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Abstract BackgroundGenomic 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. ResultsHere, 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. ConclusionsOur 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 Paneth cells (PCs), a specialized secretory cell type in the small intestine, are increasingly recognized as having an essential role in host responses to microbiome and environmental stresses. Whether and how commensal and pathogenic microbes modify PC composition to modulate inflammation remain unclear. Using newly developed PC‐reporter mice under conventional and gnotobiotic conditions, we determined PC transcriptomic heterogeneity in response to commensal and invasive microbes at single cell level. Infection expands the pool of CD74⁺PCs, whose number correlates with auto or allogeneic inflammatory disease progressions in mice. Similar correlation was found in human inflammatory disease tissues. Infection‐stimulated cytokines increase production of reactive oxygen species (ROS) and expression of a PC‐specific mucosal pentraxin (Mptx2) in activated PCs. A PC‐specific ablation ofMyD88reduced CD74⁺PC population, thus ameliorating pathogen‐induced systemic disease. A similar phenotype was also observed in mice lacking Mptx2. Thus, infection stimulates expansion of a PC subset that influences disease progression. 

Normalization is a critical step in quantitative analyses of biological processes. Recent works show that cross-platform integration and normalization enable machine learning (ML) training on RNA microarray and RNA-seq data, but no independent datasets were used in their studies. Therefore, it is unclear how to improve ML modelling performance on independent RNA array and RNA-seq based datasets. Inspired by the house-keeping genes that are commonly used in experimental biology, this study tests the hypothesis that non-differentially expressed genes (NDEG) may improve normalization of transcriptomic data and subsequently cross-platform modelling performance of ML models. Microarray and RNA-seq datasets of the TCGA breast cancer were used as independent training and test datasets, respectively, to classify the molecular subtypes of breast cancer. NDEG (p>0.85) and differentially expressed genes (DEG, p<0.05) were selected based on the p values of ANOVA analysis and used for subsequent data normalization and classification, respectively. Models trained based on data from one platform were used for testing on the other platform. Our data show that NDEG and DEG gene selection could effectively improve the model classification performance. Normalization methods based on parametric statistical analysis were inferior to those based on nonparametric statistics. In this study, the LOG_QN and LOG_QNZ normalization methods combined with the neural network classification model seem to achieve better performance. Therefore, NDEG-based normalization appears useful for cross-platform testing on completely independent datasets. However, more studies are required to examine whether NDEG-based normalization can improve ML classification performance in other datasets and other omic data types. 

Intestinal microbiota confers susceptibility to diet-induced obesity yet many probiotic species that synthesize tryptophan (trp) actually attenuate this effect, however the underlying mechanisms are unclear. We monocolonized germ-free (GF) mice with a widely consumed probiotic Lacticaseibacillus rhamnosus GG (LGG) under trp-free or -sufficient dietary conditions. We obtained untargeted metabolomics from the mouse feces and serum using liquid chromatography-mass spectrometry and obtained intestinal transcriptomic profiles via bulk-RNA sequencing. When comparing LGG-monocolonized mice with GF mice, we found a synergy between LGG and dietary trp in markedly promoting the transcriptome of fatty acid metabolism and -oxidation. Upregulation was specific and was not observed in transcriptomes of trp-fed conventional mice and mice monocolonized with Ruminococcus gnavus. Metabolomics showed that fecal and serum metabolites were also modified by LGG-host-trp interaction. We developed an R-Script based MEtabolome-TRanscriptome Correlation Analysis (METRCA) algorithm and uncovered LGG- and trp-dependent metabolites that were positively or negatively correlated with fatty acid metabolism and -oxidation gene networks. This high throughput metabolome-transcriptome correlation strategy can be used in similar investigations to reveal potential interactions between specific metabolites and functional or disease-related transcriptomic networks. 

BACKGROUND & AIMS: Lacticaseibacillus rhamnosus GG (LGG) is the world’s most consumed probiotic species but its mechanism of action on intestinal permeability and differentiation as well as its interactions with an essential source of signaling metabolites, dietary tryptophan, are incompletely studied. METHODS: Untargeted metabolomic and transcriptomic analysis were performed for LGG mono-colonized germ-free (GF) mice fed with tryptophan (trp)-free or -sufficient diets. LGG-derived metabolites were profiled in vitro under anaerobic and aerobic conditions. Multiomic correlations were performed using a newly developed metabolome-transcriptome correlating bioinformatic algorism. Newly uncovered gut barrier-modulating metabolites whose abundances are regulated by LGG and dietary trp were functionally tested in Trans-Epithelial Electrical Resistance (TEER) assay, mouse enteroid, and dextran sulfate sodium (DSS) experimental colitis. The contribution of trp-methylnicotinamide (MNA) pathway to barrier protection is delineated at specific tight junction (TJ) proteins and enterocyte-promoting factors with gain and loss of function approaches. RESULTS: LGG, strictly in the presence of dietary trp, promotes the enterocyte program and the expression of multiple TJ genes, particularly Ocln. Fecal and serum metabolites that are synergistically stimulated by LGG and dietary trp are identified. Functional evaluations revealed a novel LGG-stimulated trp-dependent Vitamin B3 metabolism pathway, with MNA unexpectedly being the most robust barrier-protective metabolite in vitro and in vivo. Reduced serum MNA is significantly associated with increased disease activity in IBD patients. Exogenous MNA enhances gut barrier in homeostasis and robustly promotes colonic healing in DSS colitis. MNA is sufficient to promote intestinal epithelial Ocln and RNF43, a master inhibitor of Wnt pathway. Blocking trp or Vitamin B3 absorption abolishes barrier recovery in vivo. CONCLUSIONS: Our study uncovers a novel LGG-regulated dietary trp-dependent production of MNA that protects gut barrier against colitis. 

Chinese government lifted its “Zero COVID-19” policy in December 2022. The estimated COVDI-19 new cases and deaths after the policy change are 167–279 million (about 12.0% to 20.1% of the Chinese population) and 0.68–2.1 million, respectively. Recent data also revealed continuous drops in fertility rate and historically lowest growth in gross domestic production in China. Thus, balancing COVID-19 control and economic recovery in China is of paramount importance yet very difficult. Supply chain disruption, essential service reduction and shortage of intensive care units have been discussed as the challenges associated with lifting “Zero COVID-19” policy. The additional challenges may include triple epidemic of COVID-19, respiratory syncytial virus and influenza, mental health issues of healthcare providers, care givers and patients, impact on human mobility, lack of robust genomic and epidemiological data and long COVID-19. However, the policy-associated opportunities and other challenges are largely untouched, but warrant attention of and prompt reactions by the policy makers, healthcare providers, public health officials and other stakeholders. The associated benefits are quick reach of herd immunity, boost of economy and businesses activities and increase in social activities. At this moment, we must embrace the policy change, effectively mitigate its associated problems and timely and effectively maximize its associated benefits. 

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.