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Picchetta L et al., The American Journal of Human Genetics - Maternal Age, Meiotic Recombination Failure, and Triploidy in Humans

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Article title:
Maternal age and genome-wide failure of meiotic recombination are associated with triploid conceptions in humans

First author:
Picchetta L

Journal:
The American Journal of Human Genetics

DOI:
10.1016/j.ajhg.2025.09.014

Reference:
Picchetta L., Ottolini C.S., Tao X., Zhan Y., Jobanputra V., Vallejo C.M., et al.. Maternal age and genome-wide failure of meiotic recombination are associated with triploid conceptions in humans. The American Journal of Human Genetics, 112, 2665-2678. (2025). https://doi.org/10.1016/j.ajhg.2025.09.014

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On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/maternal-age-meiotic-recombination-failure-and-triploidy-in-humans

Aslanzadeh V et al., Nature Communications - Deep mutational scanning of the insulin receptor guides precision therapy for insulin resistance

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Article title:
Deep mutational scanning of the human insulin receptor ectodomain to inform precision therapy for insulin resistance

First author:
Aslanzadeh V

Journal:
Nature Communications

DOI:
10.1038/s41467-025-64178-4

Reference:
Aslanzadeh V., Brierley G.V., Kumar R., Çubuk H., Vigouroux C., Matreyek K.A., et al.. Deep mutational scanning of the human insulin receptor ectodomain to inform precision therapy for insulin resistance. Nature Communications, 16. (2025). https://doi.org/10.1038/s41467-025-64178-4

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Official website https://basebybase.com

On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/deep-mutational-scanning-of-the-insulin-receptor-guides-precision-therapy-for-insulin-resistance

Baird D et al., Nature Communications - Low circulating miR-190a-5p predicts progression of chronic kidney disease

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Article title:
Low circulating miR-190a-5p predicts progression of chronic kidney disease

First author:
Baird D

Journal:
Nature Communications

DOI:
10.1038/s41467-025-64168-6

Reference:
Baird D.P., Zang J., Connor K.L., Teenan O., Reck M., Cairns C., et al.. Low circulating miR-190a-5p predicts progression of chronic kidney disease. Nature Communications, 16. (2025). https://doi.org/10.1038/s41467-025-64168-6

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On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/low-circulating-mir-190a-5p-predicts-progression-of-chronic-kidney-disease

Qu Y et al., Cell Genomics - DeepScence: Detecting Senescent Cells at Single-Cell and Spatial Resolution

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Article title:
Single-cell and spatial detection of senescent cells using DeepScence

First author:
Qu Y

Journal:
Cell Genomics

DOI:
10.1016/j.xgen.2025.101035

Reference:
Qu Y., Ji B., Dong R., Gu L., Chan C., Xie J., et al.. Single-cell and spatial detection of senescent cells using DeepScence. Cell Genomics, 5, 101035. (2025). https://doi.org/10.1016/j.xgen.2025.101035

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Episode link: https://basebybase.com/episodes/deepscence-detecting-senescent-cells-at-single-cell-and-spatial-resolution

Oosterheert W et al., Cell - Molecular Squeezing: How Coronin, Cofilin, and AIP1 Rapidly Disassemble Actin Filaments

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Article title:
Choreography of rapid actin filament disassembly by coronin, cofilin, and AIP1

First author:
Oosterheert W

Journal:
Cell

DOI:
10.1016/j.cell.2025.09.016

Reference:
Oosterheert W., Boiero Sanders M., Hofnagel O., Bieling P., Raunser S.. Choreography of rapid actin filament disassembly by coronin, cofilin, and AIP1. Cell, 188, 6845-6860.e27. (2025). https://doi.org/10.1016/j.cell.2025.09.016

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On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/molecular-squeezing-how-coronin-cofilin-and-aip1-rapidly-disassemble-actin-filaments

Schoenle A et al., Trends in Genetics - Protist Genomics: Key to Understanding Eukaryotic Evolution

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Article title:
Protist genomics: key to understanding eukaryotic evolution

First author:
Schoenle A

Journal:
Trends in Genetics

DOI:
10.1016/j.tig.2025.05.004

Reference:
Schoenle A., Francis O., Archibald J.M., Burki F., de Vries J., Dumack K., et al.. Protist genomics: key to understanding eukaryotic evolution. Trends in Genetics, 41, 868-882. (2025). https://doi.org/10.1016/j.tig.2025.05.004

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On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/protist-genomics-key-to-understanding-eukaryotic-evolution

Ćorović M et al., Trends in Genetics - m6A in the coding sequence: linking deposition, translation, and decay

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Article title:
m6A in the coding sequence: linking deposition, translation, and decay

First author:
Ćorović M

Journal:
Trends in Genetics

DOI:
10.1016/j.tig.2025.06.002

Reference:
Ćorović M., Hoch-Kraft P., Zhou Y., Hallstein S., König J., Zarnack K.. m6A in the coding sequence: linking deposition, translation, and decay. Trends in Genetics, 41, 963-973. (2025). https://doi.org/10.1016/j.tig.2025.06.002

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On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/m6a-in-the-coding-sequence-linking-deposition-translation-and-decay

Matar O et al., Trends in Genetics - Animal origins: looping back in time

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Article title:
Animal origins: looping back in time

First author:
Matar O

Journal:
Trends in Genetics

DOI:
10.1016/j.tig.2025.06.013

Reference:
Matar O., Marlétaz F.. Animal origins: looping back in time. Trends in Genetics, 41, 849-850. (2025). https://doi.org/10.1016/j.tig.2025.06.013

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Episode link: https://basebybase.com/episodes/animal-origins-looping-back-in-time

Robles-Remacho A et al., Trends in Genetics - A concise review of emerging methods to map microRNA (miRNA) expression in tissues with spatial context, covering imaging and sequencing approaches, technical barriers, bioinformatics needs, and clinical implications. Key terms: spatial miRNomics, microRNA, spatial transcriptomics, in situ polyadenylation, Patho-DBiT.

Study Highlights:
Spatial miRNA profiling is an emerging field with established singleplex imaging methods and new sequencing-based strategies that use in situ polyadenylation to capture nonpolyadenylated RNAs. STRS and Patho-DBiT demonstrated the feasibility of detecting hundreds to thousands of miRNAs in tissue sections, including FFPE samples, while imaging-based multiplexing remains limited. Bioinformatic tools such as miTEA-HiRes enable inference of miRNA activity from spatial transcriptomics, but challenges persist in sample fixation, retention, sensitivity for low-abundance miRNAs, and rRNA interference.

Conclusion:
Spatial miRNomics is now technically feasible and has revealed tissue- and disease-specific miRNA patterns, but further advances in multiplexed imaging, single-cell resolution, standardized workflows, and tailored bioinformatics are required to make spatial miRNA profiling routine and clinically actionable.

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Article title:
Spatially resolved microRNA expression in tissues: technologies, challenges, and opportunities

First author:
Robles-Remacho A

Journal:
Trends in Genetics

DOI:
10.1016/j.tig.2025.06.005

Reference:
Robles-Remacho A., Zou Y., Grillo M., Nilsson M. Spatially resolved microRNA expression in tissues: technologies, challenges, and opportunities. Trends in Genetics. 2025. https://doi.org/10.1016/j.tig.2025.06.005

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

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Episode link: https://basebybase.com/episodes/spatially-resolved-microrna-expression-in-tissues-technologies-challenges-and-opportunities

Huang X et al., Trends in Genetics - A concise review of methodological advances for identifying introgressed loci across genomes. The article surveys summary statistics, probabilistic modeling, and supervised learning approaches, their applications beyond humans, and the main challenges for robust inference and software implementation. Key terms: introgression, genomic-landscapes, probabilistic-modeling, supervised-learning, summary-statistics.

Study Highlights:
The review organizes recent progress into three methodological categories: summary statistics, probabilistic models, and supervised learning, highlighting complementary strengths. It documents applications across diverse taxa, including examples of adaptive and ghost introgression. The authors note key challenges such as confounding by ILS and population structure, model misspecification, and analysis opacity. They call for accessible implementations, transparent workflows, and systematic benchmarking to improve reliability.

Conclusion:
Progress requires leveraging complementary methods, improving transparency and benchmarking, and developing robust, accessible tools that scale across taxa and complex evolutionary scenarios.

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Article title:
Genomic landscapes of introgression

First author:
Huang X

Journal:
Trends in Genetics

DOI:
10.1016/j.tig.2025.07.001

Reference:
Huang X., Hackl J., Kuhlwilm M. Genomic landscapes of introgression. Trends in Genetics (2025). https://doi.org/10.1016/j.tig.2025.07.001

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

Support:
Base by Base is independent and ad-free — no sponsors, no paywall. If an episode was worth your time, chip in and keep the papers audited and the original songs coming:
❤️ Support monthly: https://buy.stripe.com/cNifZhclVebvagk2JDgEg01
☕ One-time donation: https://donate.stripe.com/7sY4gz71B2sN3RWac5gEg00
More at basebybase.com

On PaperCast Base by Base you'll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

Episode link: https://basebybase.com/episodes/decoding-genomic-landscapes-of-introgression