B and T cells are integral parts of the immune system and are implicated in many diseases, e.g. autoimmunity. Towards understanding the biology of B and T cells and subsets thereof, their transcriptomes can be analyzed using single-cell RNA sequencing. In some studies, the V(D)J transcripts encoding the variable regions of the B- and T-cell antigen receptors have been removed before the analyses. However, a systematic analysis of the effects of including versus excluding these genes is currently lacking. We have investigated the effects of these transcripts on unsupervised clustering and down-stream analyses of single-cell RNA sequencing data from B and T cells. We found that exclusion of the B-/T-cell receptor genes prior to unsupervised clustering resulted in clusters that represented biologically meaningful subsets, such as subsets of memory B and memory T cells. Furthermore, pseudo-time and trajectory inference analyses of early B-lineage cells resulted in a developmental pathway from progenitor to immature B cells. In contrast, when the B-/T-cell receptor genes were not removed, with the PCs used for clustering consisting of up to 70% V-genes, this resulted in some clusters being defined exclusively by V-gene segments. These did not represent biologically meaningful subsets; for instance in the early B-lineage cells, these clusters contained cells representing all developmental stages. Thus, in studies of B and T cells, to derive biologically meaningful results, it is imperative to remove the gene sequences that encode B- and T-cell receptors.

Odorant receptors (ORs) obey mutual exclusivity and monoallelic mode of expression. Efforts are ongoing to decipher the molecular mechanism that drives the 'one-neuron-one-receptor' rule of olfaction. Recently, single-cell profiling of olfactory sensory neurons (OSNs) revealed the expression of multiple ORs in the immature neurons, suggesting that the OR gene choice mechanism is much more complex than previously described by the silence-all-and-activate-one model. These results also led to the genesis of two possible mechanistic models i.e. winner-takes-all and stochastic selection. We developed Reverse Cell Tracking (RCT), a novel computational framework that facilitates OR-guided cellular backtracking by leveraging Uniform Manifold Approximation and Projection embeddings from RNA Velocity Workflow. RCT-based trajectory backtracking, coupled with statistical analysis, revealed the OR gene choice bias for the transcriptionally advanced (highest expressed) OR during neuronal differentiation. Interestingly, the observed selection bias was uniform for all ORs across different spatial zones or their relative expression within the olfactory organ. We validated these findings on independent datasets and further confirmed that the OR gene selection may be regulated by Upf3b. Lastly, our RNA dynamics-based tracking of the differentiation cascade revealed a transition cell state that harbors mixed molecular identities of immature and mature OSNs, and their relative abundance is regulated by Upf3b.

Antiviral defenses are one of the significant roles of RNA interference (RNAi) in plants. It has been reported that the host RNAi mechanism machinery can target viral RNAs for destruction because virus-derived small interfering RNAs (vsiRNAs) are found in infected host cells. Therefore, the recognition of plant vsiRNAs is the key to understanding the functional mechanisms of vsiRNAs and developing antiviral plants. In this work, we introduce a deep learning-based stacking ensemble approach, named computational prediction of plant exclusive virus-derived small interfering RNAs (COPPER), for plant vsiRNA prediction. COPPER used word2vec and fastText to generate sequence features and a hybrid deep learning framework, including a convolutional neural network, multiscale residual network and bidirectional long short-term memory network with a self-attention mechanism to enable precise predictions of plant vsiRNAs. Extensive benchmarking experiments with different sequence homology thresholds and ablation studies illustrated the comparative predictive performance of COPPER. In addition, the performance comparison with PVsiRNAPred conducted on an independent test dataset showed that COPPER significantly improved the predictive performance for plant vsiRNAs compared with other state-of-the-art methods. The datasets and source codes are publicly available at https://github.com/yuanyuanbu/COPPER.

To survive and establish a niche for themselves, bacteria constantly evolve. Toward that, they not only insert point mutations and promote illegitimate recombinations within their genomes but also insert pieces of 'foreign' deoxyribonucleic acid, which are commonly referred to as 'genomic islands' (GEIs). The GEIs come in several forms, structures and types, often providing a fitness advantage to the harboring bacterium. In pathogenic bacteria, some GEIs may enhance virulence, thus altering disease burden, morbidity and mortality. Hence, delineating (i) the GEIs framework, (ii) their encoded functions, (iii) the triggers that help them move, (iv) the mechanisms they exploit to move among bacteria and (v) identification of their natural reservoirs will aid in superior tackling of several bacterial diseases, including sepsis. Given the vast array of comparative genomics data, in this short review, we provide an overview of the GEIs, their types and the compositions therein, especially highlighting GEIs harbored by two important pathogens, viz. Acinetobacter baumannii and Klebsiella pneumoniae, which prominently trigger sepsis in low- and middle-income countries. Our efforts help shed some light on the challenges these pathogens pose when equipped with GEIs. We hope that this review will provoke intense research into understanding GEIs, the cues that drive their mobility across bacteria and the ways and means to prevent their transfer, especially across pathogenic bacteria.

The dramatic changes in physiology at high altitude (HA) as a result of the characteristic hypobaric hypoxia condition can modify innate and adaptive defense mechanisms of the body. As a consequence, few sojourners visiting HA with mild or asymptomatic infection may have an enhanced susceptibility to high-altitude pulmonary edema (HAPE), an acute but severe altitude sickness. It develops upon rapid ascent to altitudes above 2500 m, in otherwise healthy individuals. Though HAPE has been studied extensively, an elaborate exploration of the HA disease burden and the potential risk factors associated with its manifestation are poorly described. The present review discusses respiratory tract infection (RTI) as an unfamiliar but important risk factor in enhancing HAPE susceptibility in sojourners for two primary reasons. First, the symptoms of RTI s resemble those of HAPE. Secondly, the imbalanced pathways contributing to vascular dysfunction in HAPE also participate in the pathogenesis of the infectious processes. These pathways have a crucial role in shaping host response against viral and bacterial infections and may further worsen the clinical outcomes at HA. Respiratory tract pathogenic agents, if screened in HAPE patients, can help in ascertaining their role in disease risk and also point toward their association with the disease severity. The microbial screenings and identifications of pathogens with diseases are the foundation for describing potential molecular mechanisms underlying host response to the microbial challenge. The prior knowledge of such infections may predict the manifestation of disease etiology and provide better therapeutic options.