Biology subjects

Alanis-Lobato, G.

Publications and source records attributed to Alanis-Lobato, G..

6 recordsLinked to original sources

bioRxiv2024

Marsupial single-cell transcriptomics provides an atlas of developmental heterochrony

Single-cell transcriptomics has revealed conserved and divergent programmes of organogenesis in mammals, but existing studies have focused on eutherians. Marsupials exhibit short gestation and complete development externally, necessitating accelerated differentiation of anterior features required for locomotion and feeding. As such, they represent a unique outgroup with which to understand temporal shifts in development, known as heterochrony. Here, we generate the first single-cell transcriptomic atlas of gastrulation and early organogenesis in a marsupial, the opossum Monodelphis domestica. We find that anterior prioritisation is achieved by earlier initiation and shorter duration of transcriptional programmes relative to eutherians. The result is uncoupling of transcriptional and morphological progression, revealing unforeseen diversity in the order of developmental sequences in mammals. We uncover novel tissues for which heterochrony has not previously been documented. Our findings indicate that accelerated activation of transcriptional programmes facilitates the rapid growth needed for survival of the marsupial neonate.

developmental biology↗

bioRxiv2023

MICA: A multi-omics method to predict gene regulatory networks in early human embryos

Recent advances in single-cell-omics have been transformative to characterise cell types in challenging to study biological contexts. It is technically difficult to infer transcription factor-gene regulatory interactions from these datasets, especially in contexts with limited single-cell sample number such as the early human embryo. Here we systematically assessed the application of four different linear or non-linear gene regulatory network (GRN) prediction strategies to single-cell simulated and human embryo transcriptome datasets. We also compared how gene expression normalisation methods impact on regulatory network predictions, finding that TPM (transcripts per million reads) outperformed alternative methods. We identified more reproducible GRN inferences using a non-linear method based on mutual information (MI) applied to single-cell transcriptome datasets refined with chromatin accessibility (CA) (called MICA), compared to alternative network prediction methods tested. MICA captures complex non-monotonic dependencies and feedback loops. Using MICA, we generated the first GRN inferences in early human development. MICA predicted co-localisation of the AP-1 transcription factor subunit proto-oncogene JUND and the TFAP2C transcription factor AP-2{psi} in human preimplantation embryos. These interactions were supported by previous findings in other developmental and stem cell contexts. Overall, our comparative analysis of gene regulatory network prediction methods defines a pipeline that can be applied to single-cell multi-omics datasets in especially challenging contexts to infer interactions between transcription factor expression and target gene regulation.

developmental biology↗

bioRxiv2022

Role of Yap1 in adult neural stem cell activation

Most adult hippocampal neural stem cells (NSCs) remain quiescent with only a minor portion undergoing active proliferation and neurogenesis. The molecular mechanisms that trigger eventually the transition from quiescence to activation are still poorly understood. Here, we found the activity of the transcriptional activator Yap1 to be enriched in active NSCs. Genetic deletion of Yap1 led to a significant reduction in the relative proportion of active NSCs supporting a physiological role of Yap1 in regulating the transition from quiescence to activation. Overexpression of wild type Yap1 in adult NSCs did not induce NSC activation suggesting tight upstream control mechanisms, but overexpression of a gain-of-function mutant (Yap1-5SA) elicited cell cycle entry in NSCs and hilar astrocytes. Consistent with a role of Yap1 in NSC activation, single cell RNA sequencing revealed the partial induction of an activated NSC gene expression program. Yet, Yap1-5SA expression also induced Taz and other key components of the Yap/Taz regulon previously identified in glioblastoma stem cell-like cells. Consequently, dysregulated Yap1 activity led to repression of hippocampal neurogenesis, promoting aberrant differentiation instead.

neuroscience↗

bioRxiv2020

Frequent loss-of-heterozygosity in CRISPR-Cas9-edited early human embryos

CRISPR-Cas9 genome editing is a promising technique for clinical applications, such as the correction of disease-associated alleles in somatic cells. The use of this approach has also been discussed in the context of heritable editing of the human germline. However, studies assessing gene correction in early human embryos report low efficiency of mutation repair, high rates of mosaicism and the possibility of unintended editing outcomes that may have pathologic consequences. We developed computational pipelines to assess single-cell genomics and transcriptomics datasets from OCT4 (POU5F1) CRISPR-Cas9-targeted and control human preimplantation embryos. This allowed us to evaluate on-target mutations that would be missed by more conventional genotyping techniques. We observed loss-of-heterozygosity in edited cells that spanned regions beyond the POU5F1 on-target locus, as well as segmental loss and gain of chromosome 6, on which the POU5F1 gene is located. Unintended genome editing outcomes were present in approximately 16% of the human embryo cells analysed and spanned 4 to 20kb. Our observations are consistent with recent findings indicating complexity at on-target sites following CRISPR-Cas9 genome editing. Our work underscores the importance of further basic research to assess the safety of genome editing techniques in human embryos, which will inform debates about the potential clinical use of this technology.

developmental biology↗

bioRxiv2017

The latent geometry of the human protein interaction network

To mine valuable information from the complex architecture of the human protein interaction network (hPIN), we require models able to describe its growth and dynamics accurately. Here, we present evidence that uncovering the latent geometry of the hPIN can ease challenging problems in systems biology. We embedded the hPIN to hyperbolic space, whose geometric properties reflect the characteristic scale invariance and strong clustering of the network. Interestingly, the inferred hyperbolic coordinates of nodes capture biologically relevant features, like protein age, function and cellular localisation. We also realised that the shorter the distance between two proteins in the embedding space, the higher their connection probability, which resulted in the prediction of plausible protein interactions. Finally, we observed that proteins can efficiently communicate with each other via a greedy routeing process, guided by the latent geometry of the hPIN. When analysed from the appropriate biological context, these efficient communication channels can be used to determine the core members of signal transduction pathways and to study how system perturbations impact their efficiency.

systems biology↗

bioRxiv2017

A reliable and unbiased human protein network with the disparity filter

The living cell operates thanks to an intricate network of protein interactions. Proteins activate, transport, degrade, stabilise and participate in the production of other proteins. As a result, a reliable and systematically generated protein wiring diagram is crucial for a deeper understanding of cellular functions. Unfortunately, current human protein networks are noisy and incomplete. Also, they suffer from both study and technical biases: heavily studied proteins (e.g. those of pharmaceutical interest) are known to be involved in more interactions than proteins described in only a few publications. Here, we use the experimental evidence supporting the interaction between proteins, in conjunction with the so-called disparity filter, to construct a reliable and unbiased proteome-scale human interactome. The application of a global filter, i.e. only considering interactions with multiple pieces of evidence, would result in an excessively pruned network. In contrast, the disparity filter preserves interactions supported by a statistically significant number of studies and does not overlook small-scale protein associations. The resulting disparity-filtered protein network covers 67% of the human proteome and retains most of the networks weight and connectivity properties.

bioinformatics↗