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Pablo, J.

Publications and source records attributed to Pablo, J..

3 recordsLinked to original sources

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↗

Probing Multiple Algorithms to Calculate Brain Age: Examining Reliability, Relations with Demographics, and Predictive Power

The calculation of so-called "brain age" has been an emerging biomarker in aging research. Data suggests that discrepancies between chronological age and the predicted age of the brain may be predictive of mortality and morbidity (for review, see Cole, Marioni, Harris, & Deary, 2019). However, with these promising results come technical complexities of how to calculate brain age. Various groups have deployed methods leveraging different statistical approaches, often crafting novel algorithms for assessing this biomarker. There remain many open questions about the reliability, collinearity, and predictive power of different algorithms. Here, we complete a rigorous systematic comparison of three commonly used, previously published brain age algorithms (XGBoost, brainageR, and DeepBrainNet) to serve as a foundation for future applied research. First, using multiple datasets with repeated MRI scans, we calculated two metrics of reliability (intraclass correlations and Bland-Altman bias). We then considered correlations between brain age variables, chronological age, biological sex, and image quality. We also calculated the magnitude of collinearity between approaches. Finally, we used canonical regression and machine learning approaches to identify significant predictors across brain age algorithms related to clinical diagnoses of mild cognitive impairment or Alzheimers Disease. Using a large sample (N=2557), we find all three commonly used brain age algorithms demonstrate excellent reliability (r>.9). We also note that brainageR and DeepBrainNet are reasonably correlated with one another, and that the XGBoost brain age is strongly related to image quality. Finally, and notably, we find that XGBoost brain age calculations were more sensitive to the detection of clinical diagnoses of mild cognitive impairment or Alzheimers Disease. We close this work with recommendations for future research studies focused on brain age.

neuroscience↗