Biology subjects

Singh, H. R.

Publications and source records attributed to Singh, H. R..

3 recordsLinked to original sources

bioRxiv2026

Cep192 insufficiency underlies haploid instability in human cells

Mammalian somatic haploid cells offer advantages for genome engineering, yet rapid diploidization limits their utility. Here, we reveal that a haploidy-specific attenuation of mitotic spindle bipolarization, independent of previously characterized centrosome loss, underlies haploid instability in human cells. Comparative imaging and structure-function analyses demonstrate that the halved absolute dosage of the pericentriolar scaffolding protein Cep192 prevents its centrosomal accumulation to the threshold required for Aurora A-Eg5 axis. Consequently, haploids exhibit innate fragility in centrosome separation and spindle maintenance. Supplementing Cep192 restored spindle bipolarization to diploid levels and, when combined with genetic enhancement of the acentrosomal spindle pathway, profoundly stabilized the haploid state. Moreover, a genome-wide CRISPR-activation screen leveraging the above principle identified novel haploid-stabilizing genes, including the glutamate transporter SLC1A2. Our findings uncover an absolute-dosage scaling limit of mitotic scaffolding in haploids and establish genetic enhancement of spindle fidelity as an effective strategy for engineering stable animal haploid bioresources. TeaserMolecular elucidation of haploidy-linked fragility in mitotic spindle architecture enables engineering stable human haploid cells.

cell biology↗

bioRxiv2025

Mesoscale landscaping of the TRIM protein family reveals a novel human condensatopathy

The mesoscale organization of cells is central to cellular physiology and pathology. Cellular condensates often form via biomolecular phase separation, mediated by intrinsically disordered regions (IDRs) and represent a key mechanism for mesoscale organization. The TRI-partite Motif (TRIM) family of ubiquitin ligases is implicated in diverse cellular functions and disease, yet the role of biomolecular condensation in TRIM family organization remains understudied. Here, we systematically investigate the mesoscale localization of 72 TRIM proteins, revealing that a majority form condensates in distinct cellular compartments. IDR content correlates with dynamic condensate formation, suggesting a critical role in mesoscale organization. Focusing on TRIM8, associated with a neuro-renal disorder, we demonstrate that disease-causing truncations of the TRIM8 C-terminal IDR result in a condensatopathy, characterized by disrupted condensation, proteasomal regulation, and TAK1/NF{kappa}B signaling. Functional assays in cellular and animal models link these disruptions to podocyte dysfunction and impaired response to injury. Our findings establish a framework for understanding condensatopathies and the mesoscale principles governing TRIM family organization and function.

cell biology↗

bioRxiv2023

PICNIC identifies condensate-forming proteins across organisms

Biomolecular condensates are membraneless organelles that can concentrate hundreds of different proteins to operate essential biological functions. However, accurate identification of their components remains challenging and biased towards proteins with high structural disorder content with focus on self-phase separating (driver) proteins. Here, we present a machine learning algorithm, PICNIC (Proteins Involved in CoNdensates In Cells) to classify proteins involved in biomolecular condensates regardless of their role in condensate formation. PICNIC successfully predicts condensate members by identifying amino acid patterns in the protein sequence and structure in addition to the intrinsic disorder and outperforms previous methods. We performed extensive experimental validation in cellulo and demonstrated that PICNIC accurately predicts 21 out of 24 condensate-forming proteins regardless of their structural disorder content. Even though increasing disorder content was associated with organismal complexity, we found no correlation between predicted condensate proteome content and disorder content across organisms. Overall, we applied a novel machine learning classifier to interrogate condensate components at single protein and whole-proteome levels across the tree of life (picnic.cd-code.org).

bioinformatics↗