Correlation between postmortem microbial signatures and substance abuse disorders.

The microbiota gut-brain-axis is a bidirectional circuit that links the neural, endocrine, and immunological systems with gut microbial communities. The gut microbiome plays significant roles in human mind and behavior, specifically pain perception, learning capacity, memory, and temperament. Studies have shown that disruptions in the gut microbiota have been associated with substance use disorders. The interplay of gut microbiota in substance abuse disorders has not been elucidated; however, postmortem microbiome profiles may produce promising avenues for future forensic investigations. The goal of the current study was to determine gut microbiome composition in substance abuse disorder cases using transverse colon tissues of 21 drug overdose versus 19 non-overdose-related cases. We hypothesized that postmortem samples of the same cause of death will reveal similar microbial taxonomic relationships. We compared microbial diversity profiles using amplicon-based sequencing of the 16S rRNA gene V4 hypervariable region. The results demonstrated that the microbial abundance in younger-aged cases were found to have significantly more operational taxonomic units than older cases. Using weighted UniFrac analysis, the influence of substances in overdose cases was found to be a significant factor in determining microbiome similarity. The results also revealed that samples of the same cause of death cluster together, showing a high degree of similarity between samples and a low degree of similarity among samples of different causes of death. In conclusion, our examination of human transverse colon microflora in decomposing remains extends emerging literature on postmortem microbial communities, which will ultimately contribute to advanced knowledge of human putrefaction.

Life and death: A systematic comparison of antemortem and postmortem gene expression.

Gene expression is the process by which DNA is decoded to produce a functional transcript. The collection of all transcripts is referred to as the transcriptome and has extensively been used to evaluate differentially expressed genes in a certain cell or tissue type. In response to internal or external stimuli, the transcriptome is greatly regulated by epigenetic changes. Many studies have elucidated that antemortem gene expression (transcriptome) may be linked to an array of disease etiologies as well as potential targets for drug discovery; on the other hand, a number of studies have utilized postmortem gene expression (thanatotranscriptome) patterns to determine cause and time of death. The "transcriptome after death" involves the study of mRNA transcripts occurring in human tissues after death (thanatos, Greek for death). While antemortem gene expression can provide a wide range of important information about the host, the determination of the communication of genes after a human dies has recently been explored. After death a plethora of genes are regulated via activation versus repression as well as diverse regulatory factors such as the absence or presence of stimulated feedback. Even postmortem transcriptional regulation contains many more cellular constituents and is massively more complicated. The rates of degradation of mRNA transcripts vary depending on the types of postmortem tissues and their combinatorial gene expression signatures. mRNA molecules have been shown to persist for extended time frames; nevertheless, they are highly susceptible to degradation, with half-lives of selected mRNAs varying between minutes to weeks for specifically induced genes. Furthermore, postmortem genetic studies may be used to improve organ transplantation techniques. This review is the first of its kind to fully explore both gene expression and mRNA stability after death and the trove of information that can be provided about phenotypical characteristics of specific genes postmortem.

Complete Genome Sequence of a Wild-Type Isolate of Caulobacter vibrioides Strain CB2.

The complete genome of Caulobacter vibrioides strain CB2 consists of a 4,123,726-bp chromosome, a GC content of 67.2%, and 3,896 coding DNA sequences. It has no rearrangements but numerous indels relative to the reference NA1000 genome. This will allow us to study the impact of horizontal gene transfer on caulobacter genomes.

A comparison of the Caulobacter NA1000 and K31 genomes reveals extensive genome rearrangements and differences in metabolic potential.

The genus Caulobacter is found in a variety of habitats and is known for its ability to thrive in low-nutrient conditions. K31 is a novel Caulobacter isolate that has the ability to tolerate copper and chlorophenols, and can grow at 4 ° C with a doubling time of 40 h. K31 contains a 5.5 Mb chromosome that codes for more than 5500 proteins and two large plasmids (234 and 178 kb) that code for 438 additional proteins. A comparison of the K31 and the Caulobacter crescentus NA1000 genomes revealed extensive rearrangements of gene order, suggesting that the genomes had been randomly scrambled. However, a careful analysis revealed that the distance from the origin of replication was conserved for the majority of the genes and that many of the rearrangements involved inversions that included the origin of replication. On a finer scale, numerous small indels were observed. K31 proteins involved in essential functions shared 80-95% amino acid sequence identity with their C. crescentus homologues, while other homologue pairs tended to have lower levels of identity. In addition, the K31 chromosome contains more than 1600 genes with no homologue in NA1000.

Correction of the Caulobacter crescentus NA1000 genome annotation.

Bacterial genome annotations are accumulating rapidly in the GenBank database and the use of automated annotation technologies to create these annotations has become the norm. However, these automated methods commonly result in a small, but significant percentage of genome annotation errors. To improve accuracy and reliability, we analyzed the Caulobacter crescentus NA1000 genome utilizing computer programs Artemis and MICheck to manually examine the third codon position GC content, alignment to a third codon position GC frame plot peak, and matches in the GenBank database. We identified 11 new genes, modified the start site of 113 genes, and changed the reading frame of 38 genes that had been incorrectly annotated. Furthermore, our manual method of identifying protein-coding genes allowed us to remove 112 non-coding regions that had been designated as coding regions. The improved NA1000 genome annotation resulted in a reduction in the use of rare codons since noncoding regions with atypical codon usage were removed from the annotation and 49 new coding regions were added to the annotation. Thus, a more accurate codon usage table was generated as well. These results demonstrate that a comparison of the location of peaks third codon position GC content to the location of protein coding regions could be used to verify the annotation of any genome that has a GC content that is greater than 60%.