Biosurfactants are eco-friendly surface-active molecules recommended for enhanced oil recovery techniques. In the present study, a potential lipopeptide (biosurfactant) encoding the iturin A gene was synthesized from Bacillus aryabhattai. To improvise the yield of the lipopeptide for specific applications, current research tends toward engineering and expressing recombinant peptides. An iturin A gene sequence was codon-optimized, amplified with gene-specific primers, and ligated into the pET-32A expression vector to achieve high-level protein expression. The plasmid construct was transformed into an E. coli BL21 DE3 host to evaluate the expression. The highly expressed recombinant iturin A lipopeptide was purified on a nickel nitrilotriacetic acid (Ni-NTA) agarose column. Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) revealed that the purity and molecular mass of iturin A was 41 kDa. The yield of recombinant iturin A was found to be 60 g/L with a 6.7-fold increase in comparison with our previously published study on the wild strain. The approach of cloning a functional fragment of partial iturin A resulted in the increased production of the lipopeptide. When motor oil was used, recombinant protein iturin A revealed a biosurfactant property with a 74 ± 1.9% emulsification index (E24). Purified recombinant protein iturin A was characterized by mass spectrometry. MALDI-TOF spectra of trypsin digestion (protein/trypsin of 50:1 and 25:1) showed desired digested mass peaks for the protein, further confirming the identity of iturin A. The iturin A structure was elucidated based on distinctive spectral bands in Raman spectra, which revealed the presence of a peptide backbone and lipid. Recombinant iturin A was employed for enhanced oil recovery through a sand-packed column that yielded 61.18 ± 0.85% additional oil. Hence, the novel approach of the high-level expression of iturin A (lipopeptide) as a promising biosurfactant employed for oil recovery from Bacillus aryabhattai is not much reported. Thus, recombinant iturin A demonstrated its promising ability for efficient oil recovery, finding specific applications in petroleum industries.

The rise in number of infections from multidrug-resistant (MDR) Gram-negative microbes has led to an increase in the use of a variety of ‘polymyxins’ such as colistin. Even though colistin is known to cause minor nephro- and neuro-toxicity, it is still considered as last resort antibiotic for treating MDR infections. In this study, we have applied Raman spectroscopy to understand the differences among colistin sensitive and resistant bacterial strains at community level. We have successfully generated colistin resistant clones and verified the presence of resistance-causing MCR-1 plasmid. A unique spectral profile associated with specific drug concentration has been obtained. Successful delineation between resistant and sensitive cells has also been achieved via principal component analysis. Overall findings support the prospective utility of Raman spectroscopy in identifying anti-microbial resistance.

One of the major health challenges that humans have been facing for the last few decades is antimicrobial resistance (AMR), where antibiotics stop responding to infections and, thereby, take a considerable amount of time to cure them while increasing mortality rates. There are various steps that have been taken by researchers and organizations to identify, cure, and prevent this urgent issue. Here, in this review, we have tried to illustrate how machine learning has been used with different spectroscopic analytical platforms—Raman spectroscopy (RS), Fourier-transform infrared spectroscopy (FTIR), and nuclear magnetic resonance (NMR)—to further accelerate the understanding and early detection of AMR. The combination of ML algorithms with advanced spectroscopic techniques delivers faster and deeper insights into the different mechanisms of bacterial resistance, delivering novel solutions.

Background: Cancer stem cells (CSCs) are a stubborn subpopulation that fuel therapy resistance, tumor recurrence, and metastasis. In glioblastoma multiforme (GBM) — one of the deadliest brain tumors — CSCs make treatment especially challenging. Here, we set out to isolate, characterize, and compare exosomes released by CSCs and non-CSCs in the U87 glioblastoma cell line. Methods: We grew U87 cells in serum-free, growth-factor-rich conditions to encourage tumor sphere formation, isolating CSCs. Flow cytometry confirmed CSC identity with strong CD133 positivity. To test chemo-resistance, we measured temozolomide (TMZ) IC50 values using an MTT assay. We isolated exosomes from CSCs and regular cancer cells (CCs) by ultracentrifugation, analyzed their size with NanoSight, checked their shape via TEM, and confirmed CD81 expression by flow cytometry. Their biochemical makeup was profiled using FTIR and Raman spectroscopy. Results: The isolated CSCs showed strong sphere formation and 96.7% CD133 positivity. They were notably more resistant to TMZ, with a higher IC50 (12.56 ± 0.065 μg/mL) compared to CCs (6.032 ± 0.277 μg/mL). Exosomes from CSCs were slightly smaller (157 ± 12 nm) than those from CCs (193 ± 5 nm) but maintained the classic spherical shape (50–200 nm) under TEM. FTIR and Raman spectroscopy revealed distinct molecular fingerprints between the two exosome types — especially in protein, lipid, and nucleic acid signatures. Conclusion: We successfully isolated CSCs and their exosomes from U87 cells, uncovering clear biochemical and morphological differences between CSC- and CC-derived exosomes. These findings pave the way for targeting CSC exosomes in future glioblastoma therapies.

Raman spectroscopy (RS) is rapidly becoming a key analytical tool for a wide range of microbiology applications. In combination with diverse machine learning methods, RS has demonstrated potentials to be translated in form of a culture-free, rapid, and objective tool for identifying antimicrobial resistance (AMR). Colistin is regarded as the final line of defense antibiotic for treating infections caused by gram-negative bacteria. In this study, we have employed a combinatorial approach of machine learning and RS to identify a novel spectral marker associated with phosphoethanolamine modification in lipid A moiety of colistin resistant gram-negative Escherichia coli. The visible spectral fingerprints of this marker have been validated by partial least square regression and discriminant analysis. The origin of the spectral feature has been confirmed by hyperspectral imaging and K-means clustering of a single bacterial cell. The chemical structure of the modified lipid A moiety has been verified by gold standard MALDI-TOF mass spectrometry. Our findings support futuristic applicability of this spectroscopic marker in objectively identifying colistin-sensitive and resistant strains.