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Teng, A.

Publications and source records attributed to Teng, A..

6 recordsLinked to original sources

Subtype-Specific SASP Dynamics Predict Mesenchymal Transition in Recurrent Glioblastoma

BackgroundTherapy-induced senescence is hypothesized to promote glioblastoma recurrence through senescence-associated secretory phenotype (SASP) factors, yet no longitudinal study has tracked SASP dynamics across matched primary-recurrent pairs. We used the GLASS consortium to characterize {Delta}SASP trajectories and their clinical implications. MethodsWe analyzed 167 matched primary-recurrent GBM pairs from the GLASS consortium. SASP scores were calculated from a 10-gene signature. Patients were classified by {Delta}SASP trajectory (Accumulators vs Clearers). We tested associations with proneural-to-mesenchymal transition (PMT) and validated findings in TCGA (n=52). ResultsSASP dynamics were subtype-specific: Mesenchymal tumors showed significant SASP clearance ({Delta}SASP = -0.83, p = 0.0001), while Classical tumors remained stable (p = 0.86). Among individual genes, HGF was the only factor that increased at recurrence ({Delta} = +0.23, p = 0.016), while IL1A, IL1B, and MMP9 decreased significantly. {Delta}SASP strongly predicted mesenchymal transition (OR = 5.06, 95% CI: 2.30-11.18, p < 0.0001), which itself conferred worse survival (HR = 2.48, p = 0.0001). Strikingly, Clearers were nearly completely protected from PMT (2.4% vs 27.3% in Accumulators, p = 0.0008). {Delta}SASP correlated with immunosuppressive infiltration ({rho} = 0.65 with M2 macrophages, p < 0.0001). Baseline SASP predicted worse survival (HR = 1.65, p < 0.0001), an effect that persisted in IDH-wildtype tumors (HR = 1.32, p = 0.015) and was validated in the CGGA cohort (HR = 1.35, p = 0.003). External validation confirmed the SASP-Mesenchymal association in both TCGA (d = 1.75) and CGGA (d = 1.14). ConclusionsSASP trajectories are heterogeneous and subtype-specific. Patients who fail to clear SASP represent a high-risk subgroup undergoing mesenchymal transition and immunosuppression. These Accumulators may be optimal candidates for senolytic therapy, while Clearers demonstrate effective senescence surveillance that could be therapeutically reinforced.

cancer biology↗

Boosting Transcript Assembly via Delineating Transcript Start and End Sites

Transcript assembly remains a challenging task despite the development of numerous methods. A major contributor to low assembly accuracy is the difficulty in accurately determining transcript start sites (TSSs) and end sites (TESs), due to the weak and noisy signals typically found in RNA-seq data. We present Telos, a two-stage machine learning framework for precise detection of TSSs and TESs and for transcript ranking. The method takes as input any assembly, typically generated by an existing assembler. In the first stage, Telos scores the TSSs and TESs in the input assembly using a machine learning model trained on a rich set of engineered features. These site-level scores will be passed to the second stage for transcript-level evaluation. In its second stage, Telos scores the entire transcripts by training another model that integrates features of their TSS and TES (including the inferred probabilities from the first stage), along with transcript abundance estimated by the assembler and statistics about exon lengths. We extensively evaluated Telos on ONT (cDNA and direct RNA), PacBio, and Illumina short-read RNA-seq datasets. In all cases, it consistently outperformed baseline methods. Telos is agile, but achieves substantial improvements, demonstrating the value of explicitly modeling TSS and TES, a gap in current transcript assembly tools. Telos can be paired with any assembler to accurately score the assembled transcripts. It is modular, easily extensible to emerging sequencing technologies, and hence we anticipate its broad adoption in transcriptomic studies.

bioinformatics↗

X-Atlas/Orion: Genome-wide Perturb-seq Datasets via a Scalable Fix-Cryopreserve Platform for Training Dose-Dependent Biological Foundation Models

The rapid expansion of massively parallel sequencing technologies has enabled the development of foundation models to uncover novel biological findings. While these have the potential to significantly accelerate scientific discoveries by creating AI-driven virtual cell models, their progress has been greatly limited by the lack of large-scale high-quality perturbation data, which remains constrained due to scalability bottlenecks and assay variability. Here, we introduce "Fix-Cryopreserve-ScRNAseq" (FiCS) Perturb-seq, an industrialized platform for scalable Perturb-seq data generation. We demonstrate that FiCS Perturb-seq exhibits high sensitivity and low batch effects, effectively capturing perturbation-induced transcriptomic changes and recapitulating known biological pathways and protein complexes. In addition, we release X-Atlas: Orion edition (X-Atlas/Orion), the largest publicly available Perturb-seq atlas. This atlas, generated from two genome-wide FiCS Perturb-seq experiments targeting all human protein-coding genes, comprises eight million cells deeply sequenced to over 16,000 unique molecular identifiers (UMIs) per cell. Furthermore, we show that single guide RNA (sgRNA) abundance can serve as a proxy for gene knockdown (KD) efficacy. Leveraging the deep sequencing and substantial cell numbers per perturbation, we also show that stratification by sgRNA expression can reveal dose-dependent genetic effects. Taken together, we demonstrate that FiCS Perturb-seq is an efficient and scalable platform for high-throughput Perturb-seq screens. Through the release of X-Atlas/Orion, we highlight the potential of FiCS Perturb-seq to address current scalability and variability challenges in data generation, advance foundation model development that incorporates gene-dosage effects, and accelerate biological discoveries.

genomics↗

A Novel Pan-Proteome Array for High-Throughput Profiling of the Humoral Response to Treponema pallidum subsp. pallidum: a Pre-Clinical Study

BackgroundGiven the resurgence of syphilis, research endeavors to improve current assays for serological diagnosis and management of this disease are a priority. A proteome-scale platform for high-throughput profiling of the humoral response to Treponema pallidum (T. pallidum) proteins during infection could identify antigens suitable to ameliorate the performance and capabilities of treponemal tests (TTs), which may require weeks to become positive following infection, cannot distinguish between active and previously treated infections, or assess treatment response. Additionally, because infection-induced immunity is partially protective, profiling the response to T. pallidum outer membrane proteins (OMPs) could help select vaccine candidates. MethodsWe developed a pan-proteome array (PPA) based on the Nichols and SS14 strain complete proteomes and used it to define the IgM and IgG humoral response to 1,009 T. pallidum proteins in sera collected longitudinally from long-term infected rabbits, and from rabbits that were infected, treated, and re-infected. FindingsApproximately a third of the pathogens proteome was recognized in infected animals, with a marked IgG response detectable between day-10 and day-20 post-infection. We found early, gradual, and late IgG kinetic profiles, strain-dependent differences in humoral reactivity, and post-treatment fluctuation in reactivity for several antigens. Very few antigens elicited an IgM response. Several OMPs were significantly and differentially recognized, but few elicited a robust response. InterpretationThe PPA allowed the identification of antigens that could facilitate early diagnosis and of a core set of OMP that could explain protection upon re-infection. No antigen appeared suitable to monitor treatment response. FundingNIH SBIR-R43AI149804 RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSIn April 2024, we searched the PubMed database for articles on preclinical studies using high throughput proteome arrays containing at least 10% of the predicted T. pallidum proteome that aimed at identifying antibody reactivity to T. pallidum antigens during experimental syphilis infection. We could retrieve only one manuscript. In this work, an array containing the T. pallidum partial proteome as annotated in the first sequenced Nichols strain genome (GCA_000008605.1) in 1998 was assembled using recombinant antigens expressed in Escherichia coli (E. coli). The resulting array was probed using pooled sera from three rabbits infected with the Nichols stain of T. pallidum, attained from infected animals at five time points following intratesticular infection. The small number of reactive antigens (n = 106) identified in this early study was likely to be an incomplete set of all antigens recognized during infection because not all the predicted targets in the T. pallidum proteome were successfully expressed and tested. In retrospect, additional limitations of the study included an initial suboptimal annotation of the Nichols genome used to define the pathogens proteome, which has now changed with the availability of a re-sequenced Nichols strain genome devoid of sequencing errors that affected the initial annotation process, and the refinement of bioinformatic pipelines for the identification of open reading frames (ORFs). Furthermore (as acknowledged by the authors), the possible presence of amplification errors in their expression clones might have affected the sequence of some protein targets and antibody binding to the targets. As a result, some of the T. pallidum antigens known to elicit a robust humoral response during experimental infection were not detected in this antigenic screen. Lastly, employing only the Nichols strain in this early study did not consider that a significant portion of the circulating syphilis strains belong to the SS14 clade of T. pallidum. Added value of this studyThis novel PPA, combined with a more robust experiential design than ever reported, allowed us to overcome most of the limitations associated with the study mentioned above, as we were able to a) use the most recent annotations for the selected T. pallidum strains based on accurate genome sequences, b) print the pathogens virtually complete proteome in the study array, c) analyze individual sera to account for rabbit-to-rabbit variability in the humoral response to infection rather than pooled sera, d) detect both IgM and IgG over 10 or 20 timepoints, depending on the experimental design, e) obtain information on how the humoral response evolved upon treatment and re-infection and, finally, f) evaluate all of the above in animals infected with two T. pallidum strains whose genetic background is representative of the two currently circulating clades of the syphilis agent. Implications of all the available evidenceOur study provides new and more comprehensive data on how humoral immunity for two classes of antibodies develops during infection and how it evolves in response to treatment and re-infection. The analysis of sera collected at tightly spaced time points post-inoculation and for an extensive period post-infection provides a wealth of information to improve the diagnostic performance of existing tests detecting treponemal antigens. The analysis of differential immunity specific to the pathogens putative OMPs provides a rationale for vaccine candidate selection.

microbiology↗

Age-Associated Weaker Immunity to Coronaviruses is Characteristic of Children that Develop Multisystem Inflammatory Syndrome following SARS-CoV-2 Infection

We analyzed the antibody and cytokine responses of twenty-three patients with multisystem inflammatory syndrome of children (MIS-C) that appeared with a three-to-six-week delay following a mild or asymptomatic SARS-CoV-2 infection. These responses were compared to healthy convalescent pediatric COVID-19 patients approximately twenty-eight days after the onset of symptoms. Both groups had strong IgG responses to SARS-CoV-2 spike (S) and nucleocapsid (N) proteins, but the MIS-C patients had weaker antibody responses to certain epitopes in the SARS-CoV-2 S and N proteins and to the S and N proteins of endemic human coronaviruses (HCoV) compared to pediatric convalescent COVID patients. HCoV antibody reactivity was correlated with age. In contrast, MIS-C patients had elevated serum levels of several proinflammatory cytokines compared to convalescent COVID patients, including interleukins IL-6, IL-8, IL-18 and chemokines CCL2, CCL8, CXCL5, CXCL9 and CXCL10 as well as tumor necrosis factor alpha and interferon gamma. Moreover, many cytokine responses of MIS-C patients were positively correlated with antibody responses to the SARS-CoV-2 S, N, membrane and ORF3a proteins while pediatric convalescent COVID patient cytokine responses were more often negatively correlated with antibody responses to the S, N and ORF3a proteins of SARS-CoV-2.

immunology↗

Biomedical Graph Visualizer for Identifying Drug Candidates

1.Millions of Americans suffer from illnesses with non-existent or ineffective drug treatment. Identifying plausible drug candidates is a major barrier to drug development due to the large amount of time and resources required; approval can take years when people are suffering now. While computational tools can expedite drug candidate discovery, these tools typically require programming expertise that many biologists lack. Though biomedical databases continue to grow, they have proven difficult to integrate and maintain, and non-programming interfaces for these data sources are scarce and limited in capability. This creates an opportunity for us to present a suite of user-friendly software tools to aid computational discovery of novel treatments through de novo discovery or repurposing. Our tools eliminate the need for researchers to acquire computational expertise by integrating multiple databases and offering an intuitive graphical interface for analyzing these publicly available data. We built a computational knowledge graph focused on biomedical concepts related to drug discovery, designed visualization tools that allow users to explore complex relationships among entities in the graph, and served these tools through a free and user-friendly web interface. We show that users can conduct complex analyses with relative ease and that our knowledge graph and algorithms recover approved repurposed drugs. Our evaluation indicates that our method provides an intuitive, easy, and effective toolkit for discovering drug candidates. We show that our toolkit makes computational analysis for drug development more accessible and efficient and ultimately plays a role in bringing effective treatments to all patients. Our application is hosted at: https://biomedical-graph-visualizer.wl.r.appspot.com/

bioinformatics↗