Biology subjects

Rodriguez, H.

Publications and source records attributed to Rodriguez, H..

4 recordsLinked to original sources

bioRxiv2025

Copy number variation in the genome of four South American camelid species

Copy number variation (CNV), a type of structural genomic variant, has been widely studied in humans, plants, and livestock. CNVs may contribute to phenotypic variation and traits of economic importance. Evaluating CNVs in domestic populations and their wild ancestors can reveal genetic changes associated with phenotypic differences that have emerged during domestication. In this study, whole-genome data from sixteen alpacas, six llamas, one guanaco, and one vicuna were used to investigate CNVs. A total of 4,247 CNVs were identified through genome-wide analysis. Functional analysis of Gene Ontology (GO) groups indicated that CNVs in alpacas are mainly related to immune-related genes, olfactory receptor genes, and keratinization. Species-level differences were observed, with the vicuna showing the least variation and the alpaca the most. This study provides the first CNV map for camelids and contributes to expanding knowledge of CNV diversity across the four South American camelid species.

genomics↗

bioRxiv2024

Mitochondrial dysfunction: unraveling the elusive biology behind anti-TNF response during ulcerative colitis

BackgroundRecent studies hint at mitochondrial genes influencing UC patient response to anti-TNF treatment. We evaluated this hypothesis by following a targeted strategy to identify gene expression that captures the relationship between mitochondrial dysregulation and response to treatment. Our objective was to initially examine this relationship in colon samples and subsequently assess whether the resulting signal persists in the bloodstream. MethodsWe analyzed the transcriptome of colon samples from an anti-TNF treated murine model characterized by impaired mitochondrial activity and treatment resistance. We then transferred the findings that linked mitochondrial dysfunction and compromised treatment response to an anti-TNF treated UC human cohort. We next matched differential expression in the blood using monocytes from peripheral blood of controls and IBD patients, and we evaluated a classification process at baseline with whole blood samples from UC patients. ResultsIn human colon samples, the derived gene-set from the murine model showed differential expression, primarily enriched metabolic pathways, and exhibited similar classification capacity as genes enriching inflammatory pathways. Moreover, the evaluation of the classification signal using blood samples from UC patients at baseline highlighted the involvement of mitochondrial homeostasis in treatment response. ConclusionOur results highlight the involvement of metabolic pathways and mitochondrial homeostasis in determining treatment response and their ability to provide promising classification signals with detection levels in both colon and bloodstream.

immunology↗

bioRxiv2024

ACKR3 Proximity Labeling Identifies Novel G protein- and β-arrestin-independent GPCR Interacting Proteins

The canonical paradigm of GPCR signaling recognizes G proteins and {beta}-arrestins as the two primary transducers that promote GPCR signaling. Recent evidence suggests the atypical chemokine receptor 3 (ACKR3) does not couple to G proteins, and {beta}-arrestins are dispensable for some of its functions. Here, we employed proximity labeling to identify proteins that interact with ACKR3 in cells devoid of {beta}-arrestin. We identified proteins involved in the endocytic machinery and evaluated a subset of proteins conserved across several GPCR-based proximity labeling experiments. We discovered that the bone morphogenic protein 2-inducible kinase (BMP2K) interacts with many different GPCRs with varying dependency on {beta}-arrestin. Together, our work highlights the existence of modulators that can act independently of G proteins and {beta}-arrestins to regulate GPCR signaling and provides important evidence for other targets that may regulate GPCR signaling. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=190 SRC="FIGDIR/small/577545v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@56a61org.highwire.dtl.DTLVardef@18b17baorg.highwire.dtl.DTLVardef@c2bd55org.highwire.dtl.DTLVardef@11c39b0_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2020

DreamAI: algorithm for the imputation of proteomics data

Deep proteomics profiling using labeled LC-MS/MS experiments has been proven to be powerful to study complex diseases. However, due to the dynamic nature of the discovery mass spectrometry, the generated data contain a substantial fraction of missing values. This poses great challenges for data analyses, as many tools, especially those for high dimensional data, cannot deal with missing values directly. To address this problem, the NCI-CPTAC Proteogenomics DREAM Challenge was carried out to develop effective imputation algorithms for labeled LC-MS/MS proteomics data through crowd learning. The final resulting algorithm, DreamAI, is based on an ensemble of six different imputation methods. The imputation accuracy of DreamAI, as measured by Pearson correlation, is about 15%-50% greater than existing tools among less abundant proteins, which are more vulnerable to be missed in proteomics data sets. This new tool notably enhances data analysis capabilities in proteomics research.

bioinformatics↗