Biology subjects

Reich, M.

Publications and source records attributed to Reich, M..

13 recordsLinked to original sources

bioRxiv2026

The Multi-scale Copula Index (MCI): a new information-theoretic measure of association and its applications in functional cancer genomics

Cancer genomic analyses increasingly involve large, complex datasets representing diverse omics variables, such as gene expression, mutations, copy number alterations, genetic perturbations (e.g. CRISPR or iRNA), drug sensitivity, and clinical phenotypes, making it crucial to accurately measure associations among these variables. Traditional correlation-based methods, like the Pearson correlation coefficient, often assume linearity, Gaussian distributions, and independence among variables, leading to oversimplified assumptions, less sensitive readouts and less accurate significance estimation. Information-theoretic measures overcome these limitations by being one step removed from the data and assessing dependencies at the level of probability distributions, thus providing a more data-agnostic, robust measure for identifying associations in common analysis tasks such as feature selection, pattern recognition, and predictive modeling. Building on these foundations, we introduce the Multi-scale Copula Index (MCI), a novel information-theoretic measure of association. The MCI offers significant advantages over existing methods by eliminating the need for density estimation and parameter tuning, thereby ensuring consistent estimation with reduced computational complexity. Furthermore, it enhances statistical power at lower sample sizes, exhibits strong robustness to outliers, and successfully captures both directionality and important symmetries under input-data transformations, such as reflection or complementation. Due to these strengths, the MCI is particularly useful for functional cancer genomic applications involving, e.g., transcriptional and perturbational data, with different statistical properties across a wide range of dataset sizes and complexity. Contextual associations often arise in functional genomics datasets: two genomic variables show little or no correlation in most samples, yet display a strong linkage within the subset that reflects the active biological context. These situations are ideal for quantifying associations with the MCI. We illustrate the performance of the MCI in a variety of benchmarks and cancer genomic examples and applications. We also introduce the Cumulative Entropy Diagram (CED), a graphical display that allows the visualization of the MCI between a reference vector, such as a phenotype, pathway activation or drug response, and a complete set of genomic features.

bioinformatics↗

bioRxiv2026

Sex-divergent responses to microglial depletion suggest distinct regulatory dependencies in the aged brain

Microglia are essential for brain homeostasis, yet their roles in the aged brain remain poorly defined. Using microRNA (miRNA) profiling, cellular-resolution spatial transcriptomics, and bulk proteomics in 21-month-old mice, we characterize sex-dimorphic responses to microglial depletion via CSF1R inhibition (PLX5622 treatment). Microglia-enriched miRNAs, notably miR-146a-5p and miR-223-3p, were downregulated across different brain regions in both sexes. Transcriptional responses were sex dimorphic: females showed predominantly cell-type-specific downregulation, while males showed bidirectional changes including upregulation of Lzts3, Shank3, and Fgfbp1 alongside downregulation of Ang. Proteomic changes were larger in magnitude and independent from mRNA changes: males exhibited 295 differentially expressed proteins (DEPs) versus 34 in females (8.7-fold difference). Male DEPs had opposing directional shifts in synaptic vesicle proteins (upregulated) and mitochondrial ATP synthesis machinery (downregulated). These data describe sex-dimorphic molecular consequences of microglial loss in the aged brain and identify candidate post-transcriptional mechanisms warranting further investigation.

neuroscience↗

bioRxiv2026

Probabilistic mouse-human brain correspondence by multimodal optimal transport

The mouse is the principal model for brain mechanism and disease, allowing experiments that cannot be performed in humans. However, findings often translate poorly because homologous regions differ in relative size and some human territories have no clear mouse counterpart. Here we present OTTER, which learns mouse-human brain correspondence as a probabilistic, parcel-resolution coupling using multimodal fused Gromov-Wasserstein optimal transport, integrating functional and structural connectivity with spatial position and curated homologies. OTTER recovers established homologues on a transcriptomic benchmark and preserves broad cross-species organisation along the cortical areal hierarchy. Applying the coupling to human functional connectivity reveals a graded decline in mouse-based reconstruction across evolutionarily expanded association cortex, with the lowest values in lateral prefrontal territory. Finally, the bidirectional map generates testable human predictions from mouse experiments and ranks mouse circuits corresponding to human clinical targets.

neuroscience↗

bioRxiv2025

Anti-Aβ immunotherapy-mediated amyloid clearance attenuates microglial activation without inducing exhaustion at residual plaques

Anti-amyloid {beta}-peptide (A{beta}) immunotherapy was developed to reduce amyloid plaque pathology and slow cognitive decline during progression of Alzheimers disease. Efficient amyloid plaque clearance has been proven in clinical trials testing anti-A{beta} antibodies, with the impact on cognitive endpoints correlating with the extent of plaque removal. However, treatment is associated with adverse side-effects, such as oedema and haemorrhages, which are potentially linked to the induced immune response. To improve the safety profile of these molecules, it is imperative to understand the consequences of anti-A{beta} antibody treatment on immune cell function. Here, we investigated the effects of long-term chronic anti-A{beta} treatment on amyloid plaque pathology and microglial response in the APP-SAA triple knock-in mouse model. Mice were treated weekly with anti-A{beta} antibody from 4-8 months of age. Long-term treatment with anti-A{beta} results in a robust and dose-dependent removal of amyloid plaque pathology, with a higher efficiency for removing diffuse over dense-core plaques. Analysis of the CSF proteome indicates a reduction of markers for neurodegeneration including Tau and -Synuclein, as well as immune cell related proteins. Bulk RNA-seq revealed a dose-dependent decrease in brain-wide disease-associated microglial (DAM) and glycolytic gene expression, which is supported by a parallel decrease of glucose uptake and protein levels of Triggering receptor of myeloid cells 2 (Trem2) protein, a major immune receptor involved in DAM activation of microglia. In contrast, DAM activation around remaining plaques remains high regardless of treatment dose. In addition, microglia surrounding remaining plaques display a dose-dependent increase in microglial clustering and a selective increase in antigen presenting and immune signalling proteins. These findings demonstrate that long-term chronic anti-A{beta} mediated removal of A{beta} leads to a dose dependent decrease in brain-wide microglial DAM activation and neurodegeneration, while microglia at residual plaques display a combined DAM and antigen presenting phenotype that suggests a continued treatment response. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=161 SRC="FIGDIR/small/645950v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@6d1a49org.highwire.dtl.DTLVardef@d8c14dorg.highwire.dtl.DTLVardef@7ae330org.highwire.dtl.DTLVardef@1d96cc3_HPS_FORMAT_FIGEXP M_FIG Graphical abstract: Schematic overview of the effects of chronic long-term anti-A treatment in APP-SAA mice Schematic was created with BioRender.com C_FIG

neuroscience↗

bioRxiv2024

The Plastisphere: Marine fungi communities in the plastics age

Fungi play important roles in biofilms, are very versatile in their ecological role, and are considered as plastic degraders. Here we aim to increase the resolution of the fungal members of the Plastisphere, to understand fungal substrate specificities and related potential ecological impacts. Fifteen-month-old fungal Plastisphere communities were investigated on 9 different plastic types and glass in seawater from the North Sea. By integrating scanning electron microscopy (SEM) imaging, ITS-based fingerprinting, and re-evaluated 18S rRNA gene sequence data through a fungal-specific phylogeny-based pipeline, we observed fungal Plastispheres and identified specific characteristics based on morphotypes, phylogeny, and biodiversity across different substrate types. Plastic types selected for specific fungal communities with polyolefine communities indicating significantly higher diversity compared to all other plastic types. Furthermore, specific plastic types may select for specific fungal taxa and their potential hosts, highlighting the complexity of marine biofilm food webs, and related ecological implications.

microbiology↗

bioRxiv2024

Utilization and degradation of laminarin-based substrates by marine yeasts suggests their niche-specific role in microbial loop dynamics.

In the oceans, the diversity of phytoplankton primary products supports a wide range of microbial heterotrophs, including bacteria and fungi. The organic substrate dynamics within pelagic microbial communities are strongly controlled by microorganismal interactions, resulting in a dense interactome. While the role of bacteria in the microbial loop is well documented, the degradation capacity and substrate specificity of marine fungi, as well as their role and function in metabolic guilds with bacteria, is comparatively less understood. We chose the polysaccharide laminarin, a major product of marine primary production, as well as oligomeric laminarin subunits and monomeric glucose, to study the degradation capacity of eleven marine yeast isolates from the pelagic microbial community of Helgoland Roads. Our aim was to measure yeast growth and correlate degradation yields and putative intermediate degradation products with the size of laminarin-based organic precursor substrates. We developed a reproducible, temporally resolved, high-throughput growth protocol to measure resource-specific yeast growth. Measurement of temporally fine-scaled growth kinetic models of isolates were accompanied with qualitative and quantitative chemical analyses of substrates and degradation intermediates. Our data showed that yeast growth was negatively correlated with oligomer length. Fluorophore-assisted carbohydrate electrophoresis suggested the lack of enzymatic endo-activity for laminarin in yeasts under investigation, suggesting they may occupy a niche in the microbial loop, benefitting from extracellular hydrolysis of carbohydrates by other microorganisms. In terrestrial environments, namely forest soil ecosystems, yeasts have been assigned a similar niche, supporting a prominent role of yeasts in microbial interactomes.

microbiology↗

bioRxiv2024

Enhanced legumain activity links progranulin deficiency to TDP-43 pathology in frontotemporal lobar degeneration

Loss-of-function mutations in GRN are a major cause of frontotemporal lobar degeneration (FTLD) with TDP-43-positive inclusions. Progranulin (PGRN) loss leads to lysosomal dysfunction, microglial hyperactivation, and TDP-43 deposition, yet the underlying pathomechanism remains unknown. We demonstrate that PGRN slows the maturation and limits the proteolytic activity of the lysosomal protease legumain (LGMN). Accordingly, LGMN activity is strongly elevated in Grn knockout (ko) mice, in human induced pluripotent stem cell-derived GRN ko microglia, and in FTLD-GRN patients brain. Secreted microglial LGMN is internalized by neurons, where it mediates pathological processing of TDP-43, which is prevented by selective LGMN inhibition. In contrast, AAV-mediated overexpression of LGMN in mouse brains promotes TDP-43 processing, the aggregation of phosphorylated TDP-43 and increases plasma neurofilament light chain (NfL), a marker for neuronal loss. Our findings identify LGMN as a link between PGRN haploinsufficiency and TDP-43 pathology in FTLD-GRN and suggest LGMN as a therapeutic target.

neuroscience↗

bioRxiv2023

Rescue of FTLD-associated TDP-43 pathology and neurodegeneration by peripheral AAV-mediated expression of brain-penetrant progranulin

Progranulin (PGRN) haploinsufficiency is a major risk factor for frontotemporal lobar degeneration with TDP-43 pathology (FTLD-GRN). Multiple therapeutic strategies are in clinical development to restore PGRN levels in the CNS, including gene therapy. However, a limitation of current gene therapy approaches aimed to alleviate FTLD-associated pathologies may be their inefficient brain exposure and biodistribution. We therefore developed an adeno-associated virus (AAV) targeting the liver (L) to achieve sustained peripheral expression of a transferrin receptor (TfR) binding, brain-penetrant (b) PGRN variant (AAV(L):bPGRN) in two mouse models of FTLD-GRN, namely Grn knockout and GrnxTmem106b double knockout mice. This therapeutic strategy avoids potential safety and biodistribution issues of CNS-administered AAVs while maintaining sustained levels of PGRN in the brain following a single dose. AAV(L):bPGRN treatment reduced several FTLD-GRN associated disease pathologies including severe motor function deficits, aberrant TDP-43 solubility and phosphorylation, dysfunctional protein degradation, lipid metabolism, gliosis and neurodegeneration in the brain. Translatability of our findings was confirmed in a novel human in vitro model using co-cultured human induced pluripotent stem cell (hiPSC)-derived microglia lacking PGRN and TMEM106B and wild-type hiPSC-derived neurons. As in mice, aberrant TDP-43, lysosomal dysfunction and neuronal loss were ameliorated after treatment with exogenous TfR-binding protein transport vehicle fused to PGRN (PTV:PGRN). Together, our studies suggest that peripherally administered brain-penetrant PGRN replacement strategies can ameliorate FTLD-GRN relevant phenotypes including TDP-43 pathology, neurodegeneration and behavioral deficits. Our data provide preclinical proof of concept for the use of this AAV platform for treatment of FTLD-GRN and potentially other CNS disorders. One sentence summaryPeripheral AAV-mediated delivery of brain-penetrant PGRN rescues TDP-43 pathology, neurodegeneration and motor phenotypes in FTLD-GRN models.

neuroscience↗

bioRxiv2022

Characterizing Eastern spruce budworm's large-scale dispersal events through flight behavior and stable isotope analyses

Eastern spruce budworm moth (Choristoneura fumiferana (Clem.)) mass outbreaks have widespread economic and ecological consequences. A key explanation for the large-scale spread and synchronization of these outbreaks is the long-distance dispersal (up to 450km) of moths from hotspots (high-density populations) to lower-density areas. These events have proved difficult to monitor because dispersal flights occur only a few times a year, have no consistent routes, and commonly used tracking methods (e.g., population genetics, mark-recapture, radio telemetry) are inadequate for this system. Distinguishing between local and immigrant individuals is a crucial step in identifying the physical and ecological drivers of moth dispersal. Here we test whether isotopes of hydrogen (i.e., delta notation:{delta} 2H) and strontium (i.e., strontium isotope ratios: 87Sr/86Sr), known to independently vary in space in a predictable manner, can be used to distinguish between local and immigrant adult spruce budworm moths. We used an automated pheromone trap system to collect individuals at six different sites in eastern Canada within and outside the current outbreak area of budworm moths. We first use moth flight behaviour and time of capture, currently the best available tool, to determine putative local vs. immigrant status, and then evaluate whether individual 87Sr/86Sr and{delta} 2H differ between putative classes. At all sites, we detect immigrant individuals that differ significantly from putative locals. Saliently, sites where putative locals were sampled before the occurrence of potential immigration events (~10 days) showed the strongest differences between immigrant individuals and the locals 87Sr/86Sr and{delta} 2H values. Sites where the collection of putative locals was close in time (hours) or following an immigration event had a less-clear distinction between putative immigrants and locals, and showed signs of mixing between these two groups. We speculate that recent immigration could have led to the misclassification of immigrants as putative locals. 87Sr/86Sr and{delta} 2H data generally support the adequacy of current approaches using capture-time to detect immigration events, and provide enhanced resolution to distinguish between local and immigrant individuals. We discuss the broader implication of adding isotopes to the toolkit to monitor spruce budworm dispersal and suggest next steps in implementing these tools.

ecology↗

bioRxiv2022

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R, and GenePattern Notebook implementations of CoGAPS

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. Still, inferring biological processes requires additional post hoc statistics and annotation for interpretation of features learned from software packages developed for NMF implementation. Here, we aim to introduce a suite of computational tools that implement NMF and provide methods for accurate, clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations, and open questions in the field is followed by three vignettes for the Bayesian NMF algorithm CoGAPS (Coordinated Gene Activity across Pattern Subsets). Each vignette will demonstrate NMF analysis to quantify cell state transitions in public domain single-cell RNA-sequencing (scRNA-seq) data of malignant epithelial cells in 25 pancreatic ductal adenocarcinoma (PDAC) tumors and 11 control samples. The first uses PyCoGAPS, our new Python interface for CoGAPS that we developed to enhance runtime of Bayesian NMF for large datasets. The second vignette steps through the same analysis using our R CoGAPS interface, and the third introduces two new cloud-based, plug-and-play options for running CoGAPS using GenePattern Notebook and Docker. By providing Python support, cloud-based computing options, and relevant example workflows, we facilitate user-friendly interpretation and implementation of NMF for single-cell analyses.

systems biology↗

bioRxiv2021

Transcriptomics data availability and reusability in the transition from microarray to next-generation sequencing

Over the last two decades, molecular biology has been changed by the introduction of high-throughput technologies. Data sharing requirements have prompted the establishment of persistent data archives. A standardized approach for recording and managing these data was first proposed in the Minimal Information About a Microarray Experiment (MIAME) guidelines. The Minimal Information about a high throughput nucleotide Sequencing Experiment (MINSEQE) proposal was introduced in 2008 as a logical extension of the guidelines to next-generation sequencing (NGS) technologies used for transcriptome analysis. We present a historical snapshot of the data-sharing situation focusing on transcriptomics data from both microarray and RNA-sequencing experiments published between 2009 and 2013, a period during which RNA-seq studies became increasingly popular for transcriptome analysis. We assess how much data from RNA-seq based experiments is actually available in persistent data archives, compared to data derived from microarray based experiments, and evaluate how these types of data differ. Based on this analysis, we provide recommendations to improve RNA-seq data availability, reusability, and reproducibility.

bioinformatics↗

bioRxiv2019

The Tangent copy-number inference pipeline for cancer genome analyses

MotivationSomatic copy-number alterations (SCNAs) play an important role in cancer development. Systematic noise in sequencing and array data present a significant challenge to the inference of SCNAs for cancer genome analyses. As part of The Cancer Genome Atlas (TCGA), the Broad Institute Genome Characterization Center developed the Tangent copy-number inference pipeline to generate copy-number profiles using single-nucleotide polymorphism (SNP) array and whole-exome sequencing (WES) data from over 10,000 pairs of tumors and matched normal samples. Here, we describe the Tangent pipeline, which begins with DNA sequencing data in the form of .bam files or raw SNP array probe-level intensity data, and ends with segmented copy-number calls to facilitate the identification of novel genes potentially targeted by SCNAs. We also describe a modification of Tangent, Pseudo-Tangent, which enables denoising through comparisons between tumor profiles when few normal samples are available.\n\nResultsTangent Normalization offers substantial signal-to-noise ratio (SNR) improvements compared to conventional normalization methods in both SNP array and WES analyses. The improvement in SNRs is achieved primarily through noise reduction with minimal effect on signal. Pseudo-Tangent also reduces noise when few normal samples are available. Tangent and Pseudo-Tangent are broadly applicable and enable more accurate inference of SCNAs from DNA sequencing and array data.\n\nAvailability and ImplementationTangent is available at https://github.com/coyin/tangent and as a Docker image (https://hub.docker.com/r/coyin/tangent). Tangent is also the normalization method for the Copy Number pipeline in Genome Analysis Toolkit 4 (GATK4).\n\nContactmatthew_meyerson@dfci.harvard.edu, rameen@broadinstitute.org, gadgetz@broadinstitute.org

bioinformatics↗

bioRxiv2018

Cortical network fingerprints predict deep brain stimulation outcome in dystonia

BackgroundDeep brain stimulation (DBS) is an effective evidence-based therapy for dystonia. However, no unequivocal predictors of therapy responses exist. We investigate whether patients optimally responding to DBS present distinct brain network organization and structural patterns.\n\nMethodsBased on a German multicentre cohort of eighty-two dystonia patients with segmental and generalized dystonia, who received DBS implantation in the globus pallidus internus patients were classified based on the clinical response 36 months after DBS, as superior-outcome group or moderate-outcome group, as above or below 70% motor improvement, respectively. Fifty-one patients met MRI-quality and treatment response requirements (mean age 51.3 {+/-} 13.2 years; 25 female) and were included into further analysis. From preoperative MRI we assessed cortical thickness and structural covariance, which were then fed into network analysis using graph theory. We designed a support vector machine to classify subjects for the clinical response based on group network properties and individual grey matter fingerprints.\n\nResultsThe moderate-outcome group showed cortical atrophy mainly in the sensorimotor and visuomotor areas and disturbed network topology in these regions. From all the structural integrity of the cortical mantle explained about 45% of the stimulation amplitude. Classification analyses achieved 88% of accuracy using individual grey matter atrophy patterns to predict DBS outcome.\n\nConclusionsThe analysis of cortical integrity and network properties could be developed into independent predictors to identify dystonia patients who benefit from DBS.

neuroscience↗