Unraveling Links between Chronic Inflammation and Long COVID: Workshop Report.

As COVID-19 continues, an increasing number of patients develop long COVID symptoms varying in severity that last for weeks, months, or longer. Symptoms commonly include lingering loss of smell and taste, hearing loss, extreme fatigue, and "brain fog." Still, persistent cardiovascular and respiratory problems, muscle weakness, and neurologic issues have also been documented. A major problem is the lack of clear guidelines for diagnosing long COVID. Although some studies suggest that long COVID is due to prolonged inflammation after SARS-CoV-2 infection, the underlying mechanisms remain unclear. The broad range of COVID-19's bodily effects and responses after initial viral infection are also poorly understood. This workshop brought together multidisciplinary experts to showcase and discuss the latest research on long COVID and chronic inflammation that might be associated with the persistent sequelae following COVID-19 infection.

Challenges to Using Big Data in Cancer.

Big data in healthcare can enable unprecedented understanding of diseases and their treatment, particularly in oncology. These data may include electronic health records, medical imaging, genomic sequencing, payor records, and data from pharmaceutical research, wearables, and medical devices. The ability to combine datasets and use data across many analyses is critical to the successful use of big data and is a concern for those who generate and use the data. Interoperability and data quality continue to be major challenges when working with different healthcare datasets. Mapping terminology across datasets, missing and incorrect data, and varying data structures make combining data an onerous and largely manual undertaking. Data privacy is another concern addressed by the Health Insurance Portability and Accountability Act, the Common Rule, and the General Data Protection Regulation. The use of big data is now included in the planning and activities of the FDA and the European Medicines Agency. The willingness of organizations to share data in a precompetitive fashion, agreements on data quality standards, and institution of universal and practical tenets on data privacy will be crucial to fully realizing the potential for big data in medicine.

Case Studies for Overcoming Challenges in Using Big Data in Cancer.

The analysis of big healthcare data has enormous potential as a tool for advancing oncology drug development and patient treatment, particularly in the context of precision medicine. However, there are challenges in organizing, sharing, integrating, and making these data readily accessible to the research community. This review presents five case studies illustrating various successful approaches to addressing such challenges. These efforts are CancerLinQ, the American Association for Cancer Research Project GENIE, Project Data Sphere, the National Cancer Institute Genomic Data Commons, and the Veterans Health Administration Clinical Data Initiative. Critical factors in the development of these systems include attention to the use of robust pipelines for data aggregation, common data models, data deidentification to enable multiple uses, integration of data collection into physician workflows, terminology standardization and attention to interoperability, extensive quality assurance and quality control activity, incorporation of multiple data types, and understanding how data resources can be best applied. By describing some of the emerging resources, we hope to inspire consideration of the secondary use of such data at the earliest possible step to ensure the proper sharing of data in order to generate insights that advance the understanding and the treatment of cancer.

Metabolic Regulation of Inflammation and Its Resolution: Current Status, Clinical Needs, Challenges, and Opportunities.

Metabolism and inflammation have been viewed as two separate processes with distinct but critical functions for our survival: metabolism regulates the utilization of nutrients, and inflammation is responsible for defense and repair. Both respond to an organism's stressors to restore homeostasis. The interplay between metabolic status and immune response (immunometabolism) plays an important role in maintaining health or promoting disease development. Understanding these interactions is critical in developing tools for facilitating novel preventative and therapeutic approaches for diseases, including cancer. This trans-National Institutes of Health workshop brought together basic scientists, technology developers, and clinicians to discuss state-of-the-art, innovative approaches, challenges, and opportunities to understand and harness immunometabolism in modulating inflammation and its resolution.

Early Detection of Pancreatic Cancer: Applying Artificial Intelligence to Electronic Health Records.

ABSTRACT: The potential of artificial intelligence (AI) applied to clinical data from electronic health records (EHRs) to improve early detection for pancreatic and other cancers remains underexplored. The Kenner Family Research Fund, in collaboration with the Cancer Biomarker Research Group at the National Cancer Institute, organized the workshop entitled: "Early Detection of Pancreatic Cancer: Opportunities and Challenges in Utilizing Electronic Health Records (EHR)" in March 2021. The workshop included a select group of panelists with expertise in pancreatic cancer, EHR data mining, and AI-based modeling. This review article reflects the findings from the workshop and assesses the feasibility of AI-based data extraction and modeling applied to EHRs. It highlights the increasing role of data sharing networks and common data models in improving the secondary use of EHR data. Current efforts using EHR data for AI-based modeling to enhance early detection of pancreatic cancer show promise. Specific challenges (biology, limited data, standards, compatibility, legal, quality, AI chasm, incentives) are identified, with mitigation strategies summarized and next steps identified.

Prediagnostic Image Data, Artificial Intelligence, and Pancreatic Cancer: A Tell-Tale Sign to Early Detection.

Pancreatic cancer continues to be one of the deadliest malignancies and is the third leading cause of cancer-related mortality in the United States. Based on several models, it is projected to become the second leading cause of cancer-related deaths by 2030. Although the overall survival rate for patients diagnosed with pancreatic cancer is less than 10%, survival rates are increasing in those whose cancers are detected at an early stage, when intervention is possible. There are, however, no reliable biomarkers or imaging technology that can detect early-stage pancreatic cancer or accurately identify precursors that are likely to progress to malignancy. The Alliance of Pancreatic Cancer Consortia, a virtual consortium of researchers, clinicians, and advocacies focused on early diagnosis of pancreatic cancer, was formed in 2016 to provide a platform and resources to discover and validate biomarkers and imaging methods for early detection. The focus of discussion at the most recent alliance meeting was on imaging methods and the use of artificial intelligence for early detection of pancreatic cancer.

Imaging inflammation and its resolution in health and disease: current status, clinical needs, challenges, and opportunities.

Inflammation is a normal process in our body; acute inflammation acts to suppress infections and support wound healing. Chronic inflammation likely leads to a wide range of diseases, including cancer. Tools to locate and monitor inflammation are critical for developing effective interventions to arrest inflammation and promote its resolution. To identify current clinical needs, challenges, and opportunities in advancing imaging-based evaluations of inflammatory status in patients, the U.S. National Institutes of Health convened a workshop on imaging inflammation and its resolution in health and disease. Clinical speakers described their needs for image-based capabilities that could help determine the extent of inflammatory conditions in patients to guide treatment planning and undertake necessary interventions. The imaging speakers showcased the state-of-the-art in vivo imaging techniques for detecting inflammation in different disease areas. Many imaging capabilities developed for 1 organ or disease can be adapted for other diseases and organs, whereas some have promise for clinical utility within the next 5-10 yr. Several speakers demonstrated that multimodal imaging measurements integrated with serum-based measures could improve in robustness for clinical utility. All speakers agreed that multiple inflammatory measures should be acquired longitudinally to comprehend the dynamics of unresolved inflammation that leads to disease development. They also agreed that the best strategies for accelerating clinical translation of imaging inflammation capabilities are through integration between new imaging techniques and biofluid-based biomarkers of inflammation as well as already established imaging measurements.-Liu, C. H., Abrams, N. D., Carrick, D. M., Chander, P., Dwyer, J., Hamlet, M. R. J., Kindzelski, A. L., PrabhuDas, M., Tsai, S.-Y. A., Vedamony, M. M., Wang, C., Tandon, P. Imaging inflammation and its resolution in health and disease: current status, clinical needs, challenges, and opportunities.

Twist1 and Slug mediate H2AX-regulated epithelial-mesenchymal transition in breast cells.

The epithelial-mesenchymal transition (EMT) is thought to be essential for cancer metastasis. While chromatin remodeling is involved in EMT, which processes contribute to this remodeling remain poorly investigated. Recently, we showed that silencing or removal of the histone variant H2A.X induced mesenchymal-like characteristics, including activation of the EMT transcription factors, Slug and Zeb1 in human colon cancer cells. Here, we provide the evidence that H2A.X loss in human non-tumorigenic breast cell line MCF10A results in a robust EMT activation, as substantiated by a genome-wide expression analysis. Cells deficient for H2A.X exhibit enhanced migration and invasion, along with an activation of a set of mesenchymal genes and a concomitant repression of epithelial genes. In the breast model, the EMT-related transcription factor Twist1 cooperates with Slug to regulate EMT upon H2A.X Loss. Of interest, H2A.X expression level tightly correlates with Twist1, and to a lesser extent with Slug in the panel of human breast cancer cell lines of the NCI-60 datasets. These new findings indicate that H2A.X is involved in the EMT processes in cells of different origins but pairing with transcription factors for EMT may be tissue specific.

The histone variant H2A.X is a regulator of the epithelial-mesenchymal transition.

The epithelial-mesenchymal transition (EMT), considered essential for metastatic cancer, has been a focus of much research, but important questions remain. Here, we show that silencing or removing H2A.X, a histone H2A variant involved in cellular DNA repair and robust growth, induces mesenchymal-like characteristics including activation of EMT transcription factors, Slug and ZEB1, in HCT116 human colon cancer cells. Ectopic H2A.X re-expression partially reverses these changes, as does silencing Slug and ZEB1. In an experimental metastasis model, the HCT116 parental and H2A.X-null cells exhibit a similar metastatic behaviour, but the cells with re-expressed H2A.X are substantially more metastatic. We surmise that H2A.X re-expression leads to partial EMT reversal and increases robustness in the HCT116 cells, permitting them to both form tumours and to metastasize. In a human adenocarcinoma panel, H2A.X levels correlate inversely with Slug and ZEB1 levels. Together, these results point to H2A.X as a regulator of EMT.

Cripto-1 as a novel therapeutic target for triple negative breast cancer.

Triple-negative breast cancer (TNBC) presents the poorest prognosis among the breast cancer subtypes and no current standard therapy. Here, we performed an in-depth molecular analysis of a mouse model that establishes spontaneous lung metastasis from JygMC(A) cells. These primary tumors resembled the triple-negative breast cancer (TNBC) both phenotypically and molecularly. Morphologically, primary tumors presented both epithelial and spindle-like cells but displayed only adenocarcinoma-like features in lung parenchyma. The use of laser-capture microdissection combined with Nanostring mRNA and microRNA analysis revealed overexpression of either epithelial and miRNA-200 family or mesenchymal markers in adenocarcinoma and mesenchymal regions, respectively. Cripto-1, an embryonic stem cell marker, was present in spindle-like areas and its promoter showed activity in primary tumors. Cripto-1 knockout by the CRISPR-Cas9 system inhibited tumor growth and pulmonary metastasis. Our findings show characterization of a novel mouse model that mimics the TNBC and reveal Cripto-1 as a TNBC target hence may offer alternative treatment strategies for TNBC.

Genome Sequence of Aspergillus flavus NRRL 3357, a Strain That Causes Aflatoxin Contamination of Food and Feed.

Aflatoxin contamination of food and livestock feed results in significant annual crop losses internationally. Aspergillus flavus is the major fungus responsible for this loss. Additionally, A. flavus is the second leading cause of aspergillosis in immunocompromised human patients. Here, we report the genome sequence of strain NRRL 3357.

Genome Sequence of the AIDS-Associated Pathogen Penicillium marneffei (ATCC18224) and Its Near Taxonomic Relative Talaromyces stipitatus (ATCC10500).

Penicillium marneffei can cause a fatal systemic mycosis in patients infected with the HIV. Infections are endemic in the tropical regions of southeast Asia. Here, we report the genome sequences of the type strains of P. marneffei and its avirulent near relative, Talaromyces stipitatus.

Draft Genome Sequence of the Plant-Pathogenic Soil Fungus Rhizoctonia solani Anastomosis Group 3 Strain Rhs1AP.

The soil fungus Rhizoctonia solani is a pathogen of agricultural crops. Here, we report on the 51,705,945 bp draft consensus genome sequence of R. solani strain Rhs1AP. A comprehensive understanding of the heterokaryotic genome complexity and organization of R. solani may provide insight into the plant disease ecology and adaptive behavior of the fungus.

Identification of the genomic insertion site of Pmel-1 TCR α and ß transgenes by next-generation sequencing.

The pmel-1 T cell receptor transgenic mouse has been extensively employed as an ideal model system to study the mechanisms of tumor immunology, CD8+ T cell differentiation, autoimmunity and adoptive immunotherapy. The 'zygosity' of the transgene affects the transgene expression levels and may compromise optimal breeding scheme design. However, the integration sites for the pmel-1 mouse have remained uncharacterized. This is also true for many other commonly used transgenic mice created before the modern era of rapid and inexpensive next-generation sequencing. Here, we show that whole genome sequencing can be used to determine the exact pmel-1 genomic integration site, even with relatively 'shallow' (8X) coverage. The results were used to develop a validated polymerase chain reaction-based genotyping assay. For the first time, we provide a quick and convenient polymerase chain reaction method to determine the dosage of pmel-1 transgene for this freely and publically available mouse resource. We also demonstrate that next-generation sequencing provides a feasible approach for mapping foreign DNA integration sites, even when information of the original vector sequences is only partially known.

Sequencing of mitochondrial genomes of nine Aspergillus and Penicillium species identifies mobile introns and accessory genes as main sources of genome size variability.

BACKGROUND: The genera Aspergillus and Penicillium include some of the most beneficial as well as the most harmful fungal species such as the penicillin-producer Penicillium chrysogenum and the human pathogen Aspergillus fumigatus, respectively. Their mitochondrial genomic sequences may hold vital clues into the mechanisms of their evolution, population genetics, and biology, yet only a handful of these genomes have been fully sequenced and annotated. RESULTS: Here we report the complete sequence and annotation of the mitochondrial genomes of six Aspergillus and three Penicillium species: A. fumigatus, A. clavatus, A. oryzae, A. flavus, Neosartorya fischeri (A. fischerianus), A. terreus, P. chrysogenum, P. marneffei, and Talaromyces stipitatus (P. stipitatum). The accompanying comparative analysis of these and related publicly available mitochondrial genomes reveals wide variation in size (25-36 Kb) among these closely related fungi. The sources of genome expansion include group I introns and accessory genes encoding putative homing endonucleases, DNA and RNA polymerases (presumed to be of plasmid origin) and hypothetical proteins. The two smallest sequenced genomes (A. terreus and P. chrysogenum) do not contain introns in protein-coding genes, whereas the largest genome (T. stipitatus), contains a total of eleven introns. All of the sequenced genomes have a group I intron in the large ribosomal subunit RNA gene, suggesting that this intron is fixed in these species. Subsequent analysis of several A. fumigatus strains showed low intraspecies variation. This study also includes a phylogenetic analysis based on 14 concatenated core mitochondrial proteins. The phylogenetic tree has a different topology from published multilocus trees, highlighting the challenges still facing the Aspergillus systematics. CONCLUSIONS: The study expands the genomic resources available to fungal biologists by providing mitochondrial genomes with consistent annotations for future genetic, evolutionary and population studies. Despite the conservation of the core genes, the mitochondrial genomes of Aspergillus and Penicillium species examined here exhibit significant amount of interspecies variation. Most of this variation can be attributed to accessory genes and mobile introns, presumably acquired by horizontal gene transfer of mitochondrial plasmids and intron homing.

Systematic and searchable classification of cytochrome P450 proteins encoded by fungal and oomycete genomes.

BACKGROUND: Cytochrome P450 proteins (CYPs) play diverse and pivotal roles in fungal metabolism and adaptation to specific ecological niches. Fungal genomes encode extremely variable "CYPomes" ranging from one to more than 300 CYPs. Despite the rapid growth of sequenced fungal and oomycete genomes and the resulting influx of predicted CYPs, the vast majority of CYPs remain functionally uncharacterized. To facilitate the curation and functional and evolutionary studies of CYPs, we previously developed Fungal Cytochrome P450 Database (FCPD), which included CYPs from 70 fungal and oomycete species. Here we present a new version of FCPD (1.2) with more data and an improved classification scheme. RESULTS: The new database contains 22,940 CYPs from 213 species divided into 2,579 clusters and 115 clans. By optimizing the clustering pipeline, we were able to uncover 36 novel clans and to assign 153 orphan CYP families to specific clans. To augment their functional annotation, CYP clusters were mapped to David Nelson's P450 databases, which archive a total of 12,500 manually curated CYPs. Additionally, over 150 clusters were functionally classified based on sequence similarity to experimentally characterized CYPs. Comparative analysis of fungal and oomycete CYPomes revealed cases of both extreme expansion and contraction. The most dramatic expansions in fungi were observed in clans CYP58 and CYP68 (Pezizomycotina), clans CYP5150 and CYP63 (Agaricomycotina), and family CYP509 (Mucoromycotina). Although much of the extraordinary diversity of the pan-fungal CYPome can be attributed to gene duplication and adaptive divergence, our analysis also suggests a few potential horizontal gene transfer events. Updated families and clans can be accessed through the new version of the FCPD database. CONCLUSIONS: FCPD version 1.2 provides a systematic and searchable catalogue of 9,550 fungal CYP sequences (292 families) encoded by 108 fungal species and 147 CYP sequences (9 families) encoded by five oomycete species. In comparison to the first version, it offers a more comprehensive clan classification, is fully compatible with Nelson's P450 databases, and has expanded functional categorization. These features will facilitate functional annotation and classification of CYPs encoded by newly sequenced fungal and oomycete genomes. Additionally, the classification system will aid in studying the roles of CYPs in the evolution of fungal adaptation to specific ecological niches.