Biology subjects

Loganathan, S.

Publications and source records attributed to Loganathan, S..

3 recordsLinked to original sources

bioRxiv2022

In vivo CRISPR screens reveal Serpinb9 and Adam2 as regulators of immune therapy response in lung cancer

How the genetic landscape of a tumor governs the tumors response to immunotherapy remains largely elusive. Here, we established a direct in vivo CRISPR/Cas9 gene editing methodology to assess the immune-modulatory capabilities of 573 putative cancer genes associated with altered cytotoxic activity in human cancers. Using KrasG12D- and BrafV600E-driven mouse lung cancer models, we identify Serpinb9 and Adam2 as our top immune suppressive and immune enhancing genes, respectively. Mechanistically, we show that Serpinb9 ablation in KrasG12D- and BrafV600E-mutant lung tumor cells greatly enhances the efficacy of cytotoxic T-cells in vitro and in vivo. ADAM2 is a cancer testis antigen broadly expressed in human cancers such as lung adenocarcinoma (13.9%), renal (74.7%), prostate (72.4%), uterine (28.6%) and invasive breast (9.5%) cancer. In our mouse models, we show that Adam2 expression is induced in KrasG12D- but not BrafV600E-driven murine lung tumors and that its expression is further enhanced by immunotherapy. We show that loss of Adam2 significantly decreases KrasG12D-lung tumor burden but blocks the efficacy of cytotoxic T-cells. Consistently, Adam2 overexpression dramatically increases tumor growth and enhances immunotherapy efficacy. Mechanistically, we find that Adam2s oncogenic function depends on modulating the tumor immune microenvironment by restraining productive type I and type II interferon responses as well as cytokine signaling, reducing the presentation of tumor-associated antigen, and modulating surface expression of several immunoregulatory receptors within Kras-driven lung tumors. Adam2 expression also leads to reduced levels of immune checkpoint inhibitors such as Pd-l1, Lag3, Tigit and Tim3. This reduced exhaustion within the tumor microenvironment may explain why ex vivo expanded and adoptively transferred cytotoxic T-cells show enhanced cytotoxic efficacy against Adam2 overexpressing lung tumors. Together, our study highlights the power of integrating cancer genomic with in vivo CRISPR/Cas9 screens to uncover how cancer-associated genetic alterations control responses to immunotherapies.

cancer biology↗

bioRxiv2021

Spatio-temporal feature based deep neural network for cell lineage analysis in microscopy images

BackgroundTime-lapse microscopy has been widely used in biomedical experiments because it can visualize the molecular activities of living cells in real time. However, biomedical researchers are still conducting cell lineage analysis manually. Developing automatic lineage tracing algorithms is a challenging task. In the past two decades, deep neural networks (DNNs) became have shown outstanding performance on computer vision tasks. They can learn complex visual features, capture long-range temporal dependencies, and have the potential to be used for automatic cell lineage analysis. MethodsIn this study, we propose a multi-task spatio-temporal feature based deep neural network for cell lineages analysis (Cell-STN). The Cell-STN extracts spatio-temporal features from microscopy image sequences by leveraging our convolutional long short-term memory based core block. And the proposed Cell-STN utilized a task specific network to predict the cell location, the mitosis event, and the apoptosis event in a multi-task manner. ResultsWe evaluated the Cell-STN on three in-house datasets (MCF7, U2OS, and HCT116) and one public dataset (Fluo-N2DL-HeLa). For cell tracking, we used peak-wise precision, track-wise precision, end-peak precision, and spatial distance as metrics. The overall results showed the Cell-STN models outperform other state-of-the-art cell trackers. For mitosis and apoptosis tasks, we used accuracy, F1-score, temporal distance, and spatial distance as metrics. The Cell-STN models achieved the highest performance on all datasets. ConclusionThis study presented a novel DNNs approach for cell lineage analysis in microscopy images. The Cell-STN showed outstanding performance on the four datasets. Additionally, the Cell-STN required minimal training data and can be adapted to new biological event detection tasks by appending task-specific layers. This algorithm has the potential to be used in real-world biomedical research.

bioinformatics↗

bioRxiv2020

TDP-43 proteinopathy alters the ribosome association of multiple mRNAs including the glypican Dally-like protein (Dlp)/GPC6

Amyotrophic lateral sclerosis (ALS) is a genetically heterogeneous neurodegenerative disease in which 97% of patients exhibit cytoplasmic aggregates containing the RNA binding protein TDP-43. Using tagged ribosome affinity purifications in Drosophila models of TDP-43 proteinopathy, we identified TDP-43 dependent translational alterations in motor neurons impacting the spliceosome, pentose phosphate and oxidative phosphorylation pathways. A subset of the mRNAs with altered ribosome association are also enriched in TDP-43 complexes suggesting that they may be direct targets. Among these, dlp mRNA, which encodes the glypican Dally like protein (Dlp)/GPC6, a wingless (Wg/Wnt) signaling regulator is insolubilized both in flies and patient tissues with TDP-43 pathology. While Dlp/GPC6 forms puncta in the Drosophila neuropil and ALS spinal cords, it is reduced at the neuromuscular synapse in flies suggesting compartment specific effects of TDP-43 proteinopathy. These findings together with genetic interaction data show that Dlp/GPC6 is a novel, physiologically relevant target of TDP-43 proteinopathy.

neuroscience↗