Biology subjects

Mittal, E.

Publications and source records attributed to Mittal, E..

5 recordsLinked to original sources

bioRxiv2023

3D organoid modeling identified that targeting IGF1R signaling may overcome drug resistance in breast cancer

Breast cancer is the most frequently diagnosed cancer and the second largest cause of cancer deaths in women. However, drug resistance and poor response to treatments are common. Thus, there is an unmet need to identify new treatments and effective lab-based drug testing methods. Here we established a novel 3-dimensional organoid method by co-culturing cancer cells with healthy endothelial cells for longer-term testing of new drug combinations that combat drug resistance. As a proof-of-concept we showed that paclitaxel efficacy can be improved by combining it with AKT inhibitors. In addition, we identified a new triple combination of paclitaxel, HER2 inhibitor, and IGF1R inhibitor, which is more effective in increasing cell death and reducing organoid growth. Interestingly, many IGF1R pathway members are upregulated in breast cancer patients, and high expression is associated with poor survival, indicating that IGF1R is an attractive therapeutic target. Overall, using this novel organoid method, we can mimic more accurate culture conditions and identify new targets to be tested in clinical trials. Our approach is applicable to various cancers to improve patients outcomes.

cancer biology↗

bioRxiv2022

Data mining and experimental approaches to identify combination of natural herbs against bacterial infections

Various studies have identified that natural herbs can be repurposed to treat infectious and bacterial diseases. The purpose of this study is first to test the medicinal value of five herbs including asafoetida, cumin, fenugreek, neem, and turmeric as single agent and in pairs using the bacterial zone of inhibition assay. Second, we used target and network analyses to predict the best combinations. We found that all the herbs as single agent were effective against bacterial infection in the following descending order of efficacy: cumin > turmeric > neem > fenugreek > asafoetida as compared to vehicle (ethanol) treated control. Among all the tested combinations the turmeric and fenugreek combination had the best efficacy in inhibiting the bacterial growth. Next to understand the mechanism of action and to predict the effective combinations among available herbs, we used a data mining and computational analysis approach. Using NPASS, BindingDB, and pathway analysis tools, we identified the bioactive compounds for each herb, then identified the targets for each bioactive compound, and then identified associated pathways for these targets. Then we measured the target/pathway overlap for each herb and identified that the most effective combinations were those which have non-overlapping targets/pathways. For example, we showed as a proof-of-concept that turmeric and fenugreek have the least overlapping targets/pathways and thus is most effective in inhibiting bacteria growth. Our approach is applicable to treat bacterial infections and other human diseases such as cancer. Overall, the computational prediction along with experimental validation can help identify novel combinations that have significant antibacterial activity and may help prevent drug-resistant microbial diseases in human and plants.

microbiology↗

bioRxiv2022

Computational Pipeline to Identify Gene signatures that Define Cancer Subtypes

MotivationThe heterogeneous nature of cancers with multiple subtypes makes them challenging to treat. However, multi-omics data can be used to identify new therapeutic targets and we established a computational strategy to improve data mining. ResultsUsing our approach we identified genes and pathways specific to cancer subtypes that can serve as biomarkers and therapeutic targets. Using a TCGA breast cancer dataset we applied the ExtraTreesClassifier dimensionality reduction along with logistic regression to select a subset of genes for model training. Applying hyperparameter tuning, increased the model accuracy up to 92%. Finally, we identified 20 significant genes using differential expression. These targetable genes are associated with various cellular processes that impact cancer progression. We then applied our approach to a glioma dataset and again identified subtype specific targetable genes. ConclusionOur research indicates a broader applicability of our strategy to identify specific cancer subtypes and targetable pathways for various cancers.

bioinformatics↗

bioRxiv2022

A Microfluidic Device to Simulate the Impact of Gut Microbiome in Cancer

The gut microbiome has a role in the growth of many diseases such as cancer due to increased inflammation. There is an unmet need to identify novel strategies to investigate the effect of inflammation mediated by gut microbiome on cancer cells. However, there are limited biomimetic co-culture systems that allow to test causal relationship of microbiome on cancer cells. Here we developed a microfluidic chip that can simulate the interaction of the gut microbiome and cancer cells to test the effects of bacteria and inflammatory stress on cancer cells in vitro. To quantify the effect of bacteria on the growth of colorectal cancer cells, we cultured colorectal cancer cell line with Bacillus or lipopolysaccharide (LPS), which is a purified bacterial membrane and induce major inflammatory response, in the PDMS microfluidic device. We found that both LPS and Bacillus significantly accelerate the growth of colorectal cancer cells. These results show that the increased presence of certain bacteria can promote cancer cell growth and that these microfluidic chips can be used to test the specific correlation between bacteria and cancer cell growth. These microfluidic devices can have future implications for various cancer types and to identify treatment strategies.

bioinformatics↗

bioRxiv2022

Single cell preparations of Mycobacterium tuberculosis damage the mycobacterial envelope and disrupt macrophage interactions

For decades, investigators have studied the interaction of Mycobacterium tuberculosis (Mtb) with macrophages, which serve as a major cellular niche for the bacilli. Because Mtb are prone to aggregation, investigators rely on varied methods to disaggregate the bacteria for these studies. Here, we examined the impact of routinely used preparation methods on bacterial cell envelop integrity, macrophage inflammatory responses, and intracellular Mtb survival. We found that both gentle sonication and filtering damaged the mycobacterial cell envelope and markedly impacted the outcome of macrophage infections. Unexpectedly, sonicated bacilli were hyperinflammatory, eliciting dramatically higher TLR2-dependent gene expression and elevated secretion of IL-1{beta} and TNF-. Despite evoking enhanced inflammatory responses, sonicated bacilli replicated normally in macrophages. In contrast, Mtb that had been passed through a filter induced little inflammatory response, and they were attenuated in macrophages. Previous work suggests that the mycobacterial cell envelope lipid, phthiocerol dimycocerosate (PDIM), dampens macrophage inflammatory responses to Mtb. However, we found that the impact of PDIM depended on the method used to prepare Mtb. In conclusion, widely used methodologies to disaggregate Mtb may introduce experimental artifacts in Mtb-host interaction studies, including alteration of host inflammatory signaling, intracellular bacterial survival, and interpretation of bacterial mutants.

microbiology↗