Biology subjects

Carmona, S.

Publications and source records attributed to Carmona, S..

9 recordsLinked to original sources

bioRxiv2026

KLRG1 identifies circulating cytotoxic CD4 T cells with selective anti-tumor function in human cancer

Given the critical role of CD4 T cells in anti-tumor immunity, strategies to harness these cells for cancer immunotherapy are gaining increasing interest. Historically overshadowed by CD8 T cells, cytotoxic CD4 T cells can directly kill MHC class II-expressing tumor cells. However, the defining molecular signature and the mechanisms underlying their cytolytic activity remain poorly understood, particularly in cancer patients. Here, using ex vivo single-cell transcriptomic and spatial analyses of CD4 T cells from paired blood and tumor samples of melanoma patients, we identified Killer Cell Lectin-Like Receptor G1 (KLRG1) as a defining surface marker of cytotoxic CD4 T cells. The CD4+ KLRG1+ T cell subset was notably enriched among circulating cells compared with tumor-infiltrating populations, which were instead enriched in T follicular helper (Tfh) states. Functionally, KLRG1+ CD4 T cells expressed elevated levels of cytotoxic genes and exhibited superior tumor-killing capacity compared with their KLRG1- counterparts. We demonstrated that their cytotoxicity is granulysin-dependent, as confirmed by CRISPR/Cas9-mediated gene deletion. Mechanistically, CD4 T cells spared MHC class II+ cells lacking the KLRG1 ligands CD324 and CD325, such as professional antigen-presenting cells (APCs), indicating that cytotoxicity was selectively directed towards tumor cells while preserving immune cells. Finally, by investigating how the tumor microenvironment may impair CD4 T cell cytotoxicity, we showed that tumor-derived factors, including interleukin-6 (IL-6), are key drivers promoting the transition of cytotoxic CD4 T cells toward a Tfh phenotype. In summary, our findings define KLRG1 as a defining cell surface marker of cytotoxic CD4 T cells in cancer patients, as well as a key regulator that protects MHC class II+ APCs. Moreover, targeting the IL-6 signalling pathway may enhance CD4 T cell anti-tumor cytotoxicity, offering new avenues for cancer immunotherapy.

immunology↗

bioRxiv2026

Cell type composition drives patient stratification in single-cell RNA-seq cohorts

Early transcriptomic studies demonstrated that unsupervised analysis of bulk gene expression can reveal clinically meaningful patient subgroups. Single-cell RNA sequencing (scRNA-seq) provides high-resolution characterization of cellular heterogeneity and therefore enables more refined patient stratification. Several computational approaches have been proposed to summarize single-cell data into sample-level representations for cohort-level exploratory analyses. However, these methods generally do not explicitly account for the compositional nature of cell-type proportions. Based on eleven scRNA-seq cohorts across different biological conditions, we evaluated several state-of-the-art sample representation methods for their ability to recover known biological groupings in an unsupervised setting. Surprisingly, we found that baseline approaches based on cell-type composition and pseudobulk gene expression consistently matched or outperformed more complex methods while requiring orders of magnitude fewer computational resources. In particular, centered log-ratio-transformed cell-type proportions achieved the highest stratification performance and demonstrated robustness to batch effects. The stratification signal was frequently concentrated in a small subset of highly variable cell types, and performance was robust across diverse cell type annotation strategies. Altogether, these results suggest that clinically relevant inter-sample variation in scRNA-seq cohorts is largely driven by differences in cell-type composition. Importantly, compositional representations directly link cohort-level structure to specific cell populations, enabling mechanistic interpretation and facilitating clinical translation. We provide scECODA, an open-source R package for scalable and interpretable cohort-level Exploratory COmpositional Data Analysis of scRNA-seq data, and establish cell-type compositional representations as a powerful and interpretable baseline for unsupervised patient stratification.

bioinformatics↗

bioRxiv2026

Sulcal Widening in Schizophrenia Maps onto Sulcal Hubs and Energy-Synaptic Genes

Schizophrenia (SZ) is increasingly framed as a disorder of large-scale brain networks emerging from atypical early neurodevelopment, yet how network architecture relates to cortical folding abnormalities remains unclear. Here, we introduce a sulcal morphological-centred network framework that integrates normative modelling of sulcal width with diffusion-derived structural connectivity and transcriptomic data in a large multisite cohort (n = 5,392; 377 SZ). Individuals with SZ showed widespread sulcal widening, affecting 30 of 40 sulci and most pronounced in frontal, temporal and occipital regions. Critically, sulci with higher degree centrality, reflecting greater embedding within the structural connectome, exhibited disproportionately greater widening in SZ (pspin = 0.02), indicating that network hubs of cortical folding are preferentially affected. Transcriptomic integration using partial least squares regression identified a single component explaining 56.5% of SZ-related sulcal widening variance (pperm = 0.041), implicating genes enriched for synaptic signalling and energy metabolism with adult cortical expression bias and genetic enrichment for cross-disorder psychiatric risk. In contrast, oppositely weighted genes showed prenatal expression bias and enrichment for rare disruptive variants in autism spectrum disorder. Together, these findings link aberrant sulcal morphology in SZ to the brains network topology and molecular architecture, positioning cortical folding as a network-embedded phenotype in SZ.

neuroscience↗

bioRxiv2024

Long-read transcriptomic identification of synaptic adaptation to amyloid pathology in the App(NL-G-F) knock-in mouse model of the earliest phase of Alzheimer's disease

Genome-wide association studies (GWAS) have identified a transcriptional network of Alzheimers disease (AD) risk genes that are primarily expressed in microglia and are associated with AD pathology. However, traditional short-read sequencers have limited our ability to fully characterize how GWAS variants exert their effects on gene expression regulation or alternative splicing in response to the pathology, particularly resulting in inaccurate detection of splicing. To address this gap, we utilized long-read RNA-sequencing (RNA-seq) in the AppNL-G-F knock-in mouse model to identify changes in splicing and novel transcript isoforms in response to amyloid-{beta}. We show that long-read RNA-seq can recapitulate the expected induction of microglial expressed risk genes such as Trem2 in response to amyloid-{beta} at 9 months of age associated with ageing-dependent deficiencies in spatial short-term memory in the AppNL-G-F knock-in mice. Our results not only identified novel splicing events and transcript isoform abundance in genes associated with AD, but also revealed the complex regulation of gene expression through splicing in response to amyloid plaques. Surprisingly, the regulation of alternative splicing in response to amyloid was seen in genes previously not identified as AD risk genes, expressed in microglia, neurons and oligodendrocytes, and included genes such as Syngr1 that modulate synaptic physiology. We saw alternative splicing in genes such as Ctsa, Clta, Dennd2a, Irf9 and Smad4 in mice in response to amyloid, and the orthologues of these genes also showed transcript usage changes in human AD brains. Our data suggests a model whereby induction of AD risk gene expression associated with microglial proliferation and activation is concomitant with alternative splicing in a different class of genes expressed by microglia and neurons, which act to adapt or preserve synaptic activity in response to amyloid-{beta} during early stages of the disease. Our study provides new insights into the mechanisms and effects of the regulation of genes associated with amyloid pathology, which may ultimately enable better disease diagnosis, and improved tracking of disease progression. Additionally, our findings identify new therapeutic avenues for treatment of AD.

genetics↗

bioRxiv2022

Estimation of Vertex-wise Sulcal Width Maps on Cortical Surfaces

Sulcal width, defined as the physical distance between opposing sulcal banks, has shown promise as a biomarker. We present the first method to obtain a vertex-wise representation of this metric directly on the brains cortical surface. The algorithm samples the surface at different depths and estimates the distances between the opposing sulcal banks. The method is validated against a simulated sulcus and compared to other sulcal width tools based on regions of interest, showing solid correlations between the proposed algorithm and two widely-used reference methods. A vertex-wise assessment of the sulcal width is carried out by evaluating the correlation between sulcal width and age in a sample of an aging population, revealing clusters in the central, cingulate, and temporal sulcus regions associating wider sulci for older participants. The results confirm that the algorithm is reliable for obtaining sulcal width maps, allowing for vertex-wise analyses, and providing aggregated measures similar to the existing methods. The present algorithm is publicly available via https://github.com/HGGM-LIM/SWiM.

neuroscience↗

bioRxiv2022

ABLE: Automated Brain Lines Extraction Based on Laplacian Surface Collapse

The archetypical folded shape of the human cortex has been a long-standing topic for neuroscientific research. Nevertheless, the accurate neuroanatomical segmentation of sulci remains a challenge. Part of the problem is the uncertainty of where a sulcus transitions into a gyrus and vice versa. This problem can be avoided by focusing on sulcal fundi and gyral crowns which represent the topological opposites of cortical folding. We present Automated Brain Lines Extraction (ABLE), a method based on Laplacian surface collapse to segment sulcal fundi and gyral crown lines reliably. ABLE is built to work on standard FreeSurfer outputs, and eludes the delineation of anastomotic sulci while maintaining sulcal fundi lines that traverse the regions with the highest depth and curvature. First, it segments the cortex into gyral and sulcal surfaces; then, each surface is spatially filtered. A Laplacian-collapse-based algorithm is then applied to obtain a thinned representation of the surfaces. This surface is then used for careful detection of the endpoints of the lines. Finally, sulcal fundi and gyral crown lines are obtained by eroding the surfaces while preserving the connectivity between the endpoints. The method is validated by comparing ABLE with three other sulcal extraction methods using the Human Connectome Project (HCP) test-retest database to assess the reproducibility of the different tools. The results confirm ABLE as a reliable method to obtain sulcal lines with an accurate representation of the sulcal topology while ignoring anastomotic branches and the overestimation of the sulcal fundi lines. ABLE is publicly available via https://github.com/HGGM-LIM/ABLE.

neuroscience↗

bioRxiv2019

Functional conservation of lncRNA JPX despite sequence and structural divergence

Long noncoding RNAs (lncRNAs) have been identified in all eukaryotes and are most abundant in the human genome. However, the functional importance and mechanisms of action for human lncRNAs are largely unknown. Using comparative sequence, structural, and functional analyses, we characterize the evolution and molecular function of human lncRNA JPX. We find that human JPX and its mouse homolog, lncRNA Jpx, have deep divergence in their nucleotide sequences and RNA secondary structures. Despite such differences, both lncRNAs demonstrate robust binding to CTCF, a protein that is central to Jpxs role in X chromosome inactivation. In addition, our functional rescue experiment using Jpx-deletion mutant cells, shows that human JPX can functionally complement the loss of Jpx in mouse embryonic stem cells. Our findings support a model for functional conservation of lncRNAs independent from sequence and structural changes. The study provides mechanistic insight into the evolution of lncRNA function.

molecular biology↗

bioRxiv2018

Building a National HIV Cohort from Routine Laboratory Data: Probabilistic Record-Linkage with Graphs

BackgroundChronic disease management requires the ability to link patient records across multiple interactions with the health sector. South Africas National Health Laboratory Service (NHLS) conducts all routine laboratory monitoring for the countrys national public sector HIV program. However, the absence of a validated patient identifier has limited the potential of the NHLS database for epidemiological research, policy evaluation, and longitudinal patient care. We developed and validated a record linkage algorithm, creating a unique patient identifier and enabling analysis of the NHLS database as a national HIV cohort. To our knowledge, this is the first national HIV cohort in any low-or middle-income country.\n\nMethods.We linked data on all CD4 counts, HIV viral loads (VL), and ART workup laboratory tests from 2004-2016. Each NHLS laboratory test result is associated with a name, sex, date of birth (DOB), gender, and facility. However, due to typographical and other errors and patient mobility between facilities, different patient specimens may be associated with different sets of identifying information. We developed a graph-based probabilistic record linkage algorithm and used it to construct a unique identifier for all patients with laboratory results in the national HIV program. We used standard probabilistic linkage methods with Jaro-Winkler string comparisons and weights informed by response frequency. We also used graph concepts to guide the linkage in determining whether a cluster of patient specimens could plausibly reflect a single patient. This approach allows matching thresholds to vary with the density of the network and limits over-matching.\n\nTo train and validate our approach, we constructed a quasi-gold standard based on manual review of 59,000 candidate matches associated with 1000 randomly sampled specimens. These data were divided into training and validation sets. Domain weights and graph parameters were optimized using the manually matched training data.\n\nTo evaluate performance, we calculated the probability that a true match was correctly identified by our algorithm (sensitivity, Sen) and the probability that a match identified by our algorithm was truly a match (positive predictive value, PPV) in the manually-matched data. We also assessed validity in the full cohort using proxies for under-and over-matching and assessed sensitivity vis-a-vis national identification numbers and patient folder numbers, which were available for a sub-set of records. We compared the performance of our algorithm for exact matching and a prior identifier that had been developed by the NHLS Corporate Data Warehouse.\n\nResults.As of December 2016, the NHLS database contained 117 million patient specimens with a CD4, VL, or other laboratory test used in HIV care. These specimens had 63 million unique combinations of patient identifying information. From these data, our matching algorithm identified 11.6 million unique HIV patients who had at least one CD4 count or VL result. These patients 70.9 million total specimens, with a median of 3 specimens per patient (IQR 1 to 8). Sensitivity and PPV of the algorithm were estimated to be 93.7% and 98.6% in manually-matched data, compared to 64.1% and 100.0% for the existing NHLS identifier. We estimated that in 2016 there were 3.35 million patients on ART and virologically monitored, similar to the National Department of Health estimate of 3.50 million.\n\nConclusion.We constructed a South African National HIV Cohort by applying novel graph-based probabilistic record linkage techniques to routinely collected laboratory data, with high sensitivity and positive predictive value. Information on graph structure can guide record linkage in large populations when identifying data are limited.

epidemiology↗

bioRxiv2018

Heritability and genetic variance of dementia with Lewy bodies

Recent large-scale genetic studies have allowed for the first glimpse of the effects of common genetic variability in dementia with Lewy bodies (DLB), identifying risk variants with appreciable effect sizes. However, it is currently well established that a substantial portion of the genetic heritable component of complex traits is not captured by genome-wide significant SNPs. To overcome this issue, we have estimated the proportion of phenotypic variance explained by genetic variability (SNP heritability) in DLB using a method that is unbiased by allele frequency or linkage disequilibrium properties of the underlying variants. This shows that the heritability of DLB is nearly twice as high as previous estimates based on common variants only (31% vs 59.9%). We also determine the amount of phenotypic variance in DLB that can be explained by recent polygenic risk scores from either Parkinsons disease (PD) or Alzheimers disease (AD), and show that, despite being highly significant, they explain a low amount of variance. Additionally, to identify pleiotropic events that might improve our understanding of the disease, we performed genetic correlation analyses of DLB with over 200 diseases and biomedically relevant traits. Our data shows that DLB has a positive correlation with education phenotypes, which is opposite to what occurs in AD. Overall, our data suggests that novel genetic risk factors for DLB should be identified by larger GWAS and these are likely to be independent from known AD and PD risk variants.

neuroscience↗