Predictive Modelling in Clinical Bioinformatics: Key Concepts for Startups.

Clinical bioinformatics is a newly emerging field that applies bioinformatics techniques for facilitating the identification of diseases, discovery of biomarkers, and therapy decision. Mathematical modelling is part of bioinformatics analysis pipelines and a fundamental step to extract clinical insights from genomes, transcriptomes and proteomes of patients. Often, the chosen modelling techniques relies on either statistical, machine learning or deterministic approaches. Research that combines bioinformatics with modelling techniques have been generating innovative biomedical technology, algorithms and models with biotech applications, attracting private investment to develop new business; however, startups that emerge from these technologies have been facing difficulties to implement clinical bioinformatics pipelines, protect their technology and generate profit. In this commentary, we discuss the main concepts that startups should know for enabling a successful application of predictive modelling in clinical bioinformatics. Here we will focus on key modelling concepts, provide some successful examples and briefly discuss the modelling framework choice. We also highlight some aspects to be taken into account for a successful implementation of cost-effective bioinformatics from a business perspective.

MALDI-ToF Mass Spectra Phenomic Analysis for Human Disease Diagnosis Enabled by Cutting-Edge Data Processing Pipelines and Bioinformatics Tools.

Current methods for diagnosing human disease are still incapable of rapidly and accurately screening for multiple diseases simultaneously on a large scale, and at an affordable price. MALDI-ToF mass spectrometry is an ultra-sensitive, ultra-fast, lowcost, high-throughput technology that has the potential to achieve this goal, allowing human phenotype characterization and thus phenomic screening for multiple disease states. In this review, we will discuss the main advances achieved so far, putting forward targeted applications of MALDI-ToF mass spectrometry in the service of human disease detection. This review focuses on the methodological workflow as MALDI-ToF data processing for phenomic analysis, using state-of-the-art bioinformatic pipelines and software tools. The role of mathematical modelling, machine learning, and artificial intelligence algorithms for disease screening are considered. Moreover, we present some previously developed tools for disease diagnostics and screening based on MALDI-ToF analysis. We discuss the remaining challenges that are ahead when implementing MALDI-ToF into clinical laboratories. Differentiating a standard profile from a single disease phenotype is challenging, but the potential to simultaneously run multiple algorithm screens for different disease phenotypes may only be limited by computing power once this initial hurdle is overcome. The ability to explore the full potential of human clinical phenomics may be closer than imagined; this review gives an insight into the benefits this technology may reap for the future of clinical diagnostics.

Predicting the evolution and control of the COVID-19 pandemic in Portugal.

Coronavirus disease 2019 (COVID-19) is a worldwide pandemic that has been affecting Portugal since 2 March 2020. The Portuguese government has been making efforts to contradict the exponential growth through lockdown, social distancing and the usage of masks. However, these measures have been implemented without controlling the compliance degree and how much is necessary to achieve an effective control. To address this issue, we developed a mathematical model to estimate the strength of Government-Imposed Measures (GIM) and predict the impact of the degree of compliance on the number of infected cases and peak of infection. We estimate the peak to be around 650 thousand infected cases with 53 thousand requiring hospital care by the beginning of May if no measures were taken. The model shows that the population compliance of the GIM was gradual between   30% to 75%, contributing to a significant reduction on the infection peak and mortality. Importantly, our simulations show that the infection burden could have been further reduced if the population followed the GIM immediately after their release on 18 March.

Bioinformatic identification of euploid and aneuploid embryo secretome signatures in IVF culture media based on MALDI-ToF mass spectrometry.

PURPOSE: Embryo genotyping in IVF clinics aims to identify aneuploid embryos, and current methodologies rely on costly, invasive and time-consuming approaches such as PGT-A screening. MALDI-ToF-based mass spectral analysis of embryo culture has been demonstrated to be a non-invasive, affordable and accurate technique that is able to capture secretome profiles from embryo culture media extremely quick. Thus, aneuploid embryo genotypes can be distinguished from euploids from these profiles towards the development of novel embryo selection tools. METHODS: A retrospective cohort study, including 292 spent media samples from embryo cultures collected from a single IVF clinic in USA. There were 149 euploid and 165 aneuploid embryos previously analysed by PGT-A next-generation sequencing techniques. Secretome mass spectra of embryos were generated using MALDI-ToF mass spectrometry in the UK. Data was systematically analysed using a fully automated and ultra-fast bioinformatic pipeline developed for the identification of mass spectral signatures. RESULTS: Distinct spectral patterns were found for euploid and aneuploid genotypes in embryo culture media. We identified 12 characteristic peak signatures for euploid and 17 for aneuploid embryos. Data analysis also revealed a high degree of complementarity among regions showing that 22 regions are required to differentiate between genotypes with a sensitivity of 84% and a false positive rate of 18%. CONCLUSION: Ultra-fast and fully automated screening of an embryo genotype is possible based on multiple combinations of specific mass spectral peak signatures. This constitutes a breakthrough towards the implementation of non-invasive and ultra-fast tools for embryo selection immediately prior to transfer.

Copper-substituted forms of the wild type and C42A variant of rubredoxin.

In order to gain insights into the interplay between Cu(I) and Cu(II) in sulfur-rich protein environments, the first preparation and characterization of copper-substituted forms of the wild-type rubredoxin (Rd) from Desulfovibrio vulgaris Hildenborough are reported, as well as those of its variant C42A-Rd. The initial products appear to be tetrahedral Cu(I)(S-Cys)n species for the wild type (n=4) and the variant C42A (n=3, with an additional unidentified ligand). These species are unstable to aerial oxidation to products, whose properties are consistent with square planar Cu(II)(S-Cys)n species. These Cu(II) intermediates are susceptible to auto-reduction by ligand S-Cys to produce stable Cu(I) final products. The original Cu(I) center in the wild-type system can be regenerated by reduction, suggesting that the active site can accommodate Cu(I)(S-Cys)2 and Cys-S-S-Cys fragments in the final product. The absence of one S-Cys ligand prevents similar regeneration in the C42A-Rd system. These results emphasize the redox instability of Cu(II)-(S-Cys)n centers.