5-gene differential expression predicts stability of human intestinal allografts.

In intestinal allografts, endoscopy and histology detect the injury once changes in the bowel wall architecture have occurred. We aimed to identify a molecular signature that could predict early deterioration, within histologically indistinguishable biopsies with "minimal changes" (MC) pathology. Sixty biopsies from 12 adult recipients were longitudinally taken during 8years post-transplant. They were classified as either stable (STA) or non-stable (NSTA) according to the prospectively recorded number, frequency and severity of rejection events of the allograft. In a discovery set of MC samples analyzed by RNA-Seq, 816 genes were differentially expressed in STA vs NSTA biopsies. A group of 5 genes (ADH1C, SLC39A4, CYP4F2, OPTN and PDZK1) correctly classified all NSTA biopsies in the discovery set and all STA biopsies from an independent set. These results were validated by qPCR in a new group of MC biopsies. Based on a logistic regression model, a cutoff of 0.28 predicted the probability of being a NSTA biopsy with 85% sensitivity and 69% specificity. In conclusion, by analyzing MC samples early after transplantation, the expression of a 5-gene set may predict the evolution of the bowel allograft. This prognostic biomarker may be of help to personalize care of the intestinal transplant recipient.

Benchmarking of microbiome detection tools on RNA-seq synthetic databases according to diverse conditions.

Motivation: Here, we performed a benchmarking analysis of five tools for microbe sequence detection using transcriptomics data (Kraken2, MetaPhlAn2, PathSeq, DRAC and Pandora). We built a synthetic database mimicking real-world structure with tuned conditions accounting for microbe species prevalence, base calling quality and sequence length. Sensitivity and positive predictive value (PPV) parameters, as well as computational requirements, were used for tool ranking. Results: GATK PathSeq showed the highest sensitivity on average and across all scenarios considered. However, the main drawback of this tool was its slowness. Kraken2 was the fastest tool and displayed the second-best sensitivity, though with large variance depending on the species to be classified. There was no significant difference for the other three algorithms sensitivity. The sensitivity of MetaPhlAn2 and Pandora was affected by sequence number and DRAC by sequence quality and length. Results from this study support the use of Kraken2 for routine microbiome profiling based on its competitive sensitivity and runtime performance. Nonetheless, we strongly endorse to complement it by combining with MetaPhlAn2 for thorough taxonomic analyses. Availability and implementation: https://github.com/fjuradorueda/MIME/ and https://github.com/lola4/DRAC/. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

DNA Methylation Description of Hippocampus, Cortex, Amygdala, and Blood of Drug-Resistant Temporal Lobe Epilepsy.

Epigenetic changes such as DNA methylation were observed in drug-resistant temporal lobe epilepsy (DR-TLE), a disease that affects 25-30% of epilepsy patients. The main objective is to simultaneously describe DNA methylation patterns associated with DR-TLE in hippocampus, amygdala, surrounding cortex to the epileptogenic zone (SCEZ), and peripheral blood. An Illumina Infinium MethylationEPIC BeadChip array was performed in 19 DR-TLE patients and 10 postmortem non-epileptic controls. Overall, 32, 59, and 3210 differentially methylated probes (DMPs) were associated with DR-TLE in the hippocampus, amygdala, and SCEZ, respectively. These DMP-affected genes were involved in neurotrophic and calcium signaling in the hippocampus and voltage-gated channels in SCEZ, among others. One of the hippocampus DMPs (cg26834418 (CHORDC1)) showed a strong blood-brain correlation with BECon and IMAGE-CpG, suggesting that it could be a potential surrogate peripheral biomarker of DR-TLE. Moreover, in three of the top SCEZ's DMPs (SHANK3, SBF1, and MCF2L), methylation status was verified with methylation-specific qPCR. The differentially methylated CpGs were classified in DMRs: 2 in the hippocampus, 12 in the amygdala, and 531 in the SCEZ. We identified genes that had not been associated to DR-TLE so far such as TBX5, EXOC7, and WRHN. The area with more DMPs associated with DR-TLE was the SCEZ, some of them related to voltage-gated channels. The DMPs found in the amygdala were involved in inflammatory processes. We also found a potential surrogate peripheral biomarker of DR-TLE. Thus, these results provide new insights into epigenetic modifications involved in DR-TLE.

Comparative analysis of Penicillium genomes reveals the absence of a specific genetic basis for biocontrol in Penicillium rubens strain 212.

Penicillium rubens strain 212 (PO212) is a filamentous fungus belonging to the division Ascomycete. PO212 acts as an effective biocontrol agent against several pathogens in a variety of horticultural crops including Fusarium oxysporum f.sp. lycopersici, causing vascular wilt disease in tomato plants. We assembled draft genomes of two P. rubens strains, the biocontrol agent PO212 and the soil isolate S27, which lacks biocontrol activity. We also performed comparative analyses of the genomic sequence of PO212 with that of the other P. rubens and P. chrysogenum strains. This is the first Penicillium strain with biocontrol activity whose genome has been sequenced and compared. PO212 genome size is 2,982 Mb, which is currently organized into 65 scaffolds and a total of 10,164 predicted Open Reading Frames (ORFs). Sequencing confirmed that PO212 belongs to P. rubens clade. The comparative analysis of the PO212 genome with the genomes of other P. rubens and Penicillium chrysogenum strains available in databases showed strong conservation among genomes, but a correlation was not found between these genomic data and the biocontrol phenotype displayed by PO212. Finally, the comparative analysis between PO212 and S27 genomes showed high sequence conservation and a low number of variations mainly located in ORF regions. These differences found in coding regions between PO212 and S27 genomes can explain neither the biocontrol activity of PO212 nor the absence of such activity in S27, opening a possible avenue toward transcriptomic and epigenetic studies that may shed light on this mechanism for fighting plant diseases caused by fungal pathogens. The genome sequences described in this study provide a useful novel resource for future research into the biology, ecology, and evolution of biological control agents.