Biology subjects

Pani, S.

Publications and source records attributed to Pani, S..

5 recordsLinked to original sources

bioRxiv2025

Acyl-protein thioesterase 1 (LYPLA1) activity promotes the growth of MDA-MB-468 triple-negative breast cancer cells.

Protein S-acylation is a lipid-based, often reversible post-translational modification that can regulate many aspects of protein behavior, including subcellular localization, protein-interactions, and activity. Emerging evidence has identified roles for individual protein acyltransferases encoded by the ZDHHC in cancers, yet the roles of de-S-acylation enzymes are less clear. Recent evidence suggests that acyl-protein thioesterase (APT1)/LYPLA1 can impact epithelial-mesenchymal transition and metastasis. This study integrates patient datasets, CRISPR dependency data, and in vitro assays to find APT1 as a context-dependent vulnerability in triple-negative breast cancer (TNBC). Despite the highest protein abundance in luminal MCF7 cells, basal-like MDA-MB-468 cells exhibited the most prominent specific APT1 activity, reflecting subtype-specific regulation. Inhibition of APT1 with ML348 increased S-acylation of nuclear and mitochondrial proteins without altering global acylation. Functionally, APT1 inhibition reduced cell proliferation while inducing minimal apoptosis, consistent with cytostatic growth arrest. Cell-cycle analysis revealed G1 accumulation and reduced S/G2 transition, linking proteomic changes to impaired replication. These findings establish APT1 as a regulator of TNBC proliferation through dynamic de-S-acylation of cell-cycle and mitochondrial proteins, highlighting it as a potential therapeutic vulnerability in aggressive breast cancers.

cancer biology↗

bioRxiv2025

Separation measurement of two freely rotating dipole emitters at near optimal precision

According to Rayleighs criterion, two incoherent emitters with a separation below the diffraction limit are not resolvable with a conventional fluorescence microscope. One method of Super-Resolution Microscopy (SRM) circumvents the diffraction-limited resolution by precisely estimating the position of spatiotemporally independent emitters. However, these methods of SRM techniques are not optimal for estimating the separation of two simultaneously excited emitters. Recently, a number of detection methods based on modal imaging have been developed to achieve the quantum Cramer-Rao lower bound (QCRB) to estimate the separations between two nearby emitters. The QCRB determines the minimum achievable precision for all possible detection methods. Current modal imaging techniques assume a scalar field generated from a point source, such as a distant source from an optical fiber or a pinhole. However, for fluorescently labeled samples, point emitters are single fluorophores that are modeled as dipole emitters and, in practice, are often freely rotating. Dipole radiation must be described by vectorial theory, and the assumption of a scalar field no longer holds. Here, we present a method to numerically calculate the QCRB for measuring the separation of two dipole emitters, incorporating the vectorial theory. Furthermore, we propose a near-quantum optimal detection scheme based on one of the modal imaging techniques, super-localization by image inversion interferometry (SLIVER), for estimating the separation of two freely rotating dipoles. In the proposed method, we introduce a vortex wave plate before the SLIVER detection to separate the radial and azimuthal components of the dipole radiation. With numerical simulations, we demonstrated that our method achieves non-divergent precision at any separation between two dipole emitters. We investigated several practical effects relevant to experimental measurements in super-resolution microscopy, including numerical aperture, detection bandwidth, number of estimation parameters, background, and misalignment on separation estimation. Our proposed measurement provides a near quantum-limited detection scheme for measuring the separation of two freely-rotating dipole emitters, such as fluorescently tagged molecules, which are commonly used in super-resolution microscopy.

biophysics↗

bioRxiv2024

gaftools: a toolkit for analyzing and manipulating pangenome alignments

Linear reference genomes are ubiquitously used in genomics research, despite known biases associated with their use. In recent years, there has a been a shift towards using graph-based reference genomes to address some of these biases, which has required new algorithms and file formats to be developed. This has created the need to develop new tools capable of utilizing these formats and performing operations similar to those carried out by traditional methods.In this paper we present "gaftools", a multi-purpose tool that introduces several utilities for processing graph alignments in GAF format. Gaftools enables users to index and sort alignments, with graph ordering serving as a necessary step for the sorting process. Additionally it allows users to view subsets of alignments and perform realignment using the wavefront alignment algorithm, among other useful features. Many of these functionalities are inspired by SAMtools, which provide similar operations for linear genomes, while gaftools adapts and extends them for pangenomes. Availabilitygaftools is available under MIT license at https://github.com/marschall-lab/gaftools.

bioinformatics↗

bioRxiv2024

Long-read sequencing and structural variant characterization in 1,019 samples from the 1000 Genomes Project

Structural variants (SVs) contribute significantly to human genetic diversity and disease1-4. Previously, SVs have remained incompletely resolved by population genomics, with short-read sequencing facing limitations in capturing the whole spectrum of SVs at nucleotide resolution5-7. Here we leveraged nanopore sequencing8 to construct an intermediate coverage resource of 1,019 long-read genomes sampled within 26 human populations from the 1000 Genomes Project. By integrating linear and graph-based approaches for SV analysis via pangenome graph-augmentation, we uncover 167,291 sequence-resolved SVs in these samples, considerably advancing SV characterization compared to population-wide short-read sequencing studies3,4. Our analysis details diverse SV classes--deletions, duplications, insertions, and inversions--at population-scale. LINE-1 and SVA retrotransposition activities frequently mediate transductions9,10 of unique sequences, with both mobile element classes transducing sequences at either the 3'- or 5'-end, depending on the source element locus. Furthermore, analyses of SV breakpoint junctions suggest a continuum of homology-mediated rearrangement processes are integral to SV formation, and highlight evidence for SV recurrence involving repeat sequences. Our open-access dataset underscores the transformative impact of long-read sequencing in advancing the characterisation of polymorphic genomic architectures, and provides a resource for guiding variant prioritisation in future long-read sequencing-based disease studies.

genomics↗

bioRxiv2024

SVarp: pangenome-based structural variant discovery

The linear human reference genome that we use today does not represent the haplotypic diversity of the global human population. This raises bias in genomic read alignment and limits our ability to call large structural variations (SV), especially at highly polymorphic loci. Thus, many SV alleles remain unresolved. Recent efforts to transition to a graph-based reference genome resulted in the generation of the first draft human pangenome reference, but tools to call SVs relative to the pangenome reference are presently lacking. In this study, we present the SVarp algorithm, aiming to discover haplotype resolved SVs on top of a pangenome reference using long sequencing reads. SVarp outputs local assemblies of SV alleles, termed svtigs, instead of a VCF file of SV breakpoints, which we propose as a general exchange format allowing for flexible downstream analyses. In order to assess the accuracy of svtigs, we used simulated and real human genomes. Simulations allowed us to make exact breakpoint comparisons against the true callsets. We observed [~]96% recall with deletions, insertions and duplications larger than 1,000bp, showing that SVarp can reliably detect genomic structural variants not yet represented in the graph. On the other hand, we compared SVarp output for ONT sequencing data at 20X coverage against independent genome assemblies of the same samples and found that [~]82% of our svtig predictions are validated by the assemblies by a match with more than 85% sequence identity. SVarp was implemented using C++ and its source code is available at https://github.com/asylvz/SVarp under MIT license.

bioinformatics↗