Biology subjects

Phillips, R.

Publications and source records attributed to Phillips, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Comparative Distribution of α2,3- and α2,6-Linked Sialic Acid Receptors and Influenza A(H5N1) Virus Binding in Sheep, Goats, and Alpacas

Highly pathogenic avian influenza virus (HPAIV) H5N1 continues to pose a significant threat to animal and human health. Since 2022, an expanded host range has been observed, as illustrated by the H5N1 outbreak in dairy cows. Other minor livestock species, such as sheep, goats, and alpacas, have also tested positive for H5N1 for the first time, raising concerns about its impact on previously overlooked livestock species. In this study, we first characterized the distribution of sialic acid (SA) receptors across respiratory and non-respiratory tissues of sheep, goats, and alpacas using Maackia amurensis (SA 2,3) and Sambucus nigra (SA 2,6) lectins. We found that both SA receptors are present in all tissues across species, with notable differences in their distribution and abundance. Next, we developed recombinant LPAIV H5N1 viruses over a laboratory-adapted backbone to evaluate tissue-specific virus binding profiles. The results show that a representative H5N1 virus from clade 2.3.4.4b binds effectively to respiratory and non-respiratory tissues across all species. The strongest virus binding was observed in sheep, followed by goats and, lastly, alpacas. Similar binding patterns were observed with an LPAIV version of clade 2.3.4.4b and a Gs/Gd lineage precursor prototype virus. Finally, we show that H1N1 and H3N2 viruses also bind to respiratory tissues of sheep and goats. These findings provide important insights into the potential susceptibility of minor livestock species to HPAIV H5N1 and other influenza subtypes. Our results contribute to a better understanding of influenza tissue tropism across livestock species and its potential implications for zoonotic transmission.

microbiology↗

bioRxiv2026

The Trajectory Statistics of Biological Exploratory Dynamics

Biological processes, from molecules diffusing to their regulatory destinations to whales pursuing food or mates, are widely exploratory. Such behaviors are effective when some verifiable functional state or outcome can be reached regardless of initial conditions. In these cases, systems repeatedly undergo distinct and abortive trajectories; the process only ends when the system finds 'right' outcomes. This dominance of the terminal condition (and indifference to the initial state) provokes a very different perspective than the conventional 'dynamical systems' framework that has been a centerpiece of the quantitative sciences for centuries. We hypothesize that many of these problems defy the initial-condition driven or gradients on landscapes so useful in problems ranging from mechanics to electrodynamics to chemical kinetics to mass and heat transport. We examine several mathematical frameworks that capture and unify key aspects of exploratory dynamics. One powerful way of thinking of such processes is the geometric distribution, where repeated failures are punctuated by a successful trajectory. We develop intuitions for how random walks with resets accelerate search processes. We highlight fresh and surprising behaviors of search under drift, cues, and checkpoints. Last, appreciating the probability of trajectories conditioned on satisfying macroscopic final outcomes reveals a language for the potency of variation sculpted by selection in their broadest forms. This can give the fictitious appearance of the future making itself known in the present, but we view this as conceptually similar to the way 'fictitious forces' arise in non-inertial reference frames. These approaches stress unity, open questions, and applications across a range of biological phenomena.

biophysics↗

bioRxiv2026

Energetic gradients emerge in developing motor-microtubule structures

Living matter produces a variety of beautiful spatiotemporal structures and patterns that are not enduringly present in their nonliving counterparts. These ordered, non-equilibrium steady states are often sustained through the consumption of energy. Here, we investigate the energetic cost of assembling an ordered aster from an initially disordered, uniform mixture of cytoskeletal microtubules and kinesin motors. Using a calibrated fluorescent ATP reporter, we measure reproducible radial ATP gradients on scales of tens of microns that establish within, and persist over, tens of minutes, alongside coupled spatial gradients in motor density. These appreciable gradients are predicted by a reaction-diffusion model that acknowledges the localization of ATP consumption to regions where both molecular motors and microtubules are sufficiently abundant to encourage consumption, as confirmed by finite element modeling. With our results, we compare the power per volume required by our cytoskeletal networks with the known power per volume expenditure in cells. Comparison of our measured results with estimates of the dissipative processes available to motor-microtubule mixtures leads to the hypothesis that maintaining spatial motor gradients dominates the energetic demand in this system. Our direct quantification of energetic fluxes across space unlocks future explorations of what steady states are accessible to cells, and how the cytoskeleton drives broad spatial organization. SignificanceHow much energy do organisms pay to form and maintain their organizing biochemical patterns? Existing measurements of cellular metabolism and energy expenditures largely resolve net or supply-side biochemical fluxes, without spatial information, impeding the study of this basic question. Here, we develop an experimental approach to directly measure the distributions of biochemical energy that respond to power expenditures of cytoskeletal motor-microtubule networks as they form aster structures, reminiscent of those found in the mitotic spindle. As these structures self-assemble, calibrated readouts in real molecular units register large, reproducible, and long-lived gradients of ATP. We interpret these measurements by developing theory to account for the functional destinies of energy expenditure. These advances clarify outstanding questions of energy in living matter.

biophysics↗

bioRxiv2026

Informational blueprints reveal condition-dependent gene regulatory architectures

While coding regions in the genome have a direct interpretation in terms of protein products, significant fractions are non-coding and yet control essential biological functions. Unlike the genetic code, there is no "lookup table" that identifies where regulatory proteins, known as transcription factors (TFs), bind. Here, we extract these binding sites by distilling sequences of nucleotide letters into collective coordinates (hyperletters) representing the binding sites that are active under specific environmental conditions. Going beyond local information footprints between individual bases and expression levels, our information blueprint algorithm compresses the global information by optimising filters that simultaneously scan an entire promoter sequence. Inspired by renormalisation-group techniques, we identify TF binding sites as coarse-grained variables combining groups of correlated mutations with the highest collective impact on gene expression. We validate our approach on experimental data for E. coli and discover novel regulatory elements illustrating its deployment at scale across growth conditions.

genomics↗

bioRxiv2026

Comparative Performance of Portable DNA Extraction Protocols and Bioinformatics Workflows for Rapid Detection of Pathogens and Antimicrobial Resistance Using Oxford Nanopore Sequencing.

Oxford Nanopore technology (ONT) enables rapid, portable pathogen identification and detection of antimicrobial resistance (AMR). Still, the reliability of downstream genomic analyses is highly dependent on DNA extraction quality, particularly in resource-limited settings. This study comparatively evaluated four portable bacterial DNA extraction protocols, derived from three commercial kits, to determine their impact on nanopore sequencing performance, bioinformatics workflow completion, and field deployability. Six Gram-negative bacterial isolates (Escherichia coli, n= 4; Pseudomonas sp., n= 1; and Salmonella sp., n= 1) were processed using four extraction protocols: SwiftX DNA, SwiftX DNA with proteinase K (ProtK), SwiftX ParaBact, and NucleoSpin Microbial. Twenty-four DNA extracts (6 isolates x 4 protocols) were sequenced on a single multiplexed MinION R10.4.1 flow cell. Sequencing data were analysed using validated Galaxy-based generic and species-specific pipelines, with workflow completion defined as successful progression through quality control, assembly, virulence, plasmid and AMR detection modules. DNA purity varied substantially by extraction protocol and was strongly associated with workflow success. NucleoSpin Microbial achieved 100% workflow completion, SwiftX ParaBact achieved 83%, while both SwiftX DNA-based protocols failed to complete full workflows. Higher A260/A280 ratios were strongly correlated with successful workflow completion (Spearmans {rho} = 0.767, p < 0.0001). Importantly, key AMR genes required to classify isolates as multidrug resistant were consistently detected using both NucleoSpin Microbial and SwiftX ParaBact extractions. However, NucleoSpin Microbial assemblies showed significantly higher contiguity and enabled broader and more complete detection of virulence factors, pathogenicity islands, plasmid replicons, and accessory AMR genes, reflecting enhanced genomic resolution. IMPORTANCERapid whole-genome sequencing is increasingly used to detect antimicrobial resistance and guide public health responses, but its reliability depends strongly on how bacterial DNA is extracted. In this study, we show that DNA extraction method choice has a major impact on Oxford Nanopore sequencing performance across clinically relevant bacteria. While silica-column-based extraction maximised genomic completeness and analytical depth, paramagnetic bead-based reverse purification offered superior portability with sufficient resolution for frontline AMR surveillance. These findings highlight a practical trade-off between field deployability and high-resolution genomic characterisation in low-resource settings.

genomics↗

bioRxiv2026

A Multiscale Model of Collective Decision-Making in Hybrid Aspen Tree Tissues Describes Bud-Dormancy Break

The mechanisms underlying cellular coordination within tissues remain enigmatic. Most models focus on interactions between just two levels of organization - cell and tissue - and do not leverage data across deeper hierarchies that best represent living processes, with many spatial and temporal scales interacting. Integrating many scales, from molecular to cellular to tissular to organismal to populational, may be necessary to fully elucidate tissue function, especially in cases of sparse data at each level. Here, we investigate multiscale, robust regulation of tissue-level decision-making, using experimental studies of cold induced dormancy release in terminal buds of hybrid aspen trees as our case study. We develop a network model of terminal bud meristematic tissue, incorporating expression data from a key cold induced regulator gene, FLOWERING LOCUS T (FT1), which controls bud dormancy release, combined with data on variability in cell-to-cell communication controlled by FT1 mediated regulation of plasmodesmata. The model can explain dormancy breaking under constant temperature, but not variable temperature. We introduce constraints from organismal-level data and show how the presence of coordinated cellular interactions within individual plant tissues is necessary to reproduce data of population-level statistics. Our findings demonstrate how mechanisms of tissue function may be better constrained when data are used across more scales. They also hint at potential tantalizing new insights such as how tissue function might not be solely dictated bottom-up from molecular interactions, but also top-down from constraints imposed by the organismal and population context. Both implications illustrate the critical importance of incorporating cross-scale information processing in modeling biological decision-making. Significance StatementBiological hierarchies involve decision-making mediated via nested feedback loops. Data-informed modeling of this hierarchal complexity remains challenging. Here, we leverage unique features of plant biology - stationary growth, prolonged decision-making, and physical structure - to study multiscale dynamics determining cellular mechanisms of bud dormancy breaking in aspen trees. We examine how tissue function can be driven bottom-up from gene regulatory networks and top-down from organismal population-level statistics. Modeling experimental data collected at genetic, cellular, and organismal levels, reveals how population-level data allow constraining mechanisms of cellular coordination within individual plants when cellular data are sparse. Our findings demonstrate how multiscale methods can combat data sparsity and suggest new ways to study cellular coordination within organisms could be dictated by organismal population-level constraints.

synthetic biology↗

bioRxiv2025

Reverse Engineering the Evolutionary Logic of Cytoskeletal Dynamics

Eukaryotic cells generate mechanical forces through cytoskeletal filaments actively reorganized by families of molecular motors. However, how motor sequence specifies filament organization dynamics remains unclear. We developed ActiveDROPS, a cell-free platform that expresses kinesin variants in bacterial lysate droplets and quantitatively maps the resulting microtubule dynamics into a common phenotype space. Across a library of twelve kinesin-1 homologs, we observed three phenotypes: "Slow-Sustained" flows that activate after 8 h, reach peak mean velocities of 24 nm/s and decay after [~]32 h; "Fast-Burst" flows that activate within minutes and dissipate within 2 h, reaching velocities up to 900 nm/s; and a [~]36-h "Multiphase" progression through nematic, rotational, and contractile states. Microtubule gliding assays and molecular dynamics simulations using AlphaFold-predicted structures linked the "Fast-Burst" phenotype to generally faster motility and more favorable motor-tubulin interactions than "Slow-Sustained". By replacing the microtubule-binding region of a "Slow-Sustained" motor with that of a "Fast-Burst" motor, we generated a "Fast-Sustained" chimera with flows that activate within 1 h, peak at 70 nm/s and persist for 16 h, showing that recombination can reprogram the parental relationship between speed and timing of microtubule-motor self-organization. These results reveal a constrained logic through which kinesin sequence shapes cytoskeletal dynamics, providing a framework for dissecting the mechanical repertoire available to cellular systems.

synthetic biology↗

bioRxiv2025

Force Propagation in Active Cytoskeletal Networks

Conventional materials science establishes clear structure-function relationships: grain boundaries modulate strength by blocking dislocations, and defect density limits conductivity through electron scattering. Active matter presents a fundamentally different challenge, components continuously consume energy to generate forces, creating a dual identity as both structural elements and force-generating machines. This material-machine duality raises a fundamental question: which structural parameters govern functional behavior in systems where constituents actively generate forces rather than merely responding to external inputs? Here we demonstrate that percolation is the critical mechanism that transforms active cytoskeletal networks from energy-dissipating materials into work-performing machines. Using light-controlled microtubule-kinesin networks, we show that increasing bundle length from 0.9 {micro}m to 5 {micro}m triggers a percolation transition that enables global force organization. Networks below the threshold remain incapable of performing coordinated work, while networks above it develop correlation lengths exceeding 240 {micro}m, generate 25-fold stronger forces, and extract 1000-fold more mechanical work, powering transport of live cells across 800 {micro}m distances. Network simulations reveal that introducing just 5-10% longer bundles creates a connected component containing 90% of microtubules, establishing the precise threshold that drives this material-to-machine transition. Our findings establish percolation as the fundamental mechanism governing whether active matter systems function as passive materials or as coordinated machines--providing design principles for both synthetic and biological systems. increasing bundle length from 0.9 {micro}m to 5 {micro}m increasing bundle length from 0.9 {micro}m to 5 {micro}m

biophysics↗

bioRxiv2025

Biological Processes as Exploratory Dynamics

Many biological processes can be thought of as the result of an underlying dynamics in which the system repeatedly undergoes distinct and abortive trajectories with the dynamical process only ending when some specific process, purpose, structure or function is achieved. A classic example is the way in which microtubules attach to kinetochores as a prerequisite for chromosome segregation and cell division. In this example, the dynamics is characterized by apparently futile time histories in which microtubules repeatedly grow and shrink without chromosomal attachment. We hypothesize that for biological processes for which it is not the initial conditions that matter, but rather the final state, this kind of exploratory dynamics is biologys unique and necessary solution to achieving these functions with high fidelity. This kind of cause and effect relationship can be contrasted to examples from physics and chemistry where the initial conditions determine the outcome. In this paper, we examine the similarities of many biological processes that depend upon random trajectories starting from the initial state and the selection of subsets of these trajectories to achieve some desired functional final state. We begin by reviewing the long history of the principles of dynamics, first in the context of physics, and then in the context of the study of life. These ideas are then stacked up against the broad categories of biological phenomenology that exhibit exploratory dynamics. We then build on earlier work by making a quantitative examination of a succession of increasingly sophisticated models for exploratory dynamics, all of which share the common feature of being a series of repeated trials that ultimately end in a "winning" trajectory. We also explore the ways in which microscopic parameters can be tuned to alter exploratory dynamics as well as the energetic burden of performing such processes. OutlookIt is a great privilege to take part in this special volume dedicated to the life and work of Prof. Erich Sackmann (1934-2024). For one of us (RP), at the time of making a switch from traditional condensed matter physics to a life engaged in the study of life, he went to a meeting near Munich which completely opened his eyes to the ways in which the approach of physics could be brought to bear on the study of the living. Sackmanns work was an inspiring presence at that meeting. One of the hallmarks of his work was a principled approach to dissecting biological processes over a range of scales and phenomena. One common thread to much of his work was that it acknowledged the dynamical character of living organisms. The present paper attempts to follow in the tradition of Sackmanns studies of dynamics by suggesting a new way of looking at many biological processes all through the unifying perspective of what we will call exploratory dynamics.

biophysics↗

bioRxiv2025

The Dynamics of Inducible Genetic Circuits

Genes are connected in complex networks of interactions where often the product of one gene is a transcription factor that alters the expression of another. Many of these networks are based on a few fundamental motifs leading to switches and oscillators of various kinds. And yet, there is more to the story than which transcription factors control these various circuits. These transcription factors are often themselves under the control of effector molecules that bind them and alter their level of activity. Traditionally, much beautiful work has shown how to think about the stability of the different states achieved by these fundamental regulatory architectures by examining how parameters such as transcription rates, degradation rates and dissociation constants tune the circuit, giving rise to behavior such as bistability. However, such studies explore dynamics without asking how these quantities are altered in real time in living cells as opposed to at the fingertips of the synthetic biologists pipette or on the computational biologists computer screen. In this paper, we make a departure from the conventional dynamical systems view of these regulatory motifs by using statistical mechanical models to focus on endogenous signaling knobs such as effector concentrations rather than on the convenient but more experimentally remote knobs such as dissociation constants, transcription rates and degradation rates that are often considered. We also contrast the traditional use of Hill functions to describe transcription factor binding with more detailed thermodynamic models. This approach provides insights into how biological parameters are tuned to control the stability of regulatory motifs in living cells, sometimes revealing quite a different picture than is found by using Hill functions and tuning circuit parameters by hand.

biophysics↗

bioRxiv2025

The Environment-Dependent Regulatory Landscape of the E. coli Genome

1All cells respond to changes in both their internal milieu and the environment around them through the regulation of their genes. Despite decades of effort, there remain huge gaps in our knowledge of both the function of many genes (the so-called y-ome) and how they adapt to changing environments via regulation. Here we describe a joint experimental and theoretical dissection of the regulation of a broad array of more than 100 biologically interesting genes in E. coli across 39 diverse environments, enabling us to identify the binding sites and transcription factors that mediate regulatory control. Using a combination of mutagenesis, massively parallel reporter assays, mass spectrometry, and tools from information theory and statistical physics, we go from complete ignorance of a promoters environment-dependent regulatory architecture to a quantitative description of its binding sites, candidate transcription factors that bind them where identifiable, and the conditions under which they act. As proof of principle of the biological insights to be gained from such a study, we chose a combination of genes from the y-ome, toxin-antitoxin pairs, and genes hypothesized to be part of regulatory modules; we discovered a host of new insights into their underlying regulatory landscape and resulting biological function. We highlight discoveries for y-ome genes, including transcription start sites and transcription factor binding sites at base-pair resolution, and their dependence on growth conditions.

molecular biology↗

bioRxiv2025

Exploring influenza A virus receptor distribution in the lactating mammary gland of domesticated livestock and in human breast tissue.

The spread of the highly pathogenic avian influenza (HPAI) H5N1 virus among dairy cattle illustrates the adaptability of influenza A viruses (IAV) to infect non-traditional species. While IAV-specific sialic acid (SA) receptors have been identified in the mammary glands of dairy cattle, their presence in pigs, sheep, goats, and alpacas has not been studied until now. The zoonotic transmission of HPAI H5N1 to dairy and poultry farm workers during outbreaks raises public health concerns. This study employed lectin histochemistry to examine the mammary glands of livestock and humans. We found that these tissues were rich in SA 2,6-Gal receptors, followed by SA 2,3-Gal receptors, essential for IAV binding. Notably, the A(H5N1) clade 2.3.4.4b virus could bind to mammary tissue from both cattle and pigs. These findings highlight the potential for HPAI H5N1 to infect and spread within the mammary glands of production animals and humans.

immunology↗

bioRxiv2025

Loss of CARM1 alters the developmental programming of Glioma stem-like cells and creates a druggable NGFR/NTRK dependency

A key driver of Glioblastoma (GBM) heterogeneity and therapy resistance is the capacity of glioma stem-like cells (GSCs) to hijack developmental signaling programs. However, it remains unclear how GSCs regulate these adapted developmental signaling pathways and how these pathways might be therapeutically exploited. The arginine methyltransferase, CARM1, has been shown to play critical roles in maintaining stem cell pluripotency, preventing differentiation, and recently was discovered to be upregulated in Glioblastoma. To date, there is little to no understanding of the role that CARM1 plays in regulating developmental processes in Glioblastoma. To address this gap in knowledge, we applied a multi-omics approach to characterize developmental processes that are specifically regulated by CARM1 in GSCs. We found that loss of CARM1 results in dysregulation of several developmental markers: ARX, GFAP, NGFR, PDGFRA and results in both a proteomic and transcriptomic shift towards the radial glia cell lineage. Moreover, CARM1 depleted cells reprogram their signaling to develop an increased survival dependency on NGFR/NTRK signaling and are hypersensitive to the FDA approved brain penetrant NTRK inhibitor--Entrectinib. Mechanistically, we find that NFIA is a CARM1 substrate and can repress NGFR signaling just as CARM1 does, and thus the CARM1/NFIA relationship is likely a key regulator of NGFR/NTRK signaling in GSCs. Altogether, we demonstrate that CARM1 regulates the cell lineage of GSCs at the transcriptomic and proteomic level, and naturally represses NGFR/NTRK signaling--likely through CARM1 dependent methylation of NFIA. Further, CARM1 depletion leads GSCs to develop a survival dependency on NGFR/NTRK signaling and creates a therapeutic vulnerability to NTRK inhibition.

cancer biology↗

bioRxiv2024

Motor-driven microtubule diffusion in a photobleached dynamical coordinate system

Motor-driven cytoskeletal remodeling in cellular systems can often be accompanied by a diffusive-like effect at local scales, but distinguishing the contributions of the ordering process, such as active contraction of a network, from this active diffusion is difficult to achieve. Using light-dimerizable kinesin motors to spatially control the formation and contraction of a microtubule network, we deliberately photobleach a grid pattern onto the filament network serving as a transient and dynamic coordinate system to observe the deformation and translation of the remaining fluorescent squares of microtubules. We find that the network contracts at a rate set by motor speed but is accompanied by a diffusive-like spread throughout the bulk of the contracting network with effective diffusion constant two orders of magnitude lower than that for a freely-diffusing microtubule. We further find that on micron scales, the diffusive timescale is only a factor of {approx} 3 slower than that of advection regardless of conditions, showing that the global contraction and long-time relaxation from this diffusive behavior are both motor-driven but exhibit local competition within the network bulk. O_TEXTBOXSignificance StatementCytoskeletal reorganization can come at the cost of local diffusive-like disordering effects from the same active elements, but distinguishing these processes can be challenging. By photobleaching an actively contracting microtubule network, we show that the bulk redistribution of filaments exhibit a diffusion-like reorganization that can be tuned by the effective motor speed. By tuning these parameters, we show a conserved relationship between active contraction rates and effective diffusion constants, suggesting that while advection of the cytoskeletal network dominates over scales of tens to hundreds of microns, motors additionally induce a diffusive-like effect that begins to compete with advection at micron scales. C_TEXTBOX

biophysics↗

bioRxiv2024

Necromass chemistry interacts with soil mineral and microbial properties to determine fungal carbon and nitrogen persistence in soils

Despite the importance of mineral-associated organic matter (MAOM) in long-term soil carbon (C) and nitrogen (N) persistence, and the significant contribution of fungal necromass to this pool, the factors controlling the formation of fungal-derived MAOM remain unclear. This study investigated how fungal necromass chemistry, specifically melanin, interacts with soil mineral properties and microbial communities to influence MAOM formation and persistence. We cultured the fungus Hyaloscypha bicolor to produce {superscript 1}3C- and {superscript 1}{square}N-labeled necromass with varying melanin content (high or low) and incubated it in both live and sterile soils collected from six Indiana forests that differed in their clay and iron oxide (FeOx) content. After 38 days, we found that seven times more fungal-derived N was incorporated into MAOM than fungal-derived C, with fungal N comprising 20% of the MAOM-N pool. Low melanin necromass formed more MAOM-C than high melanin necromass, although site-level differences in overall MAOM formation were substantial. Soil clay and FeOx content were strong predictors of MAOM formation, explaining [~]60% and [~]68% of the variation in MAOM-C and MAOM-N, respectively. However, microbial communities significantly influenced MAOM formation, with MAOM-C formation enhanced and MAOM-N formation reduced in sterile soils. Furthermore, the relative abundance of fungal saprotrophs was negatively correlated, and bacterial richness was positively correlated with MAOM formation, and these relationships were influenced by necromass melanin content. This study reveals that microbial communities and soil properties interactively mediate the incorporation of fungal necromass C and N into MAOM, with microbes differentially influencing C and N incorporation, and these processes being further modulated by necromass melanization.

ecology↗

bioRxiv2024

Machine learning models can identify individuals based on a resident oral bacteriophage family

Metagenomic studies have revolutionized the study of novel phages. However these studies trade depth of coverage for breadth. We show that the targeted sequencing of a small region of a phage terminase family can provide sufficient sequence diversity to serve as an individual-specific barcode or a "phageprint, defined as the relative abundance profile of the variants within a terminase family. By collecting [~]700 oral samples from [~]100 individuals living on multiple continents, we found a consistent trend wherein each individual harbors one or two dominant variants that coexist with numerous low-abundance variants. By tracking phageprints over the span of a month across ten individuals, we observed that phageprints were generally stable, and found instances of concordant temporal fluctuations of variants shared between partners. To quantify these patterns further, we built machine learning models that, with high precision and recall, distinguished individuals even when we eliminated the most abundant variants and further downsampled phageprints to 2% of the remaining variants. Except between partners, phageprints are dissimilar between individuals, and neither country-of-residence, genetics, diet nor cohabitation seem to play a role in the relatedness of phageprints across individuals. By sampling from six different oral sites, we were able to study the impact of millimeters to a few centimeters of separation on an individuals phageprint and found that such limited spatial separation results in site-specific phageprints.

bioinformatics↗

bioRxiv2024

The proteome is a terminal electron acceptor

Microbial metabolism is impressively flexible, enabling growth even when available nutrients differ greatly from biomass in redox state. E. coli, for example, rearranges its physiology to grow on reduced and oxidized carbon sources through several forms of fermentation and respiration. To understand the limits on and evolutionary consequences of metabolic flexibility, we developed a mathematical model coupling redox chemistry with principles of cellular resource allocation. Our integrated model clarifies key phenomena, including demonstrating that autotrophs grow slower than heterotrophs because of constraints imposed by intracellular production of reduced carbon. Our model further indicates that growth is improved by adapting the redox state of biomass to nutrients, revealing an unexpected mode of evolution where proteins accumulate mutations benefiting organismal redox balance. One sentence summaryMicrobial proteins adapt their composition on physiological and evolutionary timescales to ensure organismal redox balance.

microbiology↗

bioRxiv2024

Dissecting endogeneous genetic circuits from first principles

For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs). However, to improve the versatility and scalability of MPRA pipelines, we need a "theory of the experiment" to help us better understand the impact of various biological and experimental parameters on the interpretation of experimental data. These parameters include binding site copy number, where a large number of specific binding sites may titrate away transcription factors, as well as the presence of overlapping binding sites, which may affect analysis of the degree of mutual dependence between mutations in the regulatory region and expression levels. To that end, in this paper we create tens of thousands of synthetic single-cell gene expression outputs using both equilibrium and out-of-equilibrium models. These models make it possible to imitate the summary statistics (information footprints and expression shift matrices) used to characterize the output of MPRAs and from this summary statistic to infer the underlying regulatory architecture. Specifically, we use a more refined implementation of the so-called thermodynamic models in which the binding energies of each sequence variant are derived from energy matrices. Our simulations reveal important effects of the parameters on MPRA data and we demonstrate our ability to optimize MPRA experimental designs with the goal of generating thermodynamic models of the transcriptome with base-pair specificity. Further, this approach makes it possible to carefully examine the mapping between mutations in binding sites and their corresponding expression profiles, a tool useful not only for better designing MPRAs, but also for exploring regulatory evolution. Author summaryWith the rapid advancement of sequencing technology, there has been an exponential increase in the amount of data on the genomic sequences of diverse organisms. Nevertheless, deciphering the sequence-phenotype mapping of the genomic data remains a formidable task, especially when dealing with non-coding sequences such as the promoter. In current databases, annotations on transcription factor binding sites are sorely lacking, which creates a challenge for developing a systematic theory of transcriptional regulation. To address this gap in knowledge, high-throughput methods such as massively parallel reporter assays (MPRAs) have been employed to decipher the regulatory genome. In this work, we make use of thermodynamic models to computationally simulate MPRAs in the context of transcriptional regulation and produce thousands of synthetic MPRA datasets. We examine how well typical experimental and data analysis procedures of MPRAs are able to recover common regulatory architectures under different sets of experimental and biological parameters. By establishing a dialogue between high-throughput experiments and a physical theory of transcription, our efforts serve to both improve current experimental procedures and enhancing our broader understanding of the sequence-function landscape of regulatory sequences.

genomics↗