Considered one of the most devastating coral disease outbreaks in history, stony coral tissue loss disease (SCTLD) is currently spreading throughout Florida’s coral reefs and the greater Caribbean. SCTLD affects at least two dozen different coral species and has been implicated in extensive losses of coral cover. Here we show Pseudoalteromonas sp. strain McH1-7 has broad-spectrum antibacterial activity against SCTLD-associated bacterial isolates. Chemical analyses indicated McH1-7 produces at least two potential antibacterials, korormicin and tetrabromopyrrole, while genomic analysis identified the genes potentially encoding an L-amino acid oxidase and multiple antibacterial metalloproteases (pseudoalterins). During laboratory trials, McH1-7 arrested or slowed disease progression on 68.2% of diseased Montastraea cavernosa fragments treated (n = 22), and it prevented disease transmission by 100% (n = 12). McH1-7 is the most chemically characterized coral probiotic that is an effective prophylactic and direct treatment for the destructive SCTLD as well as a potential alternative to antibiotic use.

Over the last 25 years, biology has entered the genomic era and is becoming a science of ‘big data’. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3–4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.

Exercise offers short-term and long-term health benefits, including an increased metabolic rate and energy expenditure in myocardium. The newly-discovered exercise-induced myokine, irisin, stimulates conversion of white into brown adipocytes as well as increased mitochondrial biogenesis and energy expenditure. Remarkably, irisin is highly expressed in myocardium, but its physiological effects in the heart are unknown. The objective of this work is to investigate irisin’s potential multifaceted effects on cardiomyoblasts and myocardium. For this purpose, H9C2 cells were treated with recombinant irisin produced in yeast cells (r-irisin) and in HEK293 cells (hr-irisin) for examining its effects on cell proliferation by MTT [3-(4, 5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] assay and on gene transcription profiles by qRT-PCR. R-irisin and hr-irisin both inhibited cell proliferation and activated genes related to cardiomyocyte metabolic function and differentiation, including myocardin, follistatin, smooth muscle actin, and nuclear respiratory factor-1. Signal transduction pathways affected by r-irisin in H9C2 cells and C57BL/6 mice were examined by detecting phosphorylation of PI3K-AKT, p38, ERK or STAT3. We also measured intracellular Ca²⁺ signaling and mitochondrial thermogenesis and energy expenditure in r-irisin-treated H9C2 cells. The results showed that r-irisin, in a certain concentration rage, could activate PI3K-AKT and intracellular Ca²⁺ signaling and increase cellular oxygen consumption in H9C2 cells. Our study also suggests the existence of irisin-specific receptor on the membrane of H9C2 cells. In conclusion, irisin in a certain concentration rage increased myocardial cell metabolism, inhibited cell proliferation and promoted cell differentiation. These effects might be mediated through PI3K-AKT and Ca²⁺ signaling, which are known to activate expression of exercise-related genes such as follistatin and myocardin. This work supports the value of exercise, which promotes irisin release.

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering– and deep learning–based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

Ribosomally synthesized and post-translationally modified peptides, or RiPPs, which have mainly isolated from microbes as well as plants and animals, are an ever-expanding group of peptidic natural products with diverse chemical structures and biological activities. They have emerged as a major category of secondary metabolites partly due to a myriad of microbial genome sequencing endeavors and the availability of genome mining software in the past two decades. Heterologous expression of RiPP gene clusters mined from microbial genomes, which are often silent in native producers, in surrogate hosts such as Escherichia coli and Streptomyces strains can be an effective way to elucidate encoded peptides and produce novel derivatives. Emerging strategies have been developed to facilitate the success of the heterologous expression by targeting multiple synthetic biology levels, including individual proteins, pathways, metabolic flux and hosts. This review describes recent advances in heterologous production of RiPPs, mainly from microbes, with a focus on E. coli and Streptomyces strains as the surrogate hosts.