Biology subjects

Kazakov, A.

Publications and source records attributed to Kazakov, A..

14 recordsLinked to original sources

bioRxiv2026

Genome-wide CRISPRi mapping of mycobacteriophage interaction determinants in Mycobacterium smegmatis mc2155

Bacteriophages are highly specific predators of bacterial hosts, but the molecular mechanisms governing their entry across the structurally complex mycobacterial cell envelope remain poorly defined. Utilizing a genome-wide CRISPR interference library in the model organism Mycobacterium smegmatis against a phylogenetically representative collection of 70 mycobacteriophages, we mapped the functional architecture of host susceptibility determinants. This parallelized genetic screen revealed a spatial continuum of infection requirements spanning from outer leaflet surface structures down to deep cytoplasmic machinery. The four major outer membrane Mycobacterium smegmatis porins (Msp) emerged as the dominant candidate receptor family for 46 diverse phages, representing the first protein receptors identified within this group. Interrogating the asymmetric outer bilayer also implicated cell envelope glycolipids as candidate receptors for 14 phages, resolving into glycopeptidolipid sub-lineages. Beneath the mycomembrane, arabinogalactan functions as a candidate secondary receptor for 5 phages, while the inner membrane lipid transporter MmpL11 and the global nucleoid-associated protein Lsr2 represent determinant factors for 14 and 31 phages respectively. While physical deletions of major porins trigger spontaneous regulatory pathways that upregulate silent backup paralogues, acute transcriptional knockdowns circumvent suppressor accumulation, revealing unmasked single-gene dependencies. Structural analysis demonstrated that host determinant usage decouples entirely from whole-genome viral phylogeny, indicating that standard phylogenetic groupings fail to predict surface recognition profiles. Ultimately, mapping this host interactome reveals an evolutionary strategy where phages target highly redundant or abundant host machinery to exploit fixed physiological liabilities, presenting a robust molecular blueprint for the rational engineering of escape-resistant therapeutic phage cocktails.

microbiology↗

bioRxiv2026

Genome-wide characterization of host factors involved in single-stranded RNA and DNA phage infection pathways

Single stranded RNA (ssRNA) and single stranded DNA (ssDNA) bacteriophages represent a key component of the global virome, yet the host genetic networks supporting their infection cycles remain poorly understood. Here, we present a comprehensive, genome-wide mapping of the genetic landscape regulating infection cycles for F pilus-dependent ssRNA and ssDNA phages in Escherichia coli. Genetic screens across ssRNA phages spanning all four genogroups of the Leviviricetes revealed a highly conserved network of host dependencies, with the notable exception of the F plasmid gene traD. While primary structural receptor components and dsbA mediated disulfide bond formation are universally required across all lineages to ensure F pilus integrity, traD exhibits a strict genogroup-specific requirement during entry, showing variable essentiality across different viral groups despite sharing an identical primary receptor. Our gene dosage screens revealed that an elevated copy number of the hslU protease or the RNA chaperone stpA restricts infection, identifying clear genetic barriers that can perturb the viral life cycle. Parallel assays with filamentous ssDNA phages produced host factor profiles consistent with published literature, while revealing additional variations in host dependency. These screens confirmed that ssDNA phages strictly rely on the host TolQRA complex for entry downstream of pilus engagement. The assays tracked prominent negative fitness signatures across homeostatic clusters, highlighting how the physiological burden of continuous, non-lytic virion extrusion strains the host envelope. Finally, this comparative approach traced the selectivity of our isolation host (E. coli HSF) to a horizontally acquired capsule architecture from Klebsiella. This surface shield excludes a large panel of double stranded DNA phages isolated on diverse E. coli strains, while allowing virions from ssDNA and ssRNA phages to engage the extended F pilus and bypass the barrier via native pilus retraction. Together, this work provides a systematic, class-wide map of single stranded phage-host interactions, bridging classical genetics with modern viral discovery while establishing a robust host platform to access uncultured viral diversity and a functional blueprint to design next generation diagnostics, protein antibiotics, and biocontrol tools to halt horizontal gene transfer.

microbiology↗

bioRxiv2026

A comprehensive phage-bacteria interaction atlas links phage lineage and capsule serotype to genome-guided machine learning prediction in Klebsiella pneumoniae

Klebsiella pneumoniae is a WHO critical-priority pathogen for which strain-specific bacteriophages are being explored as precision antimicrobials, yet rapid phage-host matching remains a major barrier to therapeutic deployment. We constructed a comprehensive interaction atlas comprising 84 taxonomically diverse phages and 101 globally sourced, clinically representative K. pneumoniae strains, including multidrug-resistant isolates. Systematic pairwise profiling produced 8,484 interaction measurements, of which 2,656 (31.3%) scored positive for bacterial clearance. Genus was the dominant phage-side determinant of host range, while capsule K-serotype was the strongest host-side determinant of susceptibility; aggregate defense, prophage, plasmid, and antimicrobial-resistance features contributed comparatively little. A genome-guided machine learning model predicted interactions without curated host annotations (AUROC, 0.882; AUPR, 0.765), outperforming a model based only on phage genus and K-serotype and modestly exceeding a curated genomic baseline. The model recovered capsule- and lipopolysaccharide-biosynthesis genes, canonical receptors and defense-associated features as major predictors using SHAP analysis. Feasibility tests of expert- and model-selected cocktails exposed a translational constraint. Although all formulations suppressed growth in vitro, only the specific cocktail whose phages replicated robustly within the murine gut reduced colonization, suggesting in vivo amplification rather than predicted host range as the limiting factor for therapeutic efficacy. Together with the activity of a model-selected cocktail built for an isolate completely excluded from training, these results provide a species-wide resource for K. pneumoniae phage matching and support a hybrid workflow combining genome-based ranking with targeted phenotypic validation.

microbiology↗

bioRxiv2026

Surface architecture of the bacterial envelope determines phage adsorption route in pathogenic Escherichia coli O157:H7

The outermost surface layers of Gram-negative bacteria determine phage access to terminal receptors, yet their genetic basis has been mapped almost exclusively in laboratory strains that lack them. Here we apply genome-wide RB-TnSeq fitness profiling to four Escherichia coli O157:H7 strains from distinct phylogenetic clades sharing the O157 O-antigen, using 38 phages with terminal receptors previously mapped in E. coli K-12 strain. RB-TnSeq fitness landscapes across all four pathogenic backgrounds were mostly similar, and dominated by surface-associated loci, including the gfc-etk group 4 capsule operon, O-antigen biosynthesis genes, LPS core assembly genes and outer membrane proteins. Disruption of gfc-etk abolished infection in 11 genetically diverse myoviruses, establishing the O-antigen capsule as a widespread required primary recognition substrate. O-antigen loci generated two classes of fitness score patterns. For 10 phages, disruption increased infectivity, indicating it is a barrier to receptor access; for 3 others, disruption abolished infectivity, demonstrating it can also be a primary recognition substrate. Outer membrane protein receptor identity was conserved across laboratory and pathogenic backgrounds, with the same proteins recognized in both K-12 and O157:H7, while glycan layer state determines whether these receptors are reached. These results demonstrate that outer surface glycan layers can act as primary and optional recognition substrates for phage infection, or as physical barriers preventing terminal receptor access. Extending the ability to probe phage-targeted receptors beyond outer membrane proteins provides a framework for incorporating glycan layer state into predictive models of phage-host interactions.

microbiology↗

bioRxiv2026

Mapping Genome-wide Transcription Factor Binding Sites in two Rhodanobacter strains Isolated from Extreme Environments

Identification of target genes for bacterial transcription factors (TFs) provide a deeper understanding of bacterial response to environmental conditions. We focused on two Rhodanobacter sp. isolates from the subsurface of the U.S. Department of Energy Oak Ridge Reservation (ORR), an environment characterized by high-concentration mixed-waste contamination plumes. Bacterial strains in this subsurface ecosystem experience changing and complex environmental conditions, including variable nutrient and oxygen availability, fluctuating pH, nitrate and metals. We examined 91 DNA-binding TFs from 17 TF families in two Rhodanobacter strains, R. denitrificans FW104-10B01 and R. thiooxydans FW510-R12, using both DNA affinity purification sequencing (DAP-seq) and comparative genomics. Target genes regulated by 31 TFs were identified. Despite being isolated from different wells, the two strains shared a highly similar core regulatory network, with orthologous TFs regulating nearly identical sets of genes. These conserved networks governed essential survival functions, including nutrient uptake (e.g., phosphate), carbohydrate metabolism, motility, and stress responses to heavy metals and oxidative environment. TF-binding sequence motifs were also inferred for each of the 31 TFs. Key results include the elucidation of a global regulator Clp which regulates a large number of genes, particularly those involved in biofilm formation and motility. Functional validation through RT-qPCR confirmed that Clp activates the expression of genes critical for type IV pilin structure and flagellar regulation. Our work highlights the advantages of synergistically employing DAP-seq and comparative genomics, which enabled us to corroborate findings and obtain a larger view of the regulatory landscape of these non-model denitrifying bacteria.

microbiology↗

bioRxiv2026

Comprehensive interaction profiling and machine learning prediction of bacteriophage infectivity across clinically diverse Pseudomonas aeruginosa

The rise of antibiotic-resistant bacterial infections has driven renewed interest in bacteriophage therapy, where viruses that specifically kill bacteria are used as targeted antimicrobials. Pseudomonas aeruginosa, a WHO critical-priority pathogen that causes severe infections in hospitalized and immunocompromised patients, presents a major challenge for phage therapy because of its extraordinary genetic diversity. Phages effective against one bacterial strain often fail against others, and existing cross-resistance-profiling approaches require iterative empirical testing of each new patient isolate. To establish a genome-based framework for rapid phage-isolate matching, we assembled a collection of 95 genomically diverse P. aeruginosa phages representing 20 genera and tested each against 99 genetically diverse clinical isolates, generating 9,405 infection outcome measurements. Bacterial O-antigen serotype emerged as the dominant determinant of strain susceptibility, while defense systems, anti-defense systems, and prophage burden contributed smaller strain-specific effects. The full curated multivariate model explained 47% of strain-susceptibility variance. Machine-learning models integrating these features and pangenome-derived gene clusters reached a per-strain AUROC of 0.86. In an in vivo proof-of-concept test against a single held-out strain, the ML-designed cocktail produced a [~]12-fold greater median CFU reduction than the expert-designed cocktail (q = 0.045), with both cocktails substantially reducing burden relative to the untreated control ([~]113-fold for ML, [~]9-fold for CG; both q < 10{square}3). SHAP analysis of the model identified bacterial surface-architecture genes (LPS biosynthesis, outer membrane proteins, type IV pili) as the dominant predictors, with defense-system content modulating which specific phages succeed against a strain rather than uniformly damping susceptibility. Together, these results establish a genome-based framework for predicting phage susceptibility in genetically diverse clinical isolates.

microbiology↗

bioRxiv2026

Metagenome-guided substrate selection enriches Terriglobus, reveals co-occurring taxa, and enables isolation of a novel species

Microbes that remain uncultivated occupy nearly every ecosystem on the planet; this is particularly true in soils, where despite their prevalence, the roles of rarely cultivated microbes in driving biogeochemical cycles and ecosystem function remain poorly explored. We combine metagenome-informed substrate selection with enrichment sub-communities to generate reduced-complexity communities that preserve co-occurrence and expand experimental access to underrepresented soil lineages without requiring prior isolation of each member. Carbohydrate-active enzyme (CAZyme) profiles from soil-derived genomes were used to select carbon compounds predicted to enrich difficult to culture taxa, including members of the phylum Acidobacteriota. Based on 16S rRNA amplicon sequencing, we reproducibly enriched Terriglobus (Acidobacteriota) on multiple metagenome-guided substrates. Select communities with consistent presence and varying abundance of Terriglobus were passaged in a longitudinal design to generate 89 metagenomes; genus-level profiling revealed that community composition varied between biological replicates but remained consistent within replicates over time, providing diverse Acidobacteriota-containing configurations for downstream analysis. Association network inference identified a core set of co-occurring taxa that positively tracked with Terriglobus across the longitudinal series. In parallel, the substrate-guided approach led to isolation of a novel Terriglobus species, the first cultured representative of its GTDB species cluster. Together, these results establish a generalizable strategy for generating communities enriched with rarely cultivated taxa, yielding tractable systems for studying microbial interactions and community assembly in soil.

microbiology↗

bioRxiv2026

Enabling the prediction of phage receptor specificity from genome data

Predicting which receptor a phage binds to from genome sequence alone has remained an intractable challenge, principally because the experimental phenotypic data required to train and validate predictive models have not been available at sufficient scale. Here we address this by conducting 1,050 genome-wide genetic screens across 255 taxonomically diverse Escherichia coli dsDNA phages, assigning host receptors to 193 phages across 19 receptor classes. Comparative genomics and AlphaFold3 structural modelling resolved the sequence determinants of specificity to defined receptor-binding protein domains and individual residues. Machine learning models trained on this dataset predicted host receptor identity from phage genome sequence alone without prior annotation of receptor-binding genes, achieving perfect precision and greater than 80% recall on 49 independently validated phages, and yielding predictions for 1,060 of 1,875 E. coli phage genomes in NCBI. Domain swaps redirected receptor specificity as predicted, and a single amino acid substitution proved both necessary and sufficient to switch recognition between two distinct porins. These results demonstrate that systematic phenotyping at scale makes sequence-based prediction of molecular interaction specificity tractable, with direct implications for phage-based medicine, microbiome engineering and the broader challenge of inferring host-pathogen interaction outcomes from sequence.

microbiology↗

bioRxiv2025

Phylogeny-agnostic strain-level prediction of phage-host interactions from genomes

Bacteriophages offer promising alternatives to antibiotics for treating drug-resistant infections and engineering microbiomes, but applications are limited by inability to select phages infecting specific bacterial strains. Selecting suitable phages requires either one-to-one experimental assays or strain-level predictions of phage-host interactions. Existing computational approaches either predict host taxonomy at broad ranks unsuitable for strain-level targeting or require species-specific mechanistic knowledge limiting generalizability. Here, we present a phylogenyagnostic machine learning framework predicting strain-level phage-host interactions across diverse bacterial genera from genome sequences alone. Systematically optimizing the workflow over 13.2 million training runs across six datasets (115,037 interactions, 949 bacterial strains, 518 phages), we achieved performance matching species-specific methods (AUROC 0.67-0.94) while eliminating phylogenetic constraints. Comprehensive feature engineering identifies biologically interpretable genetic determinants while minimizing overfitting in sparse, imbalanced datasets. Experimental validation through 1,240 novel interactions confirmed generalizability (AUROC 0.84), while genome-wide RB-TnSeq screens verified that 68.6% of experimentally identified infection mediators were captured computationally, including receptors and cell wall biosynthesis pathways. Model-guided cocktail design achieved up to 97.5% bacterial coverage with five phages, and up to a 3.1-fold improvement in single-phage selection over promiscuity-based selection. This platform enables rational phage therapy design and precision microbiome engineering with applications in combating antimicrobial resistance across clinical, agricultural, and industrial contexts.

microbiology↗

bioRxiv2024

Insular cortex encodes task alignment

Animals can learn complex behaviors. Animal behavior in the lab has traditionally been studied via summary statistics such as trial-based success rates. However, animal behavior is much more fine-grained: a trial in an experiment often consists of multiple actions, and more than one strategy can lead to a successful completion of a trial. To understand how the brain controls behavior, a fine-grained yet compact description of behavior is necessary. We describe here an approach for estimating the strategies animals use for a large family of tasks from first principles. Using reinforcement learning with informational constraints on policies, we compute a rich set of candidate policies with a small number of meaningful parameters, and match observed behavior to these policies. In a sample rat task, our approach revealed ongoing learning for more than 100 days after the saturation of success rates. Moreover, we showed that many neurons in the insular cortex of rats track the instantaneous task engagement of the rats with a resolution of a few minutes. Due to its generic formulation in reinforcement learning terminology, our work is directly applicable to the majority of animal tasks in use today.

neuroscience↗

bioRxiv2024

Intratumoral CXCL12 Gradients Contextualize Tumor Cell Invasion, Migration and Immune Suppression in Breast Cancer

Although the CXCL12/CXCR4 pathway has been prior investigated for its prometastatic and immuno- suppressive roles in the tumor microenvironment, evidence on the spatiotemporal regulation of these hallmarks has been lacking. Here, we demonstrate that CXCL12 forms a gradient specifically around cancer cell intravasation doorways, also known as Tumor Microenvironment of Metastasis (TMEM) doorways, thus facilitating the chemotactic translocation of prometastatic tumor cells expressing CXCR4 toward the perivascular TMEM doorways for subsequent entry into peripheral circulation. Fur- thermore, we demonstrate that the CXCL12-rich micro-environment around TMEM doorways may cre- ate immunosuppressive niches, whereby CD8+ T cells, despite being attracted to these regions, often exhibit reduced effector functions, limiting their efficacy. While the CXCL12/CXCR4 pathway can mini- mally influence the overall composition of immune cell populations, it biases the distribution of CD8+ T cells away from TMEM doorways, justifying its prior-established role as immunosuppressive factor for CD8+ T cells. Our research suggests that the complex interactions between CXCL12 and the various tumor and immune cell types contributes not only to the completion of the initial steps of the metastatic cascade, but also offers an immunological "sanctuary" to prometastatic tumor cells homed around TMEM doorways. Overall, our study enhances our current understanding on the mechanisms, via which CXCL12 orchestrates tumor cell behavior and immune dynamics, potentially guiding future thera- peutic strategies to combat breast cancer metastasis and improve anti-tumor immunity.

cancer biology↗

bioRxiv2024

Task-related activity in auditory cortex enhances sound representation

In auditory-guided tasks, sound presentations often occupy a small fraction of the total task time. We studied here neuronal dynamics that spanned trial duration. Many neurons had large, slow, firing rate modulations, which were not driven by sounds, were larger than the sound evoked responses, and were locked to specific time points during the task, similar to responses of hippocampal time-sensitive neurons. Concurrently, responses to sounds differed between active behavior and passive listening conditions: in the active sessions, the on-going activity just before sound presentations was higher and the responses to target stimuli were weaker but more informative about the task-relevant sounds. We show that the slow firing rate modulations caused the increased on-going activity. Using a model, we demonstrate that higher on-going activity led to more synaptic depression of the cortico-cortical synapses, reducing the tendency to produce population spikes and resulting in weaker but more informative responses.

neuroscience↗

bioRxiv2021

The RIFF: an automated environment for studying the neural basis of auditory-guided complex behavior

Behavior consists of the interaction between an organism and its environment, and is controlled by the brain. Brain activity varies at sub-second time scales, but behavioral measures are usually coarse (often consisting of only binary trial outcomes). To overcome this mismatch, we developed the RIFF: a programmable interactive arena for freely-moving rats with multiple feeding areas, multiple sound sources, high-resolution behavioral tracking, and simultaneous electrophysiological recordings. We describe two complex tasks implemented in the RIFF. Rats quickly learned these tasks and developed anticipatory behavior. Neurons in auditory cortex and posterior insula showed sensitivity to non-auditory parameters such as location and pose. Our combination of wireless electrophysiology and detailed behavioral documentation in a controlled environment produces insights into the cognitive capabilities and learning mechanisms of rats and opens the way to a better understanding of how brains control behavior.

neuroscience↗

bioRxiv2018

Acoustic Calibration in an Echoic Environment

Background: The sound fed to a loudspeaker may significantly differ from that reaching the ear of the listener. The transformation from one to the other consists of spectral distortions with strong dependence on the relative locations of the speaker and the listener as well as on the geometry of the environment. With the increased importance of research in awake, freely-moving animals in large arenas, it becomes important to understand how animal location influences the corresponding spectral distortions.\n\nNew Method: We describe a full calibration pipeline that includes spatial sampling and estimation of the spectral distortions. We estimated the impulse responses of the environment using Golay complementary sequences.\n\nUsing those sequences, we also describe an acoustic 3D localization method for freely moving animals.\n\nResults: In our arena, the impulse responses are dominated by a small number of strong reflections. We use this understanding to provide guidelines for designing the geometry of the environment as well as the presented sounds, in order to provide more uniform sound levels throughout the environment. Our 3D localization method achieves a 1 cm precision through the utilization of sound cues only.\n\nComparison with Existing Methods: To our knowledge, this is the first description of a large-scale acoustic calibration pipeline with acoustic localization for neuroscience studies.\n\nConclusions: Principled sampling of large arena allows for better design and control of the acoustic information provided to freely-moving animals.

animal behavior and cognition↗