Agent-Agnostic Biosurveillance

• Agent-agnostic biosurveillance is a method that leverages host immune responses and multi-omics data to detect unknown and emergent pathogens.
• It integrates transcriptomics, proteomics, metabolomics, and cytokine profiling to identify biosignatures independent of pre-defined agents.
• Practical applications include immune system-on-a-chip platforms, multiplexed assays, and wastewater analysis for early detection and intervention.

Agent-agnostic biosurveillance refers to the systematic detection of pathogenic threats without prior specification or enumeration of candidate biothreat agents. Rather than screening for known organisms or toxins, agent-agnostic strategies focus on the measurement and integration of host response signatures—primarily immunological and multi-omics readouts—that indicate exposure to novel, emergent, or engineered bioagents. This paradigm shift is motivated by limitations observed in list-based surveillance postures, as evidenced by the global response to SARS-CoV-2, and enabled by technological advances in high-dimensional assay, analytics, and in vitro modeling of host-pathogen interactions (Lin et al., 2023).

1. Immunological and Conceptual Foundations

Agent-agnostic biosurveillance is rooted in the immunological observation that the human host, and specifically its immune system, functions as a universal biosensor. Host recognition of danger occurs in a two-phase process:

• Innate immune recognition operates via pattern-recognition receptors (PRRs), including Toll-like receptors, C-type lectins, and NOD-like receptors, which detect pathogen-associated molecular patterns (PAMPs).
• Adaptive immune selection involves T-cell and B-cell receptor repertoires, shaped by negative selection to avoid self-reactivity but broadly reactive to non-self epitopes, including those not present in the human proteome (e.g., anti-α1-3 galactose responses) (Lin et al., 2023).

Perturbations in host receptor repertoires, phospho-signaling states, transcriptome dynamics, cytokine secretomes, and metabolic flux thus serve as high-sensitivity markers of exposure to unknown agents, independent of their genetic or antigenic characterization.

A long-term ambition is the development of "immune-system-on-a-chip" platforms. Here, engineered PRR panels or surrogate immune cells are exposed to complex environmental or clinical samples. Positive activation (measured by multiplexed surface markers or secretomes) provides an agnostic readout of bioagent exposure. Programs like DARPA's Friend-or-Foe and self-replicating RNA vaccine trials illustrate efforts to field-deploy such biosensing modalities (Lin et al., 2023).

2. Technical Assays for Host Response Profiling

Biosurveillance pipelines targeting bioagent-agnostic signatures ("BASs" — Editor's term) integrate orthogonal molecular assays. These span bulk and single-cell readouts across transcriptomes, proteomes, secretomes, and metabolomes. Key assays include:

Modality	Data Structure / Readout	Principal Outputs
Transcriptomics (bulk/single-cell RNA-seq)	$y_{gi}$: gene $g$ in sample/cell $i$; modeled as $y_{gi} \sim NB(\mu_{gi}, \phi_g)$	Fold-change $\beta_g$, $p$-values, gene-set scores
Proteomics (MS, CyTOF)	Spectral intensities, isotope/metal-tag counts	Quantitation, PTM profiles, single-cell expression
Metabolomics (LC-MS/MS, NMR)	$(m/z)$–intensity spectra, retention times	Metabolite abundance, pathway enrichment
Cytokine/Secretome Profiling	Fluorescence/luminescence intensity	Cytokine/chemokine concentration
Single-Cell Multi-Modal (CITE-seq, TEA-seq)	Joint mRNA/protein/chromatin/epitope readouts	Cell atlases, lineage/trajectory inference

Combining these modalities is central to defining BASs that are robust to agent novelty and diversity (Lin et al., 2023).

3. Computational and Statistical Integration Strategies

Discovery and validation of BASs require integration of heterogeneous, high-dimensional datasets. Key analytical modules include:

• Preprocessing and Harmonization: Batch effects are addressed with mixed-effects models, empirical Bayes (e.g., ComBat), or deep generative models (variational autoencoders). Metadata standardization aligns with FAIR data principles, encoding relevant covariates and assay parameters.
• Feature Selection/Dimensionality Reduction: Approaches range from univariate tests (e.g., limma-moderated statistics, Benjamini–Hochberg FDR) to multivariate methods—Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Elastic-Net regularized logistic regression,

$$L(\beta) = -\sum_{i}[y_i \log p_i + (1-y_i) \log(1-p_i)] + \lambda(\alpha\|\beta\|_1 + (1-\alpha)/2\|\beta\|_2^2)$$

• Network/Pathway-Centric Models: Weighted gene co-expression network analysis (WGCNA), graph-regularized factorization, and Bayesian network models support modular and causal inference of host response pathways.
• Machine Learning and Cross-Validation: Supervised classifiers (random forests, support vector machines, gradient boosting) are assessed by nested cross-validation and permutation testing, with cross-validated ROC AUC as a metric:

$$\mathrm{AUC} = \int_{0}^{1} \mathrm{TPR}\left(\mathrm{FPR}^{-1}(u)\right) du$$

Continual learning frameworks (e.g., online gradient descent) enable streaming data integration.

4. Analytical Limitations and Priority Research Directions

Contemporary limitations in agent-agnostic biosurveillance analytics include:

• No Reference Immune Baseline: There is no consensus model of a "generic healthy" immune state. Establishing longitudinal, standardized, multi-omic cohorts is imperative to calibrate baseline variance and detection thresholds.
• Metadata and Nomenclature Inconsistency: Variable usage of gene/protein identifiers (NCBI Gene vs. UniProt), study-specific labels, and heterogeneous ontologies impede dataset harmonization. Development of automated ID mapping and interoperable ontologies is critical.
• Batch Effect and Harmonization Challenges: Manual tuning often dominates current pipelines; robust automated harmonizers suitable for discrete and continuous batch variables in multi-cellular datasets are needed.
• Algorithmic Scalability: Modern ML models are resource-intensive. Efficient architectures (e.g., compressed deep nets, federated learning, summary transfer) and real-time inference pipelines are required for operational biosurveillance.
• De novo Omics Inference: Agent-agnostic approaches cannot assume existing reference databases. Advances in long-read assembly (for genomics/transcriptomics), peptide de novo sequencing (deep learning-enabled), and metabolite structural inference (spectrum-to-structure models) are essential.
• Multi-Modal Fusion Methods: Generalizable algorithms for heterogeneous, noisy, and incomplete data fusion (joint-matrix factorization, multi-view deep learning, graph neural networks) are immature and lack field-wide benchmarking (Lin et al., 2023).

5. Illustrative Applications and Validation Studies

Empirical demonstrations of agent-agnostic biosurveillance include:

• Mass Cytometry (CyTOF): Spitzer & Nolan (2016) measured over 40 protein markers on individual immune cells. Unsupervised clustering yielded activation modules (e.g., CD69$^\text{high}$ CD38$g$0 T cells) that generalized across distinct bacterial species, supporting the concept of a BAS that transcends agent identity.
• Multi-Analyte LFIA: Klebes et al. (2022) developed a lateral flow immunoassay simultaneously quantifying host IL-6 response and pathogen DNA. In wound swab testing, the assay distinguished infected from sterile wounds with $g$190% accuracy, agnostic to bacterial species.
• Wastewater Surveillance: Cui et al. (2021) integrated single-cell Raman Stable Isotope Probing and metagenomics, paired with ML classification, to detect antimicrobial-resistant strains in wastewater prior to their appearance as clinical isolates (Lin et al., 2023).

6. Synthesis and Future Perspectives

Agent-agnostic biosurveillance synthesizes fundamental immunology and multi-modal measurement to robustly detect novel pathogenic threats. This approach does not depend on predefined agent lists, instead leveraging conserved host response architectures—quantified via transcriptomic, proteomic, metabolic, and functional phenotyping—and integrated via scalable computational pipelines. Advancing the field requires concerted investment in standardizing data curation, overcoming computational and analytical bottlenecks, and field-validating BASs. Comprehensive BAS frameworks are projected to underpin future biosurveillance infrastructures capable of outpacing the evolving landscape of pathogenic threats (Lin et al., 2023).