Biology subjects

Chaussabel, D.

Publications and source records attributed to Chaussabel, D..

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

From Home to Transcriptome: Comparing the transcriptomic profile of induced immune response via lipopolysaccharide stimulation in homeRNA and venous blood

Remote blood sampling offers multiple advantages over traditional clinic-based blood sampling studies, including greater patient inclusion, more frequent sampling, and broader geographical reach. Combining remote blood sampling with transcriptomic analysis opens potential in translational applications for capturing acute and dynamic immune responses to various exposures. In this study, we establish the feasibility of homeRNA, a capillary blood collection and RNAlater-based stabilization kit, for use in downstream bulk RNA-sequencing applications via capturing a lipopolysaccharide (LPS)-induced inflammatory response. We also compared the baseline gene expression profiles and induced inflammatory response following LPS stimulation between homeRNA-stabilized samples and venous blood stabilized with RNAlater or PAXgene. We found that homeRNA was successfully able to capture an inflammatory response to LPS, specifically targeting various cytokines (e.g., IL6, IL12B, IL1B), chemokines (e.g., CCL3, CXCL10, CCL4), and other transcriptional factors in the toll-like receptor pathway, the primary pathway activated during LPS stimulation. Importantly, we also found that homeRNA captured a LPS-induced inflammatory response comparable to that of venous blood samples stabilized with either RNAlater or PAXgene. Overall, this work demonstrates that the homeRNA platform is compatible with downstream bulk RNA-sequencing analysis and can capture transcriptomic immune responses to a known stimulus which are analogous to results in traditional stabilized venous blood samples.

bioengineering↗

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

Temporal Dynamics of Transcriptional Responses to Repeated mRNA Vaccination: Insights from Third Dose Profiling

mRNA vaccines have played a crucial role in combating the COVID-19 pandemic, but the long-term dynamics of immune responses to repeated vaccination remain poorly understood. In this study, we extend our previous work on first and second dose responses by characterizing the immune signatures elicited by a third dose of COVID-19 mRNA vaccines using high-resolution temporal profiling of blood transcriptomes collected daily for 9 days post-vaccination. We observed distinct patterns of gene expression related to interferon responses, inflammation, erythroid cell signatures, and plasmablast activity across the three doses. While the first dose elicited a modest response primarily characterized by interferon signaling, the second dose induced a robust, polyfunctional response. The third dose, administered approximately nine months later, maintained this polyfunctional character and matched the second dose in magnitude, though with distinct temporal dynamics. The interferon component peaked on day 2 (similar to the first dose) rather than day 1 (as seen in the second dose), while the erythroid signature showed a markedly different trajectory, with sustained elevation rather than decrease over the following week. Notably, we observed a progressive amplification of the plasmablast response across the three doses, with an earlier peak (day 4) compared to other vaccines, potentially a unique feature of mRNA vaccines. These findings demonstrate that the heightened, polyfunctional responsiveness induced by the second dose is robustly maintained even after a prolonged interval, suggesting effective immune memory. Our results contribute to understanding mRNA vaccine-induced immunity, with implications for optimizing booster strategies and developing next-generation vaccines.

immunology↗

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↗

bioRxiv2024

Tackling the Complexity of Spatial Transcriptomics Data Interpretation with Large Language Models

Spatial transcriptomics offers unprecedented insights into the complex cellular landscapes of tissues, particularly in cancer research where understanding the tumor microenvironment is crucial. However, interpreting the vast and intricate data generated by this technology remains a significant challenge. This study explores the potential of Large Language Models (LLMs) to assist in the analysis and interpretation of spatial transcriptomic data from a murine melanoma tumor model. We first evaluated the performance of multiple LLM models in describing and quantifying spatial gene expression patterns. Our benchmarking revealed that spatial transcriptomics data interpretation proved challenging for most models, with only a few demonstrating sufficient capability for this complex task. Using Claude 3.5 Sonnet, which showed the highest accuracy in spot quantification and pattern recognition, we developed a systematic workflow for analyzing the tumor immune landscape. The model first assisted in identifying and prioritizing panels of M1 and M2 macrophage-associated markers through knowledge-driven scoring. It then demonstrated remarkable ability to integrate spatial expression data with extensive immunological knowledge, providing sophisticated interpretation of local immune organization. When analyzing individual tumor regions, the model identified coordinated immunosuppressive mechanisms including metabolic barriers and disrupted pro-inflammatory signaling cascades, findings that both aligned with and extended current understanding of tumor immunology. This study highlights the potential of LLMs as powerful assistive tools in spatial transcriptomics analysis, capable of combining advanced pattern recognition with extensive knowledge integration to enhance data interpretation. While significant development work remains to make such workflows scalable, our proof of concept demonstrates that LLMs can help accelerate the translation of spatial transcriptomics data into biological insights.

cancer biology↗

bioRxiv2024

Your Blood is Out for Delivery: Considerations of Shipping Time and Temperature on Degradation of RNA from Stabilized Whole Blood

Remote research studies are an invaluable tool for reaching populations in geographical regions with limited access to large medical centers or universities. To expand the remote study toolkit, we have previously developed homeRNA, which allows for at-home self-collection and stabilization of blood and demonstrated the feasibility of using homeRNA in high temperature climates. Here, we expand upon this work through a systematic study exploring the effects of high temperature on RNA integrity through in-lab and field experiments. Compared to the frozen controls (overall mean RIN of 8.2, n = 8), samples kept at 37{degrees}C for 2, 4, and 8 days had mean RINs of 7.6, 5.9, and 5.2 (n = 3), respectively, indicating that typical shipping conditions ([~]2 days) yield samples suitable for downstream RNA sequencing. Shorter time intervals (6 hours) resulted in minimal RNA degradation (median RIN of 6.4, n = 3) even at higher temperatures (50{degrees}C) compared to the frozen control (mean RIN of 7.8, n = 3). Additionally, we shipped homeRNA-stabilized blood from a single donor to 14 different states and back during the summer with continuous temperature probes (7.1 median RIN, n = 42). Samples from all locations were analyzed with 3 mRNA-seq to assess differences in gene counts, with the transcriptomic data suggesting that there was no preferential degradation of transcripts as a result of different shipping times, temperatures, and regions. Overall, our data support that homeRNA can be used in elevated temperature conditions, enabling decentralized sample collection for telemedicine, global health, and clinical research.

bioengineering↗

bioRxiv2024

Biological modifications of the immune response to COVID-19 vaccine in patients treated with anti-CD20 agents and immune-checkpoint inhibitors

Investigating the impact of immune-modulating therapies on mRNA vaccine efficacy transcends the immediate context of the COVID-19 pandemic. This study focuses on the differential immune responses to the third dose of COVID-19 mRNA vaccine among healthy volunteers, cancer patients treated with immune-checkpoint inhibitors (ICIs), and those treated with the anti-CD20 antibody rituximab. Utilizing RNA sequencing, serology, and interferon-{gamma} release assessment, we charted the temporal dynamics of the immune response in such cohorts. Our findings indicate that ICIs maintain an immune profile similar to that of healthy individuals, whereas treatment with rituximab is associated with impairment of type I interferon response and the upregulation of transcripts pertaining to regulatory T cells, with a global dysfunction of both humoral and cellular immunity. This research deepens our understanding of the sophisticated interplay within the immune system in health and disease states, potentially informing therapeutic strategies across a spectrum of immunological conditions. Significance statementOur study examines how cancer treatments that modify the immune system affect transcriptional, serological, and cellular responses to a model for repeated antigenic stimulation in humans, represented by the SARS-CoV-2 booster vaccine. Specifically, we investigated patients treated with rituximab (RTX), which impairs antibody production, and immune checkpoint inhibitors (ICI), which can trigger autoimmune disorders. We discovered that RTX-treated patients not only exhibit a reduced antibody response but actually show a diminished interferon-mediated immune response, indicating a broader immune disruption than anticipated. Conversely, ICI-treated patients responded to the vaccine similarly to healthy individuals, suggesting that fears of adverse vaccine reactions in these patients may be unfounded. This research highlights important considerations for the clinical management of cancer patients receiving these treatments.

immunology↗

bioRxiv2024

IL-3-driven T cell-basophil crosstalk enhances anti-tumor immunity

Cytotoxic T lymphocytes (CTLs) are pivotal in combating cancer, yet their efficacy is often hindered by the immunosuppressive tumor microenvironment, resulting in exhaustion. This study investigates the role of interleukin (IL)-3 in orchestrating anti-tumor immunity through CTL modulation. Intratumoral CTLs undergo a progressive decline in IL-3 production, which is correlated with impaired cytotoxic function. Augmenting IL-3, through intraperitoneal administration, IL-3-expressing melanoma cells, or IL-3-engineered CD8+ T cells, confers protection against tumor progression, concomitant with increased CTL activity. CTLs are critical in this therapeutic efficacy as IL-3 demonstrates no impact on tumor growth in RAG1 knockout mice or following CD8+ T cell-depletion. Rather than acting directly, CTL-derived IL-3 exerts its influence on basophils, synergistically amplifying anti-tumor immunity within CTLs. Introducing IL-3-activated basophils retards tumor progression, whereas basophil depletion diminishes the effectiveness of IL-3 supplementation. Furthermore, IL-3 prompts basophils to produce IL-4, which subsequently elevates IFN-{gamma} production and viability of CTLs. Notably, the importance of basophil-derived IL-4 is evident from the absent benefits in IL-3-supplementated, IL-4 knockout tumor-bearing mice. Overall, this research unveils IL-3-mediated CTL-basophil crosstalk in regulating anti-tumor immunity and offers the prospect of harnessing IL-3 sustenance as a promising approach for optimizing and enhancing cancer immunotherapy. Significance StatementThis study elucidates the critical role of IL-3 in orchestrating anti-tumor immunity, particularly within the context of CTLs and melanoma growth. It reveals a progressive decline in CTL-derived IL-3 during tumor progression, correlated with CTL exhaustion--a formidable barrier in cancer immunotherapy. Intriguingly, augmentation of IL-3, achieved through diverse means, effectively impedes tumor progression by enhancing CTL activity. This research unveils a novel mechanism: IL-3-mediated crosstalk between CTLs and IL-4-producing basophils, resulting in the rejuvenation of CTLs and amplifying their anti-tumor ability. These insights hold promise for the advancement and optimization of cancer immunotherapeutic strategies, deepening our comprehension of CTL dynamics within the tumor microenvironment, and advancing our ability to combat cancer effectively.

immunology↗

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↗

bioRxiv2022

At-home blood collection and stabilization in high temperature climates using homeRNA

Expanding whole blood sample collection for transcriptome analysis beyond traditional phlebotomy clinics will open new frontiers for remote immune research and telemedicine. Determining the stability of RNA in blood samples exposed to high ambient temperatures (>30{degrees}C) is necessary for deploying home-sampling in settings with elevated temperatures (e.g., studying physiological response to natural disasters that occur in warm locations or in the summer). Recently, we have developed homeRNA, a technology that allows for self-blood sampling and RNA stabilization remotely. homeRNA consists of a lancet-based blood collection device, the Tasso-SST which collects up to 0.5 mL of blood from the upper arm, and a custom-built stabilization transfer tube containing RNAlater. In this study, we investigated the robustness of our homeRNA kit in high temperature settings via two small pilot studies in Doha, Qatar (no. participants = 8), and the Western and South Central USA during the summer of 2021, which included a heatwave of unusually high temperatures in some locations (no. participants = 11). Samples collected from participants in Doha were subjected to rapid external temperature fluctuations from being moved to and from air-conditioned areas and extreme heat environments (up to 41{degrees}C external temperature during brief temperature spikes). In the USA pilot study, regions varied in outdoor temperature highs (between 25{degrees}C and 43.4{degrees}C). All samples that returned a RNA integrity number (RIN) value from the Doha, Qatar group had a RIN [≥]7.0, a typical integrity threshold for downstream transcriptomics analysis. RIN values for the Western and South Central USA samples (n=12 samples) ranged from 6.9-8.7 with 9 out of 12 samples reporting RINs [≥]7.0. Overall, our pilot data suggest that homeRNA can be used in some regions that experience elevated temperatures, opening up new geographical frontiers in disseminated transcriptome analysis for applications critical to telemedicine, global health, and expanded clinical research. Further studies, including our ongoing work in Qatar, USA, and Thailand, will continue to test the robustness of homeRNA.

bioengineering↗

bioRxiv2021

High temporal resolution transcriptomic profiling delineates distinct patterns of interferon response following Covid-19 mRNA vaccination and SARS-CoV2 infection

Knowledge of the mechanisms underpinning the development of protective immunity conferred by mRNA vaccines is fragmentary. Here we investigated responses to COVID-19 mRNA vaccination via ultra-low-volume sampling and high-temporal-resolution transcriptome profiling (23 subjects across 22 timepoints, and with 117 COVID-19 patients used as comparators). There were marked differences in the timing and amplitude of the responses to the priming and booster doses. Notably, we identified two distinct interferon signatures. The first signature (A28/S1) was robustly induced both post-prime and post-boost and in both cases correlated with the subsequent development of antibody responses. In contrast, the second interferon signature (A28/S2) was robustly induced only post-boost, where it coincided with a transient inflammation peak. In COVID19 patients, a distinct phenotype dominated by A28/S2 was associated with longer duration of intensive care. In summary, high-temporal-resolution transcriptomic permitted the identification of post- vaccination phenotypes that are determinants of the course of COVID-19 disease.

immunology↗

bioRxiv2020

Blood transcriptional module repertoire analysis and visualization using R

Transcriptional modules have been widely used for the analysis, visualization and interpretation of transcriptome data. We have previously described the construction and characterization of generic and reusable blood transcriptional module repertoires. The third and latest version that we have recently made available comprises 382 functionally annotated gene sets (modules) and encompasses 14,168 transcripts. We developed R scripts for performing module repertoire analyses and custom fingerprint visualization. These are made available here along with detailed descriptions. An illustrative public transcriptome dataset and corresponding intermediate output files are also included as supplementary material. Briefly, the steps involved in module repertoire analysis and visualization include: First, the annotation of the gene expression data matrix with module membership information. Second, running of statistical tests to determine for each module the proportion of its constitutive genes which are differentially expressed. Third, the results are expressed "at the module level" as percent of genes increased or decreased and plotted in a fingerprint grid format. A parallel workflow has been developed for computing module repertoire changes for individual samples rather than groups of samples. Such results are plotted in a heatmap format. The use case that is presented illustrates the steps involved in the generation of blood transcriptome repertoire fingerprints of septic patients at both group and individual levels.

bioinformatics↗

bioRxiv2020

A modular framework for the development of targeted Covid-19 blood transcript profiling panels

Covid-19 morbidity and mortality are associated with a dysregulated immune response. Tools are needed to enhance existing immune profiling capabilities in affected patients. Here we aimed to develop an approach to support the design of focused blood transcriptome panels for profiling the immune response to SARS-CoV-2 infection. We designed a pool of candidates based on a pre-existing and well-characterized repertoire of blood transcriptional modules. Available Covid-19 blood transcriptome data was also used to guide this process. Further selection steps relied on expert curation. Additionally, we developed several custom web applications to support the evaluation of candidates. As a proof of principle, we designed three targeted blood transcript panels, each with a different translational connotation: therapeutic development relevance, SARS biology relevance and immunological relevance. Altogether the work presented here may contribute to the future expansion of immune profiling capabilities via targeted profiling of blood transcript abundance in Covid-19 patients.

immunology↗

bioRxiv2020

Pazopanib induces dramatic but transient contraction of myeloid suppression compartment in favor of adaptive immunity

Anti-angiogenic tyrosine-kinase inhibitors (TKIs) and immune checkpoint blockade (ICB) constitute the backbone of metastatic renal cell carcinoma (mRCC) treatment. The development of the optimal combinatorial or sequential approach is hindered by the lack of comprehensive data regarding TKI-induced immunomodulation and its kinetics. Through the use of orthogonal transcriptomic and phenotyping platforms combined with functional analytic pipelines, we demonstrated that the anti-angiogenic TKI pazopanib induces a dramatic and coherent reshaping of systemic immunity in mRCC patients, downsizing the myeloid-derived suppressor cell (MDSC) compartment in favor of a strong enhancement of cytotoxic T and Natural Killer (NK) cell effector functions. The intratumoral expression level of a MDSC signature here generated was strongly associated with poor prognosis in mRCC patients. The marked but transient nature of this immunomodulation, peaking at the 3rd month of treatment, provides the rationale for the use of TKIs as a preconditioning strategy to improve the efficacy of ICB.

cancer biology↗

bioRxiv2020

Multi-organ transcriptomic profiling reveals differential regulation of the immune system among the brain-liver-fats organ network of short-term fasted mice

Different fasting regimens are known to promote health, mitigate chronic immunological disorders, and improve age-related pathophysiological parameters in animals and humans. Indeed, several clinical trials are currently ongoing using fasting as a potential therapy for a wide range of conditions. Fasting alters metabolism by acting as a reset for energy homeostasis. However, the molecular mechanisms underlying the beneficial effects of short-term fasting (STF) are still not well understood, particularly at the systems or multi-organ level. Here, we investigated the dynamic gene expression patterns associated with six periods of STF in nine different mouse organs. We cataloged the transcriptional dynamics within and between organs during STF and discovered differential temporal effects of STF among organs. Using gene ontology enrichment analysis, we identified an organ network sharing 37 common biological pathways perturbed by STF. This network incorporates the brain, liver, interscapular brown adipose tissue, and posterior-subcutaneous white adipose tissue, hence we named it the brain-liver-fats organ network. Using Reactome pathways analysis, we identified the immune system, dominated by T cell regulation processes, as a central and prominent target of systemic modulations during STF in this organ network. The changes we identified in specific immune components point to the priming of adaptive immunity and parallel the fine-tuning of innate immune signaling. Our study provides a comprehensive multi-organ transcriptomic profiling of mice subjected to multiple periods of STF, and adds new insights into the molecular modulators involved in the systemic immuno-transcriptomic changes that occur during short-term energy loss.

systems biology↗