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Journal of Cell Science Jun 2024Some chemotherapy drugs modulate the formation of stress granules (SGs), which are RNA-containing cytoplasmic foci contributing to stress response pathways. How SGs...
Some chemotherapy drugs modulate the formation of stress granules (SGs), which are RNA-containing cytoplasmic foci contributing to stress response pathways. How SGs mechanistically contribute to pro-survival or pro-apoptotic functions must be better defined. The chemotherapy drug lomustine promotes SG formation by activating the stress-sensing eIF2α kinase HRI (encoded by the EIF2AK1 gene). Here, we applied a DNA microarray-based transcriptome analysis to determine the genes modulated by lomustine-induced stress and suggest roles for SGs in this process. We found that the expression of the pro-apoptotic EGR1 gene was specifically regulated in cells upon lomustine treatment. The appearance of EGR1-encoding mRNA in SGs correlated with a decrease in EGR1 mRNA translation. Specifically, EGR1 mRNA was sequestered to SGs upon lomustine treatment, probably preventing its ribosome translation and consequently limiting the degree of apoptosis. Our data support the model where SGs can selectively sequester specific mRNAs in a stress-specific manner, modulate their availability for translation, and thus determine the fate of a stressed cell.
Topics: Humans; RNA, Messenger; Early Growth Response Protein 1; Lomustine; Stress Granules; Apoptosis; Antineoplastic Agents, Alkylating
PubMed: 38940347
DOI: 10.1242/jcs.261825 -
ChemMedChem Jun 2024Sirtuin 6 (Sirt6), an NAD+-dependent deacylase, has emerged as a promising target for aging-related diseases and cancer. Advancing the medicinal chemistry of Sirt6...
Sirtuin 6 (Sirt6), an NAD+-dependent deacylase, has emerged as a promising target for aging-related diseases and cancer. Advancing the medicinal chemistry of Sirt6 modulators is crucial for the development of chemical probes aimed at unraveling the intricate biological functions of Sirt6 and unlocking its therapeutic potential. A proprietary DNA-encoded library yielded Sirt6 inhibitor 2-Pr, displaying remarkable inhibitory activity and isoform-selectivity, and featuring a chemical structure distinct from reported Sirt6 modulators. In this study, we explore the inhibitory mechanism of 2-Pr, evaluating the impact of chemical modifications and presenting a crystal structure of the Sirt6/ADP-ribose/2-Pr complex. Notably, co-crystal structure analysis reveals an unexpected and unprecedented binding mode of Sirt6, with 2-Pr spanning the acyl channel of the enzyme, extending into the acetyl-lysine binding pocket, and reaching toward the C-site. This unique binding mode guides potential avenues for developing potent and selective Sirt6 inhibitors.
PubMed: 38940296
DOI: 10.1002/cmdc.202400273 -
Epigenomics Jun 2024
PubMed: 38940212
DOI: 10.1080/17501911.2024.2365615 -
Bioinformatics (Oxford, England) Jun 2024Shotgun metagenomics allows for direct analysis of microbial community genetics, but scalable computational methods for the recovery of bacterial strain genomes from...
SUMMARY
Shotgun metagenomics allows for direct analysis of microbial community genetics, but scalable computational methods for the recovery of bacterial strain genomes from microbiomes remains a key challenge. We introduce Floria, a novel method designed for rapid and accurate recovery of strain haplotypes from short and long-read metagenome sequencing data, based on minimum error correction (MEC) read clustering and a strain-preserving network flow model. Floria can function as a standalone haplotyping method, outputting alleles and reads that co-occur on the same strain, as well as an end-to-end read-to-assembly pipeline (Floria-PL) for strain-level assembly. Benchmarking evaluations on synthetic metagenomes show that Floria is > 3× faster and recovers 21% more strain content than base-level assembly methods (Strainberry) while being over an order of magnitude faster when only phasing is required. Applying Floria to a set of 109 deeply sequenced nanopore metagenomes took <20 min on average per sample and identified several species that have consistent strain heterogeneity. Applying Floria's short-read haplotyping to a longitudinal gut metagenomics dataset revealed a dynamic multi-strain Anaerostipes hadrus community with frequent strain loss and emergence events over 636 days. With Floria, accurate haplotyping of metagenomic datasets takes mere minutes on standard workstations, paving the way for extensive strain-level metagenomic analyses.
AVAILABILITY AND IMPLEMENTATION
Floria is available at https://github.com/bluenote-1577/floria, and the Floria-PL pipeline is available at https://github.com/jsgounot/Floria_analysis_workflow along with code for reproducing the benchmarks.
Topics: Metagenome; Metagenomics; Haplotypes; Software; Humans; Genome, Bacterial; Microbiota; Bacteria; High-Throughput Nucleotide Sequencing; Sequence Analysis, DNA
PubMed: 38940183
DOI: 10.1093/bioinformatics/btae252 -
Bioinformatics (Oxford, England) Jun 2024Exponential growth in sequencing databases has motivated scalable De Bruijn graph-based (DBG) indexing for searching these data, using annotations to label nodes with...
MOTIVATION
Exponential growth in sequencing databases has motivated scalable De Bruijn graph-based (DBG) indexing for searching these data, using annotations to label nodes with sample IDs. Low-depth sequencing samples correspond to fragmented subgraphs, complicating finding the long contiguous walks required for alignment queries. Aligners that target single-labelled subgraphs reduce alignment lengths due to fragmentation, leading to low recall for long reads. While some (e.g. label-free) aligners partially overcome fragmentation by combining information from multiple samples, biologically irrelevant combinations in such approaches can inflate the search space or reduce accuracy.
RESULTS
We introduce a new scoring model, 'multi-label alignment' (MLA), for annotated DBGs. MLA leverages two new operations: To promote biologically relevant sample combinations, 'Label Change' incorporates more informative global sample similarity into local scores. To improve connectivity, 'Node Length Change' dynamically adjusts the DBG node length during traversal. Our fast, approximate, yet accurate MLA implementation has two key steps: a single-label seed-chain-extend aligner (SCA) and a multi-label chainer (MLC). SCA uses a traditional scoring model adapting recent chaining improvements to assembly graphs and provides a curated pool of alignments. MLC extracts seed anchors from SCAs alignments, produces multi-label chains using MLA scoring, then finally forms multi-label alignments. We show via substantial improvements in taxonomic classification accuracy that MLA produces biologically relevant alignments, decreasing average weighted UniFrac errors by 63.1%-66.8% and covering 45.5%-47.4% (median) more long-read query characters than state-of-the-art aligners. MLAs runtimes are competitive with label-combining alignment and substantially faster than single-label alignment.
AVAILABILITY AND IMPLEMENTATION
The data, scripts, and instructions for generating our results are available at https://github.com/ratschlab/mla.
Topics: Algorithms; Sequence Alignment; Software; Computational Biology; Sequence Analysis, DNA; Databases, Genetic
PubMed: 38940164
DOI: 10.1093/bioinformatics/btae226 -
Bioinformatics (Oxford, England) Jun 2024Recently developed spatial lineage tracing technologies induce somatic mutations at specific genomic loci in a population of growing cells and then measure these...
MOTIVATION
Recently developed spatial lineage tracing technologies induce somatic mutations at specific genomic loci in a population of growing cells and then measure these mutations in the sampled cells along with the physical locations of the cells. These technologies enable high-throughput studies of developmental processes over space and time. However, these applications rely on accurate reconstruction of a spatial cell lineage tree describing both past cell divisions and cell locations. Spatial lineage trees are related to phylogeographic models that have been well-studied in the phylogenetics literature. We demonstrate that standard phylogeographic models based on Brownian motion are inadequate to describe the spatial symmetric displacement (SD) of cells during cell division.
RESULTS
We introduce a new model-the SD model for cell motility that includes symmetric displacements of daughter cells from the parental cell followed by independent diffusion of daughter cells. We show that this model more accurately describes the locations of cells in a real spatial lineage tracing of mouse embryonic stem cells. Combining the spatial SD model with an evolutionary model of DNA mutations, we obtain a phylogeographic model for spatial lineage tracing. Using this model, we devise a maximum likelihood framework-MOLLUSC (Maximum Likelihood Estimation Of Lineage and Location Using Single-Cell Spatial Lineage tracing Data)-to co-estimate time-resolved branch lengths, spatial diffusion rate, and mutation rate. On both simulated and real data, we show that MOLLUSC accurately estimates all parameters. In contrast, the Brownian motion model overestimates spatial diffusion rate in all test cases. In addition, the inclusion of spatial information improves accuracy of branch length estimation compared to sequence data alone. On real data, we show that spatial information has more signal than sequence data for branch length estimation, suggesting augmenting lineage tracing technologies with spatial information is useful to overcome the limitations of genome-editing in developmental systems.
AVAILABILITY AND IMPLEMENTATION
The python implementation of MOLLUSC is available at https://github.com/raphael-group/MOLLUSC.
Topics: Animals; Mice; Cell Movement; Cell Division; Cell Lineage; Likelihood Functions; Phylogeography; Mutation; Phylogeny
PubMed: 38940146
DOI: 10.1093/bioinformatics/btae221 -
Bioinformatics (Oxford, England) Jun 2024High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to...
MOTIVATION
High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. Simultaneously, chromatin structure analyses struggle with extremely sparse contact matrices. To address this problem, computational methods to enhance low-coverage contact matrices have been developed, but existing methods are largely based on resolution enhancement methods for natural images and hence often employ models that do not distinguish between biologically meaningful contacts, such as loops and other stochastic contacts.
RESULTS
We present Capricorn, a machine learning model for Hi-C resolution enhancement that incorporates small-scale chromatin features as additional views of the input Hi-C contact matrix and leverages a diffusion probability model backbone to generate a high-coverage matrix. We show that Capricorn outperforms the state of the art in a cross-cell-line setting, improving on existing methods by 17% in mean squared error and 26% in F1 score for chromatin loop identification from the generated high-coverage data. We also demonstrate that Capricorn performs well in the cross-chromosome setting and cross-chromosome, cross-cell-line setting, improving the downstream loop F1 score by 14% relative to existing methods. We further show that our multiview idea can also be used to improve several existing methods, HiCARN and HiCNN, indicating the wide applicability of this approach. Finally, we use DNA sequence to validate discovered loops and find that the fraction of CTCF-supported loops from Capricorn is similar to those identified from the high-coverage data. Capricorn is a powerful Hi-C resolution enhancement method that enables scientists to find chromatin features that cannot be identified in the low-coverage contact matrix.
AVAILABILITY AND IMPLEMENTATION
Implementation of Capricorn and source code for reproducing all figures in this paper are available at https://github.com/CHNFTQ/Capricorn.
Topics: Chromatin; Machine Learning; Humans; Computational Biology; Algorithms; Software
PubMed: 38940142
DOI: 10.1093/bioinformatics/btae211 -
Bioinformatics (Oxford, England) Jun 2024Improvements in nanopore sequencing necessitate efficient classification methods, including pre-filtering and adaptive sampling algorithms that enrich for reads of...
SUMMARY
Improvements in nanopore sequencing necessitate efficient classification methods, including pre-filtering and adaptive sampling algorithms that enrich for reads of interest. Signal-based approaches circumvent the computational bottleneck of basecalling. But past methods for signal-based classification do not scale efficiently to large, repetitive references like pangenomes, limiting their utility to partial references or individual genomes. We introduce Sigmoni: a rapid, multiclass classification method based on the r-index that scales to references of hundreds of Gbps. Sigmoni quantizes nanopore signal into a discrete alphabet of picoamp ranges. It performs rapid, approximate matching using matching statistics, classifying reads based on distributions of picoamp matching statistics and co-linearity statistics, all in linear query time without the need for seed-chain-extend. Sigmoni is 10-100× faster than previous methods for adaptive sampling in host depletion experiments with improved accuracy, and can query reads against large microbial or human pangenomes. Sigmoni is the first signal-based tool to scale to a complete human genome and pangenome while remaining fast enough for adaptive sampling applications.
AVAILABILITY AND IMPLEMENTATION
Sigmoni is implemented in Python, and is available open-source at https://github.com/vshiv18/sigmoni.
Topics: Humans; Algorithms; Nanopore Sequencing; Software; Nanopores; Genome, Human; Genomics; Sequence Analysis, DNA
PubMed: 38940135
DOI: 10.1093/bioinformatics/btae213 -
Annals of Agricultural and... Jun 2024The NAA10 gene encodes N-alpha-acetyltransferase 10 which plays an important role in cell growth, differentiation, DNA damage, metastasis, apoptosis, stress response and... (Review)
Review
The NAA10 gene encodes N-alpha-acetyltransferase 10 which plays an important role in cell growth, differentiation, DNA damage, metastasis, apoptosis, stress response and autophagy. Defects in the NAA10 gene correlate with the diagnosis of NAA10-related syndrome (Ogden syndrome). The most common symptoms of NAA10-related syndrome are: global developmental delay, non-verbal or limited speech, autism spectrum disorder, feeding difficulties, motor delay, muscle tone disturbances, and long QT syndrome. To-date, there are about 100 patients who have been reported with this condition. The case report presents the clinical study of a girl aged 4 years and 3 months diagnosed with Ogden syndrome. She had many characteristic features of the disorder, as well as precocious puberty. This girl represents the case of a patient with p.Arg83Cys mutation in NAA10 gene as well as precocious puberty.
Topics: Humans; Female; Puberty, Precocious; N-Terminal Acetyltransferase A; N-Terminal Acetyltransferase E; Child, Preschool; Mutation
PubMed: 38940118
DOI: 10.26444/aaem/171758 -
Journal of Integrative Neuroscience May 2024Parkinson's disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta region of... (Review)
Review
Parkinson's disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta region of the midbrain and the formation of intracellular protein aggregates known as Lewy bodies, of which a major component is the protein α-synuclein. Several studies have suggested that mitochondria play a central role in the pathogenesis of PD, encompassing both familial and sporadic forms of the disease. Mitochondrial dysfunction is attributed to bioenergetic impairment, increased oxidative stress, damage to mitochondrial DNA, and alteration in mitochondrial morphology. These alterations may contribute to improper functioning of the central nervous system and ultimately lead to neurodegeneration. The perturbation of mitochondrial function makes it a potential target, worthy of exploration for neuroprotective therapies and to improve mitochondrial health in PD. Thus, in the current review, we provide an update on mitochondria-based therapeutic approaches toward α-synucleinopathies in PD.
Topics: Humans; Parkinson Disease; Synucleinopathies; Mitochondria; Animals; alpha-Synuclein
PubMed: 38940084
DOI: 10.31083/j.jin2306109