Indonesia is one of the most biodiverse countries in the world and a promising resource for novel natural compound producers. Actinomycetes produce about two thirds of all clinically used antibiotics. Thus, exploiting Indonesia’s microbial diversity for actinomycetes may lead to the discovery of novel antibiotics. A total of 422 actinomycete strains were isolated from three different unique areas in Indonesia and tested for their antimicrobial activity. Nine potent bioactive strains were prioritized for further drug screening approaches. The nine strains were cultivated in different solid and liquid media, and a combination of genome mining analysis and mass spectrometry (MS)-based molecular networking was employed to identify potential novel compounds. By correlating secondary metabolite gene cluster data with MS-based molecular networking results, we identified several gene cluster-encoded biosynthetic products from the nine strains, including naphthyridinomycin, amicetin, echinomycin, tirandamycin, antimycin, and desferrioxamine B. Moreover, 16 putative ion clusters and numerous gene clusters were detected that could not be associated with any known compound, indicating that the strains can produce novel secondary metabolites. Our results demonstrate that sampling of actinomycetes from unique and biodiversity-rich habitats, such as Indonesia, along with a combination of gene cluster networking and molecular networking approaches, accelerates natural product identification.
A three-dimensional model of the variable domain of the atrazine-specific Fab fragment K411B was constructed by molecular modeling using known structures of highly homologous immunoglobulins as templates. Molecular dynamic simulations and cross-reactivity data were used to predict residues responsible for the binding of the hapten 4-chloro-6-(isopropylamino)-1,3,5-triazine-2-(6-aminohexanecarboxylic acid) (iPr/Cl/C6) instead of atrazine. Specific binding pockets could be defined for the chlorine, the isopropylamino group and the C6-spacer of the hapten. The influence of various amino acids on hapten binding was investigated by site-directed mutagenesis, and the effect of these mutations was analyzed by capture ELISA using the hapten iPr/Cl/C6 and 4-amino-6-chloro-1,3,5-triazine-2-(6-aminohexanecarboxylic acid) (H/Cl/C6). GlyH100a seems to be important in determining the conformation of the heavy-chain complementarity determining region H3; replacing it with any other residue prevented the binding of the hapten. Altering residues responsible for the binding of the chlorine atom (TrpH33, GluH50 and TyrL96) decreased the affinity significantly. Hapten-spacer recognition can be attributed to the interaction with PheL32; replacing PheL32 by leucine reduced the affinity towards iPr/Cl/C6. A triple mutant Fab fragment (GlnL89Glu, ValH37Ile and GluL3Val) showed an affinity 5-fold greater towards iPr/Cl/C6 compared to the wild-type K411B, as a result of better recognition of the isopropylamino group of iPr/Cl/C6.
Whole-genome sequencing (WGS) has played a significant role in understanding the epidemiology and biology of SARS-CoV-2 virus. Here, we investigate the use of SARS-CoV-2 WGS in Southeast and East Asian countries as a genomic surveillance during the COVID-19 pandemic. Nottingham–Indonesia Collaboration for Clinical Research and Training (NICCRAT) initiative has facilitated collaboration between the University of Nottingham and a team in the Research Center for Biotechnology, National Research and Innovation Agency (BRIN), to carry out a small number of SARS-CoV-2 WGS in Indonesia using Oxford Nanopore Technology (ONT). Analyses of SARS- CoV-2 genomes deposited on GISAID reveal the importance of clinical and demographic metadata collection and the importance of open access and data sharing. Lineage and phylogenetic analyses of two periods defined by the Delta variant outbreak reveal that: (1) B.1.466.2 variants were the most predominant in Indonesia before the Delta variant outbreak, having a unique spike gene mutation N439K at more than 98% frequency, (2) Delta variants AY.23 sub-lineage took over after June 2021, and (3) the highest rate of virus transmissions between Indonesia and other countries was through interactions with Singapore and Japan, two neighbouring countries with a high degree of access and travels to and from Indonesia.
Background Indonesia is one of the Southeast Asian countries with high case numbers of COVID-19 with up to 4.2 million confirmed cases by 29 October 2021. Understanding the genome of SARS-CoV-2 is crucial for delivering public health intervention as certain variants may have different attributes that can potentially affect their transmissibility, as well as the performance of diagnostics, vaccines, and therapeutics. Objectives We aimed to investigate the dynamics of circulating SARS-CoV-2 variants over a 15-month period in Bogor and its surrounding areas in correlation with the first and second wave of COVID-19 in Indonesia. Methods Nasopharyngeal and oropharyngeal swab samples collected from suspected patients from Bogor, Jakarta and Tangerang were confirmed for SARS-CoV-2 infection with RT-PCR. RNA samples of those confirmed patients were subjected to whole genome sequencing using the ARTIC Network protocol and sequencer platform from Oxford Nanopore Technologies (ONT). Results We successfully identified 16 lineages and six clades out of 202 samples (male n = 116, female n = 86). Genome analysis revealed that Indonesian lineage B.1.466.2 dominated during the first wave (n = 48, 23.8%) while Delta variants (AY.23, AY.24, AY.39, AY.42, AY.43 dan AY.79) were dominant during the second wave (n = 53, 26.2%) following the highest number of confirmed cases in Indonesia. In the spike protein gene, S_D614G and S_P681R changes were dominant in both B.1.466.2 and Delta variants, while N439K was only observed in B.1.466.2 (n = 44) and B.1.470 (n = 1). Additionally, the S_T19R, S_E156G, S_F157del, S_R158del, S_L452R, S_T478K, S_D950N and S_V1264L changes were only detected in Delta variants, consistent with those changes being characteristic of Delta variants in general. Conclusions We demonstrated a shift in SARS-CoV-2 variants from the first wave of COVID-19 to Delta variants in the second wave, during which the number of confirmed cases surpassed those in the first wave of COVID-19 pandemic. Higher proportion of unique mutations detected in Delta variants compared to the first wave variants indicated potential mutational effects on viral transmissibility that correlated with a higher incidence of confirmed cases. Genomic surveillance of circulating variants, especially those with higher transmissibility, should be continuously conducted to rapidly inform decision making and support outbreak preparedness, prevention, and public health response.
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