SPICE: discovery of phenotype-determining component interplays

Enhancement mechanism of xanthan gum production in Xanthomonas campestris induced by atmospheric and room-temperature plasma (ARTP) mutagenesis

Xanthan gum, produced by the aerobic fermentation of carbohydrates by Xanthomonas campestris, is a significant natural and industrial biopolymer known for its exceptional properties. Enhancing the yield of xanthan gum production remains a critical challenge. This study employed atmospheric and room temperature plasma (ARTP) technology to induce mutagenesis in X. campestris, resulting in a high-yielding strain, X20. The X20 mutant exhibited a substantial increase in xanthan gum yield, ranging from 13.3 % to 30.0 % over the starting strain across NaCl concentrations of 0, 6.0, and 8.0 g/L, along with improved viscosity and molecular weight. In the whole genome of X20 mutant, a total of 80 variant sites differing from the reference genome were identified, involving 76 mutated genes. Among these, 19 were missense mutations primarily associated with the two-component system. Transcriptome analysis highlighted their role in enhancing flagellar movement, biofilm formation, and metabolic synthesis, thereby elevating the capability of the mutant strain in xanthan gum production. This study demonstrates the potential of ARTP as an effective tool for microbial mutagenesis breeding, providing theoretical guidance for future studies on the synthesis regulation of xanthan gum and the engineering modification of X. campestris.

Machine learning for biomarker identification in cancer research - developments toward its clinical application

The patterns identified from the systematically collected molecular profiles of patient tumor samples, along with clinical metadata, can assist personalized treatments for effective management of cancer patients with similar molecular subtypes. There is an unmet need to develop computational algorithms for cancer diagnosis, prognosis and therapeutics that can identify complex patterns and help in classifications based on plethora of emerging cancer research outcomes in public domain. Machine learning, a branch of artificial intelligence, holds a great potential for pattern recognition in cryptic cancer datasets, as evident from recent literature survey. In this review, we focus on the current status of machine learning applications in cancer research, highlighting trends and analyzing major achievements, roadblocks and challenges toward its implementation in clinics.

Complex biomarker discovery in neuroimaging data: Finding a needle in a haystack

Neuropsychiatric disorders such as schizophrenia, bipolar disorder and Alzheimer's disease are major public health problems. However, despite decades of research, we currently have no validated prognostic or diagnostic tests that can be applied at an individual patient level. Many neuropsychiatric diseases are due to a combination of alterations that occur in a human brain rather than the result of localized lesions. While there is hope that newer imaging technologies such as functional and anatomic connectivity MRI or molecular imaging may offer breakthroughs, the single biomarkers that are discovered using these datasets are limited by their inability to capture the heterogeneity and complexity of most multifactorial brain disorders. Recently, complex biomarkers have been explored to address this limitation using neuroimaging data. In this manuscript we consider the nature of complex biomarkers being investigated in the recent literature and present techniques to find such biomarkers that have been developed in related areas of data mining, statistics, machine learning and bioinformatics.