Biology subjects

Jain, E.

Publications and source records attributed to Jain, E..

5 recordsLinked to original sources

bioRxiv2025

Single cell decomposition of multicellular aging programs associated with impaired lung regeneration

Aging impairs the regenerative capacity of mammalian organs and is a major risk factor for organ fibrosis. Mechanisms underlying persistent fibrosis after lung injury in old individuals remain unclear. We used longitudinal single-cell RNA-seq after lung injury and dissected aging effects computationally and experimentally at baseline and during repair. In old mice, sustained fibroblast activation in the resolution phase of fibrosis was associated with prolonged epithelial senescence and persistent epithelial-mesenchymal crosstalk. Single-cell interpretable tensor decomposition analysis revealed that aging most strongly affected T/B-lymphocytes and macrophages. Notably, we identified a Granzyme K-high CD8+ T cell state that was unique to aged mice, co-localized with epithelial progenitors, and its co-culture or Gzmk treatments in lung organoids impaired progenitor function by inducing stem cell senescence. In summary, our study highlights the effects of immune aging on epithelial progenitor function and provides a time-resolved high resolution map of lung regeneration in the context of aging.

cell biology↗

bioRxiv2023

Technical Note: DeepLabCut-Display: open-source desktop application for visualizing and analyzing two-dimensional gait parameter data in livestock

AbstractGait assessments are a key part of determining the wellbeing of livestock. Techniques for gait assessment have traditionally involved human-eye inspections or reflective markers, but markerless computer vision methods have been developed in recent years. Despite many computer vision tools providing high-quality pose estimations in an efficient manner, they lack post-processing functionality. A review of model performance and calculation of gait parameters is a necessary step to fully harness the capability of this new technology. Thus, this study developed DeepLabCut-Display, an open-source desktop software application. DeepLabCut-Display allows a user to upload the video and coordinate data associated with the output of DeepLabCut, a prominent pose-estimation software tool. A user can review the video and coordinate data in parallel, filter points by a likelihood threshold, and automatically calculate gait parameters. Specific video frames, filtered data, and gait parameters can be exported from the application for further usage. The source code is publicly hosted on GitHub alongside installation and usage instructions. DeepLabCut-Display, the product of interdisciplinary and collaborative design between software developers and animal scientists, will alleviate a critical bottleneck in processing of data for locomotor analysis in livestock. Summary StatementO_LIDeepLabCut-Display is a utility to dynamically visualize raw marker coordinates, and to automatically produce gait parameters for locomotion analysis of horses and other livestock. C_LI Lay SummaryArtificial intelligence systems that can predict and track the positions of objects are now being applied in many fields, including animal science. Veterinarians and animal scientists use these systems to create pose estimations, a digital label of anatomical landmarks overlaid on a video of an animal in motion. They are used to quantify the subjects motion and detect anomalies that may be indicative of disease or injury. Pose estimation systems are efficient and accurate, but they lack features like data visualization and post-processing analysis that are necessary to make determinations about the animals motion. This study developed DeepLabCut-Display, a software application that can visualize the data from a pose estimation system and provides a set of tools for further analysis. After a user is done with analysis, they can save the results to their computer. The application was made by a collaboration between software developers and animal scientists, highlighting how interdisciplinary teams are effective at producing useful software.

animal behavior and cognition↗

bioRxiv2023

Primary human lung fibroblasts exhibit trigger- but not disease-specific cellular senescence and impair alveolar epithelial cell progenitor function

Aging is the main risk factor for chronic lung diseases including idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD). Accordingly, hallmarks of aging such as cellular senescence are increased in different cell types such as fibroblasts in the lungs of these patients. However, whether the senescent phenotype of fibroblasts derived from IPF or COPD differs is still unknown. Therefore, we characterized senescence at baseline and after exposure to disease-relevant insults (H2O2, bleomycin, and TGF-{beta}1) in cultured primary human lung fibroblasts (phLF) from control donors, IPF, or COPD patients. We found that phLF from different disease-origins have a low baseline senescence. H2O2 and bleomycin treatment induced a senescent phenotype in phLF whereas TGF-{beta}1 only had a pro-fibrotic effect. Interestingly, we did not observe any differences in susceptibility to senescence induction in phLF based on disease origin. However, exposure to different stimuli resulted in different senescent programs in phLF. Moreover, senescent phLF reduced colony formation efficiency of alveolar epithelial progenitor cells. In conclusion, the senescent phenotype of phLF is mainly determined by the senescence inducer and impairs alveolar epithelial progenitor capacity in vitro.

cell biology↗

bioRxiv2019

The Angiosarcoma Project: enabling genomic and clinical discoveries in a rare cancer through patient-partnered research

Despite collectively accounting for 25% of tumors in U.S. adults, rare cancers have significant unmet clinical needs as they are difficult to study due to low incidence and geographically dispersed patient populations. We sought to assess whether a patient-partnered research approach using online engagement can overcome these challenges and accelerate scientific discovery in rare cancers, focusing on angiosarcoma (AS), an exceedingly rare sarcoma with a dismal prognosis and an annual U.S. incidence of 300 cases. Here, we describe the development of the Angiosarcoma Project (ASCproject), an initiative enabling patients across the U.S. and Canada to remotely share their clinical information and biospecimens for research. The project generates and publicly releases clinically annotated genomic data on tumor and germline specimens on an ongoing basis. Over 18 months, 338 AS patients registered for the ASCproject, comprising a significant fraction of all patients. Whole exome sequencing of 47 AS tumors revealed several recurrently mutated genes, including KDR, TP53, and PIK3CA. Activating mutations in PIK3CA were observed nearly exclusively in primary breast AS, suggesting a therapeutic rationale in these patients. AS of the head, neck, face, and scalp (HNFS) was associated with high tumor mutation burden and a dominant mutational signature of UV light exposure, suggesting that UV damage may be a causative factor in HNFS AS and that this AS subset might be amenable to immune checkpoint inhibitor therapy. Medical record review revealed two patients with HNFS AS received off-label treatment with anti-PD-1 therapy and experienced exceptional responses, highlighting immune checkpoint inhibition as a therapeutic avenue for HNFS AS. This patient-partnered approach has catalyzed an opportunity to discover the etiology and potential therapies for AS patients. Collectively, this proof of concept study demonstrates that empowering patients to directly participate in research can overcome barriers in rare diseases and enable biological and clinical discoveries.

cancer biology↗

bioRxiv2019

Prevalence and mutational determinants of high tumor mutation burden in breast cancer

BackgroundHigh tumor mutation burden (TMB) has been associated with benefit to immunotherapy in multiple tumor types. However, the prevalence of hypermutated breast cancer is not well described. The aim of this study is to evaluate frequency, mutational patterns, and genomic profile of hypermutated breast cancer.\n\nPatients and MethodsWe used de-identified data from individuals with primary or metastatic breast cancer from six different publicly available genomic studies. The prevalence of hypermutated breast cancer was determined among 3969 patients samples that underwent whole exome sequencing or gene panel sequencing. Samples were classified as having high TMB if they had [≥]10 mutations per megabase (mut/Mb). An additional 8 patients were identified from a Dana-Farber Cancer Institute cohort for inclusion in the hypermutated cohort. Among patients with high TMB, the mutational patterns, and genomic profile were determined. A subset of patients was treated with regimens containing PD-1 inhibitors.\n\nResultsThe median TMB was 2.63 mut/Mb. Median TMB significantly varied according to tumor subtype (HR-/HER2-> HER2+ > HR+/HER2-, p < 0.05) and sample type (metastatic > primary, p 2.2x10-16). Hypermutated tumors were found in 198 patients (5%), with an enrichment in metastatic versus primary tumors (8.4% versus 2.9%, p = 6.5 x 10-14). APOBEC activity (59.2%), followed by mismatch repair deficiency (MMRd; 36.4%), were the most common mutational processes among hypermutated tumors. Three patients with hypermutated breast cancer--including two with a dominant APOBEC activity signature and one with a dominant MMRd signature--treated with pembrolizumab-based therapies derived an objective and durable response to therapy.\n\nConclusionHypermutation occurs in 5% of all breast cancers, with an enrichment in metastatic tumors. Different mutational signatures are present in this population, with APOBEC activity being the most common dominant process. Preliminary data suggest that hypermutated breast cancers are more likely to benefit from PD-1 inhibitors.\n\nKey MessageHigh tumor mutation burden is found in 5% of all breast cancers and is more common in metastatic tumors. While different mutational signatures are present in hypermutated tumors, APOBEC activity is the most common dominant process. Preliminary data suggest that those tumors are more likely to benefit from PD-1 inhibitors.

genomics↗