Ecology

Discovery of closely related phage sequences with the conserved genetic context of bS21Multiple phage-related sequences with a conserved genomic context were detected from several freshwater metagenome-assembled datasets (see Methods). Genes for bS21, TerL, PVP, prohead core scaffolding, and protease protein (hereafter prohead protease for short), and MCP are encoded in the genomic region. BLASTp search of the TerL sequences against the ggKbase sequences (ggkbase.berkeley.edu) obtained a total of 47 unique scaffolds with the conserved genomic region (Supplementary Table 1). Two related phages were included as outgroups for comparative analyses. The corresponding samples were collected from freshwater lakes or reservoirs (one from a wastewater treatment plant), and all but three were from the oxic layer (see Methods for details).General features of manually curated genomesAll the 49 phage sequences were manually curated to fill scaffolding gaps and fix the assembly errors, and nine of them (including one outgroup phage) were curated to completion (circular and no gaps or local assembly errors) (Supplementary Table 1). A total of 14 related phage genomes from IMG/VR were also included for further analyses. The eight bS21-encoding complete genomes had genome lengths of 293–331 kbp, GC contents of 31.0–33.7% and encoded 350–413 protein-coding genes (coding density, 91.1–94.9%), with 5–25 (average 17) tRNA genes. No alternative coding signal (i.e., stop codon reassignment) was detected in any genome. In comparison, the outgroup complete genome has a size of 308 kbp (450 protein-coding genes, 6 tRNAs, 94.7% coding density) and GC content of 27.3%.Genomic context of bS21 in phagesGenomic context analyses for bS21 genes showed a highly conserved gene architecture across phage genomes in proximity to the region encoding bS21 (see Fig. 1a for example). Specifically, we found that bS21 was consistently located in between two hypothetical protein families (positions 1 and –1 in Fig. 1b and Supplementary Table 2), with core structural proteins—including the TerL, PVP, prohead protease, and MCP—generally located within five genes in both the upstream and downstream DNA. Other hypothetical proteins were also consistently found in this region, although their positions were more variable upstream (positions –4 through –10, Fig. 1b). Importantly, the bS21 gene was consistently encoded in the reverse strand relative to the conserved hypothetical and structural protein genes (Fig. 1a and Supplementary Fig. 1).Fig. 1: Genetic context of the genes encoding bS21 in the phage genomes.a Examples of genetic context of phage genomes with and without bS21. The annotation of protein-coding genes is the same as indicated in b by different colors. Those in white are genes not shown in subfigure (b). b Summary of genetic context of all phage genomes encoding bS21. The relative position of genes near the bS21 gene is shown, and the size of circles indicates the number of phages with a gene belonging to a given protein family (annotation shown on right) at that relative position. Only the 12 most frequent families are shown. The details of the genetic context are shown in Supplementary Fig. 1.Full size imagePhylogeny of bS21-encoding phagesPhylogenetic analyses based on TerL suggested the phages belonging to several groups, we thus assigned them to clades a–e (Fig. 2 and Supplementary Table 1). Most of the phages belong to clades c, d, and e, and they have a broader environmental distribution than clades a and b. Interestingly, we found that some phages within a single clade were from distant sampling sites. Closer inspection indicated they also shared large genomic fragments with high similarity (82–98% for nucleotide sequences; Supplementary Fig. 2). Comparative genome-wide analyses of the complete genomes from the same site but sampled at different time points showed sequence variations in some genes (Supplementary Fig. 3).Fig. 2: The phylogeny of bS21 phages based on the large terminal (TerL) protein sequences.Two closely related phages without bS21 encoded were included as outgroups (shown at the top of the tree). The genomes are assigned to five clades (a, b, c, d, and e) based on the topology of the phylogenetic tree. The numbers in the brackets following the scaffold names indicate the total counts of the same scaffold detected from the corresponding sampling sites. The genomes that were manually curated to completion (circular and no gap) are indicated by squares, and the genome sizes are shown in brackets.Full size imageTerL phylogeny, constructed using sequences from this study and NCBI RefSeq sequences, indicated the most closely related classified phages belong to Caudovirales of either the Myoviridae or Ackermannviridae (Supplementary Fig. 4). A phage baseplate assembly protein was encoded in most curated genomes. This is an important building block for members of Siphoviridae and Myoviridae [8], so we concluded that the bS21-encoding phages are myoviruses.Predicted bacterial hosts of bS21-encoding phagesTo predict host-phage relationships we first used CRISPR-Cas spacers targeting. While none of the 16.5k unique spacers from the relevant metagenomes targeted any of the curated phage genomes from the same sampling sites, a single cross-site target was detected. Specifically, MIW1_072018_0_1um_scaffold_78 was targeted by a spacer (24 nt and no mismatch) from a MIW2 Flavobacterium genome (affiliation: Bacteroidetes, Flavobacteria). We then predicted the bacterial hosts based on the bacterial taxonomic affiliations of the phage gene inventories as previously described [2] (Supplementary Table 3). The results indicated that all of the phages infect members of Bacteroidetes, which were detected in 43 out of 45 samples (Fig. 3 and Supplementary Table 4). The two metagenomic samples without Bacteroidetes identified were both collected via filtering through 0.2 μm and onto 0.1 μm pore size filters. Bacteroidetes were detected in both of the corresponding 0.2 μm fraction samples (Fig. 3).Fig. 3: The relative abundance of the Bacteroidetes classes in all the analyzed samples in this study.The microbial communities were profiled based on ribosomal protein S3 (rpS3) assigned to the Bacteroidetes classes. The sampling sites were indicated by colored names, and the filter sizes used during sampling are shown by circles. The three pairs of filter samples are indicated by colored stars.Full size imageWe profiled the co-detection of phage clades and Bacteroidetes classes to test for specific connections (Supplementary Fig. 5). However, this was uninformative because most samples contained more than one class. However, phages from clades a and b are unlikely to infect class Bacteroidia members, as they did not co-occur in any sample.Comparison of bacterial and phage-encoded bS21Phylogenetic analyses revealed that bS21 protein sequences from phages (this study) and the bacterial bS21 sequences (from the corresponding samples and NCBI RefSeq) clustered separately (Supplementary Fig. 6). The bacterial bS21 sequences that are most similar to phage bS21 were from Bacteroidetes, mostly from the Flavobacteriia class (Supplementary Table 5). We aligned and compared the Bacteroidetes and phage bS21 sequences and mapped the divergent and non-divergent residues to the model of the ribosome of Flavobacterium johnsoniae (Fig. 4a). Multiple divergent positions are located at the beginning of the bS21 sequences and four residues (Arg21, Phe23, Asp25, and Thr28) were significantly divergent (Fig. 4b).Fig. 4: Conservation and differences between phage and bacterial bS21.a Location of bS21 (blue) within the 16S rRNA (green) and the ASD (magenta) of the F. johnsoniae ribosome (PDB ID: 7JIL) [9]. bS21 is in the neck region of the 16S rRNA, interacting closely with the 3’ end of the 16S rRNA, where the ASD is located. The 16S rRNA is shown from the subunit interface direction. b Zebra2 divergency results from an alignment of phage and bacterial bS21 sequences mapped on F. johnsoniae bS21. Divergent positions between phage and bacterial bS21 are shown with red. c Zebra2 conservation results from the same alignment as in (b) mapped on F. johnsoniae bS21 with conserved residues shown in yellow. The stacking interaction between Tyr54 and Adenine 1534 is indicated. d The sequence logo and consensus sequences of phage and bacterial bS21 alignments and the corresponding position of Tyr54 in F. johnsoniae bS21 in the alignment are highlighted. The C-terminal parts are highlighted with gray backgrounds.Full size imageBacteroidetes usually lack the SD sequences. It was recently reported that the bS21 Tyr54 (numbering in F. johnsoniae) is an important residue for blocking the ASD in the 16S rRNA within the ribosome [9]. Our analyses predict that all the analyzed bacterial and phage bS21 in this study have an amino acid with an aromatic ring (often Tyr54 but in a few cases His54, and in one case Phe54) at the position of Tyr54 in F. johnsoniae (Fig. 4c, d and Supplementary Fig. 6). This conservation of the aromatic property in phage bS21 should ensure stacking interaction with Adenine 1534 (numbering in F. johnsoniae 16S) from the ASD. In that way, phage bS21 mimics Bacteroidetes bS21 in the region where it binds the ribosome but differs from it in the region where the mRNA would bind.In contrast, the C-terminal regions of both the bacterial and phage bS21 sets were highly divergent (Fig. 4d). However, the phage C-terminal regions are generally conserved within the clades defined based on TerL phylogeny (Fig. 2 and Supplementary Fig. 7).Metabolic potentials of bS21-encoding phagesFunctional annotation of the predicted protein-coding genes revealed that in addition to bS21, these phages carry other genes related to protein production and stability (Supplementary Table 6). Examples include protein folding chaperones and Clp protease, suggesting the importance of controlling the proteostasis network of the cell. Interestingly, we also identified many genes involved in sugar-related chemistry and polysaccharide biosynthesis. Many of these genes were predicted to perform chemical transformations related to the biosynthesis of lipopolysaccharide, a major component of the Gram-negative bacterial outer membrane. We interpret this as a potential mechanism to remodel the cell surface and prevent superinfection by competitor phages, a strategy common to the phage lysogenic cycle. These phages lack detectable integration machinery (no gene for integrase or resolvase was detected), suggesting the possibility of a non-integrative long-term infection state such as pseudolysogeny [10].Clustering analyses of 22 phages with a minimum genome size of 100 kbp (including the two outgroup genomes) based on the presence/absence of protein families indicated they shared a total of 16 protein families (Supplementary Fig. 8 and Supplementary Table 7). Phosphate starvation-inducible protein PhoH (“fam582”) was the only predicted protein detected in all 22 phages (excluding the shared predicted proteins in the conserved rpS21-encoding region described above). Other common protein families include those related to DNA replication (e.g., DNA primase/helicase, DNA polymerase, HNH endonuclease, thymidylate synthase (EC:2.1.1.45), deoxyuridine 5’-triphosphate nucleotidohydrolase (EC:3.6.1.23)), those associated with virion assembly (e.g., a phage tail sheath protein, phage baseplate assembly protein W), and those for other functions (e.g., chaperone ATPase, alpha-amylase, DegT/DnrJ/EryC1/StrS aminotransferase).Temporal and spatial distribution and activity of bS21-encoding phages in Lake RotseeTo reveal the spatial and temporal distribution of the bS21-encoding phages, we focused on the Lake Rotsee data and profiled phage occurrence based on the sequencing coverage in the metagenomic datasets. The Lake Rotsee samples were collected from the oxic (7 samples) and anoxic (3 samples) layers of the water column. The bS21-encoding phages were readily detected in oxic samples, especially in the under-ice samples when the whole water column was oxic (Fig. 5a).Fig. 5: The spatial and temporal distribution and activity of bS21 phages at Lake Rotsee.a The sequencing coverage of each phage genome in each metagenomic dataset is shown in the heatmaps. The phages are phylogenetically clustered based on their TerL protein sequences (bootstraps shown in numbers), the colored backgrounds are the same as shown in Fig. 2 for different clades. The sampling time points and depths are shown on the left, and the oxygen conditions are indicated by colored circles on the right. Two replicates were sequenced from the 15 m sample collected in 2018. b The percentage of mapped RNA reads to the phage genomes in the corresponding samples (rows labeled in (a)). The mapped RNA reads had a minimum similarity of 98% to the phage genomes. No RNA data were generated for the three samples collected on October 10, 2017. See the figure legend for each genome in the upper right, the circular genomes have names in bold font.Full size imageRotsee Lake RNA reads were mapped to the phage genomes curated from this site to reveal the transcriptional activities of bS21-encoding phages (Fig. 5b). In general, the phages were likely to be most transcriptionally active in the oxic water columns. A total of 736 genes were transcribed in at least one sample (Supplementary Table 8), those for MCP, an AAA ATPase, tail sheath protein, bS21, FKBP-type peptidyl-prolyl cis-trans isomerase, and a methyltransferase FkbM domain protein are among the top 100 most highly transcribed. The high transcriptional activities of MCP in five phages indicated they were in the late stage of replication at the time of sampling.The transcriptional behavior of phage bS21 genesTo seek evidence of a transcriptional relationship involving bS21 and other genes we focused on the three phages that were most active based on the transcriptional level of their 19 shared single-copy genes (Fig. 6a). bS21 had very similar (but slightly lower) transcriptional activities as a neighboring gene (hereafter, bS21_CN gene) encoded on the opposite strand. The bS21_CN gene encodes a hypothetical protein (protein family: fam498) and was not detected in the two outgroup phages without bS21 (Supplementary Table 6). Interestingly, a comparison of the phylogenies of bS21 and bS21_CN showed a very similar evolutionary pattern (Supplementary Fig. 9), likely suggesting their potential functional relationship in the bS21-encoding phages.Fig. 6: The transcription levels of bS21 and core structural protein genes.a The normalized transcriptional level (NTL) of shared single-copy protein families of three phages (indicated by arrows in Fig. 5b) with ≥1000 RNA reads mapped. Two families (including MCP) are listed on a different scale due to their much higher transcription levels. Refer to Fig. 5 for shape symbols that designate phage genomes and samples. b Examples of RNA mapping profiles indicating the co-transcription of some genes neighboring bS21. Hypothetical protein genes are shown in white.Full size imageInspection of the RNA reads mapping profiles indicated that the conserved region encoding bS21 and core structural proteins was not transcribed as an operon, whereas bS21 and bS21_CN, MCP and its upstream hypothetical protein gene, and prohead protease and its downstream hypothetical protein gene may each be transcribed together (Fig. 6b). Given the observed RNA expression patterns, we conclude that the phage-encoded bS21 genes were actively transcribed during late-stage replication, along with other core structural proteins.Genomic context of bS21 genes in published phage genomesTo determine whether the phage bS21 genes are generally co-located with those for core structural proteins in diverse phages, we profiled the genomic context of bS21 in 900 published bS21-encoding phages [2, 11] (Supplementary Table 9). Functional annotations were performed for the upstream and downstream ten genes of the bS21 genes using pVOG (Supplementary Table 10). Of the 20 most abundant pVOGs, 6 were related to core structural assembly (Fig. 7a), i.e., prohead protease (n = 310), MCP (n = 154), PVP (n = 120), TerL (n = 78), neck protein (n = 70), and a tail sheath protein (n = 29). A total of 388 genomes contained at least one of these genes within ten genes of bS21, and eight had all of these six core structural proteins in close proximity. Three pVOGs were related to DNA processing, i.e., an exonuclease (n = 37), an endonuclease (n = 32), DNA helicase (n = 30). Other pVOGs included Hsp20 heat shock protein (n = 127), two ATP-dependent CLP proteases (n = 50 and 47, respectively), and lysozyme (for lysis; n = 29). Interestingly, the prohead protease and the MCP pVOG genes are very close to the bS21 gene (generally 2–4 genes; Fig. 7b), as in the bS21-encoding phage genomes analyzed in this study (2–6 genes away; Fig. 1 and Supplementary Fig. 1).Fig. 7: Neighboring genes within 10 genes of bS21 in published bS21-encoding phage genomes.a The annotation and corresponding functional category (if assigned) of the 20 most commonly detected pVOG genes and their predicted functions are shown on the left, the total number of genomes with the gene are shown on the right. b The distribution of the distance of each gene to bS21 in the genomes. The position of genes next to bS21 (thus distance = 1) is highlighted using a red dashed line. The average distance of each gene to bS21 is shown on the left. c The predicted hosts of bS21-encoding phages with the top 4 most abundant genes detected within 10 genes of bS21. The total count of hosts is shown on the right.Full size imageWe respectively predicted the hosts of the bS21-encoding phages with the four most dominant pVOGs within ten genes of bS21 (Fig. 7c and Supplementary Table 11). The bacterial hosts are diverse and include Proteobacteria, Bacteroidetes, and Firmicutes. More

Co-culture dynamicsThis study was designed to enhance understanding of metabolite release and utilization across bloom stages in a simple community of phytoplankton and heterotrophic bacteria. The synthetic community was established with the diatom T. pseudonana and the bacterial strains R. pomeroyi DSS-3, Stenotrophomonas sp. SKA14, and P. dokdonensis MED152. These bacterial strains have high genetic similarity to isolates from phytoplankton cultures [14] and represent taxa that are common in phytoplankton blooms. Metabolites derived from the diatom were the sole source of carbon available for the bacteria, since no organic substrates were added. In addition, none of the bacteria can assimilate nitrate, and usable nitrogen was only available as diatom or bacterial extracellular products. The diatom had its highest specific growth rate of 1.65 d−1 during days 0–3, after which the rate declined (Fig. 1A). The total abundance of heterotrophic bacteria increased steadily but there was a succession that favored P. dokdonensis through day 15, and then R. pomeroyi by day 20; Stenotrophomonas disappeared from the model system by day 3 (Fig. 1B). The presence of bacteria did not affect the growth of diatoms based on comparisons of abundance in co-cultures versus axenic cultures at day 15 (Fig. 1A), as has been found previously [14, 26]. Inorganic nutrients were not limiting ( >5 μM at day 15; Table S1).Fig. 1: Time course of microbial abundances.A Cell abundance based on flow cytometric analysis for co-cultures (5 time points) and axenic cultures (day 15 only) (n = 3). The intensive sampling dates for the early and late bloom comparisons are marked with gray boxes. B Mean relative abundance of bacterial species is based on CFUs (n = 3). The day 0 samples were collected 8 h after inoculation.Full size imageDiatom endometabolite shiftsAnalyses focused on the day 3 (early bloom) and day 15 (late bloom) co-culture time points, for which a complete set of metabolomic and transcriptomic data were collected. Twenty-two diatom endometabolites that were annotated with high confidence by NMR analysis (Table S2) and quantified after normalizing to diatom cell number revealed that endometabolome composition differed substantially between bloom stages. Metabolites with significantly different cellular concentrations included nine compounds that were higher in intracellular concentration during the late bloom; these were arginine, valine, lysine, DHPS, glycerol-3-phosphate, phosphorylcholine, DMSP, glycine betaine, and homarine (T-test; P  More

Viral-like particle abundanceThe 10 sampling sites were equidistantly (average distance: 1.6 km) distributed between 1689 and 717 m above sea level in a 95.7 km2, pristine catchment and covered a flow-connected distance of 14.3 km (Fig. 1, Methods).Fig. 1: No evidence for a downstream accumulation of VLPs.Viral-like particles (VLP) were purified from 10 sites sampled during four seasons along an altitudinal gradient in an alpine stream (Vièze, Switzerland) (a). Neither VLP abundance (b) nor Virus-to-Prokaryote Ratios (VPR; (c)) showed pronounced spatial or temporal trends.Full size imageViral-like particle (VLP) counts normalized to areal coverage of the stream biofilm ranged from 2.8 × 109 to 3.4 × 1010 VLP m−2. On average, VLP abundance was highest in summer with 1.87 ± 0.75 × 1010 VLP m−2; however, there were no statistically significant seasonal differences in VLP abundance (repeated-measures ANOVA, F = 0.87, p = 0.47). VLP numbers did not exhibit a continuous spatial tendency, except during fall when VLP numbers increased significantly with downstream distance (r = 0.81, p 0.7 and/or pident >0.4). Indeed, 90 of the 203 putative viral depolymerases showed significant sequence similarity with 198 vOTU sequences (i.e., 6% of the overall vOTU diversity). We were able to obtain taxonomic classification for 80 of these 198 vOTUs, and found that all large Caudovirales families were represented (i.e., Myoviridae, n = 31, Siphoviridae, n = 17, Podoviridae, n = 15, Autographiviridae, n = 13, Ackermannviridae, n = 2, and Herelleviridae, n = 1). This suggests that depolymerase activity may be widespread among viruses infecting bacteria in stream biofilms. Although both the number of potential depolymerases included in our database and the number of classified vOTUs was limited, we observed that depolymerase-harboring Myoviridae vOTUs corresponded the expectation based on the overall relative abundance of Myoviridae, pointing toward the importance of dispersal for this important viral family. Siphoviridae, in contrast, were relatively underrepresented among depolymerase-harboring vOTUs. In combination with neutral model predictions, this may point towards a fundamental difference between Siphoviridae and Myoviridae in infecting stream biofilm bacteria. While Myoviridae may rather rely on efficiently spreading across distant biofilm patches facilitated by an ability to decompose the EPS matrix, many members of Siphoviridae seem to lack this ability.To investigate our second hypothesis, that lysogeny might be a successful viral life cycle strategy to spread locally within biofilm patches, we used BACPHLIP [36]. BACPHLIP predicted with high probability ( >75%) a lysogenic life cycle for 58 out of 256 complete viral genomes and a lytic life cycle for 177 viral genomes. For the remaining 21 complete viral genomes in our dataset, BACPHLIP did not result in sufficiently high prediction probability (i.e., More

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29 March 2022

Dozens of unidentified bat species likely live in Asia — and could host new viruses

Study suggests some 40% of horseshoe bats in the region have yet to be formally described.

Smriti Mallapaty

Smriti Mallapaty

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There could be more species of horseshoe bat than previously thought.Credit: Chien Lee/Nature Picture Library

A genomic analysis suggests that there are probably dozens of unknown species of horseshoe bats in southeast Asia1. Horseshoe bats (Rhinolophidae) are considered the reservoir of many zoonotic viruses — which jump from animals to people — including the close relatives of the viruses that caused severe acute respiratory syndrome and COVID-19. Identifying bat species correctly might help pinpoint geographical hotspots with a high risk of zoonotic disease, says Shi Zhengli, a virologist at the Wuhan Institute of Virology in China. “This work is important,” she says. The study was published in Frontiers in Ecology and Evolution on 29 March.Better identification of unknown bat species could also support the search for the origins of SARS-CoV-2 by narrowing down where to look for bats that may harbour close relatives of the virus, says study co-author Alice Hughes, a conservation biologist at the University of Hong Kong. The closest known relatives of SARS-CoV-2 have been found in Rhinolophus affinis bats in Yunnan province, in southwestern China2, and in three species of horseshoe bat in Laos3.Cryptic speciesHughes wanted to better understand the diversity of bats in southeast Asia and find standardized ways of identifying them. So she and her colleagues captured bats in southern China and southeast Asia between 2015 and 2020. They took measurements and photographs of the bats’ wings and noseleaf — “the funky set of tissue around their nose”, as Hughes describes it — and recorded their echolocation calls. They also collected a tiny bit of tissue from the bats’ wings to extract genetic data.To map the bats’ genetic diversity, the team used mitochondrial DNA sequences from 205 of their captured animals, and another 655 sequences from online databases — representing a total of 11 species of Rhinolophidae. As a general rule, the greater the difference between two bats’ genomes, the more likely the animals represent genetically distinct groups, and therefore different species.The researchers found that each of the 11 species were probably actually multiple species, possibly including dozens of hidden species across the whole sample. Hidden, or ‘cryptic’, species are animals that seem to belong to the same species but are actually genetically distinct. For example, the genetic diversity of Rhinolophus sinicus suggests that the group could be six separate species. Overall, they estimated that some 40% of the species in Asia have not been formally described.“It’s a sobering number, but not terribly surprising,” says Nancy Simmons, a curator at the American Museum of Natural History in New York City. Rhinolophid bats are a complex group and there has been only a limited sampling of the animals, she says.However, relying on mitochondrial DNA could mean that the number of hidden species is an overestimate. That is because mitochondrial DNA is inherited only from the mother, so could be missing important genetic information, says Simmons. Still, the study could lead to a burst of research into naming new bat species in the region, she says.Further evidenceThe findings corroborate other genetic research suggesting that there are many cryptic species in southeast Asia, says Charles Francis, a biologist at the Canadian Wildlife Service, Environment and Climate Change Canada, in Ottawa, who studies bats in the region. But, he says, the estimates are based on a small number of samples.Hughes’ team used the morphological and acoustic data to do a more detailed analysis of 190 bats found in southern China and Vietnam and found that it supported their finding that many species had not been identified in those regions. The study makes a strong argument for “the use of multiple lines of evidence when delineating species”, says Simmons.Hughes says her team also found that the flap of tissue just above the bats’ nostrils, called the sella, could be used to identify species without the need for genetic data. Gábor Csorba, a taxonomist at the Hungarian Natural History Museum in Budapest, says this means that hidden species could be identified without doing intrusive morphology studies or expensive DNA analyses.

doi: https://doi.org/10.1038/d41586-022-00776-2

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Our results show that the average diel activity patterns of Ae. aegypti populations in both Miami, FL and in Brownsville, TX were very similar; they both had two peaks, one in the early morning and the other in the evening, and the average host-seeking peaks are between 7:00 and 8:00 and between 19:00 and 20:00 (Fig. 4). Similar observations were previously reported by several investigators3,4,10,11,12 and the bimodal diel activity pattern is the most frequently reported for Ae. aegypti populations worldwide. However, variations between peak activity have been detected between populations. In East Africa, for instance, Trpis et al.3 reported peak activity at 7:00 and at 19:00, whereas McClelland10 reported peak activity two or three hours after sunrise (9:00 or 10:00) and one or two hours before sunset (17:00 or 16:00). Similarly, in the United States, Smith et al.7 observed a bimodal diel activity pattern for Ae. aegypti, but the evening peak was earlier, between 17:00 and 19:00. Despite these variations, the spacing of the peaks is similar in all these studies despite the fact that these studies were conducted in ecologically and climatically diverse locations.The activity patterns observed at site 3 in Brownsville (Fig. 2) and at site 1 in Miami (Fig. 1) were trimodal. In Brownsville, the trimodal activity peaks were between 6:30 and 7:30, 9:30 and 10:30, and 18:30 and 19:30 (Fig. 2), and in Miami the trimodal peaks were between 7:00 and 8:00, 9:00 and 10:00 and between 19:00 and 20:00 (Fig. 1). Interestingly, the timing of the third peak was similar in both Brownsville site 3 and Miami site 1 suggesting similar underlying factors despite geographic distance, different ecology, and different climate. Brownsville, Texas, is in the Lower Rio Grande Alluvial Floodplain ecoregion. The climate is humid subtropical and urbanization has removed most of the indigenous palm trees and floodplain forests vegetation (https://www.epa.gov/sites/default/files/2018-05/documents/brownsvilletx.pdf). Miami is in the Tropical Florida Ecoregion. Similar to Brownsville, Texas, urbanization and agriculture has replaced most of the indigenous Pine Rockland vegetation. Trimodal biting patterns for Ae. aegypti have been observed before in Trinidad by Chadee and Martinez4, but the middle peak was observed at 11:00 which is half an hour to an hour later than what we observed in Miami and Brownsville, respectively (Figs. 1 and 2). While the morning and evening peaks coincide with human outdoor activity, the middle peak occurs during high heat conditions and the factors that lead to this peak or its importance in the epidemiology of Ae. aegypti-borne arboviral diseases are currently not known. The studies by McClelland13 observed multiple activity peaks in an East African population of Ae. aegypti. The significance of the different activity patterns to the epidemiology of Ae. aegypti-borne arboviral diseases are currently unknown and we think they need more investigation especially since Ae. aegypti-borne arboviral infections have been rising in the recent past14,15.We observed that the host-seeking activity peaks were consistent between 5:45 and 7:30 and between 18:00 and 20:45 (Figs. 1 and 2). These observations are important in planning and conducting control operations directed at the adult Ae. aegypti female populations. During the 2016 Zika outbreak, there was no specific information on the host-seeking activity patterns of Ae. aegypti in Miami Dade County and the adulticide treatment implemented as part of an integrated approach targeted the morning activity16. The integrated approach effectively reduced the vector population and interrupted the transmission of the Zika virus; however, it highlighted the need for site-specific information on the diel activity patterns of Ae. aegypti in Miami Dade County in particular and the CONUS in general. There have been sporadic Ae. aegypti-borne arboviral disease outbreaks in Miami Dade County, FL and the city of Brownsville, TX17,18,19,20,21, in the future we will be better prepared to conduct effective adulticide applications with the current knowledge of the diel activity patterns of Ae. aegypti in these areas. Furthermore, we are now better equipped to educate the public on how to minimize exposure to Ae. aegypti-borne arboviral diseases by avoiding outdoor activities during peak biting activity periods.In our studies, we used BG-Sentinel 2 traps and monitored them every hour, twenty-four hours a day over 96 h, a method with some similarities to that used by Smith et al.7. In the past, diel biting activity studies were carried out using human landing catches following the methods primarily established by Haddow22. To our knowledge, only two studies have previously used sampling procedures not based on human landing catches to study the biting activity patterns of Ae. aegypti; the study by Ortega-Lopez et al.6 used mosquito electrocuting traps, and the study by Smith et al.7 used a mechanical rotator mosquito trap. In the present study, the use of BG-Sentinel II traps had the advantage that it was specifically designed to capture female host-seeking Ae. aegypti8,9. In addition, attached BG-Counter devices can keep track of the number of mosquitoes captured per specified unit time and environmental conditions, and store the information in a cloud server. However, the BG-Sentinel 2 traps collected a wide variety of mosquito species, (Table 1), and to keep track of specific species captured each hour, we had to monitor them every hour.Overall, we present data on the diel activity of Ae. aegypti populations in two cities in the southern United States. In both cities the activity patterns were bimodal; there were peaks of activity in the mornings and the evenings. The significance of these observations is that these peaks can be targeted to improve the effectiveness of adulticide treatments aimed at controlling Ae. aegypti adult populations. Using BG-Sentinel 2 traps eliminates individual variations associated with human landing catches and the associated danger of infections from wild mosquitoes especially during ongoing outbreaks. More