Surveillance for Sri Lankan cassava mosaic virus (SLCMV) in Cambodia and Vietnam one year after its initial detection in a single plantation in 2015.

Cassava mosaic disease, one of the ten most economically important crop viral diseases in the world, was first reported in Southeast Asia from a single plantation in Cambodia in 2015. To determine the presence and incidence of Sri Lankan cassava mosaic virus (SLCMV) one year after first detection, a total of 6,480 samples from 419 fields were systematically collected from cassava production areas across Cambodia (3,840 samples; 240 fields) and Vietnam (2,640samples; 179 fields) in the 2016 cropping season. Using PCR-based diagnostics, we identified 49 SLCMV-infected plants from nine fields, representing 2% of the total number of fields sampled. Infected fields were geographically restricted to two provinces of Eastern Cambodia, while no infection was detected from any of the other sampled sites in either country. Symptom expression patterns in infected plants suggested that SLCMV may have been transmitted both through infected planting materials, and by Bemisia tabaci, the known whitefly vector of SLCMV. In addition, 14% of virus infected plants did not express typical symptoms of cassava mosaic disease on their leaves, highlighting that molecular-based validation is needed to confirm the presence of SLCMV in the field. None of the owners of the SLCMV-infected fields indicated acquired planting materials from the plantation in Ratanakiri where SLCMV was first reported. The surveillance baseline data generated for both countries is discussed in light of future options to control and manage cassava mosaic disease.

Continental-scale suppression of an invasive pest by a host-specific parasitoid underlines both environmental and economic benefits of arthropod biological control.

Biological control, a globally-important ecosystem service, can provide long-term and broad-scale suppression of invasive pests, weeds and pathogens in natural, urban and agricultural environments. Following (few) historic cases that led to sizeable environmental up-sets, the discipline of arthropod biological control has-over the past decades-evolved and matured. Now, by deliberately taking into account the ecological risks associated with the planned introduction of insect natural enemies, immense environmental and societal benefits can be gained. In this study, we document and analyze a successful case of biological control against the cassava mealybug, Phenacoccus manihoti (Hemiptera: Pseudococcidae) which invaded Southeast Asia in 2008, where it caused substantial crop losses and triggered two- to three-fold surges in agricultural commodity prices. In 2009, the host-specific parasitoid Anagyrus lopezi (Hymenoptera: Encyrtidae) was released in Thailand and subsequently introduced into neighboring Asian countries. Drawing upon continental-scale insect surveys, multi-year population studies and (field-level) experimental assays, we show how A. lopezi attained intermediate to high parasitism rates across diverse agro-ecological contexts. Driving mealybug populations below non-damaging levels over a broad geographical area, A. lopezi allowed yield recoveries up to 10.0 t/ha and provided biological control services worth several hundred dollars per ha (at local farm-gate prices) in Asia's four-million ha cassava crop. Our work provides lessons to invasion science and crop protection worldwide. Furthermore, it accentuates the importance of scientifically-guided biological control for insect pest management, and highlights its potentially large socio-economic benefits to agricultural sustainability in the face of a debilitating invasive pest. In times of unrelenting insect invasions, surging pesticide use and accelerating biodiversity loss across the globe, this study demonstrates how biological control-as a pure public good endeavor-constitutes a powerful, cost-effective and environmentally-responsible solution for invasive species mitigation.

RNAseq and Proteomics for Analysing Complex Oomycete Plant Interactions.

The oomycetes include some of the most devastating plant pathogens. In this review we discuss the latest results from oomycete and plant studies with emphasis on interaction studies. We focus on the outcomes of RNAseq and proteomics studies and some pitfalls of these approaches. Both pathogenic interactions and biological control are discussed. We underline the usefulness of studies at several levels of complexity from studies of one organism, up to two or more and within agricultural fields (managed settings) up to wild ecosystems. Finally we identify areas of future interest such as detailed interactome studies, dual RNAseq studies, peptide modification studies and population/meta omics with or without biological control agents.

Simplified method for predicting a functional class of proteins in transcription factor complexes.

BACKGROUND: Initiation of transcription is essential for most of the cellular responses to environmental conditions and for cell and tissue specificity. This process is regulated through numerous proteins, their ligands and mutual interactions, as well as interactions with DNA. The key such regulatory proteins are transcription factors (TFs) and transcription co-factors (TcoFs). TcoFs are important since they modulate the transcription initiation process through interaction with TFs. In eukaryotes, transcription requires that TFs form different protein complexes with various nuclear proteins. To better understand transcription regulation, it is important to know the functional class of proteins interacting with TFs during transcription initiation. Such information is not fully available, since not all proteins that act as TFs or TcoFs are yet annotated as such, due to generally partial functional annotation of proteins. In this study we have developed a method to predict, using only sequence composition of the interacting proteins, the functional class of human TF binding partners to be (i) TF, (ii) TcoF, or (iii) other nuclear protein. This allows for complementing the annotation of the currently known pool of nuclear proteins. Since only the knowledge of protein sequences is required in addition to protein interaction, the method should be easily applicable to many species. RESULTS: Based on experimentally validated interactions between human TFs with different TFs, TcoFs and other nuclear proteins, our two classification systems (implemented as a web-based application) achieve high accuracies in distinguishing TFs and TcoFs from other nuclear proteins, and TFs from TcoFs respectively. CONCLUSION: As demonstrated, given the fact that two proteins are capable of forming direct physical interactions and using only information about their sequence composition, we have developed a completely new method for predicting a functional class of TF interacting protein partners with high precision and accuracy.