In this study, a multi-metal-tolerant natural bacterial isolate Providencia rettgeri strain KDM3 from an industrial effluent in Mumbai, India, showed high cadmium (Cd) tolerance. Providencia rettgeri grew in the presence of more than 100 ppm (880 μM) Cd (LD₅₀ = 100 ppm) and accumulated Cd intracellularly. Following Cd exposure, a comparative proteome analysis revealed molecular mechanisms underlying Cd tolerance. Among a total of 69 differentially expressed proteins (DEPs) in Cd-exposed cells, de novo induction of ahpCF operon proteins and L-cysteine/L-cystine shuttle protein FliY was observed, while Dps and superoxide dismutase proteins were overexpressed, indicating upregulation of a robust oxidative stress defense. ENTRA1, a membrane transporter showing homology to heavy metal transporter, was also induced de novo. In addition, the protein disaggregation chaperone ClpB, trigger factor, and protease HslU were also overexpressed. Notably, 46 proteins from the major functional category of energy metabolism were found to be downregulated. Furthermore, the addition of P. rettgeri to Cd-spiked soil resulted in a significant reduction in the Cd content [roots (11%), shoot (50%), and grains (46%)] of the rice plants. Cd bioaccumulation of P. rettgeri improved plant growth and grain yield. We conclude that P. rettgeri, a highly Cd-tolerant bacterium, is an ideal candidate for in-situ bioremediation of Cd-contaminated agricultural soils.

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) account for 80% of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). GEP-NETs are well-differentiated tumors, highly heterogeneous in biology and origin, and are often diagnosed at the metastatic stage. Diagnosis is commonly through clinical symptoms, histopathology, and PET-CT imaging, while molecular markers for metastasis and the primary site are unknown. Here, we report the identification of multi-gene signatures for hepatic metastasis and primary sites through analyses on RNA-SEQ datasets of pancreatic and small intestinal NETs tissue samples. Relevant gene features, identified from the normalized RNA-SEQ data using the mRMRe algorithm, were used to develop seven Machine Learning models (LDA, RF, CART, k-NN, SVM, XGBOOST, GBM). Two multi-gene random forest (RF) models classified primary and metastatic samples with 100% accuracy in training and test cohorts and >90% accuracy in an independent validation cohort. Similarly, three multi-gene RF models identified the pancreas or small intestine as the primary site with 100% accuracy in training and test cohorts, and >95% accuracy in an independent cohort. Multi-label models for concurrent prediction of hepatic metastasis and primary site returned >98.42% and >87.42% accuracies on training and test cohorts, respectively. A robust molecular signature to predict liver metastasis or the primary site for GEP-NETs is reported for the first time and could complement the clinical management of GEP-NETs.