Validation of the predictive value of BDNF -87 methylation for antidepressant treatment success in severely depressed patients-a randomized rater-blinded trial.

BACKGROUND: Brain-derived neurotrophic factor (BDNF) is essential for antidepressant treatment of major depressive disorder (MDD). Our repeated studies suggest that DNA methylation of a specific CpG site in the promoter region of exon IV of the BDNF gene (CpG -87) might be predictive of the efficacy of monoaminergic antidepressants such as selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and others. This trial aims to evaluate whether knowing the biomarker is non-inferior to treatment-as-usual (TAU) regarding remission rates while exhibiting significantly fewer adverse events (AE). METHODS: The BDNF trial is a prospective, randomized, rater-blinded diagnostic study conducted at five university hospitals in Germany. The study's main hypothesis is that {1} knowing the methylation status of CpG -87 is non-inferior to not knowing it with respect to the remission rate while it significantly reduces the AE rate in patients experiencing at least one AE. The baseline assessment will occur upon hospitalization and a follow-up assessment on day 49 (± 3). A telephone follow-up will be conducted on day 70 (± 3). A total of 256 patients will be recruited, and methylation will be evaluated in all participants. They will be randomly assigned to either the marker or the TAU group. In the marker group, the methylation results will be shared with both the patient and their treating physician. In the TAU group, neither the patients nor their treating physicians will receive the marker status. The primary endpoints include the rate of patients achieving remission on day 49 (± 3), defined as a score of ≤ 10 on the Hamilton Depression Rating Scale (HDRS-24), and the occurrence of AE. ETHICS AND DISSEMINATION: The trial protocol has received approval from the Institutional Review Boards at the five participating universities. This trial holds significance in generating valuable data on a predictive biomarker for antidepressant treatment in patients with MDD. The findings will be shared with study participants, disseminated through professional society meetings, and published in peer-reviewed journals. TRIAL REGISTRATION: German Clinical Trial Register DRKS00032503. Registered on 17 August 2023.

A message passing framework with multiple data integration for miRNA-disease association prediction.

Micro RNA or miRNA is a highly conserved class of non-coding RNA that plays an important role in many diseases. Identifying miRNA-disease associations can pave the way for better clinical diagnosis and finding potential drug targets. We propose a biologically-motivated data-driven approach for the miRNA-disease association prediction, which overcomes the data scarcity problem by exploiting information from multiple data sources. The key idea is to enrich the existing miRNA/disease-protein-coding gene (PCG) associations via a message passing framework, followed by the use of disease ontology information for further feature filtering. The enriched and filtered PCG associations are then used to construct the inter-connected miRNA-PCG-disease network to train a structural deep network embedding (SDNE) model. Finally, the pre-trained embeddings and the biologically relevant features from the miRNA family and disease semantic similarity are concatenated to form the pair input representations to a Random Forest classifier whose task is to predict the miRNA-disease association probabilities. We present large-scale comparative experiments, ablation, and case studies to showcase our approach's superiority. Besides, we make the model prediction results for 1618 miRNAs and 3679 diseases, along with all related information, publicly available at  http://software.mpm.leibniz-ai-lab.de/ to foster assessments and future adoption.

MuCoMiD: A Multitask Graph Convolutional Learning Framework for miRNA-Disease Association Prediction.

Growing evidence from recent studies implies that microRNAs or miRNAs could serve as biomarkers in various complex human diseases. Since wet-lab experiments for detecting miRNAs associated with a disease are expensive and time-consuming, machine learning techniques for miRNA-disease association prediction have attracted much attention in recent years. A big challenge in building reliable machine learning models is that of data scarcity. In particular, existing approaches trained on the available small datasets, even when combined with precalculated handcrafted input features, often suffer from bad generalization and data leakage problems. We overcome the limitations of existing works by proposing a novel multitask graph convolution-based approach, which we refer to as MuCoMiD. MuCoMiD allows automatic feature extraction while incorporating knowledge from five heterogeneous biological information sources (associations between miRNAs/diseases and protein-coding genes (PCGs), interactions between protein-coding genes, miRNA family information, and disease ontology) in a multitask setting which is a novel perspective and has not been studied before. To effectively test the generalization capability of our model, we conduct large-scale experiments on the standard benchmark datasets as well as on our proposed large independent testing sets and case studies. MuCoMiD obtains significantly higher Average Precision (AP) scores than all benchmarked models on three large independent testing sets, especially those with many new miRNAs, as well as in the detection of false positives. Thanks to its capability of learning directly from raw input information, MuCoMiD is easier to maintain and update than handcrafted feature-based methods, which would require recomputation of features every time there is a change in the original information sources (e.g., disease ontology, miRNA/disease-PCG associations, etc.). We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/cmtt.

Flexible TALEs for an expanded use in gene activation, virulence and scaffold engineering.

Transcription activator-like effectors (TALEs) are bacterial proteins with a programmable DNA-binding domain, which turned them into exceptional tools for biotechnology. TALEs contain a central array of consecutive 34 amino acid long repeats to bind DNA in a simple one-repeat-to-one-nucleotide manner. However, a few naturally occurring aberrant repeat variants break this strict binding mechanism, allowing for the recognition of an additional sequence with a -1 nucleotide frameshift. The limits and implications of this extended TALE binding mode are largely unexplored. Here, we analyse the complete diversity of natural and artificially engineered aberrant repeats for their impact on the DNA binding of TALEs. Surprisingly, TALEs with several aberrant repeats can loop out multiple repeats simultaneously without losing DNA-binding capacity. We also characterized members of the only natural TALE class harbouring two aberrant repeats and confirmed that their target is the major virulence factor OsSWEET13 from rice. In an aberrant TALE repeat, the position and nature of the amino acid sequence strongly influence its function. We explored the tolerance of TALE repeats towards alterations further and demonstrate that inserts as large as GFP can be tolerated without disrupting DNA binding. This illustrates the extraordinary DNA-binding capacity of TALEs and opens new uses in biotechnology.

Epigenetic features improve TALE target prediction.

BACKGROUND: The yield of many crop plants can be substantially reduced by plant-pathogenic Xanthomonas bacteria. The infection strategy of many Xanthomonas strains is based on transcription activator-like effectors (TALEs), which are secreted into the host cells and act as transcriptional activators of plant genes that are beneficial for the bacteria.The modular DNA binding domain of TALEs contains tandem repeats, each comprising two hyper-variable amino acids. These repeat-variable diresidues (RVDs) bind to their target box and determine the specificity of a TALE.All available tools for the prediction of TALE targets within the host plant suffer from many false positives. In this paper we propose a strategy to improve prediction accuracy by considering the epigenetic state of the host plant genome in the region of the target box. RESULTS: To this end, we extend our previously published tool PrediTALE by considering two epigenetic features: (i) chromatin accessibility of potentially bound regions and (ii) DNA methylation of cytosines within target boxes. Here, we determine the epigenetic features from publicly available DNase-seq, ATAC-seq, and WGBS data in rice.We benchmark the utility of both epigenetic features separately and in combination, deriving ground-truth from RNA-seq data of infections studies in rice. We find an improvement for each individual epigenetic feature, but especially the combination of both.Having established an advantage in TALE target predicting considering epigenetic features, we use these data for promoterome and genome-wide scans by our new tool EpiTALE, leading to several novel putative virulence targets. CONCLUSIONS: Our results suggest that it would be worthwhile to collect condition-specific chromatin accessibility data and methylation information when studying putative virulence targets of Xanthomonas TALEs.

PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting.

Plant-pathogenic Xanthomonas bacteria secrete transcription activator-like effectors (TALEs) into host cells, where they act as transcriptional activators on plant target genes to support bacterial virulence. TALEs have a unique modular DNA-binding domain composed of tandem repeats. Two amino acids within each tandem repeat, termed repeat-variable diresidues, bind to contiguous nucleotides on the DNA sequence and determine target specificity. In this paper, we propose a novel approach for TALE target prediction to identify potential virulence targets. Our approach accounts for recent findings concerning TALE targeting, including frame-shift binding by repeats of aberrant lengths, and the flexible strand orientation of target boxes relative to the transcription start of the downstream target gene. The computational model can account for dependencies between adjacent RVD positions. Model parameters are learned from the wealth of quantitative data that have been generated over the last years. We benchmark the novel approach, termed PrediTALE, using RNA-seq data after Xanthomonas infection in rice, and find an overall improvement of prediction performance compared with previous approaches. Using PrediTALE, we are able to predict several novel putative virulence targets. However, we also observe that no target genes are predicted by any prediction tool for several TALEs, which we term orphan TALEs for this reason. We postulate that one explanation for orphan TALEs are incomplete gene annotations and, hence, propose to replace promoterome-wide by genome-wide scans for target boxes. We demonstrate that known targets from promoterome-wide scans may be recovered by genome-wide scans, whereas the latter, combined with RNA-seq data, are able to detect putative targets independent of existing gene annotations.

Transcriptional Reprogramming of Rice Cells by Xanthomonas oryzae TALEs.

Rice-pathogenic Xanthomonas oryzae bacteria cause severe harvest loss and challenge a stable food supply. The pathogen virulence relies strongly on bacterial TALE (transcription activator-like effector) proteins that function as transcriptional activators inside the plant cell. To understand the plant targets of TALEs, we determined the genome sequences of the Indian X. oryzae pv. oryzae (Xoo) type strain ICMP 3125T and the strain PXO142 from the Philippines. Their complete TALE repertoire was analyzed and genome-wide TALE targets in rice were characterized. Integrating computational target predictions and rice transcriptomics data, we were able to verify 12 specifically induced target rice genes. The TALEs of the Xoo strains were reconstructed and expressed in a TALE-free Xoo strain to attribute specific induced genes to individual TALEs. Using reporter assays, we could show that individual TALEs act directly on their target promoters. In particular, we show that TALE classes assigned by AnnoTALE reflect common target genes, and that TALE classes of Xoo and the related pathogen X. oryzae pv. oryzicola share more common target genes than previously believed. Taken together, we establish a detailed picture of TALE-induced plant processes that significantly expands our understanding of X. oryzae virulence strategies and will facilitate the development of novel resistances to overcome this important rice disease.