Diabetes as a risk factor for periodontal disease—plausible mechanisms

The present narrative review examines the scientific evidence of the biological mechanisms that may link periodontitis and diabetes, as a source of comorbidity. Publications regarding periodontitis and diabetes, in human, animals, and in vitro were screened for their relevance. Periodontal microbiome studies indicate a possible association between altered glucose metabolism in prediabetes and diabetes and changes in the periodontal microbiome. Coinciding with this, hyperglycemia enhances expression of pathogen receptors, which enhance host response to the dysbiotic microbiome. Hyperglycemia also promotes pro-inflammatory response independently or via the advanced glycation end product/receptor for advanced glycation end product pathway. These processes excite cellular tissue destruction functions, which further enhance pro-inflammatory cytokines expression and alteration in the RANKL/osteoprotegerin ratio, promoting formation and activation of osteoclasts. The evidence supports the role of several pathogenic mechanisms in the path of true causal comorbidity between poorly controlled diabetes and periodontitis. However, further research is needed to better understand these mechanisms and to explore other mechanisms.     

Detection of the aerolysin gene in Aeromonas hydrophila by the polymerase chain reaction.

Synthetic oligonucleotide primers were used in a polymerase chain reaction (PCR) technique to detect the gene for aerolysin in strains of Aeromonas hydrophila and to screen for identical genes in A. caviae, A. sobria, and A. veronii isolated from patients with diarrheal disease. Primers targeted a 209-bp fragment of the aer gene coding for the beta-hemolysin and detected template DNA only in the PCR using nucleic acid (NA) from hemolytic strains of A. hydrophila which were also cytotoxic to Vero and CHO cells and enterotoxic in suckling-mouse assays. PCR amplification of NA from hemolytic A. sobria or nonhemolytic A. hydrophila and A. caviae strains was consistently negative. Primer specificity was determined in the PCR by using NA extracted from 56 strains of bacteria, including hemolytic Escherichia coli and Listeria monocytogenes as well as several recognized enteric pathogens defined in terms of their toxigenicity. The detection limit for the aerolysin gene by PCR amplification was 1 ng of total NA. The PCR clearly identified aerolysin-producing strains of A. hydrophila and may have application as a species-specific virulence test because other hemolytic Aeromonas species tested were negative.     

Increased yield of full GBA sequencing in Ashkenazi Jews with Parkinson's disease

Background

Variants in GBA are the most common genetic risk factor for Parkinson's disease (PD), and are especially prevalent in the Ashkenazi Jewish (AJ) population. However, most studies on GBA in AJ genotype only seven selected Gaucher-associated pathogenic variants rather than sequencing the whole gene, which may leave carriers of PD-associated GBA variants undiscovered.

SLAM: cross-species gene finding and alignment with a generalized pair hidden Markov model

Comparative-based gene recognition is driven by the principle that conserved regions between related organisms are more likely than divergent regions to be coding. We describe a probabilistic framework for gene structure and alignment that can be used to simultaneously find both the gene structure and alignment of two syntenic genomic regions. A key feature of the method is the ability to enhance gene predictions by finding the best alignment between two syntenic sequences, while at the same time finding biologically meaningful alignments that preserve the correspondence between coding exons. Our probabilistic framework is the generalized pair hidden Markov model, a hybrid of (1). generalized hidden Markov models, which have been used previously for gene finding, and (2). pair hidden Markov models, which have applications to sequence alignment. We have built a gene finding and alignment program called SLAM, which aligns and identifies complete exon/intron structures of genes in two related but unannotated sequences of DNA. SLAM is able to reliably predict gene structures for any suitably related pair of organisms, most notably with fewer false-positive predictions compared to previous methods (examples are provided for Homo sapiens/Mus musculus and Plasmodium falciparum/Plasmodium vivax comparisons). Accuracy is obtained by distinguishing conserved noncoding sequence (CNS) from conserved coding sequence. CNS annotation is a novel feature of SLAM and may be useful for the annotation of UTRs, regulatory elements, and other noncoding features.     

Identification of verotoxin type 2 variant B subunit genes in Escherichia coli by the polymerase chain reaction and restriction fragment length polymorphism analysis.

A set of synthetic oligonucleotide primers was designed for use in a polymerase chain reaction protocol to specifically detect the B subunit genes in vtx2ha and vtx2hb, which code for the production of the VT2 (Shiga-like toxin II) variant cytotoxins VT2v-a and VT2v-b, respectively. An additional set of primers amplified a fragment common to the B subunits of the VT2 and the VT2 variant genes. Subsequent restriction endonuclease digestion of this amplicon permitted prediction of specific VT2 and variant genotypes on the basis of predetermined restriction fragment length polymorphisms. Genotypes of 21 VT2-producing strains of Escherichia coli were determined using this polymerase chain reaction-restriction fragment length polymorphism procedure. Four strains contained B subunit target sequences only for VT2 genes, 9 strains contained sequences only for VT2v-a genes, and 3 strains contained sequences only for VT2v-b. For genes in combination, one strain contained B subunit genes for both VT2 and VT2v-a and two strains contained B subunit genes for VT2 and VT2v-b. Two strains of E. coli O91:H21 contained both VT2v-a and VT2v-b B subunit genes. The VT2 reference strain of E. coli, E32511, was found to contain the targeted sequences from both VT2 and VT2v-a genes, whereas the recombinant E. coli, pEB1, possessed only that of the VT2 gene. The specific activities of extracellular VT2 determined in HeLa cells ranged from 0.3 to 41.7 TCD50 per microgram of protein in strains carrying the VT2 gene target and from 0 to 50.0 TCD50 per microgram of protein in strains carrying only the VT2 variant target (TCD50 is the tissue culture dose by which 50% of the cells were affected), suggesting that phenotypic expression does not correlate with genotype.     

Feedback and feedforward adaptation to visuomotor delay during reaching and slicing movements

It has been suggested that the brain and in particular the cerebellum and motor cortex adapt to represent the environment during reaching movements under various visuomotor perturbations. It is well known that significant delay is present in neural conductance and processing; however, the possible representation of delay and adaptation to delayed visual feedback has been largely overlooked. Here we investigated the control of reaching movements in human subjects during an imposed visuomotor delay in a virtual reality environment. In the first experiment, when visual feedback was unexpectedly delayed, the hand movement overshot the end‐point target, indicating a vision‐based feedback control. Over the ensuing trials, movements gradually adapted and became accurate. When the delay was removed unexpectedly, movements systematically undershot the target, demonstrating that adaptation occurred within the vision‐based feedback control mechanism. In a second experiment designed to broaden our understanding of the underlying mechanisms, we revealed similar after‐effects for rhythmic reversal (out‐and‐back) movements. We present a computational model accounting for these results based on two adapted forward models, each tuned for a specific modality delay (proprioception or vision), and a third feedforward controller. The computational model, along with the experimental results, refutes delay representation in a pure forward vision‐based predictor and suggests that adaptation occurred in the forward vision‐based predictor, and concurrently in the state‐based feedforward controller. Understanding how the brain compensates for conductance and processing delays is essential for understanding certain impairments concerning these neural delays as well as for the development of brain–machine interfaces.