Biology subjects

Alterovitz, G.

Publications and source records attributed to Alterovitz, G..

8 recordsLinked to original sources

bioRxiv2026

Multiscale Transcriptomic Architecture of the ALS Motor Cortex

Amyotrophic lateral sclerosis (ALS) exhibits marked clinical and molecular heterogeneity, but how this heterogeneity is organized within disease-relevant central nervous system regions remains incompletely understood. Because pooled cross-region analyses may confound regional variation with subtype structure, we focused on the motor cortex, a key site of ALS pathology, to resolve its internal transcriptomic organization. Using approximately 600 postmortem motor cortex transcriptomes from the Target ALS resource, we applied gene set variation analysis to generate sample-level pathway activity profiles and performed consensus clustering in pathway space. We identified seven reproducible ALS motor cortex subtypes characterized by distinct inflammatory, damage-response, metabolic, glial, and neuronal/synaptic programs. Donor-held-out projection recovered the seven subtype assignments with a mean balanced accuracy of 0.939, supporting the reproducibility of the pathway-defined subtype structure in unseen donors. Mapping these bulk-defined programs to an independent motor-cortex single-nucleus RNA-seq dataset revealed distinct cellular contexts, while cell-type marker adjustment showed that cellular differences contributed to, but did not fully account for, subtype-associated pathway variation. The seven subtypes also recapitulated major molecular programs reported in previous ALS subtype frameworks while resolving them into finer motor-cortex-specific states. Rather than forming isolated molecular classes, these subtypes occupied a structured pathway-space landscape with both discrete subtype-centered peaks and continuous biological gradients. Principal component analysis and representative pathway overlays defined a major axis from an upper inflammatory/damage region to a lower neuronal/metabolic/remodeling region, with C7 forming a neuronal/synaptic connector within the lower landscape. Co-expression network analysis further supported this multiscale organization, revealing family-level network backbones, subtype-specific modular refinements, and bridge-like rewiring. Metadata-informed analyses showed that this molecular architecture was not primarily organized by disease duration. Instead, the landscape was more clearly associated with clinical and genetic-background variables, including ALS/FTD family history and mutation-related signals. In particular, family-history-positive and C9orf72 repeat expansion-positive samples were more represented in the lower neuronal/metabolic/remodeling region, whereas family-history-negative samples and C9orf72-negative cases were more represented in the upper inflammatory/damage region. These findings support a model in which ALS motor cortex heterogeneity is organized as a multiscale transcriptomic architecture more closely associated with genetic-background variables than with disease duration. This framework provides a basis for subtype-aware biological comparison, future patient-stratification, biomarker, or therapeutic target-prioritization studies.

bioinformatics↗

bioRxiv2026

SpaCoEx: Sparse Gene Selection for Spatially Varying Co-expression in Spatial Transcriptomics

Spatial transcriptomics enables gene expression to be measured while preserving tissue location, but most existing analyses focus on spatial variation in individual genes or expression-defined domains. Here, we introduce SpaCoEx, a sparse spatial representation framework that integrates gene-expression levels with spatially varying gene-gene co-expression. SpaCoEx first estimates local co-expression matrices from neighboring spatial spots, maps them into a log-Euclidean representation, and performs structured gene selection by retaining or removing the full row and column associated with each gene. The selected genes are then used to construct both expression-level features and local co-expression features, which are combined through an -weighted joint representation for downstream spatial analysis. We applied SpaCoEx to human cutaneous squamous cell carcinoma and annotated human breast cancer spatial transcriptomics datasets. In the cutaneous squamous cell carcinoma dataset, SpaCoEx selected 29 of 45 keratinocyte-related genes while preserving 96.74% of the spatial co-expression variation. In the breast cancer dataset, SpaCoEx identified spatially varying co-expression between B2M and HLA-C, a biologically meaningful major histocompatibility complex (MHC) class I antigen-presentation gene pair. Their local correlation was significantly higher in cancer-associated regions than in non-cancer regions (mean difference = 0.30, spatially adjusted SE = 0.045, P<1.0x10^(-10)), whereas B2M and HLA-C expression individually did not differ significantly between cancer and non-cancer. In benchmarking against manual tissue annotations, co-expression-only SpaCoEx achieved the strongest spatial coherence (percentage of abnormal spots [PAS] = 0.076), while the joint expression/co-expression representation achieved the highest annotation agreement, with an adjusted Rand Index (ARI) of 0.584 at = 0.60 and and normalized mutual information (NMI) of 0.663 at = 0.40. By integrating marginal gene-expression information with local gene-gene co-expression structure, SpaCoEx provides a sparse, low-dimensional, and interpretable representation of spatial transcriptomics data that captures complementary aspects of tissue organization beyond expression-based variation alone.

bioinformatics↗

bioRxiv2025

A Unified Protein Embedding Model with Local and Global Structural Sensitivity

Structural comparison between proteins is key to many research tasks, including evolutionary analysis, peptidomimetics, and functional annotation. Traditional structure alignment tools based on three-dimensional protein structures, such as TM-Align, DALI, or ProBiS, are accurate, but they are computationally expensive and impractical at scale. Existing protein language models (PLMs), such as TM-Vec, improve computational efficiency but only capture global structural similarity, overlooking important motif-level structural details. In this paper, we propose a novel PLM consisting of a Siamese neural network, enabling efficient embedding-based structural comparison while also capturing both global and local structural similarity. Our model was trained on a dual loss function combining TM-score, a global similarity metric, and a variation of lDDT scores, a per-residue similarity metric. We tested against two datasets: a varied TM-score dataset from TM-Vec, and a high TM-score mutant dataset from VIPUR. Against these sets, our model achieved a TM-score MAE of 0.0741 and 0.0583, respectively, and a lDDT-score MAE of 0.0788 and 0.0038, respectively. Our model fulfills two key roles: first, it rapidly detects global structural differences. Second, it supports fine-grained structural assessments, improving sensitivity to subtle but functionally important structural changes.

bioinformatics↗

bioRxiv2025

Unveiling the Biochemical Mechanisms of Aging and the Implications of Oxidative Stress on Cellular Senescence through Multi-Omics Analysis of Fibroblasts

This research investigates the complex biochemical mechanisms underlying aging by analyzing primary human fibroblasts using a longitudinal multi-omics dataset. This dataset includes cytology, DNA methylation and epigenetic clocks, bioenergetics, mitochondrial DNA sequencing, RNA sequencing, and cytokine profiling. Key findings indicate that mitochondrial efficiency declines with age, while glycolysis becomes more prevalent to compensate for energy demands. Epigenetic clocks, such as Hannum and PhenoAge, showed strong correlations with biological age ({rho} > 0.650, p < 1e-6), validating the experimental setup and confirming that the cultured fibroblasts were aging appropriately. Fibroblasts with SURF1 mutations exhibited accelerated aging, marked by bioenergetic deficits, increased cell volume, and reduced proliferative capacity, underscoring the pivotal role of mitochondrial dysfunction in cellular senescence. Novel insights were gained from analyzing cytokines like IL18 and PCSK9, some of which were linked to age-related diseases such as Alzheimers and cardiovascular disorders. Experimental treatments revealed distinct effects on cellular aging. Dexamethasone reduced inflammation but also increased DNA methylation, induced metabolic inefficiencies, and shortened cellular lifespan. Oligomycin heightened oxidative stress and RNA degradation, emphasizing how such treatments contribute to cellular stress and metabolic imbalance while shedding light on aging mechanisms. By uncovering connections between mitochondrial dysfunction, epigenetic biomarkers, and immune dysregulation, this study identifies potential therapeutic targets for age-related diseases. Future research could validate the most promising biomarkers across diverse cell types and experimental treatments to build a more comprehensive understanding of aging.

genomics↗

bioRxiv2021

The GA4GH Variation Representation Specification (VRS): a Computational Framework for the Precise Representation and Federated Identification of Molecular Variation

Maximizing the personal, public, research, and clinical value of genomic information will require that clinicians, researchers, and testing laboratories exchange genetic variation data reliably. Developed by a partnership among national information resource providers, public initiatives, and diagnostic testing laboratories under the auspices of the Global Alliance for Genomics and Health (GA4GH), the Variation Representation Specification (VRS, pronounced "verse") is an extensible framework for the semantically precise and computable representation of variation that complements contemporary human-readable and flat file standards for variation representation. VRS objects are designed to be semantically precise representations of variation, and leverage this design to enable unique, federated identification of molecular variation. We describe the components of this framework, including the terminology and information model, schema, data sharing conventions, and a reference implementation, each of which is intended to be broadly useful and freely available for community use. The specification, documentation, examples, and community links are available at https://vrs.ga4gh.org/.

bioinformatics↗

bioRxiv2020

Berberine reverses multidrug resistance in Candida albicans by hijacking the drug efflux pump Mdr1p

Clinical use of antimicrobials faces great challenges from the emergence of multidrug resistant (MDR) pathogens. The overexpression of drug efflux pumps is one of the major contributors to MDR. It is considered as a promising approach to overcome MDR by reversing the function of drug efflux pumps. In the life-threatening fungal pathogen Candida albicans, the major facilitator superfamily (MFS) transporter Mdr1p can excrete many structurally unrelated antifungals, leading to multidrug resistance. Here we report a counterintuitive case of reversing multidrug resistance in C. albicans by using a natural product berberine to hijack the overexpressed Mdr1p for its own importation. Moreover, we illustrate that the imported berberine accumulates in mitochondria, and compromises the mitochondrial function by impairing mitochondrial membrane potential and mitochondrial Complex I. It results in the selective elimination of Mdr1p overexpressed C. albicans cells. Furthermore, we show that berberine treatment can prolong the mean survival time (MST) of mice with a blood-borne dissemination of Mdr1p overexpressed multidrug resistant candidiasis. This study provided a potential direction of novel anti-MDR drug discovery by screening for multidrug efflux pump converters.

microbiology↗

bioRxiv2020

Simple cloning of large natural product biosynthetic gene cluster by CRISPR/Cas12a-mediated fast direct capturing strategy

Directly cloning of biosynthetic gene clusters (BGCs) from microbial genomes has been revolutionizing the natural product-based drug discovery. However, it is still very challenging to efficiently clone, for example, large (> 80kb) and GC-rich (> 70%), streptomycete originating BGCs. In this study, we developed a simple, fast yet efficient and low-cost in vitro platform for direct cloning large BGCs from streptomycete genomic DNA, named as CAT-FISHING (CRISPR/Cas12a- and Agarose plug-based sysTem for Fast bIoSyntHetIc geNe cluster cloninG), by combining the advantages of CRISPR/Cas12a cleavage and bacterial artificial chromosome (BAC) library construction. CAT-FISHING was demonstrated by directly cloning large DNA fragments ranging from 47 to 139 kb with GC content of > 70% from the S. albus J1074 genome in a relatively efficient manner. Moreover, surugamides, encoded by a captured 87-kb BGC with GC content of 76%, was heterologously expressed in a Streptomyces chassis. These results indicate that CAT-FISHING is a powerful platform for BGCs batch cloning, which would be greatly beneficial to the natural products-based drug discovery. We believe that this system will lead a renaissance of interest in microorganisms as a source for drug development.

synthetic biology↗

bioRxiv2017

Enabling Precision Medicine via standard communication of NGS provenance, analysis, and results

A personalized approach based on a patients or pathogens unique genomic sequence is the foundation of precision medicine. Genomic findings must be robust and reproducible, and experimental data capture should adhere to FAIR guiding principles. Moreover, effective precision medicine requires standardized reporting that extends beyond wet lab procedures to computational methods. The BioCompute framework (https://osf.io/zm97b/) enables standardized reporting of genomic sequence data provenance, including provenance domain, usability domain, execution domain, verification kit, and error domain. This framework facilitates communication and promotes interoperability. Bioinformatics computation instances that employ the BioCompute framework are easily relayed, repeated if needed and compared by scientists, regulators, test developers, and clinicians. Easing the burden of performing the aforementioned tasks greatly extends the range of practical application. Large clinical trials, precision medicine, and regulatory submissions require a set of agreed upon standards that ensures efficient communication and documentation of genomic analyses. The BioCompute paradigm and the resulting BioCompute Objects (BCO) offer that standard, and are freely accessible as a GitHub organization (https://github.com/biocompute-objects) following the \"Open-Stand.org principles for collaborative open standards development\". By communication of high-throughput sequencing studies using a BCO, regulatory agencies (e.g., FDA), diagnostic test developers, researchers, and clinicians can expand collaboration to drive innovation in precision medicine, potentially decreasing the time and cost associated with next generation sequencing workflow exchange, reporting, and regulatory reviews.

scientific communication and education↗