Chlamydomonas reinhardtii is a unicellular green alga whose lineage diverged from land plants over 1 billion years ago. It is a model system for studying chloroplast-based photosynthesis, as well as the structure, assembly, and function of eukaryotic flagella (cilia), which were inherited from the common ancestor of plants and animals, but lost in land plants. We sequenced the ∼120-megabase nuclear genome of Chlamydomonas and performed comparative phylogenomic analyses, identifying genes encoding uncharacterized proteins that are likely associated with the function and biogenesis of chloroplasts or eukaryotic flagella. Analyses of the Chlamydomonas genome advance our understanding of the ancestral eukaryotic cell, reveal previously unknown genes associated with photosynthetic and flagellar functions, and establish links between ciliopathy and the composition and function of flagella.

Proteomics is the study of proteins, their time- and location-dependent expression profiles, as well as their modifications and interactions. Mass spectrometry is useful to investigate many of the questions asked in proteomics. Database search methods are typically employed to identify proteins from complex mixtures. However, databases are not often available or, despite their availability, some sequences are not readily found therein. To overcome this problem, de novo sequencing can be used to directly assign a peptide sequence to a tandem mass spectrometry spectrum. Many algorithms have been proposed for de novo sequencing and a selection of them are detailed in this article. Although a standard accuracy measure has not been agreed upon in the field, relative algorithm performance is discussed. The current state of the de novo sequencing is assessed thereafter and, finally, examples are used to construct possible future perspectives of the field.

Lithium is a mood stabilizing agent commonly used for the treatment of bipolar disorder. Here, we investigated the potential neuroprotective effect of lithium against paraquat toxicity and its underlying mechanisms in vitro. SH-SY5Y human neuroblastoma cells were treated with paraquat (PQ) 0.5 mM concentration after lithium pretreatment to test lithium’s capability in preventing cell toxicity. Cell death was evaluated by LDH, WST-8, and tryphan blue assay. Apoptosis was analyzed using DNA fragmentation, Annexin V immunostaining, Sub G1 cell cycle analysis, and caspase-3 activity assays. BCL2, BAX, and NRF2 protein expression were evaluated by Western-blotting and the BDNF protein level was determined with ELISA. mRNA levels of BCL2, BAX , BDNF, and NRF2 target genes (HO-1, GCS, NQO1), as well as miR-34a expression were analyzed by qPCR assay. Functional experiments were done via transfection with NRF2 siRNA and miR-34a mimic. Lithium treatment prevented paraquat induced cell death and apoptosis. Lithium treated cells showed increased anti-apoptotic protein BCL2 and decreased pro-apoptotic protein BAX expression. Lithium exerted a neurotrophic effect by increasing BDNF protein expression. It also diminished reactive oxygen species production and activated the redox sensitive transcription factor NRF2 and increased its target genes’ expression. Knockdown of NRF2 abolished neuroprotective, anti-apoptotic, and anti-oxidant effects of lithium. Furthermore, lithium significantly decreased both basal and PQ-induced expression of miR-34a. Transfection of miR-34a specific mimic reversed neuroprotective, anti-apoptotic, and anti-oxidant effects of lithium against PQ-toxicity. Our results revealed two novel mechanisms of lithium neuroprotection, namely NRF2 activation and miR-34a suppression.

Deep learning is a powerful machine learning technique that can learn from large amounts of data using multiple layers of artificial neural networks. This paper reviews some applications of deep learning in bioinformatics, a field that deals with analyzing and interpreting biological data. We first introduce the basic concepts of deep learning and then survey the recent advances and challenges of applying deep learning to various bioinformatics problems, such as genome sequencing, gene expression analysis, protein structure prediction, drug discovery, and disease diagnosis. We also discuss future directions and opportunities for deep learning in bioinformatics. We aim to provide an overview of deep learning so that bioinformaticians applying deep learning models can consider all critical technical and ethical aspects. Thus, our target audience is biomedical informatics researchers who use deep learning models for inference. This review will inspire more bioinformatics researchers to adopt deep-learning methods for their research questions while considering fairness, potential biases, explainability, and accountability.