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Karyotype engineering reveals spatio-temporal control of replication firing and gene contacts.

ABSTRACT: Eukaryotic genomes vary in terms of size, chromosome number, and genetic complexity. Their temporal organization is complex, reflecting coordination between DNA folding and function. Here, we used fused karyotypes of budding yeast to characterize the effects of chromosome length on nuclear architecture. We found that size-matched megachromosomes expand to occupy a larger fraction of the enlarged nucleus. Hi-C maps reveal changes in the three-dimensional structure corresponding to inactivated centromeres and telomeres. De-clustering of inactive centromeres results in their loss of early replication, highlighting a functional correlation between genome organization and replication timing. Repositioning of former telomere-proximal regions on chromosome arms exposed a subset of contacts between flocculin genes. Chromatin reorganization of megachromosomes during cell division remained unperturbed, and it revealed that centromere-rDNA contacts in anaphase, extending over 0.3 Mb on wild-type chromosome, cannot exceed ∼1.7 Mb. Our results highlight the relevance of engineered karyotypes to unveiling relationships between genome organization and function.

SUBMITTER: Lazar-Stefanita L

PROVIDER: S-EPMC9365758 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Karyotype engineering reveals spatio-temporal control of replication firing and gene contacts.

Lazar-Stefanita Luciana L Luo Jingchuan J Montagne Remi R Thierry Agnes A Sun Xiaoji X Mercy Guillaume G Mozziconacci Julien J Koszul Romain R Boeke Jef D JD

Cell genomics 20220810 8

Eukaryotic genomes vary in terms of size, chromosome number, and genetic complexity. Their temporal organization is complex, reflecting coordination between DNA folding and function. Here, we used fused karyotypes of budding yeast to characterize the effects of chromosome length on nuclear architecture. We found that size-matched megachromosomes expand to occupy a larger fraction of the enlarged nucleus. Hi-C maps reveal changes in the three-dimensional structure corresponding to inactivated cen ...[more]

PMID: 35983101

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Project description:MotivationEmergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies-e.g. live cell imaging, scRNAseq, agent-based models-requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties.ResultsDespite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced.Availability and implementationAll source codes for the agent-based model, emulation, and analyses are publicly available at the corresponding DOIs: 10.5281/zenodo.10622155, 10.5281/zenodo.10611675, 10.5281/zenodo.10621244, respectively.

Project description:BACKGROUND:Meningococcal meningitis is a major health problem in the "African Meningitis Belt" where recurrent epidemics occur during the hot, dry season. In Niger, a central country belonging to the Meningitis Belt, reported meningitis cases varied between 1,000 and 13,000 from 2003 to 2009, with a case-fatality rate of 5-15%. METHODOLOGY/PRINCIPAL FINDINGS:In order to gain insight in the epidemiology of meningococcal meningitis in Niger and to improve control strategies, the emergence of the epidemics and their diffusion patterns at a fine spatial scale have been investigated. A statistical analysis of the spatio-temporal distribution of confirmed meningococcal meningitis cases was performed between 2002 and 2009, based on health centre catchment areas (HCCAs) as spatial units. Anselin's local Moran's I test for spatial autocorrelation and Kulldorff's spatial scan statistic were used to identify spatial and spatio-temporal clusters of cases. Spatial clusters were detected every year and most frequently occurred within nine southern districts. Clusters most often encompassed few HCCAs within a district, without expanding to the entire district. Besides, strong intra-district heterogeneity and inter-annual variability in the spatio-temporal epidemic patterns were observed. To further investigate the benefit of using a finer spatial scale for surveillance and disease control, we compared timeliness of epidemic detection at the HCCA level versus district level and showed that a decision based on threshold estimated at the HCCA level may lead to earlier detection of outbreaks. CONCLUSIONS/SIGNIFICANCE:Our findings provide an evidence-based approach to improve control of meningitis in sub-Saharan Africa. First, they can assist public health authorities in Niger to better adjust allocation of resources (antibiotics, rapid diagnostic tests and medical staff). Then, this spatio-temporal analysis showed that surveillance at a finer spatial scale (HCCA) would be more efficient for public health response: outbreaks would be detected earlier and reactive vaccination would be better targeted.

Dataset Information

Karyotype engineering reveals spatio-temporal control of replication firing and gene contacts.

Publications

Karyotype engineering reveals spatio-temporal control of replication firing and gene contacts.

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