Biology subjects

Newman, S.

Publications and source records attributed to Newman, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Integrative multi-omic analysis identifies tumor-intrinsic p38 as a driver of immune exclusion in human epithelial cancers

Patients with tumors not responding to immune-checkpoint inhibition (ICI) often harbor a non-T cell-inflamed tumor microenvironment, characterized by the absence of IFN-{gamma}-associated CD8+ T cells and dendritic cell activation. While the role of p38 mitogen-activated protein kinases (MAPKs) in regulating dendritic and myeloid cells is established, the tumor-intrinsic immunomodulatory function of p38 remains underexplored. Here, we identify tumor cell-intrinsic p38 signaling as a target candidate associated with immune exclusion and reduced immunotherapy response. In human papillomavirus-negative head and neck squamous carcinoma (HNSCC), molecular analysis of 395 tumor tissues revealed a p38-centered network enriched in non-T cell-inflamed tumors. Multi-cancer single-cell RNA sequencing analysis of over 200,000 cells further identifies p38 activation as a potential immune-exclusion program across multiple epithelial tumor types, including HNSCC and lung squamous cell carcinoma (LUSC), supported by tissue validation in [~]250 human biospecimens using multispectral imaging and digital spatial profiling. Functional studies demonstrate that p38 knockdown or pharmacologic inhibition in HNSCC and LUSC cell lines increases T cell migration, with CXCL16 identified as a chemokine mediator in vitro; neutralization of CXCL16 attenuated this effect. Together, these findings identify tumor-intrinsic p38 activation as a driver of immune exclusion in epithelial cancers and support its potential as a therapeutic target to overcome immunotherapy resistance.

Cancer Biology↗

bioRxiv2026

Complement modulation synergizes with therapeutic hypothermia in a rat model of neonatal HIE

BackgroundNeurodevelopmental impairment remains common in neonatal hypoxic-ischemic encephalopathy (HIE) despite treatment with the standard of care, therapeutic hypothermia (TH). The complement response activates at reperfusion and is known to exacerbate neuroinflammation and injury, though its full role and interaction with hypothermia are incompletely defined. We hypothesized that modulating the complement response could improve structural and functional outcomes in HIE, and tested a novel complement therapy (CT), consisting of C3a peptides and the C5a-receptor antagonist PMX205, as both a stand-alone treatment and as an adjuvant to TH. MethodsWistar rat pups were randomized to the following treatment groups: Sham (uninjured control), NT (uninjured, normothermia/not treated control), or injured and treated with either TH, CT, or CT+TH. At term-equivalence, mild-moderate hypoxic-ischemic injury was induced by Vannuccis method. To capture the short and long-term effects of the treatments, cohorts were harvested 3 or 66-72 days post-injury, respectively. Cerebral injury was measured by quantifying levels of inflammatory markers and cerebral tissue loss, and functional outcomes were assessed in a series of behavioral tests. The data were stratified to detect sexual dimorphisms. ResultsCT and TH treatments demonstrated test and sex-dependent differences in improvement compared to untreated, injured rats. In male rats, TH treatment worsened long-term hippocampal and thalamic brain injury and functional measures of ataxia and attention. CT-treatment worsened long-term thalamic loss in females. Combining the two treatments (CT+TH) demonstrated additive improvement in both sexes, including short and long-term cortical loss and ataxia. ConclusionsComplement modulation enhances the neuroprotective effects of TH after neonatal hypoxic-ischemic injury, with sex-specific effects on inflammation and behavior. Combining complement modulation with the standard of care often demonstrated synergistic improvement in both sexes, supporting complement-targeted therapy as a promising adjunct to hypothermia in neonatal HIE. Graphical abstract. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/717097v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1025d1forg.highwire.dtl.DTLVardef@2fa4e5org.highwire.dtl.DTLVardef@1f2c1c4org.highwire.dtl.DTLVardef@8f3410_HPS_FORMAT_FIGEXP M_FIG C_FIG Created with BioRender. Saadat, A. (2026) https://BioRender.com/siwm825.

neuroscience↗

bioRxiv2025

Reliable repurposing of antibody interactome inside the cell

In biology proximity is paramount and eighty-five percent of the human proteome has at least one documented interacting monoclonal antibody. These molecules penetrate the cytoplasm poorly and are very often non-functional within the cell. Sequence analysis of 106 antibody variable domains alongside the cytoplasmic human proteome shows charge and isoelectric point are characteristics ill adapted to intracellular monodispersity. Characterisation of forty-five single-chain variable fragment (scFv) intrabodies expressed in human cells confirmed charge to have the greatest impact on solubility. We created new interdomain linkers, optimised scFv domain orientation and found variable heavy domain framework sites to be generally positively charged, and promote insolubility, but be amenable to optimisation. This is applied in combination to reduce the search space and refine the products of AI-led inverse folding to create highly soluble, abundant and thermally stable intrabodies that maintain parent antibody epitope recognition. Over six hundred intrabody sequences are described targeting sixty cytoplasmic proteins with linear, conformational, post-translational modification or oligomeric state specificity. Interactions were validated for p53, -synuclein, SOD1, polyQ, FUS/TLS, UCHL1 and GFP. This approach removes obstacles hindering intracellular repurposing of the vast sequenced antibody interactome with applications relevant to many human disease states.

molecular biology↗

bioRxiv2025

Active learnings impact on student course performance in STEM varies by type and intensity

We updated a recent meta-analysis of active learnings impact on student achievement in undergraduate STEM courses by following the same protocol to evaluate studies published from 2010-2017. We screened 1659 papers, coded 1294, and found 210 that met five pre-established inclusion criteria and six pre-established criteria for methodological quality. After further dropping 76 studies with no exam scores data, 134 of these studies contained data on student performance on identical or equivalent exams. We found that on average, active learnings effect size on exam scores was 0.519 {+/-} 0.049, meaning that when students are in active learning classes, they perform roughly half a standard deviation higher on an identical exam. Funnel plots and sensitivity analyses indicated that these results were not due to sampling bias. Active learning had a positive impact on student outcomes regardless of class size, course level, or STEM discipline, though there was heterogeneity in the effects. All of these results are very similar when compared to earlier meta-analyses, however increased resolution in the studies analyzed here revealed two novel results. First, student performance was significantly better in courses that employed high-intensity active learning, defined as students being on task at least two-thirds of class time, versus lower-intensities. Additionally, there was significant heterogeneity in efficacy across different types of active learning employed. These results suggest that most, if not all types of active learning are effective, and that when innovating in their classes, instructors should continually work to increase active learning intensity. We urge caution in interpreting the results on active learning types, however, and propose a preliminary framework for making more-sophisticated and reliable analyses of variation in course design. Finally, the evidence presented here for active learnings impact on student outcomes creates a strong foundation for faculty professional development and administration.

scientific communication and education↗

bioRxiv2024

A multiplexed and quantitative assay for small-molecule detection via photo-crosslinking of structure switching aptamers

There is an unmet need for molecular detection assays that enable the multiplexed, quantification of smallmolecule analytes. We present xPlex, an assay that combines aptamer switches with ultraviolet-crosslinkable complementary strands to record target-binding events. When the aptamers small-molecule target is present, the crosslinkable strand is displaced, enabling PCR amplification and detection of the relevant aptamer. In the absence of that target, the aptamer is readily cross-linked to the strand, preventing amplification from happening. The resulting aptamer-specific amplicons can be detected and quantified in a multiplexed fashion using high-throughput sequencing. We demonstrate quantitative performance for a pair of small-molecule analytes, dopamine and glucose, and show that this assay retains good specificity with mixtures of the two molecules at various concentrations. We further show that xPlex can effectively evaluate the specificity of cross-reactive aptamers to a range of different small-molecule analytes. We believe the xPlex assay format could offer a useful strategy for achieving multiplexed analysis of small-molecule targets in a variety of scenarios.

bioengineering↗

bioRxiv2024

Genome-wide profiling of highly similar paralogous genes using HiFi sequencing

Variant calling is hindered in segmental duplications by sequence homology. We developed Paraphase, a HiFi-based informatics method that resolves highly similar genes by phasing all haplotypes of a gene family. We applied Paraphase to 160 long (>10 kb) segmental duplication regions across the human genome with high (>99%) sequence similarity, encoding 316 genes. Analysis across five ancestral populations revealed highly variable copy numbers of these regions. We identified 23 families with exceptionally low within-family diversity, where extensive gene conversion and unequal-crossing over have resulted in highly similar gene copies. Furthermore, our analysis of 36 trios identified 7 de novo SNVs and 4 de novo gene conversion events, 2 of which are non-allelic. Finally, we summarized extensive genetic diversity in 9 medically relevant genes previously considered challenging to genotype. Paraphase provides a framework for resolving gene paralogs, enabling accurate testing in medically relevant genes and population-wide studies of previously inaccessible genes.

genomics↗

bioRxiv2023

Selective advantage of mutant stem cells in clonal hematopoiesis occurs by attenuating the deleterious effects of inflammation and aging

Clonal hematopoiesis (CH) arises when hematopoietic stem cells (HSC) acquire mutations in genes, including DNMT3A and TET2, conferring a competitive advantage through a mechanism that remains unclear. To gain insight into how CH mutations enable gradual clonal expansion, we used single-cell multi-omics with high-fidelity genotyping on CH bone marrow samples. Most of the selective advantage of mutant cells occurs within HSCs. DNMT3A and TET2-mutant clones expand further in early progenitors, while TET2 mutations accelerate myeloid maturation in a dose-dependent manner. Unexpectedly, both mutant and non-mutant HSCs from CH samples are enriched for inflammatory and aging transcriptomic signatures, compared to HSC from non-CH samples, revealing a non-cell autonomous mechanism. However, DNMT3A and TET2-mutant HSCs have an attenuated inflammatory response relative to wild-type HSCs within the same sample. Our data support a model whereby CH clones are gradually selected because they are more resistant to the deleterious impact of inflammation and aging.

cancer biology↗

bioRxiv2023

Identification of tumor-intrinsic drivers of immune exclusion in acral melanoma

BackgroundAcral melanoma (AM) has distinct characteristics as compared to cutaneous melanoma and exhibits poor response to immune checkpoint inhibitors (ICI). Tumor-intrinsic mechanisms of immune exclusion have been identified in many cancers but less studied in AM. MethodsWe characterized clinically annotated tumors from patients diagnosed with AM at our institution in correlation with ICI response using whole transcriptome RNAseq, whole exome sequencing, CD8 immunohistochemistry, and multispectral immunofluorescence imaging. A defined interferon-{gamma}-associated T cell-inflamed gene signature was used to categorize tumors into non-T cell-inflamed and T cell-inflamed phenotypes. In combination with AM tumors from two published studies, we systematically assessed the immune landscape of AM and detected differential gene expression and pathway activation in a non-T cell-inflamed tumor microenvironment (TME). Two single-cell(sc) RNAseq AM cohorts and 11 bulk RNAseq cohorts of various tumor types were used for independent validation on pathways associated with lack of ICI response. In total, 892 specimens were included in this study. Results72.5% of AM tumors showed low expression of the T cell-inflamed gene signature, with 23.9% of total tumors categorized as the non-T cell-inflamed phenotype. Patients of low CD3+CD8+PD1+ intratumoral T cell density showed poor prognosis. We identified 11 oncogenic pathways significantly upregulated in non-T cell-inflamed relative to T cell-inflamed TME shared across all three acral cohorts (MYC, HGF, MITF, VEGF, EGFR, SP1, ERBB2, TFEB, SREBF1, SOX2, and CCND1). scRNAseq analysis revealed that tumor cell-expressing pathway scores were significantly higher in low vs high T cell-infiltrated AM tumors. We further demonstrated that the 11 pathways were enriched in ICI non-responders compared to responders across cancers, including acral melanoma, cutaneous melanoma, triple-negative breast cancer, and non-small cell lung cancer. Pathway activation was associated with low expression of interferon stimulated genes, suggesting suppression of antigen presentation. Across the 11 pathways, fatty acid synthase and CXCL8 were unifying downstream target molecules suggesting potential nodes for therapeutic intervention. ConclusionsA unique set of pathways is associated with immune exclusion and ICI resistance in AM. These data may inform immunotherapy combinations for immediate clinical translation.

cell biology↗

bioRxiv2022

Dietary restriction of isoleucine increases healthspan and lifespan of genetically heterogeneous mice

Low protein (LP) diets promote health and longevity in diverse species. Although the precise components of an LP diet that mediate its beneficial effects have not been defined, reducing dietary levels of the three branched-chain amino acids (BCAAs) leucine, isoleucine and valine promotes metabolic health in both sexes, and increases lifespan while reducing frailty in male, but not female, C57BL/6J mice. Each BCAA has unique metabolic effects, and we recently showed that restriction of isoleucine is both sufficient to promote metabolic health and required for the metabolic benefits of an LP diet in male C57BL/6J mice. Here, we tested the hypothesis that specifically restricting isoleucine could promote healthy aging in genetically heterogenous UM-HET3 mice. We find that a reduced isoleucine diet improves the metabolic health of both young and old HET3 mice, promoting leanness and glycemic control. Restriction of isoleucine starting in adult, 6 month old HET3 mice reprograms hepatic metabolism in a way distinct from an LP diet. Finally, we find that a reduced isoleucine diet reduces frailty and extends the lifespan of both male and female HET3 mice, but to a much greater degree in males. Our results demonstrate that restricting dietary isoleucine can increase health span and longevity in a genetically diverse population of mice, and suggests that reducing dietary levels of isoleucine may have great potential as a geroprotective intervention.

physiology↗

bioRxiv2022

Denoising diffusion weighted imaging data using convolutional neural networks

Diffusion weighted imaging (DWI) with multiple, high b-values is critical for extracting tissue microstructure measurements; however, high b-value DWI images contain high noise levels that can overwhelm the signal of interest and bias microstructural measurements. Here, we propose a simple denoising method that can be applied to any dataset, provided a low-noise, single-subject dataset is acquired using the same DWI sequence. The denoising method uses a one-dimensional convolutional neural network (1D-CNN) and deep learning to learn from a low-noise dataset, voxel-by-voxel. The trained model can then be applied to high-noise datasets from other subjects. We validated the 1D-CNN denoising method by first demonstrating that 1D-CNN denoising resulted in DWI images that were more similar to the noise-free ground truth than comparable denoising methods, e.g., MP-PCA, using simulated DWI data. Using the same DWI acquisition but reconstructed with two common reconstruction methods, i.e. SENSE1 and sum-of-square, to generate a pair of low-noise and high-noise datasets, we then demonstrated that 1D-CNN denoising of high-noise DWI data collected from human subjects showed promising results in three domains: DWI images, diffusion metrics, and tractography. In particular, the denoised images were very similar to a low-noise reference image of that subject, more than the similarity between repeated low-noise images (i.e. computational reproducibility). Finally, we demonstrated the use of the 1D-CNN method in two practical examples to reduce noise from parallel imaging and simultaneous multi-slice acquisition. We conclude that the 1D-CNN denoising method is a simple, effective denoising method for DWI images that overcomes some of the limitations of current state-of-the-art denoising methods, such as the need for a large number of subjects for training and accounting for the rectified noise floor.

neuroscience↗

bioRxiv2021

A simple and direct method to define clonal selection in somatic mosaicism

As DNA variants accumulate in somatic stem cells, become selected or evolve neutrally, they may ultimately alter tissue function. When, and how, selection occurs in homeostatic tissues is incompletely understood. Here, we introduce SCIFER, a scalable method that identifies selection in an individual tissue, without requiring knowledge of the underlying driver event. Moreover, SCIFER infers the self-renewal and mutation dynamics of the tissues stem cells, and, if selection is present, the size and growth rate of the largest selected clone. We benchmark SCIFER with published data and then probe bone marrow of 22 non-leukemic individuals for clonal hematopoiesis (CH), identifying CH with known and unknown driver events. Unexpectedly, we find accelerated division of all stem cells in CH, compared to age-matched non-CH individuals, suggesting that the bone marrow environment alters stem cell dynamics in individuals with CH. SCIFER is broadly applicable to renewing somatic tissues to detect and quantify selection.

cancer biology↗

bioRxiv2020

Collegiate athlete brain data for white matter mapping and network neuroscience

We describe a dataset of processed data with associated reproducible preprocessing pipeline collected from two collegiate athlete groups and one non-athlete group. The dataset shares minimally processed diffusion-weighted magnetic resonance imaging (dMRI) data, three models of the diffusion signal in the voxel, full-brain tractograms, segmentation of the major white matter tracts as well as structural connectivity matrices. There is currently a paucity of similar datasets openly shared. Furthermore, major challenges are associated with collecting this type of data. The data and derivatives shared here can be used as a reference to study the effects of long-term exposure to collegiate athletics, such as the effects of repetitive head impacts. We use advanced anatomical and dMRI data processing methods publicly available as reproducible web services at brainlife.io.

neuroscience↗

bioRxiv2020

St. Jude Cloud a Pediatric Cancer Genomic Data Sharing Ecosystem

Effective data sharing is key to accelerating research that will improve the precision of diagnoses, efficacy of treatments and long-term survival of pediatric cancer and other childhood catastrophic diseases. We present St. Jude Cloud (https://www.stjude.cloud), a cloud-based data sharing ecosystem developed via collaboration between St. Jude Childrens Research Hospital, DNAnexus, and Microsoft, for accessing, analyzing and visualizing genomic data from >10,000 pediatric cancer patients, long-term survivors of pediatric cancer and >800 pediatric sickle cell patients. Harmonized genomic data totaling 1.25 petabyes on St. Jude Cloud include 12,104 whole genomes, 7,697 whole exomes and 2,202 transcriptomes, which are freely available to researchers worldwide. The resource is expanding rapidly with regular data uploads from St. Judes prospective clinical genomics programs, providing public access as soon as possible rather than holding data back until publication. Three interconnected apps within the St. Jude Cloud ecosystem--Genomics Platform, Pediatric Cancer Knowledgebase (PeCan) and Visualization Community--provide a unique experience for simultaneously performing advanced data analysis in the cloud and enhancing the pediatric cancer knowledgebase. We demonstrate the value of the St. Jude Cloud ecosystem through use cases that classify 48 pediatric cancer subtypes by gene expression profiling and map mutational signatures across 35 subtypes of pediatric cancer.

cancer biology↗

bioRxiv2020

Identifying Modules of Cooperating Cancer Drivers

AbstractIdentifying cooperating modules of driver alterations can provide biological insights to cancer causation and would advance the development of effective personalized treatments. We present Cancer Rule-Set Optimization (CRSO) for inferring the combinations of alterations that cooperate to drive tumor formation in individual patients. Application to 19 TCGA cancer types found a mean of 11 core driver combinations per cancer, comprising 2-6 alterations per combination, and accounting for a mean of 70% of samples per cancer. CRSO departs from methods based on statistical cooccurrence, which we demonstrate is a suboptimal criterion for investigating driver cooperation. CRSO identified well-studied driver combinations that were not detected by other approaches and nominated novel combinations that correlate with clinical outcomes in multiple cancer types. Novel synergies were identified in NRAS-mutant melanomas that may be therapeutically relevant. Core driver combinations involving NFE2L2 mutations were identified in four cancer types, supporting the therapeutic potential of NRF2 pathway inhibition. CRSO is available at https://github.com/mikekleinsgit/CRSO/.Competing Interest StatementThe authors have declared no competing interest.View Full Text

cancer biology↗

bioRxiv2019

A resource of targeted mutant mouse lines for 5,061 genes

The International Mouse Phenotyping Consortium reports the generation of new mouse mutant strains for over 5,000 genes from targeted embryonic stem cells on the C57BL/6N genetic background. This includes 2,850 null alleles for which no equivalent mutant mouse line exists, 2,987 novel conditional-ready alleles, and 4,433 novel reporter alleles. This nearly triples the number of genes with reporter alleles and almost doubles the number of conditional alleles available to the scientific community. When combined with more than 30 years of community effort, the total mutant allele mouse resource covers more than half of the genome. The extensively validated collection is archived and distributed through public repositories, facilitating availability to the worldwide biomedical research community, and expanding our understanding of gene function and human disease.

genetics↗

bioRxiv2019

RNAIndel: a machine-learning framework for discovery of somatic coding indels using tumor RNA-Seq data

Reliable identification of expressed somatic insertion/deletion (indels) is an unmet demand due to artifacts generated in PCR-based RNA-Seq library preparation and the lack of normal RNA-Seq data, presenting analytical challenges for discovery of somatic indels in tumor trasncriptome.\n\nBy implementing features characterized by PCR-free whole-genome and whole-exome sequencing into a machine-learning framework, we present RNAIndel, a tool for predicting somatic, germline and artifact indels from tumor RNA-Seq data alone. RNAIndel robustly predicts 87{square}93% of somatic indels from 235 samples with heterogeneous conditions, even recovering subclonal (VAF range 0.01-0.15) driver indels missed by targeted deep-sequencing, outperforming the current best-practice for RNA-Seq variant calling which had 57% sensitivity but with 12 times more false positives.\n\nRNAIndel is freely available at https://github.com/stjude/RNAIndel\n\nContactjinghui.zhang@stjude.org

bioinformatics↗

bioRxiv2018

Association of physiological variables with football subconcussive head impacts: Why measure?

Subconcussive head impacts, defined as impacts to the cranium that do not result in clinical symptoms of concussion, are gaining traction as a major public health concern. Researchers begin to suggest subconcussive impact-dependent changes in various neurological measures. However, a contribution of physiological factors such as physical exertion and muscle damage has never been accounted. We conducted a prospective longitudinal study during a high school American football season to examine the association between physiological factors and subconcussive head impact kinematics. Fifteen high-school American football players volunteered in the study. A sensor-installed mouthguard recorded the number of head impacts, peak linear (PLA: g) and peak rotational (PRA: rad/s2) head accelerations from every practice and game. Serum samples were collected at 12 time points (pre-season baseline, five in-season pre-post games, and post-season) and assessed for the creatine kinase skeletal muscle-specific isoenzyme (CK-MM), as a surrogate for skeletal muscle damage. Physical exertion was estimated in the form of excess post-exercise oxygen consumption (EPOC) from heart rate data captured during five games via a wireless heart rate monitor. A total of 9,700 hits, 214,492 g, and 19,885,037 rad/s2 were recorded from 15 players across the study period. Mixed-effect regression models indicated that head impact kinematics (frequency, PLA, and PRA) were significantly and positively associated with CK-MM increase, but not with EPOC. There was a significant and positive association between CK-MM and EPOC. These data suggest that skeletal muscle damage effects should be considered when using outcome measures that may have an interaction with muscle damage, including inflammatory biomarkers and vestibular/balance tests.

physiology↗

bioRxiv2018

Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE): A Cloud-based Platform for Curating and Classifying Germline Variants

Variant interpretation in the era of next-generation sequencing (NGS) is challenging. While many resources and guidelines are available to assist with this task, few integrated end-to-end tools exist. Here we present \"PeCanPIE\" - the Pediatric Cancer Variant Pathogenicity Information Exchange, a web- and cloud-based platform for annotation, identification, and classification of variations in known or putative disease genes. Starting from a set of variants in Variant Call Format (VCF), variants are annotated, ranked by putative pathogenicity, and presented for formal classification using a decision-support interface based on published guidelines from the American College of Medical Genetics and Genomics (ACMG). The system can accept files containing millions of variants and handle single-nucleotide variants (SNVs), simple insertions/deletions (indels), multiple-nucleotide variants (MNVs), and complex substitutions. PeCanPIE has been applied to classify variant pathogenicity in cancer predisposition genes in two large-scale investigations involving >4,000 pediatric cancer patients, and serves as a repository for the expert-reviewed results. While PeCanPIEs web-based interface was designed to be accessible to non-bioinformaticians, its back end pipelines may also be run independently on the cloud, facilitating direct integration and broader adoption. PeCanPIE is publicly available and free for research use.

bioinformatics↗