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Poisoning with Soman, an Organophosphorus Nerve Agent, Alters Fecal Bacterial Biota and 1
Urine Metabolites: a case for Novel Signatures for Asymptomatic Nerve Agent Exposure 2
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Derese Getneta, Aarti Gautam
a, Raina Kumar
a,b, Allison Hoke
a,c, Amrita K. Cheema
d, Franco 4
Rossettie, Caroline R. Schultz
f, Rasha Hammamieh
a, Lucille A. Lumley
g, and Marti Jett
a# 5
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aIntegrative Systems Biology Program, US Army Center for Environmental Health Research, 7
Fort Detrick, Maryland 8
bAdvanced Biomedical Computing Center, Frederick National Lab for Cancer Research, Fort 9
Detrick, Maryland
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cThe Geneva Foundation, US Army Center for Environmental Health Research, Fort Detrick, 11
Maryland
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dDepartments of Oncology and Biochemistry, Molecular and Cellular Biology, Georgetown 13
University Medical Center, Washington DC 14
eClinical Research Management, Silver Spring, Maryland
15
fEdmond Scientific Company, Aberdeen Proving Ground, Maryland
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gUS Army Medical Research Institute of Chemical Defense, Aberdeen Proving Ground, 17
Maryland
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Running title: Soman induced changes in microbiome and urine metabolome 19
Address correspondence to Marti Jett, Chief Scientist of Systems Biology Enterprise at 20
Present address: US Army Center for Environmental Health Research, Fort Detrick, Maryland 22
A.G., D.G., and R.K. contributed equally to this work. 23
AEM Accepted Manuscript Posted Online 14 September 2018Appl. Environ. Microbiol. doi:10.1128/AEM.00978-18This is a work of the U.S. Government and is not subject to copyright protection in the United States.Foreign copyrights may apply.
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Abstract: The experimental pathophysiology of organophosphorus (OP) chemical exposure has 25
been extensively reported. Here, we describe an altered fecal bacterial biota and urine 26
metabolome that follows intoxication with soman, a lipophilic G class chemical warfare nerve 27
agent. Non-anaesthetized Sprague-Dawley male rats were subcutaneously administered soman at 28
0.8 (sub-seizurogenic) or 1.0 (seizurogenic) of the median lethal dose (LD50) and evaluated for 29
signs of toxicity. Animals were stratified based on seizing activity to evaluate effects of soman 30
exposure on fecal bacterial biota and urine metabolites. Soman exposure reshaped fecal bacterial 31
biota by altering Facklamia, Rhizobium, Bilophila, Enterobacter, and Morganella genera of the 32
Firmicutes and Proteobacteria phyla, some of which are known to hydrolyze OPs. However, 33
analogous changes were not observed in the bacterial biota of the ileum, which remained the 34
same irrespective of dose or seizing status of animals after soman intoxication. However, at 75 35
days post soman exposure, bacterial biota stabilized and no differences were observed between 36
groups. Interestingly, when considering just the seizing status of animals, we found that the urine 37
metabolome was markedly different. Leukotriene C4, kynurenic acid, 5-hydroxyindoleacetic 38
acid, norepinephrine, and aldosterone were excreted at much higher rates at 72 hrs in seizing 39
animals, consistent with early multi-organ involvement during soman poisoning. These findings 40
demonstrate the feasibility of using the dysbiosis of fecal bacterial biota in combination with 41
urine metabolome alterations as forensic evidence for pre-symptomatic OP exposure temporally 42
to enable administration of neuroprotective therapies of the future. 43
Importance: The paucity of assays to determine physiologically relevant OP exposure presents 44
an opportunity to explore the use of fecal bacteria as sentinels in combination with urine to 45
assess changes in the exposed host. Recent advances in sequencing technologies and 46
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computational approaches have enabled researchers to survey large community level changes of 47
gut bacterial biota and metabolomic changes in various biospecimens. Here, we profiled changes 48
in fecal bacterial biota and urine metabolome following a chemical warfare nerve agent 49
exposure. The significance of this work is a proof-of-concept that fecal bacterial biota and urine 50
metabolites are two separate biospecimens rich in surrogate indicators suitable for monitoring 51
OP exposure. The larger value of such an approach is that assays developed around these 52
observations can be deployed at any setting with a moderate clinical chemistry and microbiology 53
capability. This can enable estimation of the affected radius or to screen, triage, or rule out 54
suspected cases of exposures in mass casualty scenarios, transportation accidents involving 55
hazmats, refugee movements, humanitarian missions, and training settings when coupled to an 56
established and validated decision-tree with clinical features. 57
Keywords: soman, gut microbiome, 16S rRNA gene, and urine metabolome 58
Background 59
Despite the serious health threat posed to communities, organic derivatives of phosphorus 60
containing acids have a wide range of applications in modern society (1-3). OP-containing 61
products are in excessive use world-wide for the control of agricultural or household pests. OP-62
containing pesticides account for almost 38% of all pesticides used across the globe, leading to 63
nearly 3 million poisonings, over 200,000 deaths annually, and the contamination of numerous 64
ecosystems (4). In addition, application of OP derivatives as agents of war and terrorism in the 65
form of nerve agents poses a significant threat to both civilians and the warfighter. Exposure to 66
OP leads to various degrees of neurotoxicity due to cholinergic receptor hyperactivity, mediated 67
primarily by the inhibition of acetylcholinesterase (AChE) (5). The excessive accumulation of 68
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acetylcholine leads to severe physiological complications that may manifest both as muscarinic 69
symptoms (e.g. lacrimation, salivation, diarrhea, miosis, and bradycardia) as well as nicotinic 70
symptoms (e.g. tachycardia, hypertension, convulsions, and paralysis of skeletal and respiratory 71
muscles) and death (1-3, 6, 7). 72
Soman, pinacolyl methylphosphonofluoridate or GD (German agent D), is one of the G class 73
nerve agents (volatile agents associated with inhalation toxicity) that inhibits AChE much more 74
rapidly but less specifically than V class nerve agents (viscous agents associated with 75
transdermal toxicity) (2, 6, 8). Whole body autoradiography studies in mice revealed that 76
intravenously administered tritiated-soman (3H-soman) spreads through the entire body in less 77
than 5 min (9). High levels of accumulation were noted in the lungs, skin, gallbladder, intestinal 78
lumen, and urine during the first 24 hrs. 3H-pinacolyl methylphosphoric acid (
3H-PMPA), a 79
hydrolyzed acid and primary metabolite of soman, was found to be concentrated in specific 80
organs such as lungs, heart, and kidneys within minutes of 3H-soman administration, which 81
reflected the highly reactive (i.e. rapid aging) nature of soman in vivo (10). Significant amounts 82
of soman were also detected in red blood cells, a major esterase depot, when compared to the 83
plasma. In addition, these studies revealed that the common route of excretion for PMPA, a 84
major soman metabolite, was via urine and the intestinal lumen content (9, 11). Interestingly, 85
only trace amounts of 3H-soman,
3H-PMPA, or
3H-methylphosphonic acid (hydrolyzed PMPA) 86
were observed in the central nervous system. Current clinical nerve agent exposure assessments 87
are primarily based on overt physiological reactions such as convulsions, loss of consciousness, 88
and salivation for high-dose exposures or pupil constriction and respiratory distress for low-dose 89
exposures (12, 13). Recent studies have also demonstrated the feasibility of identifying OP 90
hydrolysis products in hair and nail clippings to verify nerve agent exposure after 30 days (13, 91
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14). Hence, monitoring asymptomatic exposure or verifying suspected exposure during the pre-92
symptomatic phase using minimally invasive and rapid molecular methods is an ideal approach 93
and capability. Thus, identification of new surrogate biomarkers of toxicity and/or exposure to 94
soman and other OPs is essential both from a clinical and a public health standpoint, especially 95
for triaging population level exposures. 96
Using an omics approach, we have assessed the potential value of correlation between changes in 97
fecal bacterial biota or urine metabolites and OP exposure to determine if these biospecimens are 98
suitable for diagnostic use for exposure surveillance and monitoring in a rat model of soman 99
exposure. More importantly, we filled in a knowledge gap of how OP exposure directly or 100
indirectly impacts bacterial communities of the mammalian gut as well as alter the global urine 101
metabolic profile. For over 20 years, specific species from the Bacteroidetes and Proteobacteria 102
phyla have been implicated in enhanced biodegradation of OP pesticides in the bioremediation 103
field. Therefore, exploring the role of the microbiome in a mammalian host’s response to OP is 104
the next logical step (4, 15). Furthermore, recent advances in sequencing technologies have 105
enabled a detailed analysis of structural changes in the gut microbiome, revealing the dynamic 106
ecosystem of the bacterial biota and its essential role in health and disease. We also identified 107
urine as a suitable specimen type for investigation in this study because urine consists of 108
numerous metabolites as outputs from multiple pathways and provides a snapshot of both local 109
and systemic physiological changes (16). With these concerns in mind, we focused our efforts on 110
exploring and describing soman-induced dysbiosis of the gut microbiota and alterations in the 111
urine metabolome. 112
Results 113
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Clinical manifestation of soman insult 114
To establish soman-induced toxicity with and without seizure, Sprague-Dawley rats were 115
subcutaneously injected with saline or 0.8 LD50 (sub-seizurogenic and unmitigated by treatment) 116
or 1 LD50 (seizurogenic and mitigated by treatment) exposure equivalent of soman. To reduce 117
mortality, the animals given 1 LD50 were also administered atropine sulfate and HI-6 one minute 118
after exposure. Rats that developed seizures at 1 LD50 were given diazepam to control seizing. 119
Approximately 42% of the animals exposed to soman (0.8 or 1.0 LD50) experienced seizure 120
irrespective of dose or the medical treatment regimen administered. As expected, control rats did 121
not experience seizure from administration of the vehicle. Seizing animals experienced a notable 122
weight loss and displayed increased activity in the days immediately after soman poisoning (Fig. 123
1a and Fig. S1a). Body temperature was not altered between seizing and non-seizing groups (Fig. 124
S1b). The Racine scale score, a quantitative assessment of seizure-related activities such as 125
degrees of tremors, convulsions, and seizures, was significantly higher in seizing subjects as 126
compared to non-seizing subjects, as expected (Fig. 1b)(17-19). Based on EEG activity, body 127
weight, and Racine score, we broadly categorized our analysis groups into a non-seizing group 128
(no seizure, n= 13) or a seizing group (exposure seizure or sustained seizure, n=10). We also 129
further subdivided cohorts based on dose because of seizure differences (Fig. 1c and 1d). Fecal 130
matter, urine, and tissues were harvested from animals to examine the gut microbiota and urine 131
metabolic changes due to the soman insult. 132
Taxonomic changes to soman exposure 133
To assess the effect of soman exposure on the bacterial biota, we sequenced the V3 and V4 134
hypervariable regions of the 16S ribosomal RNA from the feces and ileum of all animals at 72 135
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hrs. Collecting and sequencing specimens from individual animals enabled us to assess the effect 136
of dose- or seizing status- driven changes in individual bacterial compositions. By measuring 137
multiple alpha diversity estimates within a dose (0.8 LD50 or 1.0 LD50) or seizing status (non-138
seizing or seizing) specimens, we found statistically significant (p<0.05) altered diversity in the 139
fecal bacterial biota of seizing animals at both sub-seizurogenic 0.8 LD50 and seizurogenic 1.0 140
LD50 (with a pronounced effect in seizing animals of the 0.8 LD50 exposure) that was markedly 141
different than control or non-seizing groups (Fig. S2a). When measuring alpha diversity between 142
the ileum specimens of control and soman-exposed animals, however, a higher degree of taxa 143
similarity was observed irrespective of dose or seizing status (Figure S2b) of specimens. The 144
bacterial biota of the ileum was not altered by exposure to soman while the fecal bacterial biota 145
was substantially altered. 146
In order to investigate if organism abundance accounted for the alpha diversity differences 147
observed between the fecal and ileum bacterial biota in response to soman insult, we rank 148
ordered all phyla identified in the study based on relative abundance (data not shown). To our 149
surprise, no differences were observed between the fecal and ileum bacterial phyla abundance 150
distribution. In both fecal and ileum specimens, Firmicutes and Bacteroidetes accounted for the 151
most abundant phyla as expected followed by Verrucomicrobia, Tenericutes, and Actinobacteria. 152
However, within the ileum the relative abundances of all the phyla were evenly distributed 153
around 10% while the phyla within the fecal specimens displayed a wide range of distribution 154
from 6% to 18% (data not shown). To further understand if soman exposure-driven bacterial 155
biota differences were attributable to dose or seizure individually, we measured beta diversity 156
using weighted UniFrac distances between specimens (fecal or ileum) and visualized the output 157
using principal coordinate analysis (PCoA) (Fig. S3). However, due to overlapping clusters, the 158
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PCoA was unable to distinguish any of the groups into significant clusters based on dose or 159
seizing status in either the ileum or fecal specimens. 160
Effect of soman exposure on fecal bacterial biota composition 161
Next, we examined the microbial communities of the feces and ileum of each subject and 162
enumerated each phylum to decipher where microbial diversity was substantially altered by 163
exposure to soman, based on dose or seizure status. Several structural changes were observed in 164
the fecal microbiota compositions correlating with dose or seizure status of animals (Fig. 2). We 165
found that the relative abundance of fecal TM7 phylum was reduced in response to increasing 166
dose of soman and seizure status of animals, concurrently. Conversely, increasing relative 167
abundance of fecal Proteobacteria and Cyanobacteria trended with increase in soman dose insult 168
and seizing status of animals (Fig S4). However, the relative abundance of Actinobacteria, 169
Firmicutes, Tenericutes, and Verrucomicrobia phyla was unchanged in response to soman dose 170
or seizing status of animals. The Bacteroidetes phylum showed a positive trend of relative 171
abundance increase, although not statistically significant. Similar microbial community 172
composition analyses were also completed for ileum specimens collected from each individual 173
subject to identify if any changes were masked during the initial analyses (Fig. S5). Consistent 174
with previous findings in the ileum, no significant structural bacterial biota changes were 175
observed in any subjects in response to increase in dose or seizing status. 176
Furthermore, to measure the bacterial biota diversity and relative abundance (with respect to 177
quantitative and qualitative analysis), we looked into higher phylogenetic resolution and 178
taxonomic breakdowns. Distinct bacterial populations were observed at the genus level of 179
Firmicutes and Proteobacteria in the fecal specimens of soman-exposed animals (Fig. 3). The 180
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Facklamia genus of the Aerococcaceae family was only detected in the fecal specimens of 181
soman-exposed rats and not in the ileum tissues content. Rhizobium, Bilophila, Enterobacter, and 182
Morganella genera of the Proteobacteria phylum exhibited expansion in the soman exposed rats 183
similar to that observed for the Facklamia genus. Interestingly, the Rhizobium genus, formerly 184
known as Agrobacterium, was primarily identified in the lower dose (0.8 LD50) soman exposure 185
group while Bilophila, Blautia, Enterobacter, and Morganella genera were detected in all soman 186
doses irrespective of seizing status of animals (Fig S6). Furthermore, several unclassified OTUs 187
were observed for Alcaligenaceae and Comamonadaceae of the Burkholderiales order and some 188
Enterobacteriaceae families, primarily in the seizing high soman dose exposure animals, and 189
these genera clusters were not observed in the control group or the low soman dose exposure 190
group. All difference in bacterial biota composition here were only observed at the 72 hr time 191
point and negligible at 75 days post exposure (data not shown). 192
Soman exposure alters the urine metabolome 193
The untargeted metabolomic profiling of biological substrates within a biospecimen provides 194
direct and simultaneous measurements of catabolic biochemical outputs that make up a given 195
phenotype (20). Comparative metabolomics profiling of urine specimens was performed to 196
assess soman exposure-associated metabolic alterations at 72 hrs post exposure, for seizing and 197
non-seizing animals. XCMS was used to pre-process metabolomics data to generate an analysis 198
matrix of mass-over-charge, retention time, fold change, and p-value for over 500 unique 199
metabolite peaks identified from both positive and negative mode analyses. Volcano plots from 200
Metaboanalyst v3.0. were used to visualize and enrich for metabolites with ≥ 2 fold change at 201
p<0.05 (Fig. S7). Furthermore, PCA was used to visualize if seizing and non-seizing groups 202
clustered separately (Fig S8). At the 72 hr time point, we found >200 metabolite peaks with 203
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>2.0 fold change, of which 9 analytes had >20 fold change in seizing rats rather than non-seizing 204
rats. Many of these metabolites with >20 fold change appear to be either food-derived 205
metabolites such as methylmaysin and acetylpicropolin, or cresol derivatives. We also found a 206
large fraction of modified amino acids (such as acetylated, methylated, or formylated) common 207
urine solutes along with many phenyl and indole compounds. We used an in-house algorithm to 208
identify bacterial metabolites that are known to be detected in urine. Hence, we identified routine 209
urine metabolites associated with microbial origin such as p-cresol, D-alanine, and phenylacetic 210
acid. Interestingly, these metabolites were detected at a significantly higher rate (>5 fold change) 211
in the urine of seizing rats than non-seizing rats. The tryptophan catabolism by-product 212
kynurenic acid, the inflammatory lipid leukotriene C4, and the neurotransmitter norepinephrine 213
were also secreted in the urine at a significantly higher rate in seizing animals (Table 1) (21). 214
However, urinary citric acids levels remained unaltered between seizing and non-seizing 215
animals, suggesting that the presence of physiological indicators of physiological dysregulation 216
(acidemia and inflammation) in the urine of soman-exposed animals was not primarily the result 217
of kidney injury. 218
To gain insights into biological networks perturbed by soman in seizing rats, we annotated all 219
molecular networks associated with candidate metabolites identified at the 72 hrs time point in 220
an unbiased manner using publicly available databases. Consistent with the neurotoxicity 221
pathology of soman assault, large proportions of the metabolites in the urine of seizing animals 222
were primarily related to nervous system signaling activity representing norepinephrine and 223
serotonergic pathway products (data not shown). Next, we narrowed down our pathway 224
annotation to only consider best peak-matched metabolites (such as highest mass accuracy 225
(lowest dppm) and frequent database hits.) and to manually remove unlikely identifications, such 226
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as drug action pathways, to restrict off-target hits in our annotation algorithm. This careful focus 227
of the data input revealed key molecular networks (catabolic processes) reflective of products 228
associated with kidney function, canonical central nervous system (CNS) inflammation, amino 229
acid and lipid metabolism, and vitamin absorption (Fig. 4). To increase the confidence in this 230
analysis, we have focused our study only on a select set of metabolites that were validated. 231
Discussion 232
Here, we employed a high throughput systems approach to survey changes in the gut bacterial 233
biota and urine metabolites and to explore new targets with potential diagnostic value for OP 234
exposure and toxicity in noninvasively collected specimens. We initially noted that seizing 235
subjects were significantly different than controls and non-seizing subjects based on multiple 236
parameters, including alpha diversity measures. However, composition analysis indicated that 237
the TM7 and Cyanobacteria phyla were probably the main drivers of the large observed alpha 238
diversity differences while the Proteobacteria phylum was a modest contributor to these 239
observed differences. Unfortunately, Cyanobacteria represent organisms such as chloroplasts, 240
and the TM7 phylum is not well understood. Due to limited knowledge, we concluded that these 241
two phyla are insufficient for drawing meaningful conclusions for the observed biological 242
differences at this moment. Thus, we focused our efforts to closely examine the genera 243
compositions of the various phyla identified in our study especially Proteobacteria. To this end, 244
we noted the presence of Facklamia genus, a Firmicutes, only in the feces and not in the ileum 245
digesta of rats exposed to soman. This genus is hardly associated with invasive disease, but it is 246
known to be occasionally isolated from specimens of urinary tract infections and 247
chorioamnionitis infections (22). We also observed Blautia, another Firmicutes, which was 248
highly enriched only in the feces of soman-exposed animals. Unfortunately, limited data is 249
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available regarding the metabolic functions specifically hydrolysis, reduction, or esterification of 250
xenobiotics for these organisms within the gut and additional investigations are needed in this 251
area. 252
Many of the organophosphate-degrading genes (opd) implicated in the enhanced degradation of 253
OPs are located on mobile elements known to be transferred between organisms (23). In 254
addition, a majority of these genes exhibit a broad specificity and activity against OPs by either 255
directly hydrolyzing the phosphoester bonds in organophosphorus compounds or by further 256
degradation of methylphosphonate-esters to reduce toxicity or reactivation of OP (4, 23). Such 257
genes have been isolated from select species of Flavobacterium, Pseudomonas, Alteromonas, 258
Burkholderia, Bacillus, Alcaligense, Enterobacter, and Rhizobium genera, to name a few. Since 259
we had identified multiple genera such as Enterobacter and Rhizobium that are known to be 260
carriers of opd genes and involved in enhanced biodegradation of OPs, we investigated opd 261
genes from pooled fecal specimens and detected several targets that indicated the expected gene 262
templates were present in the bacterial biota (Fig. S9). However, we do not have evidence for 263
either a specific species that is carrying the opd gene templates or transcript data supporting 264
active expression of the genes (no RNA or protein). Rhizobium, an Alphaproteobacteria, is an 265
aerobic, oxidase-positive, Gram-negative bacillus isolated from the soil environment and known 266
to cause plant tumors (24, 25). Currently, there is limited information about which species of 267
Rhizobium reside in the gut of mammalians as routine commensals, which will be possible to 268
identify with whole genome sequencing approach, and whether these have functional role (15, 269
24). The Bilophila genus is a Deltaproteobacteria, where B. wadsworthia is the only known 270
species in this genus. B. wadsworthia is known to thrive on taurine and H2 production, 271
especially around highly fermentative sites (26). We found that B. wadsworthia was highly 272
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enriched and detected in all soman-exposed cohorts irrespective of dose or seizing status but not 273
in control animals suggesting an unusually highly anaerobic environment could have promoted 274
this organism’s detection. Hence, we decided to validate B. wadsworthia using a PCR assay 275
targeting the 16S rRNA in fecal DNA extracts, pooled due to limited original specimens (Fig. 276
S9). We observed that B. wadsworthia levels were altered across pooled groups, especially in 277
seizing animals with high dose exposure, and were faintly expressed in controls. 278
Seizures that accompany soman intoxication lead to profound brain damage through excitotoxic 279
cell death of neurons, accompanied by neuroinflammation (27). Along this line, we detected 280
elevated levels of tryptophan catabolism (>2 fold change) revealed by the detection of kynurenic 281
acid and quinoline, in the urine of seizing animals. Kynurenic acid is a putative neuroprotective 282
metabolite and an antagonist of N-methyl-D-aspartate (NMDA) receptors (21, 28, 29). Elevated 283
levels of quinolone indicate an overall increased systemic tryptophan catabolism during soman 284
intoxication in seizing animals, presumably driven by inflammation in the brain, known to 285
induce tryptophan-degrading indoleamine-2,3-dioxygenase (IDO) (28, 29). This notion of the 286
inflammatory neuropathology of soman exposure was further supported by the detection of 287
increased systemic levels of the eicosanoid inflammatory mediator, leukotriene C4 (LTC4), in 288
seizing animals’ urine. LTC4 is one of the cysteinyl leukotrienes (cys-LTs) generated by the 289
enzymatic oxidation of arachidonic acid, a fatty acid released from neuronal membrane 290
glycerophospholipids during a secondary phase of brain injury (via a cascade of physiological 291
reactions to primary injury) (30, 31). Furthermore, after acute soman intoxication and seizure 292
onset, high levels of acetylcholine accumulate in the CNS. This is followed by decreased levels 293
of norepinephrine, aspartate, and glutamate (GLU) (i.e. excitatory amino acid (EAA) 294
neurotransmitters) in the brain (32, 33). Post neuronal damage, the sustainment of seizures 295
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involve increases in concentration of EAAs like GLU along with norepinephrine, serotonin (5-296
HT), and its’ metabolite 5-hydroxyindolacetic acid (5-HIAA) (27). In our experimental system, 297
the primary stimulant soman is known to cause severe neuropathology, specifically inflammation 298
of the CNS. Consistent with this notion, we observed and validated increased levels of the 299
neurotransmitters norepinephrine (> 11 fold change), the serotonin metabolite 5-HIAA, and 300
increased levels of the cys-LT LTC4 in the urine metabolite profile of seizing animals, clearly 301
indicating increased inflammation associated with CNS neuropathology. Aldosterone is an 302
essential mineralocorticoid hormone directly involved in the regulation of sodium absorption and 303
potassium excretion in the kidney, salivary glands, sweat glands, and colon (34). Elevated levels 304
of aldosterone are known to alter glomerular structure and function via pro-oxidative and pro-305
fibrotic changes (35). Hence, aldosterone increases glomerular permeability to albumin, leading 306
to increased protein urinary excretion. We found that urinary excretion of aldosterone is more 307
than 5 times higher in seizing rats than non-seizing rats, suggesting that symptomatic soman 308
exposure may cause proteinuria and increased electrolyte excretion. Furthermore, in this context, 309
aldosterone-driven nephropathy and renal damage of modest effect are possible secondary 310
features of soman insult that can be easily exploited for pre-symptomatic monitoring of 311
exposure. However, our experimental design did not include proper specimen preservation or 312
analysis of renal function, specifically protein in urine to use readily available gold standard 313
clinical pathology assays. 314
Xenobiotic metabolites of microbial origin have been previously detected in urine (36, 37). In 315
our study, we identified a few of the most common microbial urinary metabolites, p-cresol and 316
phenylacetic acid (38). Both of these metabolites are a result of tyrosine catabolism by colon 317
microbes then sulfated by the colonic epithelium, resulting in reabsorption back into the host. 318
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High levels of p-cresol sulfate are correlated with cardiovascular diseases as well as the mortality 319
of chronic kidney disease patients (36). Furthermore, the phenolic features of such compounds 320
are speculated to mimic neurotransmitters and interfere with the blood-brain barrier(36). 321
Consistent with our finding of dysbiosis of the gut bacterial biota, the soman-exposed animals in 322
our study had a significant increase in p-cresol (27x) and phenylacetic acid (7x) in their urine as 323
compared to non-seizing animals. However, we did not obtain enough 16S sequencing resolution 324
for species-level information in our efforts to identify organisms, especially those associated 325
with tyrosine catabolism such as Clostridium difficile (36). 326
Although this was a proof-of-concept (i.e. feasibility study) that applied untargeted technologies, 327
we were able to enrich for candidate diagnostic markers both from the bacterial biota and from 328
urine solutes and to also identify organisms with a potential role as forensic signatures of 329
exposure. With additional resources and better targeted technology, we plan to pursue a more 330
detailed analysis on a large cohort of animals, specifically to temporally populate quantitative 331
ratios of specific bacteria and urine metabolites in male and female rats. Furthermore, we plan to 332
profile multiple chemical nerve agents and pesticides. Based on these findings, one can envision 333
moderately complex assays with a correlation matrix and a decision algorithm to enhance current 334
clinical microbiology and urine chemistry workflows for diagnostics purposes. This would be 335
valuable for pre-symptomatic phase screening to enable disease modifying therapies, 336
determining the radius of impact during mass exposure, and casual/passive screening of training 337
sites and camps, as well as monitoring of refugees from non-invasive samples. 338
Methods and Materials 339
Animals 340
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Male Sprague-Dawley rats (250-300 g; Charles River Laboratories (Kingston, NY)) were 341
individually housed on a 12:12 hrs light cycle with ad libitum access to food and water. Rats 342
were weighed daily. The experimental protocol was approved by the Institutional Animal Care 343
and Use Committee at the United States Army Medical Research Institute of Chemical 344
Defense(IACUC U-908), and all procedures were conducted in accordance with the principles 345
stated in the Guide for the Care and Use of Laboratory Animals and the Animal Welfare Act of 346
1966 (P.L. 89–544), as amended. 347
348
Surgery and EEG recording 349
Rats were surgically implanted with a subcutaneous transmitter (F-40EET; Data Sciences 350
International, Inc. (DSI; St. Paul, MN)) as described in Schultz et al. (2014), to record bi-351
hemispheric cortical EEG waveform activity as well as body temperature and activity throughout 352
the duration of the experiment. Surgery was conducted under isoflurane (3 - 4% induction; 1.5 -353
3% maintenance) and rats received buprenorphine (0.03 mg/kg, sc; Reckitt Benckiser 354
Pharmaceuticals, Inc, Richmond, VA) immediately after full recovery from anesthesia. Rats 355
recovered from surgery for 7-14 days prior to soman -exposure. RPC-1 physiotel receivers (DSI) 356
were placed under the rats’ home cage for EEG acquisition (24 hrs/day) with baseline recordings 357
made at least 24 hrs prior to exposure. Data were digitized at 250 Hz and recorded using 358
Dataquest ART 4.1 (Acquisition software; DSI). 359
360
Soman Exposures 361
Rats were exposed 0.8 or 1.0 LD50 soman (0.5 mL/kg, using 157.4 and 196.8 µg/ml respectively) 362
or saline (control) and evaluated for seizure activity. Soman LD50 is 98.4 g/ml (39). Soman was 363
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obtained from the Edgewood Chemical Biological Center (Aberdeen Proving Ground, MD). To 364
promote survival, rats that received 1.0 mg/kg soman were treated with medical countermeasures 365
(MCM; an admix of 2 mg/kg atropine sulfate (ATS, Sigma-Aldrich Chemical Company, St. 366
Louis, MO, USA) and 93.6 mg/kg HI-6 (0.5 ml/kg, i.m., Starkes Associates, Buffalo, NY, USA)) 367
at 1 min after exposure and rats that developed seizures were treated with 10 mg/kg diazepam 368
(DZP; 2 ml/kg, s.c., Hospira Inc., Lake Forest, Illinois, USA) at 30 min after seizure onset (with 369
average seizure onset of 8 min). 370
371
Behavioral seizure 372
Behavioral seizures were scored using a modified Racine scale (17-19): stage 1: mastication, 373
tongue fasciculation, oral tonus; stage 2, head tremors, head bobs; stage 3, limb clonus or tonus, 374
body tremor; stage 4, rearing with forelimb clonus; and stage 5, rearing and falling with 375
generalized convulsions. For analysis, rats received a score corresponding to the maximum stage 376
reached per time interval. Observations were made continuously for up to 5 hrs after exposure to 377
soman. 378
379
EEG analysis 380
Full-power spectral analysis of EEG, identification of epileptiform activity, and other EEG 381
anomalies were analyzed according to the methods described previously (40). EEG recorded 382
seizures were confirmed through visual screening and characterized by sustained frequencies and 383
the value of the most prominent frequencies in Hz (e.g.: the highest power calculated by MatLab 384
program (www.mathworks.com) in µV2/Hz). Some soman -exposed rats developed seizure 385
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activity (SE) with seizure onset at ~ 8 min for those exposed to 1.0 LD50, while others did not 386
develop seizures. 387
388
Sample collection 389
All surfaces and tools, including the guillotine, collection foils, sample tubes, saline bottle, and 390
syringes, were sprayed with RNase AWAY®. Animals were administered Fatal-Plus® (sodium 391
pentobarbital) and once fully anesthetized the rats were euthanized using a guillotine. Urine was 392
collected directly from the bladder using a syringe with 21 gauge needle, saved in RNase-free 393
microfuge tubes, and flash frozen in liquid nitrogen. The ileum was removed and flash frozen. 394
Digesta was flushed out by rinsing with sterile saline during sample processing. Organs were 395
collected in foils and flash frozen in liquid nitrogen. Fresh fecal pellets were collected during rat 396
handling and stored at -80°C for processing. 397
398
DNA extraction 399
Samples were kept cold at all time before the extraction. The Ileum tissues were weighed and 400
homogenized in 50 mM Tris-HCl (Lonza, Walkersville, MD, USA) with 2 nM EDTA Solution 401
(Lonza) using the BeadBeater (Bio Spec Products, Inc., Bartlesville, OK, USA), the DNA 402
extraction was carried out using the QIAGEN DNeasy Blood and Tissue Kit (QIAGEN Inc., 403
Germantown, MD, USA), and RNA was extracted using TRIzol Reagent (Invitrogen, Life 404
Technologies, Grand Island, NY, USA) in conjunction with the QIAGEN miRNeasy Mini Kit 405
(QIAGEN). The fecal samples were weighed and RNA was extracted using MoBio PowerSoil 406
Total RNA Isolation Kit (MO BIO Laboratories, Inc, Carlsbad, CA, USA) and DNA was 407
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extracted using the MoBio PowerSoil DNA Elution Accessory Kit. The extracted DNA was used 408
for PCR and sequencing. 409
410
Library preparation and sequencing 411
We used primers that were previously designed to amplify the V3-V4 hyper-variable regions of 412
the 16S rRNA gene (41). A limited cycle PCR generated a single amplicon of ~460 bp, and this 413
was followed by addition of Illumina sequencing adapters and dual‐index barcodes. Using paired 414
300 bp reads, and MiSeq v3 reagents, the ends of each read were overlapped to generate high‐415
quality, full‐length reads of the V3 and V4 region in a single run. 416
417
Data Analysis 418
Sequence read quality assessment, filtering, barcode trimming, and chimera detection were 419
performed on de-multiplexed sequences using the USEARCH method in the Quantitative 420
Insights Into Microbial Ecology (QIIME) package (v.1.9.1) (42). OTUs were defined by 421
clustering with 97% sequence similarity cutoffs (at 3% divergence). The representative sequence 422
for an OTU was chosen as the most abundant sequence showing up in that OTU’s by collapsing 423
identical sequences, and choosing the one that was read the most abundant sequences. Then 424
representative sequences were aligned against Greengenes database core set (v.gg_13_8) using 425
PyNAST alignment method (www.greengenes.secondgenom.com). The minimum sequence 426
length of 150nt and the minimum percent of 75% match were used for the alignment (43, 44). 427
The RDP Classifier program (v.2.2) was used to assign the taxonomy to the representative set of 428
sequences using a pre-built database of assigned sequence of reference set (45). Alpha Diversity 429
was performed using PhyloSeq R package(www.bioconductor.org), Chao1 metric (estimates the 430
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species richness.), the observed species metric (the count of unique OTUs found in the sample) 431
and Phylogenetic Distance (PD_whole_tree) were calculated (46). Similarly, Beta Diversity were 432
calculated using QIIME and visualized using Principal Coordinate Analysis (PCoA) to visualize 433
distances between samples on an x-y-z plot. The ranked abundance profile was created using 434
BiodiversityR R Bioconductor package (2.7-2) to highlight the most abundant phylum in all 435
samples. To determine the differentially abundant taxonomic groups over different groups in 436
soman against non-soman exposed at different doses and seizing versus non-seizing groups were 437
examined by fitting linear models using moderated standard errors and the empirical Bayes 438
model following TMM normalization on OTU count. The normalized abundance profile was 439
created across different doses (0.8 and 1.0 LD50) of soman exposure compared with control 440
samples, similarly seizing versus non-seizing rats compared against non soman exposed control 441
samples. Sequences can be accessed from the NCBI Sequence Read Archive (SRA) at study 442
accession SRP116704 bioproject PRJNA401162. 443
To predict the metabolites contributed as per microbial composition we used PICRUSt (v.1.0.0), 444
open source tool that uses precomputed gene content inference for 16S rRNA. PICRSUSt uses 445
the OTU abundance count generated using ‘closed-reference’ OTU picking against Greengenes 446
database, for normalization of OTU table, each OTU was first divided by known as well as 447
predicted 16s copy number abundance (47). Final metagenome functional predictions were 448
performed by multiplying normalized OTU abundance by each predicted functional profile. 449
Statistical hypothesis testing analysis of metagenomics profiles was performed using R to 450
compare KEGG Orthologs (v.80.0) between pre- and post-Soman exposed samples and Principal 451
coordinates analysis was also performed. The predicted metagenome functional counts were 452
normalized using TMM normalization method to fit linear models using contrast function to 453
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compute fold changes by moderating the standard errors using empirical Bayes model. Logodds 454
and moderated t- statistic of differential predicted significant metabolite derived from KEGG 455
Orthologs was computed between pre- and post-soman exposed samples at different doses and 456
seizing vs non seizing groups with p value cutoff ≤ 0.05. Significant metabolites were further 457
annotated using in-house metabolite annotation function with other databases such as HMDB 458
(v.2.5), KEGG (compounds, pathways, orthologs and reactions) (v.80.0), SMPDB (v.2.0) and 459
FOODB (v.1.0). 460
Metabolomic profiling and data analysis 461
Urine samples were processed using the method of Tyburski (48). Briefly, the samples were 462
thawed on ice and vortexed. For metabolite extraction, 20 µL of urine was mixed with 80 µL of 463
50% acetonitrile (in water) containing internal standards (10 µL of debrisoquine (1mg/mL) and 464
50 µL of 4, nitro-benzoic acid (1mg/mL)). The supernatant was transferred to a fresh tube and 465
used for UPLC-ESI-Q-TOF-MS analysis (Xevo G2, Waters Corporation). Each sample (5 μL) 466
was injected onto a reverse-phase 50 × 2.1 mm BEH 1.7 μm C18 column using an Acquity 467
UPLC system (Waters Corporation, USA). The gradient mobile phase was comprised of water 468
containing 0.1% formic acid solution (A) and acetonitrile containing 0.1% formic acid solution 469
(B). Each sample was resolved for 10 min at a flow rate of 0.5 ml/min. This approach has been 470
extensively used for metabolomic profling of biofluids; UPLC gradients conditions and the mass 471
spectrometry parameters and has been described in details (49-51). The UPLC gradient consisted 472
of 100% A for 0.5 min then a ramp of curve 6 to 60% B from 0.5 min to 4.5 min, then a ramp of 473
curve 6 to 100% B from 4.5 to 8.0 min, a hold at 100% B until 9.0 min, then a ramp of curve 6 to 474
100% A from 9.0 min to 9.2 min, followed by a hold at 100% A until 10 mins. The column 475
eluent was introduced directly into the mass spectrometer by electrospray. Mass spectrometry 476
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was performed on a Quadrupole-time-of-flight mass spectrometer operating in either negative or 477
positive electrospray ionization mode with a capillary voltage of 3.2 KV and a sampling cone 478
voltage of 35 V. The desolvation gas flow was 800 L/h and the temperature was set to 350°C. 479
The cone gas flow was 50 L/h, and the source temperature was 150°C. The data was acquired in 480
the V mode with scan time of 0.3 seconds, and inter-scan delay at 0.08 seconds. Accurate mass 481
was maintained by infusing sulfadimethoxine (311.0814 m/z) in 50% aqueous acetonitrile (250 482
pg/µL) at a rate of 30 µL/min via the lockspray interface every 10 seconds. Data were acquired 483
in centroid mode from 50 to 850 m/z mass range for TOF-MS scanning, in duplicates (technical 484
replicates) for each sample in positive and negative ionization mode and checked for 485
chromatographic reproducibility. For all profiling experiments, the sample queue was staggered 486
by interspersing samples of the two groups to eliminate bias. Pooled sample injections 487
throughout the run (one pool was created by mixing 2 µL aliquot from all 110 samples) were 488
used as quality controls (QCs) to assess inconsistencies that are particularly evident in large 489
batch acquisitions in terms of retention time drifts and variation in ion intensity over time. QCs 490
were projected in the orthogonal partial least squares-discriminant analysis (OPLS-DA) model 491
along with the study samples to ensure that the technical performance did not impact the 492
biological information(52). The raw data were pre-processed using the XCMS (53) software for 493
peak detection and alignment. The resultant three dimensional data matrix consisting of 494
mass/charge ratios with retention times and feature intensities was subjected to multivariate data 495
analysis using Metaboanalyst v 3.0. Quantitative descriptors of model quality for the OPLS-DA 496
models included R2 (explained variation of the binary outcome: Treatment vs. control and Q2 497
(cross-validation based predicted variation of the binary outcome). We used score plots to 498
visualize the discriminating properties of the OPLS-DA models. The features selected via OPLS- 499
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were used for accurate mass based database search; subsequently the identity of a sub-set of 500
metabolites was confirmed using tandem mass spectrometry. 501
Ethics approval and consent to Participate 502
This research complied with the Animal Welfare Act and implementing Animal Welfare 503
Regulations, the Public Health Service Policy on Humane Care and Use of Laboratory Animals, 504
and adhered to the principles noted in The Guide for the Care and Use of Laboratory Animals 505
(NRC, 2011). 506
The views, opinions, and findings contained in this report are those of the authors and should 507
not be construed as official Department of the Army position, policy, or decision, unless so 508
designated by other official documentation. Citations of commercial organizations or trade 509
names in this report do not constitute an official Department of the Army endorsement or 510
approval of the products or services of these organizations. 511
Consent for publication 512
Not Applicable. Human subjects did not participate in this study. 513
514
Availability of data and materials 515
Data generated and analyzed during this study are included in this published article and 516
supplementary information files with the exception of raw urine mass spectrometry and 75 day 517
microbiome assay, which will be available from the corresponding author on reasonable request. 518
Sequences can be accessed from the NCBI Sequence Read Archive (SRA) at study accession 519
SRP116704 bioproject PRJNA401162. 520
521
Competing Interest 522
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The authors declare no competing interests. BARDA was not involved in the study design or in 523
the collection, analysis and interpretation of data or the decision to write this manuscript and 524
submit it for publication. The views expressed in this manuscript are those of the authors and do 525
not reflect the official policy of the Department of Army, Department of Defense or the US 526
Government. 527
528
Funding 529
Support was provided by an interagency agreement between the Biomedical Advanced Research 530
and Development Authority (BARDA), the Geneva Foundation, and the US Army Medical 531
Research Institute of Chemical Defense (USAMRICD) as well as a memorandum of agreement 532
between USAMRICD and US Army Center of Environmental Health (USACEHR). The 533
Metabolomics Shared Resource in Georgetown University (Washington DC, USA) partially 534
supported by NIH/NCI/CCSG grant P30-CA051008. 535
536
Authors’ Contributions 537
DG analyzed data and wrote the manuscript. AG coordinated microbiome and metabolomics 538
analyses and obtained the samples, AH processed microbiome specimens and completed 539
validation, RK analyzed the data, and AKC completed metabolomics analysis. FR analyzed the 540
EEG data, CS conducted animal experiments. LL, MJ and RH conceived and designed the study 541
and edited manuscript. All authors read and approved the final manuscript. 542
543
Acknowledgments 544
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We thank Ms. Kirandeep Gill (Georgetown University) for technical assistance with 545
metabolomics data as well as Dr. Matthew Rice and Dr. Julia Scheerer for editorial input. 546
547
548
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704
Figure Legends 705
Figure 1 Clinical manifestation of toxicity after soman exposure. Panel a represents trends in 706
body weight changes based on seizing status of rats before and after soman exposure. Panel b 707
represents Racine Score differences based on seizing status of rats after soman exposure. Panel c 708
and d represents seizure activities (latency to seize or seizure duration during 72hr monitoring) 709
of rats exposed to 0.8 LD50 of soman without treatment (unmitigated seizure model, n=4) or 1.0 710
LD50 of soman and treated with medical counter measure (mitigated seizure model, n=6) a 711
minute after exposure. 712
713
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29
Figure 2 Trends in relative abundance of fecal bacterial biota (phylum level) based on dose 714
(Panel a) or seizing status (Panel b) of rats 72 hrs after soman exposure. TM7 phylum exhibited 715
decreased 716
normalized counts with increasing dose or seizing status while the inverse was observed for the 717
Proteobacteria phylum. 718
719
Figure 3 3 SPADE-like analysis showing taxonomic changes in Proteobacteria and Firmicutes 720
(Bacilli only) phyla. Circle size is assigned by automatically binning normalized OTU counts 721
into any of the three sizes. Distance between circles is assigned arbitrarily. Colors are added as a 722
visual aid for following branch and do not have connotation. Panel a represents average OTU 723
count of fecal bacterial biota detected for the two phyla in soman unexposed control animals. 724
Panel b shows representative average of OTU counts of bacterial biota detected after exposure 725
(solid circles) compared to bacterial biota of unexposed control (faded circles). Unassigned OTU 726
clusters at the order or genus level are represented by blue or red circles. 727
728
Figure 4 Snapshot of validated altered urine metabolites, affected candidate site, and parent 729
pathways. Circos image represents urine metabolites significantly altered (p<0.05) in seizing 730
animals (outer ring labels on white background) and associated catabolic pathways (outer ring 731
ribbons) and sites (ribbon colors). 732
733
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Figure 1
Fig. 1 Clinical manifestation of toxicity after soman exposure. Panel a represents trends in body weight
changes based on seizing status of rats before and after soman exposure. Panel b represents Racine Score
differences based on seizing status of rats after soman exposure. Panel c and d represents seizure
activities (latency to seize or seizure duration during 72hr monitoring) of rats exposed to 0.8 LD50 of
soman without treatment (unmitigated seizure model, n=4) or 1.0 LD50 of soman and treated with medical
counter measure (mitigated seizure model, n=6) a minute after exposure.
A B
C D
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Fig. 2 Trends in relative abundance of fecal bacterial biota (phylum level) based on dose (Panel a)
or seizing status (Panel b) of rats 72 hrs after soman exposure. TM7 phylum exhibited decreased
normalized counts with increasing dose or seizing status while the inverse was observed for the
Proteobacteria phylum.
Figure 2
B
Bacteroidetes Deferribacteres Firmicutes Proteobacteria TM7
CTRL NS SE CTRL NS SE CTRL NS SE CTRL NS SE CTRL NS SE
CTRL: Control
NS: Non-seizing
SE: Seizing
0.04
0.08
0.12
A Bacteroidetes Deferribacteres Firmicutes Proteobacteria TM7
0 0.8 1.0 0 0.8 1.0 0 0.8 1.0 0 0.8 1.0 0 0.8 1.0
0.04
0.08
0.12
0: No Soman
0.8: 0.8LD50 Soman
1.0: 1.0LD50 Soman
No
rm
ali
ze
d
co
un
t N
orm
ali
ze
d
co
un
t
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Fig. 3 SPADE-like analysis showing taxonomic changes in Proteobacteria and Firmicutes (Bacilli only)
phyla. Circle size is assigned by automatically binning normalized OTU counts into any of the three sizes.
Distance between circles is assigned arbitrarily. Colors are added as a visual aid for following branch
and do not have connotation. Panel a represents average OTU count of fecal bacterial biota detected for the
two phyla in soman unexposed control animals. Panel b shows representative average of OTU counts of
bacterial biota detected after exposure (solid circles) compared to bacterial biota of
unexposed control (faded circles). Unassigned OTU clusters at the order or genus level are represented
by blue or red circles.
Figure 3
A
B
Control
Soman exposed
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Fig. 4 Snapshot of validated altered urine metabolites, affected candidate site, and parent pathways.
Circos image represents urine metabolites significantly altered (p<0.05) in seizing animals (outer ring
labels on white background) and associated catabolic pathways (outer ring ribbons) and sites
(ribbon colors).
Figure 4
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Table 1 Summary of soman-exposure altered urine solute
ESI
Mode
Fold
Change
p value
Name
Formula
RT
M/z
dppm
NEG 0.45193 0.00160 Guanosine-3',5'-
Diphosphate
C10H15N5O11P2 4.58 442.0211 9.092775
NEG 0.4864 0.00773 Citric acid C6H8O7 0.42 191.0192 2.781159
NEG 2.3912 0.04993 Uric acid C5H4N4O3 0.34 167.0199 6.901621
POS 2.2575 0.00119 Kynurenic acid C10H7NO3 1.57 190.0503 2.260052
POS 2.7 0.00409 Quinoline C9H7N 3.13 130.0645 4.847519
POS 3.4701 0.00000 Leukotriene C4 C30H47N3O9S 2.08 626.3129 3.806747
POS 11.883 0.00033 Norepinephrine
sulfate
C8H11NO6S 3.17 250.0398 7.291454
POS 3.0192 0.00018 5-
Hydroxyindoleacetic
acid
C10H9NO3 1.62 192.0666 5.670386
NEG 27.481 0.0018 p-Cresol C7H8O 3.11 107.0501 1.270889
POS 6.8123 0.0301 Phenylacetic acid C8H8O2 2.96 137.060 4.37399
POS 6.572 0.00467 N-Butyryl-L-
homoserine lactone
C8H13NO3 2.39 172.0968 0.089186
POS 3.0117 0.00435 L-Carnitine C7H15NO3 0.39 162.1123 1.041845
POS 4.0547 0.00140 Glycine C2H5NO2 2.45 76.0397 5.287456
POS 4.2532 0.00000 Neuraminic acid C9H17NO8 0.39 268.1006 7.883747
POS 6.8278 0.00337 Retinoyl
glucuronide
C26H36O8 5.48 477.2527 9.227536
POS 7.083 0.00276 Glutathione C10H17N3O6S 3.16 308.091 0.198757
POS 7.6032 0.00970 D-Alanine/L-
Alanine
C3H7NO2 0.69 90.0557 8.396408
POS 5.78 0.00861 Aldosterone C21H28O5 5.72 361.2036 7.455842
Urine metabolites whose excretion significantly differed (p<0.05) in seizing animals compared to
non-seizing animals. These metabolites were validated by an additional independent experiment.
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