Robust expansion of phylogeny for fast-growing genome sequence data

Massive sequencing of SARS-CoV-2 genomes has led to a great demand for adding new samples to a reference phylogeny instead of building the tree from scratch. To address such challenge, we proposed an algorithm 'TIPars' by integrating parsimony analysis with pre-computed ancestral sequences. Compared to four state-of-the-art methods on four benchmark datasets (SARS-CoV-2, Influenza virus, Newcastle disease virus and 16S rRNA genes), TIPars achieved the best performance in most tests. It took only 21 seconds to insert 100 SARS-CoV-2 genomes to a 100k-taxa reference tree using near 1.4 gigabytes of memory. Its efficient and accurate phylogenetic placements and incrementation for phylogenies with highly similar and divergent sequences suggest that it will be useful in a wide range of studies including pathogen molecular epidemiology, microbiome diversity and systematics.

Advances in Genetics and Epigenetic Alterations in Alzheimer’s Disease: A Notion for Therapeutic Treatment

Alzheimer’s disease (AD) is a disabling neurodegenerative disorder that leads to long-term functional and cognitive impairment and greatly reduces life expectancy. Early genetic studies focused on tracking variations in genome-wide DNA sequences discovered several polymorphisms and novel susceptibility genes associated with AD. However, despite the numerous risk factors already identified, there is still no fully satisfactory explanation for the mechanisms underlying the onset of the disease. Also, as with other complex human diseases, the causes of low heritability are unclear. Epigenetic mechanisms, in which changes in gene expression do not depend on changes in genotype, have attracted considerable attention in recent years and are key to understanding the processes that influence age-related changes and various neurological diseases. With the recent use of massive sequencing techniques, methods for studying epigenome variations in AD have also evolved tremendously, allowing the discovery of differentially expressed disease traits under different conditions and experimental settings. This is important for understanding disease development and for unlocking new potential AD therapies. In this work, we outline the genomic and epigenomic components involved in the initiation and development of AD and identify potentially effective therapeutic targets for disease control.

Deregulation of ncRNA in Neurodegenerative Disease: Focus on circRNA, lncRNA and miRNA in Amyotrophic Lateral Sclerosis

Parallel and massive sequencing of total RNA samples derived from different samples are possible thanks to the use of NGS (Next Generation Sequencing) technologies. This allowed characterizing the transcriptomic profile of both cell and tissue populations, increasing the knowledge of the molecular pathological processes of complex diseases, such as neurodegenerative diseases (NDs). Among the NDs, Amyotrophic Lateral Sclerosis (ALS) is caused by the progressive loss of motor neurons (MNs), and, to date, the diagnosis is often made by exclusion because there is no specific symptomatologic picture. For this reason, it is important to search for biomarkers that are clinically useful for carrying out a fast and accurate diagnosis of ALS. Thanks to various studies, it has been possible to propose several molecular mechanisms associated with the disease, some of which include the action of non-coding RNA, including circRNAs, miRNAs, and lncRNAs which will be discussed in the present review. The evidence analyzed in this review highlights the importance of conducting studies to better characterize the different ncRNAs in the disease to use them as possible diagnostic, prognostic, and/or predictive biomarkers of ALS and other NDs.

Biodegradation of a Complex Phenolic Industrial Stream by Bacterial Strains Isolated from Industrial Wastewaters

Molecular and metabolomic tools were used to design and understand the biodegradation of phenolic compounds in real industrial streams. Bacterial species were isolated from an industrial wastewater treatment plant of a phenol production factory and identified using molecular techniques. Next, the biodegradation potential of the most promising strains was analyzed in the presence of a phenolic industrial by-product containing phenol, alfa-methylstyrene, acetophenone, 2-cumylphenol, and 4-cumylphenol. A bacterial consortium comprising Pseudomonas and Alcaligenes species was assessed for its ability to degrade phenolic compounds from the phenolic industrial stream (PS). The consortium adapted itself to the increasing levels of phenolic compounds, roughly up to 1750 ppm of PS; thus, becoming resistant to them. In addition, the consortium exhibited the ability to grow in the presence of PS in repeated batch mode processes. Results from untargeted metabolomic analysis of the culture medium in the presence of PS suggested that bacteria transformed the toxic phenolic compounds into less harmful molecules as a survival mechanism. Overall, the study demonstrates the usefulness of massive sequencing and metabolomic tools in constructing bacterial consortia that can efficiently biodegrade complex PS. Furthermore, it improves our understanding of their biodegradation capabilities.

Application of Next Generation Semiconductor-Based Sequencing for the Identification of Apis mellifera Complementary Sex Determiner (csd) Alleles from Honey DNA

The complementary sex determiner (csd) gene plays an essential role in the sex determination of Apis mellifera L. Females develop only if fertilized eggs have functional heterozygous genotypes at this gene whereas males, being haploids, are hemizygous. Two identical csd alleles produce non vital males. In light of the recent decline in honey bee populations, it is therefore important to monitor the allele variability at this gene. In this study, we tested the application of next generation semiconductor-based sequencing technology (Ion Torrent) coupled with environmental honey DNA as a source of honey bee genome information to retrieve massive sequencing data for the analysis of variability at the hypervariable region (HVR) of the csd gene. DNA was extracted from 12 honey samples collected from honeycombs directly retrieved from 12 different colonies. A specifically designed bioinformatic pipeline, applied to analyze a total of about 1.5 million reads, identified a total of 160 different csd alleles, 55% of which were novel. The average number of alleles per sample was compatible with the number of expected patrilines per colony, according to the mating behavior of the queens. Allele diversity at the csd could also provide information useful to reconstruct the history of the honey.

Genomic mutation profile in progressive chronic lymphocytic leukemia patients prior to first-line chemoimmunotherapy with FCR and rituximab maintenance (REM)

Chronic Lymphocytic Leukemia (CLL) is the most prevalent leukemia in Western countries and is notable for its variable clinical course. This variability is partly reflected by the mutational status of IGHV genes. Many CLL samples have been studied in recent years by next-generation sequencing. These studies have identified recurrent somatic mutations in NOTCH1, SF3B1, ATM, TP53, BIRC3 and others genes that play roles in cell cycle, DNA repair, RNA metabolism and splicing. In this study, we have taken a deep-targeted massive sequencing approach to analyze the impact of mutations in the most frequently mutated genes in patients with CLL enrolled in the REM (rituximab en mantenimiento) clinical trial. The mutational status of our patients with CLL, except for the TP53 gene, does not seem to affect the good results obtained with maintenance therapy with rituximab after front-line FCR treatment.

Evaluation of the Potential of Sewage Sludge Mycobiome to Degrade High Diclofenac and Bisphenol-A Concentrations

One of the most challenging environmental threats of the last two decades is the effects of emerging pollutants (EPs) such as pharmaceutical compounds or industrial additives. Diclofenac and bisphenol A have regularly been found in wastewater treatment plants, and in soils and water bodies because of their extensive usage and their recalcitrant nature. Due to the fact of this adversity, fungal communities play an important role in being able to safely degrade EPs. In this work, we obtained a sewage sludge sample to study both the culturable and non-culturable microorganisms through DNA extraction and massive sequencing using Illumina MiSeq techniques, with the goal of finding degraders adapted to polluted environments. Afterward, degradation experiments on diclofenac and bisphenol A were performed with the best fungal degraders. The analysis of bacterial diversity showed that Dethiosulfovibrionaceae, Comamonadaceae, and Isosphaeraceae were the most abundant families. A predominance of Ascomycota fungi in the culturable and non-culturable population was also detected. Species such as Talaromyces gossypii, Syncephalastrum monosporum, Aspergillus tabacinus, and Talaromyces verruculosus had remarkable degradation rates, up to 80% of diclofenac and bisphenol A was fully degraded. These results highlight the importance of characterizing autochthonous microorganisms and the possibility of selecting native fungal microorganisms to develop tailored biotransformation technologies for EPs.

K-mer-based machine learning method to classify LTR-retrotransposons in plant genomes

Every day more plant genomes are available in public databases and additional massive sequencing projects (i.e., that aim to sequence thousands of individuals) are formulated and released. Nevertheless, there are not enough automatic tools to analyze this large amount of genomic information. LTR retrotransposons are the most frequent repetitive sequences in plant genomes; however, their detection and classification are commonly performed using semi-automatic and time-consuming programs. Despite the availability of several bioinformatic tools that follow different approaches to detect and classify them, none of these tools can individually obtain accurate results. Here, we used Machine Learning algorithms based on k-mer counts to classify LTR retrotransposons from other genomic sequences and into lineages/families with an F1-Score of 95%, contributing to develop a free-alignment and automatic method to analyze these sequences.

Proposal and validation of a method to classify genetic subtypes of diffuse large B cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease whose prognosis is associated with clinical features, cell-of-origin and genetic aberrations. Recent integrative, multi-omic analyses had led to identifying overlapping genetic DLBCL subtypes. We used targeted massive sequencing to analyze 84 diagnostic samples from a multicenter cohort of patients with DLBCL treated with rituximab-containing therapies and a median follow-up of 6 years. The most frequently mutated genes were IGLL5 (43%), KMT2D (33.3%), CREBBP (28.6%), PIM1 (26.2%), and CARD11 (22.6%). Mutations in CD79B were associated with a higher risk of relapse after treatment, whereas patients with mutations in CD79B, ETS1, and CD58 had a significantly shorter survival. Based on the new genetic DLBCL classifications, we tested and validated a simplified method to classify samples in five genetic subtypes analyzing the mutational status of 26 genes and BCL2 and BCL6 translocations. We propose a two-step genetic DLBCL classifier (2-S), integrating the most significant features from previous algorithms, to classify the samples as N12-S, EZB2-S, MCD2-S, BN22-S, and ST22-S groups. We determined its sensitivity and specificity, compared with the other established algorithms, and evaluated its clinical impact. The results showed that ST22-S is the group with the best clinical outcome and N12-S, the more aggressive one. EZB2-S identified a subgroup with a worse prognosis among GCB-DLBLC cases.

Monitoring COVID-19 Transmission Risks by Quantitative Real-Time PCR Tracing of Droplets in Hospital and Living Environments

ABSTRACT Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) environmental contamination occurs through droplets and biological fluids released in the surroundings from patients or asymptomatic carriers. Surfaces and objects contaminated by saliva or nose secretions represent a risk for indirect transmission of coronavirus disease 2019 (COVID-19). We assayed surfaces from hospital and living spaces to identify the presence of viral RNA and the spread of fomites in the environment. Anthropic contamination by droplets and biological fluids was monitored by detecting the microbiota signature using multiplex quantitative real-time PCR (qPCR) on selected species and massive sequencing on 16S amplicons. A total of 92 samples (flocked swabs) were collected from critical areas during the pandemic, including indoor (three hospitals and three public buildings) and outdoor surfaces exposed to anthropic contamination (handles and handrails, playgrounds). Traces of biological fluids were frequently detected in spaces open to the public and on objects that are touched with the hands (>80%). However, viral RNA was not detected in hospital wards or other indoor and outdoor surfaces either in the air system of a COVID hospital but only in the surroundings of an infected patient, in consistent association with droplet traces and fomites. Handled objects accumulated the highest level of multiple contaminations by saliva, nose secretions, and fecal traces, further supporting the priority role of handwashing in prevention. In conclusion, anthropic contamination by droplets and biological fluids is widespread in spaces open to the public and can be traced by qPCR. Monitoring fomites can support evaluation of indirect transmission risks for coronavirus or other flu-like viruses in the environment. IMPORTANCE Several studies have evaluated the presence of SARS-CoV-2 in the environment. Saliva and nasopharyngeal droplets can land on objects and surfaces, creating fomites. A suitable indicator would allow the detection of droplets or biofluids carrying the virus. Therefore, we searched for viral RNA and droplets and fomites on at risk surfaces. We monitored by qPCR or next generation sequencing (NGS) droplets through their microbiota. Although the study was performed during the pandemic, SARS-CoV-2 was not significantly found on surfaces, with the only exception of environmental areas near infectious patients. Conversely, anthropic contamination was frequent, suggesting a role for biofluids as putative markers of indirect transmission and risk assessment. Moreover, all SARS-CoV-2-contaminated surfaces showed droplets’ microbiota. Fomite monitoring by qPCR may have an impact on public health strategies, supporting prevention of indirect transmission similarly to what is done for other communicable diseases (e.g., influenza and influenza-like infections).