As synthetic biology advances, labeling of genes or organisms, like other high-value products, will become important not only to pinpoint their identity, origin, or spread, but also for intellectual property, classification, bio-security or legal reasons. Ideally information should be inseparably interlaced into expressed genes. We describe a method for embedding messages within open reading frames of synthetic genes by adapting steganographic algorithms typically used for watermarking digital media files. Text messages are first translated into a binary string, and then represented in the reading frame by synonymous codon choice. To aim for good expression of the labeled gene in its host as well as retain a high degree of codon assignment flexibility for gene optimization, codon usage tables of the target organism are taken into account. Preferably amino acids with 4 or 6 synonymous codons are used to comprise binary digits. Several different messages were embedded into open reading frames of T7 RNA polymerase, GFP, human EMG1 and HIV gag, variously optimized for bacterial, yeast, mammalian or plant expression, without affecting their protein expression or function. We also introduced Vigenère polyalphabetic substitution to cipher text messages, and developed an identifier as a key to deciphering codon usage ranking stored for a specific organism within a sequence of 35 nucleotides.

Metabolomics, with its wealth of data, offers a valuable avenue for enhancing predictions and decision-making in diabetes. This observational study aimed to leverage machine learning (ML) algorithms to predict the 4-year risk of developing type 2 diabetes mellitus (T2DM) using targeted quantitative metabolomics data. A cohort of 279 cardiovascular risk patients who underwent coronary angiography and who were initially free of T2DM according to American Diabetes Association (ADA) criteria was analyzed at baseline, including anthropometric data and targeted metabolomics, using liquid chromatography (LC)–mass spectroscopy (MS) and flow injection analysis (FIA)–MS, respectively. All patients were followed for four years. During this time, 11.5% of the patients developed T2DM. After data preprocessing, 362 variables were used for ML, employing the Caret package in R. The dataset was divided into training and test sets (75:25 ratio) and we used an oversampling approach to address the classifier imbalance of T2DM incidence. After an additional recursive feature elimination step, identifying a set of 77 variables that were the most valuable for model generation, a Support Vector Machine (SVM) model with a linear kernel demonstrated the most promising predictive capabilities, exhibiting an F1 score of 50%, a specificity of 93%, and balanced and unbalanced accuracies of 72% and 88%, respectively. The top-ranked features were bile acids, ceramides, amino acids, and hexoses, whereas anthropometric features such as age, sex, waist circumference, or body mass index had no contribution. In conclusion, ML analysis of metabolomics data is a promising tool for identifying individuals at risk of developing T2DM and opens avenues for personalized and early intervention strategies.

We have cloned and characterized a single copy C. reinhardtii gene containing an open reading frame of 333 nucleotides encoding a 12.7 kDa protein. The novel protein, DIP13, exhibits 60% identity with two mammalian proteins, human NA14 and an unnamed mouse protein. Homologous sequences are also present in several protozoan, trematode and fish genomes, but no homologs have been found in the completed genomes of yeast, Drosophila, C. elegans and A. thaliana. By using a specific antibody we have localized DIP13 to microtubule structures, namely basal bodies, flagellar axonemes and cytoplasmic microtubules. Anti-DIP13 antibody also specifically recognized human NA14 by immunofluorescence and stained basal bodies and flagella of human sperm cells as well as the centrosome of HeLa cells. Expression of the DIP13 open reading frame in antisense orientation in Chlamydomonas resulted in multinucleate, multiflagellate cells, which suggests a role for this protein in ensuring proper cell division. Thus, DIP13/NA14 could represent the founding members of a new class of highly conserved proteins that are associated with microtubule structures.

Uromodulin is the most abundant protein in urine. Low uromodulin has been found associated with diabetes as well as with chronic kidney disease (CKD). Whether it also predicts a future decline in kidney function is not known. We evaluated the association between serum uromodulin and kidney function in 529 patients and performed a genome-wide association study. Clinical parameters including renal function were determined at baseline and reassessed at a 4-year follow-up visit. Patients’ serum uromodulin levels were highest associated with a polymorphism in the UMOD coding region and were significantly correlated with estimated glomerular filtration rate (eGFR) (r = 0.242, P < 0.001) and, in an inverse way, with the albumin–creatinine ratio (r = −0.120, P = 0.012). Serum uromodulin concentrations were significantly lower in patients with CKD (eGFR < 60 ml/min per 1.73 m2) than in patients with normal kidney function (71.9 ± 29.0 vs. 169.1 ± 76.1 ng/ml, P < 0.001) and the same applied to patients with albuminuria (148.7 ± 72.2 vs. 167.9 ± 77.6 ng/ml, P = 0.008) or hypertension (160.9 ± 74.0 vs. 181.8 ± 87.8 ng/ml, P = 0.037). Prospectively, patients who developed CKD during the follow-up had significantly less uromodulin in serum than those who did not (126.8 ± 42.3 vs. 180.2 ± 79.1 ng/ml, P = 0.003). Serum uromodulin concentration was inversely associated with the development of CKD, even after adjustment for patients age, sex, genotype of the identified polymorphism, hypertension and diabetes status, and eGFR (odds ratio = 0.263, P = 0.019), and it significantly increased the performance of a prediction model for CKD (C-statistics 0.844 vs. 0.804, P = 0.049). The current study is the first evaluating the value of uromodulin in serum as a novel predictive biomarker for renal function and for the incidence of CKD.