Biology subjects

Ma, H. R.

Publications and source records attributed to Ma, H. R..

6 recordsLinked to original sources

bioRxiv2025

A foundation model for microbial growth dynamics

Microbial growth dynamics contain rich information about microbial populations, which support applications from antibiotic testing to microbiome engineering. However, the high dimensionality of growth data and the scarcity of large, task-specific datasets have limited generalizable modeling analysis across systems. Here, we develop a foundation model for microbial growth dynamics. It is a large-scale, self-supervised representation model trained on [~]370,000 experimental and simulated growth curves spanning diverse microbial species, environmental conditions, and community contexts. The model learns lower-dimensional latent embeddings that capture essential dynamical features of raw growth data and enable accurate reconstruction of these data. The concise representations enhance predictive performance in diverse downstream applications. Using these embedding, we achieve few-shot learning for antibiotic classification and concentration prediction, accurate forecasting of simulated and experimental communities, and inference of total abundance from relative-abundance data. By extracting transferable representations from heterogeneous datasets, our model provides a general framework for analyzing and predicting microbial community dynamics from limited measurements.

synthetic biology↗

bioRxiv2025

Engineering microbial consortia for distributed signal processing

A critical goal in biology is deducing input signals from measurable readouts. Genetic circuits have successfully been designed to respond to specific analytes and produce quantifiable outputs; however, multiplexed signal processing remains challenging. This limitation is partially due to crosstalk, or sensors non-specific responses to unintended signals. While strategies to achieve orthogonality are promising, they are time-intensive and context-dependent. Here, we introduce a new, generalizable approach that leverages microbial consortia to distribute sensory functions and computational methods to disentangle signals. Compartmentalizing sensor circuits within distinct populations simplifies experimental optimization by allowing individual populations to be exchanged without requiring genetic modifications. Our computational pipeline combines mechanistic modeling with machine learning to decode microbial communities unique temporal responses and predict multiple input concentrations. We demonstrated this platforms versatility in a variety of contexts: measuring signals with high crosstalk, detecting antibiotics with natural microbial communities, and quantifying chemicals in hospital sink wastewater. Our findings highlight how combining microbial engineering with computational strategies can produce robust, scalable biosensors for diverse applications.

synthetic biology↗

bioRxiv2025

Keystone engineering enables collective range expansion in microbial communities

Keystone engineers profoundly influence microbial communities by altering their shared environment, often by modifying key resources. Here, we show that in an antibiotic-treated microbial community, bacterial spread is controlled by keystone engineering affecting dispersal--an effect hidden in well-mixed environments. Focusing on two pathogens, non-motile Klebsiella pneumoniae and motile Pseudomonas aeruginosa, we found that both tolerate a {beta}-lactam antibiotic, with Pseudomonas being more resilient and dominating in well-mixed cultures. During range expansion, however, the antibiotic inhibits Pseudomonas ability to spread unless it is near Klebsiella--Klebsiella degrades the antibiotic to create a "clear zone" that allows Pseudomonas to expand, at the expense of Klebsiellas own growth, thus acting as a keystone engineer. As Pseudomonas spreads, it competitively suppresses Klebsiella. Our modeling and experimental analyses reveal that this keystone effect operates at a millimeter scale. We also observed similar keystone engineering by a Bacillus species isolated from a hospital sink, in both pairwise and eight-member bacterial communities with its co-isolates. These findings suggest that spatially explicit experiments are essential to understand certain keystone engineering mechanisms and have implications for surface-associated microbial communities like biofilms, as well as for diagnosing and treating polymicrobial infections involving drug-degrading, non-motile (e.g., Klebsiella), and drug-tolerant, motile (e.g., Pseudomonas) bacteria.

ecology↗

bioRxiv2024

Antibiotic-mediated microbial community restructuring is dictated by variability in antibiotic susceptibility and population interactions

It is widely known that faster-growing bacterial cells are more susceptible to antibiotics. Given this notion, it appears intuitive that antibiotic treatment would enrich slower-growing cells in a clonal population or slower-growing populations in a microbial community, which has been commonly observed. However, experimental observations also show the enrichment of faster-growing subpopulations under certain conditions. Does this apparent discrepancy suggest uniqueness about different growth environments or the role of additional confounding factors? If so, what could be the major determinant in antibiotic-mediated community restructuring? Combining modeling and quantitative measurements using a barcoded heterogeneous E. coli library, we show that the outcome of antibiotic-mediated community restructuring can be driven by two major factors. One is the variability among the clonal responses of different subpopulations to the antibiotic; the other is their interactions. Our results suggest the importance of quantitative measurements of antibiotic responses in individual clones in predicting community responses to antibiotics and addressing subpopulation interactions.

microbiology↗

bioRxiv2024

An engineered prodrug selectively suppresses β-lactam resistant bacteria in a mixed microbial setting

The rise of {beta}-lactam resistance necessitates new strategies to combat bacterial infections. We purposefully engineered the {beta}-lactam prodrug AcephPT to exploit {beta}-lactamase activity to selectively suppress resistant bacteria producing extended-spectrum-{beta}-lactamases (ESBLs). Selective targeting of resistant bacteria requires avoiding interaction with penicillin-binding proteins, the conventional targets of {beta}-lactam antibiotics, while maintaining recognition by ESBLs to activate AcephPT only in resistant cells. Computational approaches provide a rationale for structural modifications to the prodrug to achieve this biased activity. We show AcephPT selectively suppresses gram-negative ESBL-producing bacteria in clonal populations and in mixed microbial cultures, with effective selectivity for both lab strains and clinical isolates expressing ESBLs. Time-course NMR experiments confirm hydrolytic activation of AcephPT exclusively by ESBL-producing bacteria. In mixed microbial cultures, AcephPT suppresses proliferation of ESBL-producing strains while sustaining growth of {beta}-lactamase-non-producing bacteria, highlighting its potential to combat {beta}-lactam resistance while promoting antimicrobial stewardship. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/606422v1_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@cd7f9aorg.highwire.dtl.DTLVardef@1747bc6org.highwire.dtl.DTLVardef@6a3419org.highwire.dtl.DTLVardef@825aa6_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2022

Mapping single-cell responses to population-level dynamics during antibiotic treatment

Treatment of sensitive bacteria with beta-lactam antibiotics often leads to two salient population-level features: a transient increase in total population biomass before a subsequent decline, and a linear correlation between growth and killing rates. However, it remains unclear how these population-level responses emerge from collective single-cell responses. During beta-lactam treatment, it is well recognized that individual cells often exhibit varying degrees of filamentation before lysis. We show that the probability of cell lysis increases with the extent of filamentation and that this dependence is characterized by unique parameters that are specific to bacterial strain, antibiotic dose, and growth condition. Modeling demonstrates how the single-cell lysis probabilities can give rise to population-level biomass dynamics, which were experimentally validated. This mapping provides insights into how the population biomass time-kill curve emerges from single cells and allows the representation of both single-and population-level responses with universal parameters.

microbiology↗