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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. 

Extreme environmental conditions, such as heat, salinity, and decreased water availability, can have a devastating impact on plant growth and productivity, potentially resulting in the collapse of entire ecosystems. Stress-induced systemic signaling and systemic acquired acclimation play canonical roles in plant survival during episodes of environmental stress. Recent studies revealed that in response to a single abiotic stress, applied to a single leaf, plants mount a comprehensive stress-specific systemic response that includes the accumulation of many different stress-specific transcripts and metabolites, as well as a coordinated stress-specific whole-plant stomatal response. However, in nature plants are routinely subjected to a combination of two or more different abiotic stresses, each potentially triggering its own stress-specific systemic response, highlighting a new fundamental question in plant biology: are plants capable of integrating two different systemic signals simultaneously generated during conditions of stress combination? Here we show that plants can integrate two different systemic signals simultaneously generated during stress combination, and that the manner in which plants sense the different stresses that trigger these signals (i.e., at the same or different parts of the plant) makes a significant difference in how fast and efficient they induce systemic reactive oxygen species (ROS) signals; transcriptomic, hormonal, and stomatal responses; as well as plant acclimation. Our results shed light on how plants acclimate to their environment and survive a combination of different abiotic stresses. In addition, they highlight a key role for systemic ROS signals in coordinating the response of different leaves to stress. 

Abstract Identifying genes that interact to confer a biological function to an organism is one of the main goals of functional genomics. High‐throughput technologies for assessment and quantification of genome‐wide gene expression patterns have enabled systems‐level analyses to infer pathways or networks of genes involved in different functions under many different conditions. Here, we leveraged the publicly available, information‐rich RNA‐Seq datasets of the model plantArabidopsis thalianato construct a gene co‐expression network, which was partitioned into clusters or modules that harbor genes correlated by expression. Gene ontology and pathway enrichment analyses were performed to assess functional terms and pathways that were enriched within the different gene modules. By interrogating the co‐expression network for genes in different modules that associate with a gene of interest, diverse functional roles of the gene can be deciphered. By mapping genes differentially expressing under a certain condition inArabidopsisonto the co‐expression network, we demonstrate the ability of the network to uncover novel genes that are likely transcriptionally active but prone to be missed by standard statistical approaches due to their falling outside of the confidence zone of detection. To our knowledge, this is the firstA. thalianaco‐expression network constructed using the entire mRNA‐Seq datasets (>20,000) available at the NCBI SRA database. The developed network can serve as a useful resource for theArabidopsisresearch community to interrogate specific genes of interest within the network, retrieve the respective interactomes, decipher gene modules that are transcriptionally altered under certain condition or stage, and gain understanding of gene functions.