Biology subjects

Palucka, K.

Publications and source records attributed to Palucka, K..

At least 19 recordsLinked to original sources

bioRxiv2026

Clinical and Cross-Domain Validation of an LLM-Guided, Literature-Based Gene Prioritization Framework

BackgroundWe previously published a literature-based pipeline for sepsis gene prioritization (PS3 and candidate genes) using an LLM-enabled retrieval and judging framework. Here, we extend that work to ask whether these prioritized genes show independent clinical validity and whether the same strategy generalizes to a drug-obesity- infection setting. MethodsUsing the original LLM-guided workflow, we evaluated PS3 and the Candidate set in two new settings. First, we tested 28-day mortality prediction in the independent VANISH sepsis trial, benchmarking PS3 and Candidate against two established immune signatures--the Severe-or-Mild (SoM) signature and Immune Health Metric (IHM)--under a uniform logistic-regression framework with clinical covariates. Second, we applied the same genome-wide screening and tiered judging pipeline to GLP-1/obesity/infection biology centered on semaglutide, comparing Tier-1 and Tier-2 gene sets to STEP trial serum proteomics at gene and Hallmark pathway levels. In parallel, we fine-tuned an open-weight GPT-OSS-20B model on curated sepsis justifications to obtain a domain-aware "LLM-as-judge," and compared its scoring behavior with the base model on semaglutide Tier-2 genes. ResultsIn the full VANISH cohort, PS3 and the Candidate set showed moderate discrimination, whereas SoM remained the strongest single predictor of 28-day mortality. In the Critical/High APACHE II subgroup, PS3 achieved ROC and precision-recall performance comparable to, or slightly better than, SoM despite its smaller, knowledge-derived composition, indicating that literature-prioritized genes capture mortality-relevant immune dysregulation under severe illness. In the semaglutide case study, gene-level overlap between LLM-prioritized genes and differentially abundant serum proteins was modest, but Tier-1 genes recapitulated the main semaglutide-responsive metabolic programs from STEP and highlighted additional immune-metabolic pathways relevant to infection, with discordances largely explained by serum proteome coverage. The fine-tuned judge remained moderately concordant with the base GPT-OSS across mechanistic themes, preserving overall ranking while inducing systematic, biologically interpretable shifts in immune and infection-related scores. ConclusionsAn LLM-guided, literature-based gene prioritization framework yields compact gene sets that show independent sepsis mortality signal and pathway-level concordance in a semaglutide/obesity/infection setting, while a sepsis-aware LLM-as-judge provides domain-specific refinements without overturning core rankings. Together, these findings support knowledge-grounded, LLM-derived gene sets and judges as interpretable components for probing immune dysregulation across diseases and therapies. Short abstractWe previously published a literature-based pipeline for sepsis gene prioritization (Priority Set 3, PS3, and a Candidate set) using an LLM-enabled retrieval and judging framework; here, we test whether these prioritized genes show independent clinical validity and whether the same strategy generalizes to a drug-obesity-infection setting. Using the original workflow, we first evaluated 28-day mortality prediction in the independent VANISH sepsis trial, benchmarking PS3 and the Candidate set against two established immune signatures--the Severe-or-Mild (SoM) signature and Immune Health Metric (IHM)--within a uniform logistic-regression framework adjusted for clinical covariates. In the full cohort, PS3 and Candidate showed moderate discrimination, while SoM remained the strongest single predictor; however, in the Critical/High APACHE II subgroup, PS3 achieved ROC and precision-recall performance comparable to, or slightly better than, SoM despite its smaller, knowledge-driven composition, indicating that literature-prioritized genes capture mortality-relevant immune dysregulation under severe illness. We then applied the same genome-wide screening and tiered judging pipeline to GLP-1/obesity/infection biology centered on semaglutide, comparing Tier-1 and Tier-2 genes with STEP trial serum proteomics; although gene-level overlap with differentially abundant proteins was modest, Tier-1 genes recapitulated key semaglutide-responsive metabolic programs and highlighted additional immune-metabolic pathways relevant to infection, with discordances largely attributable to serum proteome coverage. Finally, supervised fine-tuning of an open-weight GPT-OSS model on curated sepsis justifications yielded a domain-aware "LLM-as-judge" that remained broadly concordant with the base model while inducing systematic, interpretable shifts in immune and infection-related scores. Together, these results support LLM-guided, literature-based gene sets and judges as compact, mechanistically interpretable components for probing immune dysregulation across diseases and therapies.

bioinformatics↗

bioRxiv2025

Automating Candidate Gene Prioritization with Large Language Models: From Naive Scoring to Literature-Grounded Validation

BackgroundIdentifying promising therapeutic targets from thousands of genes in transcriptomic studies remains a major bottleneck in biomedical research. While large language models (LLMs) show potential for gene prioritization, they suffer from hallucination and lack systematic validation against expert knowledge. MethodsWe developed a two-stage computational framework that combines LLM-based screening with literature validation for systematic gene prioritization. Starting with 10,824 genes from the BloodGen3 repertoire, we applied multi-criteria evaluation for sepsis relevance, followed by retrieval-augmented generation (RAG) using 6,346 curated sepsis publications. A novel faithfulness evaluation system verified that LLM predictions aligned with retrieved literature evidence. ResultsThe framework identified 609 sepsis-relevant genes with >94% filtering efficiency, demonstrating strong enrichment for inflammatory pathways including TNF- signaling, complement activation, and interferon responses. Literature validation yielded 30 ultra-high confidence therapeutic candidates, including both established sepsis genes (IL10, TREM1, S100A9, NLRP3) and novel targets warranting investigation. Benchmark validation against expert-curated databases achieved 71.2% recall, with systematic correlation between computational confidence and evidence quality. The final candidate set balanced discovery (11 novel genes) with validation (19 known genes), maintaining biological coherence throughout the filtering process. ConclusionsThis framework demonstrates that rigorous methodology can transform unreliable LLM outputs into systematically validated biological insights. By combining computational efficiency with literature grounding, the approach provides a practical tool for prioritizing experimental validation efforts. The modular design enables adaptation to other diseases through knowledge base substitution, offering a systematic approach to literature-guided biomarker discovery. AvailabilitySource code and implementation details are available at https://github.com/taushifkhan/llm-geneprioritization-framework, vector database at https://doi.org/10.5281/zenodo.15802241 and Interactive demonstration at https://llm-geneprioritization.streamlit.app/

bioinformatics↗

bioRxiv2025

Intron retention regulates STAT2 function and predicts immunotherapy response in lung cancer

Immunotherapy benefits only a subset of lung cancer patients, and the molecular determinants of variable outcomes remain unclear. Using long-read RNA-sequencing we mapped the landscape of alternative RNA splicing in human primary lung adenocarcinomas. We identified over 180,000 full-length mRNA isoforms, more than half of which were novel and many of which occurred in immune-related genes, particularly within the type I interferon response pathway. We discovered retained introns in the STAT2 gene that produce altered protein isoforms that regulate immune signaling and interferon responses. Furthermore, STAT2 intron retention levels predicted patient responses to checkpoint inhibitors. These findings position alternative splicing as a key regulator of tumor immune responses and reveal mRNA splicing alterations as potential biomarkers to identify patients who might respond to cancer immunotherapy.

immunology↗

bioRxiv2025

Host-specific bacterial modulation of airway gene expression and alternative splicing

The human microbiome varies extensively between individuals. While there are numerous studies investigating the effects of inter-individual differences on microbiome composition, there are few studies investigating inter-individual effects on microbial modulation of the host, or host-specific effects. To address this knowledge gap, we colonized human bronchial epithelial air-liquid interface tissue cultures generated from six different adults with one of three phylogenetically diverse bacteria and compared how each microbe differentially modulated host gene expression in each of the six donors. Microbial treatment had the strongest effect on transcription, followed by donor-specific effects. Gene pathways differed markedly in their donor- and microbe-specificity; interferon expression was highly donor-dependent while transcription of epithelial barrier and antibacterial innate immunity genes were predominantly microbially driven. Moreover, we evaluated whether microbial regulation of alternative splicing was modulated by donor. Strikingly, we found significant non-redundant, donor-specific regulation of alternative splicing exclusively in the Gram-positive commensal microbes. These findings highlight that microbial effects on the human airway epithelium are not only species-specific but also deeply individualized, scoring the importance of host context in shaping microbe-induced transcriptional and splicing responses.

microbiology↗

bioRxiv2025

Prediction of Outcome from Spatial Protein Profiling of Triple-Negative Breast Cancers

In tumors, reciprocal spatial interactions between immune cells, their mediators, the extracellular matrix, and mutated neoplastic cells impact all aspects of treatment resistance. The operational mechanisms of these interactions are foundational for developing insights and targets for cancer therapy and prevention. Spatial quantification of the tumor microenvironment system from image data has untapped potential for patient stratification. Here, we present SparTile, a powerful computational approach for analysis of multiplex proteomics images to reveal clinically relevant structural organization in the tumor microenvironment. SparTile enables the robust unbiased identification and characterization of tumor microenvironments based on spatial relationships among protein markers without the need for cell segmentation or classification. Applied to tissues of patients with triple negative breast cancer (TNBC), an aggressive subtype of breast cancer, SparTile identified repeatable microenvironments with specific cellular relationships. These included microenvironments characterized by risk markers such as Ki67+ (p-value= 0.052) and vimentin+ (p-value<0.01) tumor cells, which correlate with poor survival. Furthermore, myeloid markers in an MX1 positive tumor environment correlate with shorter survival (p-value=0.04). Finally, we identified a relative distance between tumor and myeloid cell markers as a strong prognostic risk factor in multivariate Cox models (p-value=0.01). This distance metric was externally validated on two datasets of breast cancer multiplex images (p-values<0.01). Thus, unbiased protein-based and segmentation-free spatial analysis is effective for identifying clinically relevant biomarkers from multiplex tumor images and identifies novel predictive biology.

cancer biology↗

bioRxiv2024

Deep functional profiling of gene sets using Large Language Models: A blueprint for tailored, context-aware functional annotation

In this study, we present a proof-of-concept approach leveraging Large Language Models (LLMs) for the deep functional profiling of gene sets, addressing the limitations of traditional functional annotation tools. By employing a stepwise prompting strategy with OpenAIs GPT-4, we systematically retrieve, consolidate, and score immune functions associated with each gene in a module, generating ranked lists of functions with detailed justifications and supporting literature evidence. As a blueprint for this approach, we demonstrate how this in-depth, context-aware analysis can characterize Module M10.4 from the BloodGen3 transcriptional module repertoire, confirming its central role in neutrophil-mediated innate immunity and antimicrobial defense while uncovering a nuanced network of immune functions overlooked by conventional methods. The LLM-based workflow identifies cell types and transcriptional programs driving these functions, offering a more granular and quantitative assessment of the gene sets functional associations compared to direct LLM prompting or pathway enrichment analysis. Our results showcase the potential of LLMs in providing biologically meaningful interpretations of gene expression signatures, accounting for cellular composition changes and transcriptional regulation. This initial proof-of-concept study establishes a foundation for the automated, high-throughput functional profiling of transcriptional signatures. By integrating advanced language models with traditional literature-based validation, we present a powerful strategy for unraveling the complex biology of gene sets and transcriptional modules, ultimately facilitating the translation of systems-level molecular data into actionable knowledge.

bioinformatics↗

bioRxiv2024

Automating Candidate Gene Prioritization with Large Language Models: Development and Benchmarking of an API-Driven Workflow Leveraging GPT-4

In this exploratory study, we developed an automated workflow that leverages Large Language Models, specifically GPT-4, to prioritize candidate genes for targeted assay development. The workflow automates interaction with OpenAI models and enables prompt creation, submission. It features customizable prompts designed to evaluate candidate genes based on criteria such as association with biological processes, biomarker potential, and therapeutic implications, which can be tailored for specific diseases or processes. Benchmarking experiments comparing the performance of the Application Programming Interface (API)-based automated prompting approach with manual prompting demonstrated high consistency and reproducibility in gene prioritization results. The automated method exhibited scalability by successfully prioritizing genes relevant to sepsis from the BloodGen3 repertoire, comprising 11,465 genes, distributed among 382 modules. The workflow efficiently identified sepsis-associated genes across the repertoire, revealing distinct gene clusters and providing insights into their distribution within module aggregates and individual modules. This proof-of-concept study demonstrates how LLMs can enhance gene prioritization, streamlining the identification process for targeted assays across various biological contexts. However, it also reveals the need for further validation and highlights the exploratory nature of this work due to scoring inconsistencies and the necessity for manual fact-checking. Despite these challenges, the automated workflow holds promise for accelerating targeted assay development for disease management and paves the way for future research.

bioinformatics↗

bioRxiv2023

Comprehensive single cell aging atlas of mammary tissues reveals shared epigenomic and transcriptomic signatures of aging and cancer

Aging is the greatest risk factor for breast cancer; however, how age-related cellular and molecular events impact cancer initiation is unknown. We investigate how aging rewires transcriptomic and epigenomic programs of mouse mammary glands at single cell resolution, yielding a comprehensive resource for aging and cancer biology. Aged epithelial cells exhibit epigenetic and transcriptional changes in metabolic, pro-inflammatory, or cancer-associated genes. Aged stromal cells downregulate fibroblast marker genes and upregulate markers of senescence and cancer-associated fibroblasts. Among immune cells, distinct T cell subsets (Gzmk+, memory CD4+, {gamma}{delta}) and M2-like macrophages expand with age. Spatial transcriptomics reveal co-localization of aged immune and epithelial cells in situ. Lastly, transcriptional signatures of aging mammary cells are found in human breast tumors, suggesting mechanistic links between aging and cancer. Together, these data uncover that epithelial, immune, and stromal cells shift in proportions and cell identity, potentially impacting cell plasticity, aged microenvironment, and neoplasia risk.

genomics↗

bioRxiv2023

Long-term engraftment of adult hematopoietic progenitors in a novel model of humanized mice

Pre-clinical use of humanized mice transplanted with CD34+ hematopoietic progenitor cells (HPCs) is limited by insufficient engraftment with adult HPCs. Here, we developed a novel immunodeficient mice based in NOD-SCID-Il2{gamma}c-/- (NSG) mice to support long-term engraftment with human adult HPCs and tissue colonization with human myeloid cells. As both Flt3L and IL-6 are critical for many aspects of hematopoiesis, we knock-out mouse Flt3 and knock-in human IL6 gene. The resulting mice showed an increase in the availability of mouse Flt3L to human cells, and a dose-dependent production of human IL-6 upon activation. Upon transplantation with low number of human HPCs from adult bone marrow, these humanized mice demonstrated a significantly higher engraftment with multilineage differentiation of human lymphoid and myeloid cells. Furthermore, higher frequencies of human lymphoid and myeloid cells were detected in tissues at one year after adult HPC transplant. Thus, these mice enable studies of human hematopoiesis and tissue colonization over time. SummaryPre-clinical use of humanized mice is limited by insufficient engraftment with adult hematopoietic progenitor cells (HPCs). Here, we developed a novel immunodeficient mice which support long-term engraftment with adult bone marrow HPCs and facilitate building autologous models for immuno-oncology studies.

immunology↗

bioRxiv2023

Species- and strain-effects in interferon, innate immunity, and barrier transcriptional response of microbiome interactions of 3D respiratory epithelial cultures

The microbiome modulates the respiratory epitheliums immunomodulatory functions. To explore how the microbiomes biodiversity affects microbe-epithelial interactions, we screened 58 phylogenetically diverse microbes for their transcriptomic effect on tracheobronchial air-liquid interface cell (ALI) cultures. We found distinct species- and strain-level differences in host innate immunity, and epithelial barrier response. Strikingly, we found that host interferon, an antiviral immune response, was one of the most variable host processes, and this variability was not driven by microbial phylogenetic diversity, bioburden, nor by the microbes ability to stimulate other innate immunity pathways. Our study provides a foundation for understanding how the respiratory microbiomes biodiversity affects epithelial, and particularly, antiviral innate immunity.

microbiology↗

bioRxiv2023

A 3D Printed Ventilated Perfused Lung Model Platform to Dissect the Lung's Response to Viral Infection in the Presence of Respiration

In this study, we developed a three-dimensionally (3D) printed lung model that faithfully recapitulates the intricate lung environment. This 3D model incorporated alveolar and vascular components that allow for a comprehensive exploration of lung physiology and responses to infection in vitro. In particular, we investigated the intricate role of ventilation on formation of the alveolar epithelial layer and its response to viral infections. In this regard, we subjected our 3D printed, perfused lung model to a continuous respiratory cycle at the air-liquid interface (ALI) for up to 10 days followed by infection with two viruses: influenza virus (Pr8) and respiratory syncytial virus (RSV), at two different concentrations for 24 or 48 h. The results revealed that ventilation induced increased tight-junction formation with better epithelial barrier function over time, facilitated higher expression of alveolar epithelial specific genes, enabled higher level of infection with an increased progression of viral spread and replication over time, and modulated the production of pro-inflammatory cytokines and chemokines. Our findings represent a critical step forward in advancing our understanding of lung-specific viral responses and respiratory infections in response to ventilation, which sheds light on vital aspects of pulmonary physiology and pathobiology.

bioengineering↗

bioRxiv2023

Protocol for primary human lung organoid-derived air-liquid interface in vitro model to study response to SARS-CoV-2

This article presents a comprehensive protocol for establishing primary human lung organoid-derived air-liquid interface (ALI) cultures from cryopreserved human lung tissue. These cultures serve as a physiologically relevant model to study human airway epithelium in vitro. The protocol encompasses lung tissue cryostorage, tissue dissociation, lung epithelial organoid generation, and ALI culture differentiation. It also demonstrates SARS-CoV-2 infection in these cultures as an example of their utility. Quality control steps, ALI characterization, and technical readouts for monitoring virus response are included in the study. For additional details on the use and execution of this protocol, please refer to Diana Cadena Castaneda et al (https://doi.org/10.1016/j.isci.2023.107374). HighlightsO_LIHuman lung tissue dissection, embedding in OCT blocks, and tissue cryopreservation. C_LIO_LIThawing & lung tissue dissociation for lung epithelium organoid generation. C_LIO_LIOrganoid-derived air-liquid-interface cultures for the study of viral infection. C_LIO_LIBulk RNA-Seq, flow cytometry, viral titer, and imaging to follow response to virus. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/557067v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@b47fbdorg.highwire.dtl.DTLVardef@2e6e60org.highwire.dtl.DTLVardef@508ac6org.highwire.dtl.DTLVardef@1c701a4_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2023

Spatiotemporally organized immunomodulatory response to SARS-CoV-2 virus in primary human broncho-alveolar epithelia

The COVID-19 pandemic continues to be a health crisis with major unmet medical needs. The early responses from airway epithelial cells, the first target of the virus regulating the progression towards severe disease, are not fully understood. Primary human air-liquid interface cultures representing the broncho-alveolar epithelia were used to study the kinetics and dynamics of SARS-CoV-2 variants infection. The infection measured by nucleoprotein expression, was a late event appearing between day 4-6 post infection for Wuhan-like virus. Other variants demonstrated increasingly accelerated timelines of infection. All variants triggered similar transcriptional signatures, an "early" inflammatory/immune signature preceding a "late" type I/III IFN, but differences in the quality and kinetics were found, consistent with the timing of nucleoprotein expression. Response to virus was spatially organized: CSF3 expression in basal cells and CCL20 in apical cells. Thus, SARS-CoV-2 virus triggers specific responses modulated over time to engage different arms of immune response.

immunology↗

bioRxiv2023

High-Throughput Bioprinting of the Nasal Epithelium using Patient-derived Nasal Epithelial Cells

Human nasal epithelial cells (hNECs) are an essential cell source for the reconstruction of the respiratory pseudostratified columnar epithelium composed of multiple cell types in the context of infection studies and disease modeling. Hitherto, manual seeding has been the dominant method for creating nasal epithelial tissue models. However, the manual approach is slow, low-throughput and has limitations in terms of achieving the intricate 3D structure of the natural nasal epithelium in a uniform manner. 3D Bioprinting has been utilized to reconstruct various epithelial tissue models, such as cutaneous, intestinal, alveolar, and bronchial epithelium, but there has been no attempt to use of 3D bioprinting technologies for reconstruction of the nasal epithelium. In this study, for the first time, we demonstrate the reconstruction of the nasal epithelium with the use of primary hNECs deposited on Transwell inserts via droplet-based bioprinting (DBB), which enabled high-throughput fabrication of the nasal epithelium in Transwell inserts of 24-well plates. DBB of nasal progenitor cells ranging from one-tenth to one-half of the cell seeding density employed during the conventional cell seeding approach enabled a high degree of differentiation with the presence of cilia and tight-junctions over a 4-week air-liquid interface culture. Single cell RNA sequencing of these cultures identified five major epithelial cells populations, including basal, suprabasal, goblet, club, and ciliated cells. These cultures recapitulated the pseudostratified columnar epithelial architecture present in the native nasal epithelium and were permissive to respiratory virus infection. These results denote the potential of 3D bioprinting for high-throughput fabrication of nasal epithelial tissue models not only for infection studies but also for other purposes such as disease modeling, immunological studies, and drug screening.

bioengineering↗

bioRxiv2021

Mice with a humanized immune system are resilient to transplantation of human microbiota

Human gut microbiota has co-evolved with human, and plays important roles in regulating the development and functioning of the host immune system. To study the human-specific microbiome-immunune interaction in an animal model is challenging as the animal model needs to capture both the human-specific immune functions and the human-specific microbiome composition. Here we combined two widely-used humanization procedures to generate a humanized mouse model (HMA-huCD34) with functional human leukocytes developed from engrafted huCD34+ cells and human fecal microbes introduced through fecal microbiota transplantation, and investigated how the two introduced human components interact. We found that the engrafted human leukocytes are resilient to the transplanted human microbes, while reciprocally the transplanted microbial community in the huCD34 mice was significantly different from mice without a humanized immune system. By tracking the colonization of human fecal Bacteroides strains in the mouse gut, we found that the composition of the strain population changes over time, the trajectory of which depends upon the type of mouse. On the other hand, different from Bacteroides, Akkermansia muciniphila exhibited consistent and rapid fixation of a single donor strain in all tested mice, suggesting strong purifying selection common to all mouse types. Our prospect study illustrated the complex interactions between the transplanted microbiome and different host factors, and suggested that the humanized mouse model may not faithfully reproduce the human-specific microbiome-immune interaction.

microbiology↗

bioRxiv2020

Long-read isoform sequencing reveals survival-associated splicing in breast cancer

Tumors display widespread transcriptome alterations, but the full repertoire of isoform-level alternative splicing in cancer is not known. We developed a long-read RNA sequencing and analytical platform that identifies and annotates full-length isoforms, and infers tumor-specific splicing events. Application of this platform to breast cancer samples vastly expands the known isoform landscape of breast cancer, identifying thousands of previously unannotated isoforms of which ~30% impact protein coding exons and are predicted to alter protein localization and function, including of the breast cancer-associated genes ESR1 and ERBB2. We performed extensive cross-validation with -omics data sets to support transcription and translation of novel isoforms. We identified 3,059 breast tumor-specific splicing events, including 35 that are significantly associated with patient survival. Together, our results demonstrate the complexity, cancer subtype-specificity, and clinical relevance of novel isoforms in breast cancer that are only annotatable by LR-seq, and provide a rich resource of immuno-oncology therapeutic targets.

genomics↗

bioRxiv2019

Calculating sample size for identifying cell subpopulation in single-cell RNA-seq experiments

SO_SCPLOWUMMARYC_SCPLOWSingle-cell RNA sequencing (scRNA-seq) is a rapidly developing technology for studying gene expression at the individual cell level and is often used to identify subpopulations of cells. Although the use of scRNA-seq is steadily increasing in basic and translational research, there is currently no statistical model for calculating the optimal number of cells for use in experiments that seek to identify cell subpopulations. Here, we have developed a statistical method ncells for calculating the number of cells required to detect a rare subpopulation in a homogeneous cell population for the given type I and II error. ncells defines power as the probability of separation of subpopulations which is calculated from three user-defined parameters: the proportion of rare subpopulation, proportion of up-regulated marker genes of the subpopulation, and levels of differential expression of the marker genes. We applied ncells to the scRNA-seq data on dendritic cells and monocytes isolated from healthy blood donor to show its efficacy in calculating the optimal number of cells in identifying a novel subpopulation.

bioinformatics↗

bioRxiv2019

A Transcriptome Fingerprinting Assay for Clinical Immune Monitoring

BackgroundWhile our understanding of the role that the immune system plays in health and disease is growing at a rapid pace, available clinical tools to capture this complexity are lagging. We previously described the construction of a third-generation modular transcriptional repertoire derived from genome-wide transcriptional profiling of blood of 985 subjects across 16 diverse immunologic conditions, which comprises 382 distinct modules.\n\nResultsHere we describe the use of this modular repertoire framework for the development of a targeted transcriptome fingerprinting assay (TFA). The first step consisted in down-selection of the number of modules to 32, on the basis of similarities in changes in transcript abundance and functional interpretation. Next down-selection took place at the level of each of the 32 modules, with each one of them being represented by four transcripts in the final 128 gene panel. The assay was implemented on both the Fluidigm high throughput microfluidics PCR platform and the Nanostring platform, with the list of assays target probes being provided for both. Finally, we provide evidence of the versatility of this assay to assess numerous immune functions in vivo by demonstrating applications in the context of disease activity assessment in systemic lupus erythematosus and longitudinal immune monitoring during pregnancy.\n\nConclusionsThis work demonstrates the utility of data-driven network analysis applied to large-scale transcriptional profiling to identify key markers of immune responses, which can be downscaled to a rapid, inexpensive, and highly versatile assay of global immune function applicable to diverse investigations of immunopathogenesis and biomarker discovery.

immunology↗