Biology subjects

Avino, M.

Publications and source records attributed to Avino, M..

9 recordsLinked to original sources

bioRxiv2025

The Fragile X mental retardation protein (FMRP) coordinates an epigenetic checkpoint in neural progenitor cells

Fragile X syndrome (FXS) is a neurodevelopmental disorder caused by silencing of the FMR1 gene, which encodes the multifunctional RNA-binding protein FMRP. While FMRP is best known for its roles in RNA metabolism, it can also associate with chromatin through recognition of histone H3 lysine 79 di-methylation (H3K79me2), an epigenetic mark linked to transcriptionally active genes. However, the functional relevance of this FMRP-H3K79me2 interaction has remained largely unexplored in the context of FXS pathophysiology. We assessed H3K79me2 levels during the differentiation of induced pluripotent stem cells (iPSCs) generated from both healthy individuals and FXS patients and discovered a global increase in H3K79me2 levels specifically in FXS neural progenitor cells (NPCs). Altered H3K79me2 landscape drives widespread transcriptional dysregulation, characterized by reduced expression of neurogenesis-associated genes alongside aberrant activation of programs promoting proliferation and glial lineage commitment. Functionally, these changes result in enhanced NPCs proliferation and a biased differentiation trajectory. Notably, pharmacological reduction of global H3K79me2 levels in FXS NPCs using DOT1L inhibitors effectively mitigates these defects, restoring proliferation rate and rebalancing lineage specification, thereby rescuing key aspects of the pathological phenotype. Collectively, our findings identify an H3K79me2-dependent epigenetic barrier regulating NPC proliferation and lineage commitment and link its dysregulation to the neurodevelopmental defects associated with FXS.

neuroscience↗

bioRxiv2025

Ketogenic interventions prevent alterations of the gut microbiome in transgenic Alzheimer Disease mice

Alterations in the gut microbiome constitute a feature of aging and therefore represent a therapeutic target for aging-related diseases. In this study, we investigated the impact of ketogenic interventions on the microbiome of mice genetically predisposed to Alzheimers disease (AD). AD mice exhibited several microbial alterations, notably increased levels of Bifidobacterium and decreased levels of Bacteroidetes. Ketogenic interventions, either a medium-chain triglyceride-enriched diet (MCT) or carbohydrate-free high-fat diet (CFHF), administered for 1 month restored the levels of more than 50% of the bacteria altered in AD mice, including a strong reduction in Bifidobacterium levels. Ketogenic interventions induced a shift in the gut microbiome associated with increased levels of short-chain fatty acid-producing bacteria, such as Lachnospiraceae and Muribaculaceae. MCT and CFHF also triggered diet-specific microbial changes, which may contribute to the distinct physiological effects of these diets. In conclusion, ketogenic interventions may influence AD pathophysiology by modulating the gut microbiome.

neuroscience↗

bioRxiv2024

More is better: A simple antibody-based strategy for recovering all major mouse brain cell types from multiplexed single-cell RNAseq samples.

Single-cell RNA sequencing (scRNAseq) is a powerful yet costly technique for studying cellular diversity within the complexity of organs and tissues. Here, we sought to establish an effective multiplexing strategy for the adult mouse brain that could allow multiple experimental groups to be pooled into a single sample for sequencing, reducing costs, increasing data yield, and eliminating batch effects. We first describe an optimized cold temperature single-cell dissociation protocol that permits isolation of a high yield and viability of brain cells from the adult mouse. Cells isolated using this protocol were then screened by flow cytometry using a panel of antibodies, allowing identification of a single antibody, anti-Thy1.2, that can tag the vast majority of isolated mouse brain cells. We then used this primary antibody against a "universal" neural target, together with secondary antibodies carrying sample-specific oligonucleotides and the BD Rhapsody single-cell system and show that multiple adult mouse brain samples can be pooled into a single multiplexed run for scRNAseq. Bioinformatic analyses enable efficient demultiplexing of the sequenced pooled brain sample, with high tagging efficiency and precise annotation and clustering of brain cell populations. The efficiency and flexibility of the cell dissociation protocol and the two-step multiplexing strategy simplifies experimental design, optimizes reagent usage, eliminates sequencing batch effects and reduces overall experimental costs.

molecular biology↗

bioRxiv2023

Tracking the progression of Alzheimer's disease with peripheral blood monocytes

BackgroundAlzheimers disease (AD) is the most common form of dementia with the symptoms gradually worsening over the years. However, the driving pathological processes occur well before the appearance of symptoms. AD patients display signs of systemic inflammation, suggesting that it could precede the well-established AD hallmarks. We recently showed that the innate immune response in the form of monocyte activation is detectable at the pre-clinical stage. ObjectivesOur goal here is to characterize changes of gene expression in peripheral blood monocytes from patients at different stages of AD progression and validate potential biomarkers for a better prognosis and diagnosis of AD clinical spectrum. ResultsWe performed a whole transcriptome analysis on monocytes purified from healthy subjects, Mild Cognitive Impairment (MCI) and AD patients, and established the list of genes differentially expressed in monocytes during the disease evolution. We observed that, in the top 500 genes differentially expressed, a majority of these genes were upregulated (65%) during AD progression. These genes are mainly involved in chemokine/cytokine-mediated signaling pathways. We further confirmed several biomarkers by quantitative PCR and immunoblotting and showed that they are often deregulated at pre-clinical stages of the disease (MCI stage), supporting the hyperactivation of monocytes in MCI patients. PerspectivesOur findings provide evidence that the pre-clinical stage of AD can be detected in monocytes using a specific set of biomarkers, highlighting the importance to study the early innate immune response in AD. Our results open the possibility to use these biomarkers with different diagnostic methodologies to better predict and efficiently treat AD.

neuroscience↗

bioRxiv2023

Impact of in vivo cyclic reprogramming on the choroid plexus

In vivo reprogramming using the transient expression of Oct3/4, Sox2, Klf4 and c{square}Myc (OSKM) transcription factors can be used to induce tissue regeneration. A cyclic regime for short{square}term OSKM expression has been shown to promote regeneration of several organs however its impact on the brain remains largely unknown. We investigated the effects of a cyclic short-term OSKM expression on the choroid plexus (CP), a highly vascularized tissue found within the brain ventricles which is responsible for producing the cerebrospinal fluid (CSF). Transient reprogramming was done on 8-week-old mice carrying the polycistronic OSKM cassette under tetracycline operator (tetO) and confirmed the successful transient reprogramming. We then performed the analysis of the CP at cellular and molecular levels. The CP tissue exhibited minor morphological changes in height and area of epithelial cells. We did not observe any significant differences in the integrity of the brain-CSF barrier but noticed an increase of NKCC1 expression, a protein involved in CSF production. A whole transcriptome analysis (RNA-seq) was also carried on the tissue and showed no difference in gene expression after the transient reprogramming, at the exception of blood-related genes. Our results indicate that surprisingly the CP mainly remains insensible to in vivo transient reprogramming as only morphological and protein changes were observed in the tissue, suggesting that translational changes might be at stake during the reprogramming process but are not present at the transcriptomic level. Our results also highlight that more tailored strategies need to be developed for exploring the potential of CP reprogramming in regenerative medicine.

neuroscience↗

bioRxiv2019

Detection of novel HIV-1 drug resistance mutations by support vector analysis of deep sequence data and experimental validation

The global HIV-1 pandemic comprises many genetically divergent subtypes. Most of our understanding of drug resistance in HIV-1 derives from subtype B, which predominates in North America and western Europe. However, about 90% of the pandemic represents non-subtype B infections. Here, we use deep sequencing to analyze HIV-1 from infected individuals in Uganda who were either treatment-naive or who experienced virologic failure on ART without the expected patterns of drug resistance. Our objective was to detect potentially novel associations between mutations in HIV-1 integrase and treatment outcomes in Uganda, where most infections are subtypes A or D. We retrieved a total of 380 archived plasma samples from patients at the Joint Clinical Research Centre (Kampala), of which 328 were integrase inhibitor-naive and 52 were raltegravir (RAL)-based treatment failures. Next, we developed a bioinformatic pipeline for alignment and variant calling of the deep sequence data obtained from these samples from a MiSeq platform (Illumina). To detect associations between within-patient polymorphisms and treatment outcomes, we used a support vector machine (SVM) for feature selection with multiple imputation to account for partial reads and low quality base calls. Candidate point mutations of interest were experimentally introduced into the HIV-1 subtype B NL4-3 backbone to determine susceptibility to RAL in U87.CD4.CXCR4 cells. Finally, we carried out replication capacity experiments with wild-type and mutant viruses in TZM-bl cells in the presence and absence of RAL. Our analyses not only identified the known major mutation N155H and accessory mutations G163R and V151I, but also novel mutations I203M and I208L as most highly associated with RAL failure. The I203M and I208L mutations resulted in significantly decreased susceptibility to RAL (44.0-fold and 54.9-fold, respectively) compared to wild-type virus (EC50=0.32 nM), and may represent novel pathways of HIV-1 resistance to modern treatments.\n\nAuthor summaryThere are many different types of HIV-1 around the world. Most of the research on how HIV-1 can become resistant to drug treatment has focused on the type (B) that is the most common in high-income countries. However, about 90% of infections around the world are caused by a type other than B. We used next-generation sequencing to analyze samples of HIV-1 from patients in Uganda (mostly infected by types A and D) for whom drug treatment failed to work, and whose infections did not fit the classic pattern of adaptation based on B. Next, we used machine learning to detect mutations in these virus populations that could explain the treatment outcomes. Finally, we experimentally added two candidate mutations identified by our analysis to a laboratory strain of HIV-1 and confirmed that they conferred drug resistance to the virus. Our study reveals new pathways that other types of HIV-1 may use to evolve resistance to drugs that make up the current recommended treatment for newly diagnosed individuals.

microbiology↗

bioRxiv2018

Tree Shape-based approaches for the Comparative study of Cophylogeny

Cophylogeny is the congruence of phylogenetic relationships between two different groups of organisms due to their long-term interaction, such as between host and pathogen species. Discordance between host and pathogen phylogenies may occur due to pathogen host-switch events, pathogen speciation within a host species, and extinction. Here, we investigated the use of tree shape distance measures to quantify the degree of cophylogeny for the comparative analysis of host-pathogen interactions across taxonomic groups.\n\nWe firstly implemented a coalescent model to simulate pathogen phylogenies within a fixed host tree, given the cospeciation probability, migration rate between hosts, and pathogen speciation rate within hosts.\n\nNext, we used simulations from this model to evaluate 13 distance metrics between these trees and the host tree, including Robinson-Foulds distance and two kernel distances that we developed for labeled and unlabeled trees, which use branch lengths and can accommodate trees of different sizes. Finally, we used these distance metrics to revisit actual datasets from published cophylogenetic studies across all taxonomic groups, where authors described the observed associations as representing a high or low degree of cophylogeny.\n\nOur simulation analyses demonstrated that some metrics are more informative than others with respect to specific coevolution parameters. For example, the Sim metric was the most responsive to variation in coalescence rates, whereas the unlabeled kernel metric was the most responsive to cospeciation probabilities. We also determined that distance metrics were more informative about the model parameters when the underlying parameter values did not assume extreme values, e.g., rapid host switching. When applied to real datasets, projection of these trees associations into a parameter space defined by the 13 distance metrics revealed some clustering of studies reporting low concordance. This suggested that different investigators are describing concordance in a consistent way across biological systems, and that these expert subjective assessments can be at least partly quantified using distance metrics.\n\nOur results support the hypothesis that tree distance measures can be useful for quantifying host and pathogen cophylogeny. This motivates the usage of distance metrics in the field of coevolution and supports the development of simulation-based methods, i.e., approximate Bayesian computation, to estimate coevolutionary parameters from the discordant shapes of host and pathogen trees. [tree shape; cophylogeny; codivergence; coevolution; host switching; tree metrics; kernel]

evolutionary biology↗

bioRxiv2018

Evidence for a Recombinant Origin of HIV-1 group M from Genomic Variation

Reconstructing the early dynamics of the HIV-1 pandemic can provide crucial insights into the socioeconomic drivers of emerging infectious diseases in human populations, including the roles of urbanization and transportation networks. Current evidence indicates that the global pandemic comprising almost entirely of HIV-1/M originated around the 1920s in central Africa. However, these estimates are based on molecular clock estimates that are assumed to apply uniformly across the virus genome. There is growing evidence that recombination has played a significant role in the early history of the HIV-1 pandemic, such that different regions of the HIV-1 genome have different evolutionary histories. In this study, we have conducted a dated-tip analysis of all near full-length HIV-1/M genome sequences that were published in the GenBank database. We used a sliding window approach similar to the bootscanning method for detecting breakpoints in intersubtype recombinant sequences. We found evidence of substantial variation in estimated root dates among windows, with an estimated mean time to the most recent common ancestor (tMRCA) of 1922. Estimates were significantly autocorrelated, which was more consistent with an early recombination event than with stochastic error variation in phylogenetic reconstruction and dating analyses. A piecewise regression analysis supported the existence of at least one recombination breakpoint in the HIV-1/M genome with interval-specific means around 1929 and 1913, respectively. This analysis demonstrates that a sliding window approach can accommodate early recombination events outside the established nomenclature of HIV-1/M subtypes, although it is difficult to incorporate the earliest available samples due to their limited genome coverage.

evolutionary biology↗

bioRxiv2018

An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes

For many disease-causing virus species, global diversity is clustered into a taxonomy of subtypes with clinical significance. In particular, the classification of infections among the subtypes of human immunodeficiency virus type 1 (HIV-1) is a routine component of clinical management, and there are now many classification algorithms available for this purpose. Although several of these algorithms are similar in accuracy and speed, the majority are proprietary and require laboratories to transmit HIV-1 sequence data over the network to remote servers. This potentially exposes sensitive patient data to unauthorized access, and makes it impossible to determine how classifications are made and to maintain the data provenance of clinical bioinformatic workflows. We propose an open-source supervised and alignment-free subtyping method (KO_SCPCAPAMERISC_SCPCAP) that operates on k-mer frequencies in HIV-1 sequences. We performed a detailed study of the accuracy and performance of subtype classification in comparison to four state-of-the-art programs. Based on our testing data set of manually curated real-world HIV-1 sequences (n = 2, 784), Kameris obtained an overall accuracy of 97%, which matches or exceeds all other tested software, with a processing rate of over 1,500 sequences per second. Furthermore, our fully standalone general-purpose software provides key advantages in terms of data security and privacy, transparency and reproducibility. Finally, we show that our method is readily adaptable to subtype classification of other viruses including dengue, influenza A, and hepatitis B and C virus.

bioinformatics↗