emPAI-assisted strategy enhances screening and assessment of Mycobacterium tuberculosis infection serological markers

Characterization and optimization of 5´ untranslated region containing poly-adenine tracts in Kluyveromyces marxianus using machine-learning model

BACKGROUND

The 5´ untranslated region (5´ UTR) plays a key role in regulating translation efficiency and mRNA stability, making it a favored target in genetic engineering and synthetic biology. A common feature found in the 5´ UTR is the poly-adenine (poly(A)) tract. However, the effect of 5´ UTR poly(A) on protein production remains controversial. Machine-learning models are powerful tools for explaining the complex contributions of features, but models incorporating features of 5´ UTR poly(A) are currently lacking. Thus, our goal is to construct such a model, using natural 5´ UTRs from Kluyveromyces marxianus, a promising cell factory for producing heterologous proteins.

RESULTS

We constructed a mini-library consisting of 207 5´ UTRs harboring poly(A) and 34 5´ UTRs without poly(A) from K. marxianus. The effects of each 5´ UTR on the production of a GFP reporter were evaluated individually in vivo, and the resulting protein abundance spanned an approximately 450-fold range throughout. The data were used to train a multi-layer perceptron neural network (MLP-NN) model that incorporated the length and position of poly(A) as features. The model exhibited good performance in predicting protein abundance (average R2 = 0.7290). The model suggests that the length of poly(A) is negatively correlated with protein production, whereas poly(A) located between 10 and 30 nt upstream of the start codon (AUG) exhibits a weak positive effect on protein abundance. Using the model as guidance, the deletion or reduction of poly(A) upstream of 30 nt preceding AUG tended to improve the production of GFP and a feruloyl esterase. Deletions of poly(A) showed inconsistent effects on mRNA levels, suggesting that poly(A) represses protein production either with or without reducing mRNA levels.

CONCLUSION

The effects of poly(A) on protein production depend on its length and position. Integrating poly(A) features into machine-learning models improves simulation accuracy. Deleting or reducing poly(A) upstream of 30 nt preceding AUG tends to enhance protein production. This optimization strategy can be applied to enhance the yield of K. marxianus and other microbial cell factories.

Discovery of Schistosoma mekongi circulating proteins and antigens in infected mouse sera

Schistosomiasis is a neglected tropical disease caused by an infection of the parasitic flatworms schistosomes. Schistosoma mekongi is a restricted Schistosoma species found near the Mekong River, mainly in southern Laos and northern Cambodia. Because there is no vaccine or effective early diagnosis available for S. mekongi, additional biomarkers are required. In this study, serum biomarkers associated with S. mekongi-infected mice were identified at 14-, 28-, 42-, and 56-days post-infection. Circulating proteins and antigens of S. mekongi in mouse sera were analyzed using mass spectrometry-based proteomics. Serine protease inhibitors and macrophage erythroblast attacher were down-regulated in mouse sera at all infection timepoints. In addition, 54 circulating proteins and 55 antigens of S. mekongi were identified. Notable circulating proteins included kyphoscoliosis peptidase and putative tuberin, and antigens were detected at all four infection timepoints, particularly in the early stages (12 days). The putative tuberin sequence of S. mekongi was highly similar to homologs found in other members of the genus Schistosoma and less similar to human and murine sequences. Our study provided the identity of promising diagnostic biomarkers that could be applicable in early schistosomiasis diagnosis and vaccine development.