Biology subjects

Alvarez, G.

Publications and source records attributed to Alvarez, G..

5 recordsLinked to original sources

bioRxiv2023

casmini-tool: a comprehensive database for efficient and specific guide RNA design using dCasMINI

The dCasMINI protein is a hypercompact, nuclease-inactivated CRISPR-Cas system engineered for transcriptional modulation and epigenetic editing [Xu et al., 2021]. The small size of dCas-MINI (529 amino acids), less than half the size of comparable Cas9 molecules, makes it ideal for AAV-based therapies which are frequently limited by AAVs small cargo capacity. Unlike Cas9 or Cas12a, there are no available computational tools for designing dCasMINI guides. To facilitate and accelerate the development of dCasMINI-based applications, we synthesized knowledge regarding dCasMINI guide design and built a website to assist researchers in designing optimal guides for dCasMINI-based experiments for transcriptional inhibition (CRISPRi) and activation (CRISPRa); to ensure that our tool would be useful for therapeutic guide design, in which a guides off-target safety profile is of paramount importance, we specifically optimized alignment parameters for high-sensitivity to comprehensively report genome-wide off-targets. To investigate dCasMINIs full protospacer adjacent motif (PAM) profile, we engineered libraries of PAMs and exhaustively characterized dCasMINIs ability to activate a locus with different PAMs. We also experimentally investigated the importance of each nucleotide position on the guide RNAs ability to activate its target, and characterized a 6bp high-fidelity seed region at the 5 end of the protospacer sequence which we identified to be intolerant to mismatches and deletions, and thus critical for true binding events. Taken together, our tool offers CRISPRi/a guide design for every protein-coding gene in the human genome along with comprehensive off-target prediction, incorporating the most up-to-date information about dCasMINIs full PAM and protospacer design rules. The tool is freely available to use at www.casmini-tool.com.

bioengineering↗

bioRxiv2023

Scaling models of visual working memory to natural images

Over the last few decades, psychologists have developed precise quantitative models of human recall performance in visual working memory (VWM) tasks. However, these models are tailored to a particular class of artificial stimulus displays and simple feature reports from participants (e.g., the color or orientation of a simple object). Our work has two aims. The first is to build models that explain peoples memory errors in continuous report tasks with natural images. Here, we use image generation algorithms to generate continuously varying response alternatives that differ from the stimulus image in natural and complex ways, in order to capture the richness of peoples stored representations. The second aim is to determine whether models that do a good job of explaining memory errors with natural images also explain errors in the more heavily studied domain of artificial displays with simple items. We find that: (i) features taken from state-of-the-art deep encoders predict trial-level difficulty in natural images better than several reasonable baselines; and (ii) the same visual encoders can reproduce set-size effects and response bias curves in the artificial stimulus domains of orientation and color. Moving forward, our approach offers a scalable way to build a more generalized understanding of VWM representations by combining recent advances in both AI and cognitive modeling.

neuroscience↗

bioRxiv2022

Precision Discovery of Novel Inhibitors of Human Cancer Target HsMetAP1 from Vast Unexplored Metagenomic Diversity

Microbial natural products have long been a rich source of human therapeutics. While the chemical diversity encoded in the genomes of microbes is large, this modality has waned as fermentation-based discovery methods have suffered from rediscovery, inefficient scaling, and incompatibility with target-based discovery paradigms. Here, we leverage a metagenomic partitioning strategy to sequence soil microbiomes at unprecedented depth and quality. We then couple these data with target-focused, in silico search strategies and synthetic biology to discover multiple novel natural product inhibitors of human methionine aminopeptidase-1 (HsMetAP1), a validated oncology target. For one of these, metapeptin B, we demonstrate sub-micromolar potency, strong selectivity for HsMetAP1 over HsMetAP2 and elucidate structure-activity relationships. Our approach overcomes challenges of traditional natural product methods, accesses vast, untapped chemical diversity in uncultured microbes, and demonstrates computationally-enabled precision mining of modulators of human proteins.

microbiology↗

bioRxiv2022

Large-Scale Benchmarking of Diverse Artificial Vision Models in Prediction of 7T Human Neuroimaging Data

The rapid development and open-source release of highly performant computer vision models offers new potential for examining how different inductive biases impact representation learning and emergent alignment with the high-level human ventral visual system. Here, we assess a diverse set of 224 models, curated to enable controlled comparison of different model properties, testing their brain predictivity using large-scale functional magnetic resonance imaging data. We find that models with qualitatively different architectures (e.g. CNNs versus Transformers) and markedly different task objectives (e.g. purely visual contrastive learning versus vision-language alignment) achieve near equivalent degrees of brain predictivity, when other factors are held constant. Instead, variation across model visual training diets yields the largest, most consistent effect on emergent brain predictivity. Overarching model properties commonly suspected to increase brain predictivity (e.g. greater effective dimensionality; learnable parameter count) were not robust indicators across this more extensive survey. We highlight that standard model-to-brain linear re-weighting methods may be too flexible, as most performant models have very similar brain-predictivity scores, despite significant variation in their underlying representations. Broadly, our findings point to the importance of visual diet, challenge common assumptions about the methods used to link models to brains, and more concretely outline future directions for leveraging the full diversity of existing open-source models as tools to probe the common computational principles underlying biological and artificial visual systems.

neuroscience↗

bioRxiv2021

Revealing capture sites and movements by strontium isotope analyses in bones of Caiman yacare in the Beni river floodplain, Bolivia

Studying the distribution of organisms and their movements is fundamental to understand population dynamics. Most studies indicated that crocodilians do not move around much but several studies demonstrated that some species showed movement patterns. Detection of these movements along the individual life is still a challenge. In this study we analyzed the variation of strontium isotopic ratio (87Sr/86Sr) in the femur bones of 70 Caimanya care individuals caught in 16 sites located in five hydrological sectors of the Beni river floodplain in Bolivia. Our results demonstrated for the first time that such a methodology could yield indications about the capture sites and reconstruct individual life history. Analyses of the outer part of the femur of 70 individuals showed that capture sites could be differentiated between sectors and even between sites or groups of sites in each sector. Studies of complete 87Sr/86Sr profiles along the femur, representing the individuals entire life, were performed on 33 yacares. We found that most of the individuals did not show any significant isotopic variation throughout their lives. This absence of variation could result from a high fidelity to the birth site, and/or from an insignificant isotopic variation between the water bodies through which the animal has potentially moved. However, 24% of the analyzed individuals presented significant variations that can be considered as movements between different habitats. Based on the observed low proportion of moving yacares, we advocated that each water body should be considered an individual management unit.

ecology↗