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Abstract Aerobic methanotrophic bacteria are the primary organisms that consume atmospheric methane (CH₄) and have potential to mitigate the climate-active gas. However, a limited understanding of the genetic determinants of methanotrophy hinders the development of biotechnologies leveraging these unique microbes. Here, we developed and optimized a methanotroph CRISPR interference (CRISPRi) system to enable functional genomic screening. We built a genome-wide single guide RNA (sgRNA) library in the industrial methanotroph,Methylococcus capsulatus, consisting of ∼45,000 unique sgRNAs mediating inducible, CRISPRi-dependent transcriptional repression. A selective screen during growth on CH₄identified 233 genes whose transcription repression resulted in a fitness defect and repression of 13 genes associated with a fitness advantage. Enrichment analysis of the 233 putative essential genes linked many of the encoded proteins with critical cellular processes like ribosome biosynthesis, translation, transcription, and other central biosynthetic metabolism, highlighting the utility of CRISPRi for functional genetic screening in methanotrophs, including the identification of novel essential genes.M. capsulatusgrowth was inhibited when the CRISPRi system was used to individually target genes identified in the screen, validating their essentiality for methanotrophic growth. Collectively, our results show that the CRISPRi system and sgRNA library developed here can be used for facile gene-function analyses and genomic screening to identify novel genetic determinants of methanotrophy. These CRISPRi screening methodologies can also be applied to high-throughput engineering approaches for isolation of improved methanotroph biocatalysts. 

Abstract As noncommunicable diseases (NCDs) pose a significant global health burden, identifying effective diagnostic and predictive markers for these diseases is of paramount importance. Epigenetic modifications, such as DNA methylation, have emerged as potential indicators for NCDs. These have previously been exploited in other contexts within the framework of neural network models that capture complex relationships within the data. Applications of neural networks have led to significant breakthroughs in various biological or biomedical fields but these have not yet been effectively applied to NCD modeling. This is, in part, due to limited datasets that are not amenable to building of robust neural network models. In this work, we leveraged a neural network trained on one class of NCDs, cancer, as the basis for a transfer learning approach to non-cancer NCD modeling. Our results demonstrate promising performance of the model in predicting three NCDs, namely, arthritis, asthma, and schizophrenia, for the respective blood samples, with an overall accuracy (f-measure) of 94.5%. Furthermore, a concept based explanation method called Testing with Concept Activation Vectors (TCAV) was used to investigate the importance of the sample sources and understand how future training datasets for multiple NCD models may be improved. Our findings highlight the effectiveness of transfer learning in developing accurate diagnostic and predictive models for NCDs.