuals, that autoantibodies targeting G protein-coupled receptors (GPCR) and RAS-related molecules associate with the clinical severity of COVID-19. Patients with moderate and severe disease are characterized by higher autoantibody levels than healthy controls and those with mild COVID-19 disease. Among the anti-GPCR autoantibodies, machine learning classification identifies the chemokine receptor CXCR3 and the RAS-related molecule AGTR1 as targets for antibodies with the strongest association to disease severity. Besides antibody levels, autoantibody network signatures are also changing in patients with intermediate or high disease severity. Although our current and previous studies identify anti-GPCR antibodies as natural components of human biology, their production is deregulated in COVID-19 and their level and pattern alterations might predict COVID-19 disease severity.

Pathway Enrichment Analysis of Microarray Data

Microarray analyses usually result in a list of differential genes that need to be annotated to link them the phenotype being studied, help planning validation experiments and interpretation of the results. Pathway enrichment analyses are frequently used for such purpose, where pathways are human created models of molecular activities and processes. While different types of pathway enrichment are available, we focus this protocol on the most frequent type-overrepresentation analysis. Many databases collect different sets of pathways and curate different sets of genes for the same pathways, so it is important to carefully choose the most suitable pathway source to perform enrichment analysis. To provide a comprehensive pathway analysis, in this protocol we will use pathDIP, which supports comprehensive enrichment analysis by integrating 22 main pathway databases. We will also describe the steps needed to visualize the enriched pathways using GSOAP.

Detecting Rewiring Events in Protein-Protein Interaction Networks Based on Transcriptomic Data

Proteins rarely carry out their cellular functions in isolation. Instead, eukaryotic proteins engage in about six interactions with other proteins on average. The aggregated protein interactome of an organism forms a “hairy ball”-type protein-protein interaction (PPI) network. Yet, in a typical human cell, only about half of all proteins are expressed at a particular time. Hence, it has become common practice to prune the full PPI network to the subset of expressed proteins. If RNAseq data is available, one can further resolve the specific protein isoforms present in a cell or tissue. Here, we review various approaches, software tools and webservices that enable users to construct context-specific or tissue-specific PPI networks and how these are rewired between two cellular conditions. We illustrate their different functionalities on the example of the interactions involving the human TNR6 protein. In an outlook, we describe how PPI networks may be integrated with epigenetic data or with data on the activity of splicing factors.

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data

The advent of single-cell RNA sequencing (scRNA-seq) technologies has revolutionized transcriptomic studies. However, large-scale integrative analysis of scRNA-seq data remains a challenge largely due to unwanted batch effects and the limited transferabilty, interpretability, and scalability of the existing computational methods. We present single-cell Embedded Topic Model (scETM). Our key contribution is the utilization of a transferable neural-network-based encoder while having an interpretable linear decoder via a matrix tri-factorization. In particular, scETM simultaneously learns an encoder network to infer cell type mixture and a set of highly interpretable gene embeddings, topic embeddings, and batch-effect linear intercepts from multiple scRNA-seq datasets. scETM is scalable to over 106 cells and confers remarkable cross-tissue and cross-species zero-shot transfer-learning performance. Using gene set enrichment analysis, we find that scETM-learned topics are enriched in biologically meaningful and disease-related pathways. Lastly, scETM enables the incorporation of known gene sets into the gene embeddings, thereby directly learning the associations between pathways and topics via the topic embeddings.

Insights into the pathogenesis of psoriatic arthritis from genetic studies

Psoriatic arthritis (PsA) is a relatively common inflammatory arthritis, a spondyloarthritis (SpA), that occurs most often in patients with psoriasis, a common immune-mediated inflammatory skin disease. Both psoriasis and PsA are highly heritable. Genetic and recent genomic studies have identified variants associated with psoriasis and PsA, but variants differentiating psoriasis from PsA are few. In this review, we describe recent developments in understanding the genetic burden of PsA, linkage, association and epigenetic studies. Using pathway analysis, we provide further insights into the similarities and differences between PsA and psoriasis, as well as between PsA and other immune-mediated inflammatory diseases, particularly ankylosing spondylitis, another SpA. Environmental factors that may trigger PsA in patients with psoriasis are also reviewed. To further understand the pathogenetic differences between PsA and psoriasis as well as other SpA, larger cohort studies of well-phenotyped subjects with integrated analysis of genomic, epigenomic, transcriptomic, proteomic and metabolomic data using interomic system biology approaches are required.

Rho-GTPase pathways may differentiate treatment response to TNF-alpha and IL-17A inhibitors in psoriatic arthritis

Biological therapies have dramatically improved the therapeutic landscape of psoriatic arthritis (PsA); however, 40–50% of patients are primary non-responders with response rates declining significantly with each successive biological therapy. Therefore, there is a pressing need to develop a coherent strategy for effective initial and subsequent selection of biologic agents. We interrogated 40 PsA patients initiating either tumour necrosis factor inhibitors (TNFi) or interleukin-17A inhibitors (17Ai) for active PsA. Patients achieving low disease activity according to the Disease Activity Index for PsA (DAPSA) at 3 months were classified as responders. Baseline and 3-month CD4⁺ transcript profiling were performed, and novel signaling pathways were identified using a multi-omics profiling and integrative computational analysis approach. Using transcriptomic data at initiation of therapy, we identified over 100 differentially expressed genes (DEGs) that differentiated IL-17Ai response from non-response and TNFi response from non-response. Integration of cell-type-specific DEGs with protein–protein interactions and further comprehensive pathway enrichment analysis revealed several pathways. Rho GTPase signaling pathway exhibited a strong signal specific to IL-17Ai response and the genes, RAC1 and ROCKs, are supported by results from prior research. Our detailed network and pathway analyses have identified the rewiring of Rho GTPase pathways as potential markers of response to IL17Ai but not TNFi. These results need further verification.

Towards a unified open access dataset of molecular interactions

The International Molecular Exchange (IMEx) Consortium provides scientists with a single body of experimentally verified protein interactions curated in rich contextual detail to an internationally agreed standard. In this update to the work of the IMEx Consortium, we discuss how this initiative has been working in practice, how it has ensured database sustainability, and how it is meeting emerging annotation challenges through the introduction of new interactor types and data formats. Additionally, we provide examples of how IMEx data are being used by biomedical researchers and integrated in other bioinformatic tools and resources.

Extracellular Matrix Injury of Kidney Allografts in Antibody-Mediated Rejection: A Proteomics Study

BACKGROUND

Antibody-mediated rejection (AMR) accounts for >50% of kidney allograft loss. Donor-specific antibodies (DSA) against HLA and non-HLA antigens in the glomeruli and the tubulointerstitium cause AMR while inflammatory cytokines such as TNFα trigger graft injury. The mechanisms governing cell-specific injury in AMR remain unclear.

METHODS

Unbiased proteomic analysis of laser-captured and microdissected glomeruli and tubulointerstitium was performed on 30 for-cause kidney biopsy specimens with early AMR, acute cellular rejection (ACR), or acute tubular necrosis (ATN).

RESULTS

A total of 107 of 2026 glomerular and 112 of 2399 tubulointerstitial proteins was significantly differentially expressed in AMR versus ACR; 112 of 2026 glomerular and 181 of 2399 tubulointerstitial proteins were significantly dysregulated in AMR versus ATN (P<0.05). Basement membrane and extracellular matrix (ECM) proteins were significantly decreased in both AMR compartments. Glomerular and tubulointerstitial laminin subunit γ-1 (LAMC1) expression decreased in AMR, as did glomerular nephrin (NPHS1) and receptor-type tyrosine-phosphatase O (PTPRO). The proteomic analysis revealed upregulated galectin-1, which is an immunomodulatory protein linked to the ECM, in AMR glomeruli. Anti-HLA class I antibodies significantly increased cathepsin-V (CTSV) expression and galectin-1 expression and secretion in human glomerular endothelial cells. CTSV had been predicted to cleave ECM proteins in the AMR glomeruli. Glutathione S-transferase ω-1, an ECM-modifying enzyme, was significantly increased in the AMR tubulointerstitium and in TNFα-treated proximal tubular epithelial cells.

CONCLUSIONS

Basement membranes are often remodeled in chronic AMR. Proteomic analysis performed on laser-captured and microdissected glomeruli and tubulointerstitium identified early ECM remodeling, which may represent a new therapeutic opportunity.

The IMEx Coronavirus interactome: an evolving map of Coronaviridae-Host molecular interactions

The current Coronavirus Disease 2019 (COVID-19) pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions enables studying fine-grained resolution of the mechanisms behind the virus biology and the human organism response. Here we present a curated dataset of physical molecular interactions, manually extracted by IMEx Consortium curators focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family. Currently, the dataset comprises over 2,200 binarized interactions extracted from 86 publications. The dataset can be accessed in the standard formats recommended by the Proteomics Standards Initiative (HUPO-PSI) at the IntAct database website ( www.ebi.ac.uk/intact ), and will be continuously updated as research on COVID-19 progresses.

Urinary proteomics links keratan sulfate degradation and lysosomal enzymes to early type 1 diabetes

Diabetes is the leading cause of end-stage renal disease worldwide. Our understanding of the early kidney response to chronic hyperglycemia remains incomplete. To address this, we first investigated the urinary proteomes of otherwise healthy youths with and without type 1 diabetes and subsequently examined the enriched pathways that might be dysregulated in early disease using systems biology approaches. This cross-sectional study included two separate cohorts for the discovery (N = 30) and internal validation (N = 30) of differentially excreted proteins. Discovery proteomics was performed on a Q Exactive Plus hybrid quadrupole-orbitrap mass spectrometer. We then searched the pathDIP, KEGG, and Reactome databases to identify enriched pathways in early diabetes; the Integrated Interactions Database to retrieve protein-protein interaction data; and the PubMed database to compare fold changes of our signature proteins with those published in similarly designed studies. Proteins were selected for internal validation based on pathway enrichment and availability of commercial enzyme-linked immunosorbent assay kits. Of the 2451 proteins identified, 576 were quantified in all samples from the discovery cohort; 34 comprised the urinary signature for early diabetes after Benjamini-Hochberg adjustment (Q < 0.05). The top pathways associated with this signature included lysosome, glycosaminoglycan degradation, and innate immune system (Q < 0.01). Notably, all enzymes involved in keratan sulfate degradation were significantly elevated in urines from youths with diabetes (|fold change| > 1.6). Increased urinary excretion of monocyte differentiation antigen CD14, hexosaminidase A, and lumican was also observed in the validation cohort (P < 0.05). Twenty-one proteins from our signature have been reported elsewhere as potential mediators of early diabetes. In this study, we identified a urinary proteomic signature for early type 1 diabetes, of which lysosomal enzymes were major constituents. Our findings highlight novel pathways such as keratan sulfate degradation in the early kidney response to hyperglycemia.

Germline Mutation in MUS81 Resulting in Impaired Protein Stability is Associated with Familial Breast and Thyroid Cancer

Multiple primary thyroid cancer (TC) and breast cancer (BC) are commonly diagnosed, and the lifetime risk for these cancers is increased in patients with a positive family history of both TC and BC. Although this phenotype is partially explained by TP53 or PTEN mutations, a significant number of patients are negative for these alterations. We judiciously recruited patients diagnosed with BC and/or TC having a family history of these tumors and assessed their whole-exome sequencing. After variant prioritization, we selected MUS81 c.1292G>A (p.R431H) for further investigation. This variant was genotyped in a healthy population and sporadic BC/TC tissues and investigated at the protein level and cellular models. MUS81 c.1292G>A was the most frequent variant (25%) and the strongest candidate due to its function of double-strand break repair. This variant was confirmed in four relatives from two families. MUS81 p.R431H protein exhibited lower expression levels in tumors from patients positive for the germline variant, compared with wild-type BC, and normal breast and thyroid tissues. Using cell line models, we showed that c.1292G>A induced protein instability and affected DNA damage response. We suggest that MUS81 is a novel candidate involved in familial BC/TC based on its low frequency in healthy individuals and proven effect in protein stability.

Physiological Tau Interactome in Brain and Its Link to Tauopathies

Alzheimer's disease (AD) and most of the other tauopathies are incurable neurodegenerative diseases with unpleasant symptoms and consequences. The common hallmark of all of these diseases is tau pathology, but its connection with disease progress has not been completely understood so far. Therefore, uncovering novel tau-interacting partners and pathology affected molecular pathways can reveal the causes of diseases as well as potential targets for the development of AD treatment. Despite the large number of known tau-interacting partners, a limited number of studies focused on in vivo tau interactions in disease or healthy conditions are available. Here, we applied an in vivo cross-linking approach, capable of capturing weak and transient protein-protein interactions, to a unique transgenic rat model of progressive tau pathology similar to human AD. We have identified 175 potential novel and known tau-interacting proteins by MALDI-TOF mass spectrometry. Several of the most promising candidates for possible drug development were selected for validation by coimmunoprecipitation and colocalization experiments in animal and cellular models. Three proteins, Baiap2, Gpr37l1, and Nptx1, were confirmed as novel tau-interacting partners, and on the basis of their known functions and implications in neurodegenerative or psychiatric disorders, we proposed their potential role in tau pathology.

Complexities in Genetics of Psoriatic Arthritis

PURPOSE OF THE REVIEW

To provide a general overview and current challenges regarding the genetics of psoriatic disease. With the use of integrative medicine, multiple candidate loci identified to date in psoriatic disease will be annotated, summarized, and visualized. Recent studies reporting differences in genetic architecture between psoriatic arthritis and cutaneous-only psoriasis will be highlighted.

RECENT FINDINGS

Focusing on functional pathways that connect previously identified genetic variants can increase our understanding of psoriatic diseases. The genetic architecture differs between psoriatic arthritis and cutaneous-only psoriasis with arthritis-specific signals in linkage disequilibrium independent of the published psoriasis signals. Integrative medicine is helpful in understanding cellular mechanisms of psoriatic diseases. Careful selection of the psoriatic disease cohort has translated into mechanistic differences among psoriatic arthritis and cutaneous psoriasis.

The IMEx coronavirus interactome: an evolving map of Coronaviridae-host molecular interactions

The current coronavirus disease of 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus (SARS-CoV)-2, has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions can provide fine-grained resolution of the mechanisms behind the virus biology and the human organism response. We present a curated dataset of physical molecular interactions focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family that has been manually extracted by International Molecular Exchange (IMEx) Consortium curators. Currently, the dataset comprises over 4400 binarized interactions extracted from 151 publications. The dataset can be accessed in the standard formats recommended by the Proteomics Standards Initiative (HUPO-PSI) at the IntAct database website (https://www.ebi.ac.uk/intact) and will be continuously updated as research on COVID-19 progresses.