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Genomic safe harbors are regions of the genome that can maintain transgene expression without disrupting the function of host cells. Genomic safe harbors play an increasingly important role in improving the efficiency and safety of genome engineering. However, limited safe harbors have been identified.

Here, we develop a framework to facilitate searches for genomic safe harbors by integrating information from polymorphic mobile element insertions that naturally occur in human populations, epigenomic signatures, and 3D chromatin organization. By applying our framework to polymorphic mobile element insertions identified in the 1000 Genomes project and the Genotype-Tissue Expression (GTEx) project, we identify 19 candidate safe harbors in blood cells and 5 in brain cells. For three candidate sites in blood, we demonstrate the stable expression of transgene without disrupting nearby genes in host erythroid cells. We also develop a computer program, Genomics and Epigenetic Guided Safe Harbor mapper (GEG-SH mapper), for knowledge-based tissue-specific genomic safe harbor selection.

Our study provides a new knowledge-based framework to identify tissue-specific genomic safe harbors. In combination with the fast-growing genome engineering technologies, our approach has the potential to improve the overall safety and efficiency of gene and cell-based therapy in the near future.

 

Abstract Spatially resolved transcriptomics technologies enable the measurement of transcriptome information while retaining the spatial context at the regional, cellular or sub-cellular level. While previous computational methods have relied on gene expression information alone for clustering single-cell populations, more recent methods have begun to leverage spatial location and histology information to improve cell clustering and cell-type identification. In this study, using seven semi-synthetic datasets with real spatial locations, simulated gene expression and histology images as well as ground truth cell-type labels, we evaluate 15 clustering methods based on clustering accuracy, robustness to data variation and input parameters, computational efficiency, and software usability. Our analysis demonstrates that even though incorporating the additional spatial and histology information leads to increased accuracy in some datasets, it does not consistently improve clustering compared with using only gene expression data. Our results indicate that for the clustering of spatial transcriptomics data, there are still opportunities to enhance the overall accuracy and robustness by improving information extraction and feature selection from spatial and histology data. 

Objective: Mortality-trends from alcoholic liver disease (ALD) have recently increased and they differ by various factors in the U.S. However, these trends have only been analyzed using univariate models and in reality they may be influenced by various factors. We thus examined trends in age-standardized mortality from ALD among U.S. adults for 1999-2017, using multivariable piecewise log-linear models. Methods: We collected mortality-data from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research database, using the Underlying Cause of Death. Results: We identified 296,194 deaths from ALD and 346,386 deaths indirectly attributable to ALD during the period from 1999-2017. The multivariable-adjusted, age-standardized ALD mortality was stable during 1999-2006 (annual percentage change [APC]=-2.24, P=0.24), and increased during 2006-2017 (APC=3.18, P<0.006). Their trends did not differ by sex, race, age or urbanization. Subgroup analyses revealed upward multivariable-adjusted, age-standardized mortality-trends in alcoholic fatty liver (APC=4.64, P<0.001), alcoholic hepatitis (APC=4.38, P<0.001), and alcoholic cirrhosis (APC=5.33, P<0.001), but downward mortality-trends in alcoholic hepatic failure (APC=-1.63, P=0.006) and unspecified ALD (APC=-0.86, P=0.013). Strikingly, non-alcoholic cirrhosis also had an upward multivariable-adjusted, age-standardized mortality-trend (APC=0.69, P=0.046). By contrast, recent mortality-trends were stable for all cause of deaths (APC=-0.39, P=0.379) and downward for malignant neoplasms excluding liver cancer (APC=-2.82, P<0.001), infections (APC=-2.60, P<0.001), cardiovascular disease (APC=-0.69, P=0.044) and respiratory disease (APC=-0.56, P=0.002). The adjusted mortality with ALD as a contributing cause of death also had an upward trend during 2000-2017 (APC=5.47, P<0.001). Strikingly, common comorbidities of ALD, including hepatocellular carcinoma, cerebrovascular and ischemic heart cardiovascular diseases and sepsis, had upward trends during the past 14 to 16 years. Conclusions: ALD had an upward multivariable-adjusted, age-standardized mortality-trend among U.S. adults, without significant differences by sex, race, age or urbanization. Three ALD subtypes (alcoholic fatty liver, alcoholic hepatitis and alcoholic cirrhosis) and non-alcoholic cirrhosis had upward morality-trends, while other ALD subtypes and other causes of death did not. 

Proteomics plays a vital role in biomedical research in the post-genomic era. With the technological revolution and emerging computational and statistic models, proteomic methodology has evolved rapidly in the past decade and shed light on solving complicated biomedical problems. Here, we summarize scientific research and clinical practice of existing and emerging high-throughput proteomics approaches, including mass spectrometry, protein pathway array, next-generation tissue microarrays, single-cell proteomics, single-molecule proteomics, Luminex, Simoa and Olink Proteomics. We also discuss important computational methods and statistical algorithms that can maximize the mining of proteomic data with clinical and/or other 'omics data. Various principles and precautions are provided for better utilization of these tools. In summary, the advances in high-throughput proteomics will not only help better understand the molecular mechanisms of pathogenesis, but also to identify the signature signaling networks of specific diseases. Thus, modern proteomics have a range of potential applications in basic research, prognostic oncology, precision medicine, and drug discovery.