Plant-parasitic nematodes (PPNs) cause serious harm to agricultural production. Bacillus firmus shows excellent control of PPNs and has been produced as a commercial nematicide. However, its nematicidal factors and mechanisms are still unknown. In this study, we showed that B. firmus strain DS-1 has high toxicity against Meloidogyne incognita and soybean cyst nematode. We sequenced the whole genome of DS-1 and identified multiple potential virulence factors. We then focused on a peptidase S8 superfamily protein called Sep1 and demonstrated that it had toxicity against the nematodes Caenorhabditis elegans and M. incognita. The Sep1 protein exhibited serine protease activity and degraded the intestinal tissues of nematodes. Thus, the Sep1 protease of B. firmus is a novel biocontrol factor with activity against a root-knot nematode. We then used C. elegans as a model to elucidate the nematicidal mechanism of Sep1, and the results showed that Sep1 could degrade multiple intestinal and cuticle-associated proteins and destroyed host physical barriers. The knowledge gained in our study will lead to a better understanding of the mechanisms of B. firmus against PPNs and will aid in the development of novel bio-agents with increased efficacy for controlling PPNs.

RNA splicing is an essential step in producing mature messenger RNA (mRNA) and other RNA species. Harnessing RNA splicing modifiers as a new pharmacological modality is promising for the treatment of diseases caused by aberrant splicing. This drug modality can be used for infectious diseases by disrupting the splicing of essential pathogenic genes. Several antisense oligonucleotide splicing modifiers were approved by the U.S. Food and Drug Administration (FDA) for the treatment of spinal muscular atrophy (SMA) and Duchenne muscular dystrophy (DMD). Recently, a small-molecule splicing modifier, risdiplam, was also approved for the treatment of SMA, highlighting small molecules as important warheads in the arsenal for regulating RNA splicing. The cellular targets of these approved drugs are all mRNA precursors (pre-mRNAs) in human cells. The development of novel RNA-targeting splicing modifiers can not only expand the scope of drug targets to include many previously considered “undruggable” genes but also enrich the chemical-genetic toolbox for basic biomedical research. In this review, we summarized known splicing modifiers, screening methods for novel splicing modifiers, and the chemical space occupied by the small-molecule splicing modifiers.

Risdiplam is the first approved small-molecule splicing modulator for the treatment of spinal muscular atrophy (SMA). Previous studies demonstrated that risdiplam analogues have two separate binding sites in exon 7 of the SMN2 pre-mRNA: (i) the 5′-splice site and (ii) an upstream purine (GA)-rich binding site. Importantly, the sequence of this GA-rich binding site significantly enhanced the potency of risdiplam analogues. In this report, we unambiguously determined that a known risdiplam analogue, SMN-C2, binds to single-stranded GA-rich RNA in a sequence-specific manner. The minimum required binding sequence for SMN-C2 was identified as GAAGGAAGG. We performed all-atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method, which captured spontaneous binding of a risdiplam analogue to the target nucleic acids. We uncovered, for the first time, a ligand-binding pocket formed by two sequential GAAG loop-like structures. The simulation findings were highly consistent with experimental data obtained from saturation transfer difference (STD) NMR and structure-affinity-relationship studies of the risdiplam analogues. Together, these studies illuminate us to understand the molecular basis of single-stranded purine-rich RNA recognition by small-molecule splicing modulators with an unprecedented binding mode.

The ongoing COVID-19/Severe Acute Respiratory Syndrome CoV-2 (SARS-CoV-2) pandemic has become a significant threat to public health and has hugely impacted societies globally. Targeting conserved SARS-CoV-2 RNA structures and sequences essential for viral genome translation is a novel approach to inhibit viral infection and progression. This new pharmacological modality compasses two classes of RNA-targeting molecules: 1) synthetic small molecules that recognize secondary or tertiary RNA structures and 2) antisense oligonucleotides (ASOs) that recognize the RNA primary sequence. These molecules can also serve as a “bait” fragment in RNA degrading chimeras to eliminate the viral RNA genome. This new type of chimeric RNA degrader is recently named ribonuclease targeting chimera or RIBOTAC. This review paper summarizes the sequence conservation in SARS-CoV-2 and the current development of RNA-targeting molecules to combat this virus. These RNA-binding molecules will also serve as an emerging class of antiviral drug candidates that might pivot to address future viral outbreaks.

Background In polyglutamine (polyQ) disease, the investigation of the prediction of a patient's age at onset (AAO) facilitates the development of disease‐modifying intervention and underpins the delay of disease onset and progression. Few polyQ disease studies have evaluated AAO predicted by machine‐learning algorithms and linear regression methods. Objective The objective of this study was to develop a machine‐learning model for AAO prediction in the largest spinocerebellar ataxia type 3/Machado–Joseph disease (SCA3/MJD) population from mainland China. Methods In this observational study, we introduced an innovative approach by systematically comparing the performance of 7 machine‐learning algorithms with linear regression to explore AAO prediction in SCA3/MJD using CAG expansions of 10 polyQ‐related genes, sex, and parental origin. Results Similar prediction performance of testing set and training set in each models were identified and few overfitting of training data was observed. Overall, the machine‐learning‐based XGBoost model exhibited the most favorable performance in AAO prediction over the traditional linear regression method and other 6 machine‐learning algorithms for the training set and testing set. The optimal XGBoost model achieved mean absolute error, root mean square error, and median absolute error of 5.56, 7.13, 4.15 years, respectively, in testing set 1, with mean absolute error (4.78 years), root mean square error (6.31 years), and median absolute error (3.59 years) in testing set 2. Conclusion Machine‐learning algorithms can be used to predict AAO in patients with SCA3/MJD. The optimal XGBoost algorithm can provide a good reference for the establishment and optimization of prediction models for SCA3/MJD or other polyQ diseases. © 2020 International Parkinson and Movement Disorder Society