Hyunjin Shim

Urban composting supports soil health but also intersects with food safety, where compost is produced near farms and communities. Here, we profiled temporal microbiome dynamics across a 6-week heat compost cycle from the urban compost piles using paired physicochemical panels and long-read metagenomics. Nutrient composition and pH shifted with compost age, coinciding with stage-structured microbial succession, including temperature-linked turnover of compost communities from mesophilic to thermotolerant taxa. Bacterial profiles included the presence of antimicrobial resistance genes and foodborne-associated genera early in the cycle, with reduced representation during the thermophilic phase. Analysis of previously unclassified long reads reveals an extensive repertoire of putative bacteriophages, including several complete genomes and candidates linked to foodborne bacteria, and their abundance is coupled to the host abundance. Together, these results support thermophilic composting as a key mitigation step for microbiological hazards in urban-adjacent systems and identify compost piles as a promising reservoir for discovering candidate lytic phages for downstream isolation and host-range testing.

Human intelligence is experiential, intuitive, and adaptable, while artificial intelligence is data-driven, algorithmic, and task-specific. These key differences have been well described, but the long-term implications that follow from their different rates and modes of evolution remain underexamined. This Perspective argues that biological cognition accumulates knowledge slowly, whereas silicon-based systems can preserve information at scales and speeds that far exceed human capacities. Furthermore, human intelligence is bounded by mortality and generational transmission, whereas artificial intelligence can persist through evolutionary timescales. We outline how these asymmetries matter for the future of knowledge, framed through three lenses - biologist, researcher, and educator. We synthesize current examples to illustrate the impact of artificial intelligence on human knowledge and education, and conclude with design principles that cultivate diverse and durable knowledge creation and retention.

Bacteriophages offer advantages over small-molecule antibiotics, including host specificity and general compatibility with the human phagenome. However, their evolvability as replicating biological entities introduces therapeutic unpredictability and risks of phage-bacteria co-evolution. Here, we retain the targeting benefits of phages while avoiding genetic replication by engineering genetic-information-free, protein-only phages (POPs). These genome-free particles self-assemble in a cell-free protein synthesis system from modular, de novo gene fragments encoding only structural and antimicrobial proteins. Using Enterobacteria phage T7 and its susceptible bacterial host as a model, we test the hypothesis that POPs stochastically encapsulate small antimicrobial proteins during self-assembly and deliver them into bacteria during adsorption via an ejectome-mediated injection mechanism. A computational survey of T7 small proteins revealed early and mid-genome enrichments of hypothetical proteins and capsid volume sufficient to accommodate multiple small proteins in the absence of the ∼40-kb genome. In time-series antimicrobial susceptibility assays (48-72 h), POPs produced initial growth inhibition comparable to wild-type T7 at the highest doses with a linear dose-effect relationship and a minimum inhibitory concentration-like threshold. These results establish the feasibility of genetic-information-free POPs as protein-based antimicrobials that couple phage receptor specificity with minimal biosafety risks, supporting the development of more stable and predictable phage-inspired therapeutics.

Bacteriophages are promising alternative antimicrobial agents due to their high specificity for host bacteria and minimal immunogenicity in humans. However, their therapeutic application is limited by their nature as biological entities, with potential evolutionary consequences. In this study, we address these challenges by repurposing only the structural components of bacteriophages as vesicles to deliver antibiotics directly into the cytoplasm of bacterial hosts. This approach is based on two key hypotheses: first, antibiotics such as β-lactams remain effective against resistant bacteria if injected directly into the cytoplasm, bypassing resistance mechanisms; second, phage structures can be synthesized and self-assembled in vitro using modular genomes and cell-free protein expression to carry small molecules as cargo. To test these hypotheses, we utilized T7 phages and penicillin-resistant Escherichia coli as a model system. First, we designed the T7 phage genome into a modular format containing only the genes encoding structural components and synthesized the gene fragments via de novo gene synthesis. These phage structures were then rebooted in vitro using cell-free protein expression in the presence of penicillin G, allowing the antibiotics to be incorporated as cargo during the self-assembly process. Finally, we tested the antimicrobial activity of these antibiotic-loaded phage syringes against penicillin-resistant E. coli. The results demonstrate that phage syringes effectively reduce the host population compared to negative controls, including free penicillin and water. This study highlights the potential of using phage structures as antibiotic delivery vehicles, offering a novel strategy to overcome both the limitations of small-molecule antibiotics and phage therapy.

Background Next-generation sequencing necessitates a minimum quantity and concentration of DNA/RNA samples, typically achieved through amplification using the PCR technique. However, this amplification step introduces several drawbacks to biological insights, including PCR bias and the loss of epigenetic information. The advent of long-read sequencing technologies facilitates direct sequencing, with the primary constraint being the limited amount of DNA/RNA present in biological samples. Methods Here, we present a novel method called Noise-Augmented Direct (NAD) sequencing that enables the direct sequencing of target DNA even when it falls below the minimum quantity and concentration required for long-read sequencing by augmenting with noise DNA and adaptive sampling. Adaptive sampling is an emerging technology of nanopore sequencing, allowing the enhanced sequencing of target DNA by selectively depleting noise DNA. In this study, we use the DNA standard of the Lambda phage genome as the noise DNA to augment samples containing low amounts of bacterial genomes (1 ng to 300 ng). Results The results with cost-effective flow cells indicate that NAD sequencing successfully detects the target DNA with an input quantity as low as 1 ng, and the bacterial genome of Salmonella enterica can be assembled to 30% completion at an accuracy of 98% with an input quantity of 3 ng. With high throughput flow cells, the bacterial genome of Pseudomonas aeruginosa was assembled to near completion (99.9%) at an accuracy of 99.97% with an input quantity of 300 ng. Conclusions This proof-of-concept study demonstrates the potential of NAD sequencing in enhancing the robustness of long-read sequencing for small input DNA/RNA samples with noise augmentation and adaptive sampling.

Biological information is encoded in a sequence of biochemical molecules such as nucleic acids and amino acids, and nanopore sequencing is a long-read sequencing technology capable of directly decoding these molecules into human-readable signals. The long reads from nanopore sequencing offer the advantage of obtaining contiguous information, which is particularly beneficial for decoding complex or repetitive regions in a genome. In this study, we investigated the efficacy of nanopore sequencing in decoding biological information from distinctive genomes in metagenomic samples, which pose significant challenges for traditional short-read sequencing technologies. Specifically, we sequenced blood and fecal samples from mice infected with Trypanosoma brucei, a unicellular protozoan known for its hypervariable and dynamic regions that help it evade host immunity. Such characteristics are also prevalent in other host-dependent parasites, such as bacteriophages. The taxonomic classification results showed a high proportion of nanopore reads identified as T. brucei in the infected blood samples, with no significant identification in the control blood samples and fecal samples. Furthermore, metagenomic de novo assembly of these nanopore reads yielded contigs that mapped to the reference genome of T. brucei in the infected blood samples with over 96 % accuracy. This exploratory work demonstrates the potential of nanopore sequencing for the challenging task of classifying and assembling hypervariable and dynamic genomes from metagenomic samples.

Expanding genetic codes from natural standard nucleotides to artificial non-standard nucleotides marks a significant advancement in synthetic biology, with profound implications for biotechnology and medicine. Decoding the biological information encoded in these non-standard nucleotides presents new challenges, as traditional sequencing technologies are unable to recognize or interpret novel base pairings. In this perspective, we explore the potential of nanopore sequencing, which is uniquely suited to decipher both standard and non-standard nucleotides by directly measuring the biophysical properties of nucleic acids. Nanopore technology offers real-time, long-read sequencing without the need for amplification or synthesis, making it particularly advantageous for expanded genetic systems like Artificially Expanded Genetic Information Systems (AEGIS). We discuss how the adaptability of nanopore sequencing and advancements in data processing can unlock the potential of these synthetic genomes and open new frontiers in understanding and utilizing expanded genetic codes.

Biological information is encoded in a sequence of biochemical molecules such as nucleic acids and amino acids, and nanopore sequencing is a long-read sequencing technology capable of directly decoding these molecules into human-readable signals. The long reads from nanopore sequencing offer the advantage of obtaining contiguous information, which is particularly beneficial for decoding complex or repetitive regions in a genome. In this study, we investigated the efficacy of nanopore sequencing in decoding biological information from distinctive genomes in metagenomic samples, which pose significant challenges for traditional short-read sequencing technologies. Specifically, we sequenced blood and fecal samples from mice infected with Trypanosoma brucei , a unicellular protozoan known for its hypervariable and dynamic regions that help it evade host immunity. Such characteristics are also prevalent in other host-dependent parasites, such as bacteriophages. The taxonomic classification results showed a high proportion of nanopore reads identified as T. brucei in the infected blood samples, with no significant identification in the control blood samples and fecal samples. Furthermore, metagenomic de novo assembly of these nanopore reads yielded contigs that mapped to the reference genome of T. brucei in the infected blood samples with over 96% accuracy. This exploratory work demonstrates the potential of nanopore sequencing for the challenging task of classifying and assembling hypervariable and dynamic genomes from metagenomic samples.

Bacteriophages are the most prevalent biological entities in the biosphere. However, limitations in both medical relevance and sequencing technologies have led to a systematic underestimation of the genetic diversity within phages. This underrepresentation not only creates a significant gap in our understanding of phage roles across diverse biosystems but also introduces biases in computational models reliant on these data for training and testing. In this study, we focused on publicly available genomes of bacteriophages infecting high-priority ESKAPE pathogens to show the extent and impact of this underrepresentation. First, we demonstrate a stark underrepresentation of ESKAPE phage genomes within the public genome and protein databases. Next, a pangenome analysis of these ESKAPE phages reveals extensive sharing of core genes among phages infecting the same host. Furthermore, genome analyses and clustering highlight close nucleotide-level relationships among the ESKAPE phages, raising concerns about the limited diversity within current public databases. Lastly, we uncover a scarcity of unique lytic phages and phage proteins with antimicrobial activities against ESKAPE pathogens. This comprehensive analysis of the ESKAPE phages underscores the severity of underrepresentation and its potential implications. This lack of diversity in phage genomes may restrict the resurgence of phage therapy and cause biased outcomes in data-driven computational models due to incomplete and unbalanced biological datasets.

Bacteriophages are the most prevalent biological entities in the biosphere. However, limitations in both medical relevance and sequencing technologies have led to a systematic underestimation of the genetic diversity within phages. This underrepresentation not only creates a significant gap in our understanding of phage roles across diverse biosystems but also introduces biases in computational models reliant on these data for training and testing. In this study, we focused on publicly available genomes of bacteriophages infecting high-priority ESKAPE pathogens to show the extent and impact of this underrepresentation. First, we demonstrate a stark underrepresentation of ESKAPE phage genomes within the public genome and protein databases. Next, a pangenome analysis of these ESKAPE phages reveals extensive sharing of core genes among phages infecting the same host. Furthermore, genome analyses and clustering highlight close nucleotide-level relationships among the ESKAPE phages, raising concerns about the limited diversity within current public databases. Lastly, we uncover a scarcity of unique lytic phages and phage proteins with antimicrobial activities against ESKAPE pathogens. This comprehensive analysis of the ESKAPE phages underscores the severity of underrepresentation and its potential implications. This lack of diversity in phage genomes may restrict the resurgence of phage therapy and cause biased outcomes in data-driven computational models due to incomplete and unbalanced biological datasets.