Biology subjects

Valm, A. M.

Publications and source records attributed to Valm, A. M..

10 recordsLinked to original sources

bioRxiv2026

A decrease in specific health-associated commensals is linked to progressive periodontal tissue destruction independent of dysbiotic community profiles

Periodontitis is a chronic inflammatory disease associated with dysbiotic microbial communities that leads to destruction of the tooth-supporting tissues. The transition from host-microbial periodontal homeostasis to disease remains poorly understood. The murine ligature-induced periodontitis model was employed to characterize the temporal dynamics of the subgingival microbiome and host tissue features. Ligatures were placed in C57BL/6N mice, and collected on days 0, 1, 3, 5, and 7 post-induction. Bacterial load, alveolar bone loss, immune cells (CD45), cells with osteoclastogenic potential (TRAP) and collagen destruction were analyzed. Additionally, the V4 region of the 16S rRNA gene was sequenced for ecological analyses, including co-occurrence networks and functional prediction. Spatial distribution of the most abundant species was visualized using CLASI-FISH microscopy. Finally, association models were performed to link bacterial abundances with time and tissue parameters. The most substantial microbial shift occurred on day 1, and a dysbiotic community was established by day 3. CD45 cell infiltration increased as early as day 1, preceding the rise in TRAP cells on day 3 and the onset of tissue destruction on day 5. By day 7, predicted bacterial functions included protein export, lipid and galactose metabolism. Health-associated taxa were identified, and their abundance correlated positively with collagen integrity and negatively with immune cell infiltration and bacterial load, highlighting their role in homeostasis. These findings provide a high-resolution temporal map of microbiome-host interactions during experimental periodontitis establishment and identify specific microbial and cellular windows for potential therapeutic intervention.

microbiology↗

bioRxiv2026

Metagenomic and transcriptomic signatures of periodontitis in companion dogs

BackgroundPeriodontitis (Perio) is a progressive oral disease characterized by inflammation and degradation of the periodontal apparatus and is associated with local and systemic morbidity including loss of teeth, cardiovascular disease, and diabetes mellitus, among others. Perio is highly prevalent in domestic canines and exhibits certain parallels in pathogenesis and pathophysiology to Perio in humans, although standard treatments are less effective. In both species, a complex interplay between oral microbiota and host immune response is implicated in the etiology of Perio but is not fully understood. ResultsUsing shotgun metagenomics and RNA-seq on oral samples from companion dogs, we identify features of the oral microbiome and host transcriptional profile that are associated with Perio and its progression. We observe differences in microbiota composition between Perio and non-Perio animals that are largely consistent with what has been described in humans but also identify several species that are distinctly associated with canine Perio. We observe an abrupt shift in host gene expression related to immune response and tissue structure that is associated with disease severity, specifically the progression from mild periodontal disease (PD) to more severe Perio and the initiation of clinical attachment loss. The gingival plaque microbiota exhibits a parallel dynamic, with distinct compositional profiles in mild, moderate, and severe PD. We then examine several of the known mechanistic components of the keystone pathogen hypothesis of PD, identifying specific commonalities between canine and human pathologies, including the involvement of Porphyromonas species and related virulence factors. Additionally, we show infiltration of gingival tissue by Porphyromonas and Tannerella spp. via fluorescence microscopy. Finally, we assess correlations between host gene expression and microbial metabolic pathways which suggest additional potential virulence factors. ConclusionsThis work elucidates the metagenomic and transcriptomic signatures of Perio in companion dogs with the goals of informing veterinary medicine, evaluating the potential of canines as a model organism for the study of Perio, and clarifying the relationship between Perio development and progression, the oral microbiota, and the localized host response. Our findings provide insight into the etiopathogenesis of canine Perio and its relationship to human Perio and suggest novel targets of potential translational interest.

microbiology↗

bioRxiv2026

Cross hybridization Inference for Phylogenetic Resolution (CIPHR)-FISH enables microbiome imaging with strain level taxonomic resolution

The spatial organization of microbial communities is a critical determinant of host-microbe interactions, yet species-level mapping remains challenging due to high 16S rRNA sequence homology and spectral crosstalk in multiplexed fluorescence in situ hybridization (FISH). To address this challenge, we developed Cross-hybridization Inference for Phylogenetic Resolution (CIPHR)-FISH, a pipeline that integrates strategic probe design with supervised machine learning. CIPHR-FISH transforms probe cross-hybridization and spectral overlap, traditionally viewed as experimental noise, into informative molecular signatures. Using a gnotobiotic zebrafish model colonized with a defined mix of 10 zebrafish bacterial strains, we trained a support vector machine (SVM) on empirical hybridization patterns from pure bacterial cultures. CIPHR-FISH achieved 99.2 % macro-averaged accuracy, significantly outperforming standard linear unmixing (62.5 %), and successfully discriminated strains with 99.7% sequence homology. Applying this tool to gnotobiotic zebrafish larvae revealed distinct biogeographies: the intestinal bulb hosted highly structured, multi-layered polymicrobial aggregates, while the skin exhibited sparse, uniformly dispersed individual bacterial cells. Notably, we observed significant inter-individual variation in spatial community structure that was obscured by traditional bulk 16S rRNA sequencing. CIPHR-FISH provides a robust, scalable framework for high-resolution spatial biology by converting the limitations of molecular labeling into a rich data source for taxonomic classification. This approach enables the quantification of micro-scale ecological and stochastic forces that shape the microbiome across hosts.

microbiology↗

bioRxiv2026

BEEP Learning: Multi-View Image Decomposition for Massively Multiplexed Biological Fluorescence Microscopy

Fluorescence imaging with spectrally variant fluorophores allows the spatial mapping of biological structures with exquisite cellular and molecular specificity. However, the ability to robustly discriminate multiple fluorophores in any single imaging experiment is greatly hindered by the broad emission spectra of bio-compatible fluorophores and the large contribution of noise in low-energy regime fluorescence microscopy. In this study, we propose a novel machine learning framework, Bleaching-Excitation-Emission Photodynamics (BEEP) learning, that exploits multiple discriminatory features of fluorescent dyes to greatly expand the number of distinguishable objects in an image by integrating emission spectra, excitation variability, and bleaching dynamics into a unified multi-view, fluorescence unmixing approach. Our method is built upon a rank-one-tensor-based generalized linear model and leverages two biophysically grounded assumptions: consistent spectral and bleaching behaviors under fixed excitation, and invariant fluorophore abundances across excitations. We first extract excitation-specific spectral and bleaching signatures from reference images, and then use them to estimate abundances in complex mixtures. Experimental results on both simulated and real images of microbial populations demonstrate that our approach significantly outperforms conventional and partially multi-view methods, offering improved robustness and accuracy in highly multiplexed fluorescence imaging.

biophysics↗

bioRxiv2026

Inosine misincorporation into mRNA triggers the integrated stress response and activates an innate immune gene expression signature

The inosine triphosphate pyrophosphatase (ITPase) enzyme restricts levels of the non-canonical nucleotides (deoxy)inosine triphosphate (dITP/ITP) and prevents their aberrant misincorporation into nucleic acids. ITPase deficiency is associated with dilated cardiomyopathy and epileptic encephalopathy in humans and is usually fatal in infancy. It leads to pronounced inosine misincorporation into RNA but the cellular consequences of this misincorporation are not well understood and the pathogenic basis of ITPase deficiency remains unknown. Here we show that cellular transfection of mRNA with inosine misincorporation activates the integrated stress response (ISR) with an innate immune gene expression signature. This stress response triggers stress granule formation and is modulated by the double stranded RNA sensor protein Kinase R (PKR). Inosine nucleoside treatment of ITPase-deficient cells leads to endogenous inosine misincorporation into mRNA and activation of the ISR. Further, differentiation of human ITPase-deficient induced pluripotent stem cells into neurons results in a low-level stress response. Thus, our study establishes inosine misincorporation into mRNA as an unappreciated form of cellular stress. This is normally prevented by the ITPase enzyme, with implications for the pathogenesis of ITPase deficiency.

molecular biology↗

bioRxiv2025

LGBTQ+ realities in the biological sciences

While scientific environments have been described as unwelcoming to the LGBTQ+ community, and fields such as physics have systematically documented these challenges, the climate in biology workplaces has not been assessed. We conducted the largest survey to date of LGBTQ+ biologists to examine how their sense of belonging and perception of climate in the biology workplace and professional societies compare to that of their straight and cis peers. We surveyed 1419 biologists across five professional societies, with 486 identifying as LGBTQ+. Trans and gender non-conforming (TGNC) biologists reported lower belonging and morale within the workplace, professional societies, and the biology community compared to cis, straight biologists. They also reported being less comfortable with the climate of various professional biology environments. While LGBTQ+ biologists report that their workplaces are moderately inclusive, over 20% of all LGBTQ+ biologists and nearly 40% of TGNC biologists experience exclusionary behavior at work. This landmark survey provides the first comprehensive analysis of the LGBTQ+ climate in biology, revealing specific challenges faced by TGNC scientists and identifying interventions to enhance inclusivity for scientists. Significance StatementThis landmark study includes the largest known sample of LGBTQ+ biologists and offers the first comprehensive description of the LGBTQ+ climate in biology, differentiating between the experiences of cisgender lesbian, gay, bisexual, and queer (LGBQ) biologists and transgender and gender non-conforming (TGNC) biologists. The study found that compared to non-LGBTQ+ biologists, TGNC participants report lower belonging, morale and comfort with the climate across biology workplaces, professional societies, and the biology community. While on average LGBTQ+ participants reported that their workplaces are moderately inclusive, over 20% of all LGBTQ+ biologists and nearly 40% of TGNC biologists report experiencing exclusionary behaviors at work. The study offers immediate implications for institutional policies and professional development in the biological sciences.

scientific communication and education↗

bioRxiv2024

A Framework of Multi-View Machine Learning for Biological Spectral Unmixing of Fluorophores with Overlapping Excitation and Emission Spectra

The accuracy in assigning fluorophore identity and abundance, termed spectral unmixing, in biological fluorescence microscopy images remains challenging due to the unavoidable and significant overlap in emission spectra among fluorophores. In conventional laser scanning confocal spectral microscopy, fluorophore information is acquired by recording emission spectra with a single combination of discrete excitation wavelengths. As a matter of fact, organic fluorophores have not only unique emission spectral signatures but also have unique and characteristic excitation spectra. In this paper, we propose a generalized multi-view machine learning approach, which makes use of both excitation and emission spectra to greatly improve the accuracy in differentiating multiple highly overlapping fluorophores in a single image. By recording emission spectra of the same field with multiple combinations of excitation wavelengths, we obtain data representing these different views of the underlying fluorophore distribution in the sample. We then propose a framework of multi-view machine learning methods, which allows us to flexibly incorporate noise information and abundance constraints, to extract the spectral signatures of fluorophores from their reference images and to efficiently recover their corresponding abundances in unknown mixed images. Numerical experiments on simulated image data demonstrate the methods efficacy in improving accuracy, allowing for the discrimination of 100 fluorophores with highly overlapping spectra. Furthermore, validation on images of mixtures of fluorescently labeled E. coli demonstrates the power of the proposed multi-view strategy in discriminating fluorophores with spectral overlap in real biological images.

bioinformatics↗

bioRxiv2023

Gnotobiotic zebrafish microbiota display inter-individual variability affecting host physiology

Gnotobiotic animal models reconventionalized under controlled laboratory conditions with multi-species bacterial communities are commonly used to study host-microbiota interactions under presumably more reproducible conditions than conventional animals. The usefulness of these models is however limited by inter-animal variability in bacterial colonization and our general lack of understanding of the inter-individual fluctuation and spatio-temporal dynamics of microbiota assemblies at the micron to millimeter scale. Here, we show underreported variability in gnotobiotic models by analyzing differences in gut colonization efficiency, bacterial composition, and host intestinal mucus production between conventional and gnotobiotic zebrafish larvae re-conventionalized with a mix of 9 bacteria isolated from conventional microbiota. Despite similar bacterial community composition, we observed high variability in the spatial distribution of bacteria along the intestinal tract in the reconventionalized model. We also observed that, whereas bacteria abundance and intestinal mucus per fish were not correlated, reconventionalized fish had lower intestinal mucus compared to conventional animals, indicating that the stimulation of mucus production depends on the microbiota composition. Our findings, therefore, suggest that variable colonization phenotypes affect host physiology and impact the reproducibility of experimental outcomes in studies that use gnotobiotic animals. This work provides insights into the heterogeneity of gnotobiotic models and the need to accurately assess re-conventionalization for reproducibility in host-microbiota studies.

microbiology↗

bioRxiv2023

Fluorescence Image Data with Sparse and Low-Rank Poisson Regression

Multispectral biological fluorescence microscopy has enabled the identification of multiple targets in complex samples. The accuracy in the unmixing result degrades (1) as the number of fluorophores used in any experiment increases and (2) as the signal-to-noise ratio in the recorded images decreases. Further, the availability of prior knowledge regarding the expected spatial distributions of fluorophores in images of labeled cells provides an opportunity to improve the accuracy of fluorophore identification and abundance. We propose a regularized sparse and low-rank Poisson unmixing approach (SL-PRU) to deconvolve spectral images labeled with highly overlapping fluorophores which are recorded in low signal-to-noise regimes. Firstly, SL-PRU implements multi-penalty terms when pursuing sparseness and spatial correlation of the resulting abundances in small neighborhoods simultaneously. Secondly, SL-PRU makes use of Poisson regression for unmixing instead of least squares regression to better estimate photon abundance. Thirdly, we propose a method to tune the SL-PRU parameters involved in the unmixing procedure in the absence of knowledge of the ground truth abundance information in a recorded image. By validating on simulated and real-world images, we show that our proposed method leads to improved accuracy in unmixing fluorophores with highly overlapping spectra.

bioinformatics↗

bioRxiv2021

A feedback loop between heterochromatin and the nucleopore complex controls germ-cell to oocyte transition during Drosophila oogenesis

Germ cells differentiate into oocytes that become totipotent upon fertilization. How the highly specialized oocyte acquires this distinct cell fate is poorly understood. During Drosophila oogenesis, H3K9me3 histone methyltransferase SETDB1 translocates from the cytoplasm to the nucleus of germ cells concurrent with oocyte specification. Here, we discovered that nuclear SETDB1 is required to silence a cohort of differentiation-promoting genes by mediating their heterochromatinization. Intriguingly, SETDB1 is also required for the upregulation of 18 of the ~30 nucleoporins (Nups) that comprise the nucleopore complex (NPC). NPCs in turn anchor SETDB1-dependent heterochromatin at the nuclear periphery to maintain H3K9me3 and gene silencing in the egg chambers. Aberrant gene expression due to loss of SETDB1 or Nups results in loss of oocyte identity, cell death and sterility. Thus, a feedback loop between heterochromatin and NPCs promotes transcriptional reprogramming at the onset of oocyte specification that is critical to establish oocyte identity.

developmental biology↗