Stochastic modelling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones.

Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present the first formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein-Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution. Interestingly, sublines previously observed to be resistant to anti-CTLA-4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signaling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression.

Fear effect in a three-species food chain model with generalist predator.

Within the framework of a food web, the foraging behavior of meso-carnivorous species is influenced by fear responses elicited by higher trophic level species, consequently diminishing the fecundity of these species. In this study, we investigate a three-species food chain model comprising of prey, an intermediate predator, and a top predator. We assume that both the birth rate and intraspecies competition of prey are impacted by fear induced by the intermediate predator. Additionally, the foraging behavior of the intermediate predator is constrained due to the presence of the top predator. It is essential to note that the top predators exhibit a generalist feeding behavior, encompassing food sources beyond the intermediate predators. The study systematically determines all feasible equilibria of the proposed model and conducts a comprehensive stability analysis of these equilibria. The investigation reveals that the system undergoes Hopf bifurcation concerning various model parameters. Notably, when other food sources significantly contribute to the growth of the top predators, the system exhibits stable behavior around the interior equilibrium. Our findings indicate that the dynamic influence of fear plays a robust role in stabilizing the system. Furthermore, a cascading effect within the system, stemming from the fear instigated by top predators, is observed and analyzed. Overall, this research sheds light on the intricate dynamics of fear-induced responses in shaping the stability and behavior of multi-species food web systems, highlighting the profound cascading effects triggered by fear mechanisms in the ecosystem.

A review on phytoestrogens: Current status and future direction.

Phytoestrogens are plant secondary metabolite that is structurally and functionally similar to mammalian estrogens, which have been shown to have various health benefits in humans. Isoflavones, coumestans, and lignans are the three major bioactive classes of phytoestrogens. It has a complicated mechanism of action involving an interaction with the nuclear estrogen receptor isoforms ERα and ERß, with estrogen agonist and estrogen antagonist effects. Depending on their concentration and bioavailability in various plant sources, phytoestrogens can act as estrogen agonist or antagonists. Menopausal vasomotor symptoms, breast cancer, cardiovascular disease, prostate cancer, menopausal symptoms, and osteoporosis/bone health have all been studied using phytoestrogens as an additional standard hormone supplemental remedy. The botanical sources, techniques of identification, classification, side effects, clinical implications, pharmacological and therapeutic effects of their proposed mode of action, safety issues, and future directions for phytoestrogens have all been highlighted in this review.

Emergent dynamics of adult stem cell lineages from single nucleus and single cell RNA-Seq of Drosophila testes.

Proper differentiation of sperm from germline stem cells, essential for production of the next generation, requires dramatic changes in gene expression that drive remodeling of almost all cellular components, from chromatin to organelles to cell shape itself. Here, we provide a single nucleus and single cell RNA-seq resource covering all of spermatogenesis in Drosophila starting from in-depth analysis of adult testis single nucleus RNA-seq (snRNA-seq) data from the Fly Cell Atlas (FCA) study. With over 44,000 nuclei and 6000 cells analyzed, the data provide identification of rare cell types, mapping of intermediate steps in differentiation, and the potential to identify new factors impacting fertility or controlling differentiation of germline and supporting somatic cells. We justify assignment of key germline and somatic cell types using combinations of known markers, in situ hybridization, and analysis of extant protein traps. Comparison of single cell and single nucleus datasets proved particularly revealing of dynamic developmental transitions in germline differentiation. To complement the web-based portals for data analysis hosted by the FCA, we provide datasets compatible with commonly used software such as Seurat and Monocle. The foundation provided here will enable communities studying spermatogenesis to interrogate the datasets to identify candidate genes to test for function in vivo.

Single-Cell Transcriptomic Profiling of the Mouse Testicular Germ Cells Reveals Important Role of Phosphorylated GRTH/DDX25 in Round Spermatid Differentiation and Acrosome Biogenesis during Spermiogenesis.

Gonadotropin-regulated testicular RNA helicase (GRTH)/DDX25 is a member of DEAD-box family of RNA helicase essential for the completion of spermatogenesis and male fertility, as evident from GRTH-knockout (KO) mice. In germ cells of male mice, there are two species of GRTH, a 56 kDa non-phosphorylated form and 61 kDa phosphorylated form (pGRTH). GRTH Knock-In (KI) mice with R242H mutation abolished pGRTH and its absence leads to infertility. To understand the role of the GRTH in germ cell development at different stages during spermatogenesis, we performed single-cell RNA-seq analysis of testicular cells from adult WT, KO and KI mice and studied the dynamic changes in gene expression. Pseudotime analysis revealed a continuous developmental trajectory of germ cells from spermatogonia to elongated spermatids in WT mice, while in both KO and KI mice the trajectory was halted at round spermatid stage indicating incomplete spermatogenesis process. The transcriptional profiles of KO and KI mice were significantly altered during round spermatid development. Genes involved in spermatid differentiation, translation process and acrosome vesicle formation were significantly downregulated in the round spermatids of KO and KI mice. Ultrastructure of round spermatids of KO and KI mice revealed several abnormalities in acrosome formation that includes failure of pro-acrosome vesicles to fuse to form a single acrosome vesicle, and fragmentation of acrosome structure. Our findings highlight the crucial role of pGRTH in differentiation of round spermatids into elongated spermatids, acrosome biogenesis and its structural integrity.

Stochastic Modeling of Gene Expression Evolution Uncovers Tissue- and Sex-Specific Properties of Expression Evolution in the Drosophila Genus.

Gene expression evolution is typically modeled with the stochastic Ornstein-Uhlenbeck (OU) process. It has been suggested that the estimation of within-species variations using replicated data can increase the predictive power of such models, but this hypothesis has not been fully tested. We developed EvoGeneX, a computationally efficient implementation of the OU-based method that models within-species variation. Using extensive simulations, we show that modeling within-species variations and appropriate selection of species improve the performance of the model. Further, to facilitate a comparative analysis of expression evolution, we introduce a formal measure of evolutionary expression divergence for a group of genes using the rate and the asymptotic level of divergence. With these tools in hand, we performed the first-ever analysis of the evolution of gene expression across different body-parts, species, and sexes of the Drosophila genus. We observed that genes with adaptive expression evolution tend to be body-part specific, whereas the genes with constrained evolution tend to be shared across body-parts. Among the neutrally evolving gene expression patterns, gonads in both sexes have higher expression divergence relative to other tissues and the male gonads have even higher divergence than the female gonads. Among the evolutionarily constrained genes, the gonads show different divergence patterns, where the male gonads, and not the female gonads, show less constrained divergence than other body-parts. Finally, we show interesting examples of adaptive expression evolution, including adaptation of odor binding proteins.

Modeling the impact of precautionary measures and sanitation practices broadcasted through media on the dynamics of bacterial diseases.

The media has a significant contribution in spreading awareness by broadcasting various programs about prevalent diseases in the society along with the role of providing information, feeding news and educating a large mass. In this paper, the effect of media programs promoting precautionary measures and sanitation practices to control the bacterial infection in the community is modeled and analyzed considering the number of media programs as a dynamical variable. In the modeling phenomena, human population is partitioned into three classes; susceptible, infected and recovered. The disease is supposed to spread by direct contact of susceptible with infected individuals and indirectly by the ingestion of bacteria present in the environment. The growth in the media programs is considered proportional to the size of infected population and the impact of these programs on the indirect disease transmission rate and bacteria shedding rate by infected individuals is also considered. The feasibility of equilibria and their stability conditions are obtained. Model analysis reveals that broadcasting media programs and increasing its effectiveness shrink the size of infected class and control the spread of disease to a large extent.

Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly.

For more than 100 years, the fruit fly Drosophila melanogaster has been one of the most studied model organisms. Here, we present a single-cell atlas of the adult fly, Tabula Drosophilae, that includes 580,000 nuclei from 15 individually dissected sexed tissues as well as the entire head and body, annotated to >250 distinct cell types. We provide an in-depth analysis of cell type-related gene signatures and transcription factor markers, as well as sexual dimorphism, across the whole animal. Analysis of common cell types between tissues, such as blood and muscle cells, reveals rare cell types and tissue-specific subtypes. This atlas provides a valuable resource for the Drosophila community and serves as a reference to study genetic perturbations and disease models at single-cell resolution.

Hybrid-DCA: A double asynchronous approach for stochastic dual coordinate ascent.

In prior works, stochastic dual coordinate ascent (SDCA) has been parallelized in a multi-core environment where the cores communicate through shared memory, or in a multi-processor distributed memory environment where the processors communicate through message passing. In this paper, we propose a hybrid SDCA framework for multi-core clusters, the most common high performance computing environment that consists of multiple nodes each having multiple cores and its own shared memory. We distribute data across nodes where each node solves a local problem in an asynchronous parallel fashion on its cores, and then the local updates are aggregated via an asynchronous across-node update scheme. The proposed double asynchronous method converges to a global solution for L-Lipschitz continuous loss functions, and at a linear convergence rate if a smooth convex loss function is used. Extensive empirical comparison has shown that our algorithm scales better than the best known shared-memory methods and runs faster than previous distributed-memory methods. Big datasets, such as one of 280 GB from the LIBSVM repository, cannot be accommodated on a single node and hence cannot be solved by a parallel algorithm. For such a dataset, our hybrid algorithm takes less than 30 seconds to achieve a duality gap of 10-5 on 16 nodes each using 12 cores, which is significantly faster than the best known distributed algorithms, such as CoCoA+, that take more than 160 seconds on 16 nodes.

Bioinformatics pipeline using JUDI: Just Do It!

SUMMARY: Large-scale data analysis in bioinformatics requires pipelined execution of multiple software. Generally each stage in a pipeline takes considerable computing resources and several workflow management systems (WMS), e.g. Snakemake, Nextflow, Common Workflow Language, Galaxy, etc. have been developed to ensure optimum execution of the stages across two invocations of the pipeline. However, when the pipeline needs to be executed with different settings of parameters, e.g. thresholds, underlying algorithms, etc. these WMS require significant scripting to ensure an optimal execution. We developed JUDI on top of DoIt, a Python based WMS, to systematically handle parameter settings based on the principles of database management systems. Using a novel modular approach that encapsulates a parameter database in each task and file associated with a pipeline stage, JUDI simplifies plug-and-play of the pipeline stages. For a typical pipeline with n parameters, JUDI reduces the number of lines of scripting required by a factor of O(n). With properly designed parameter databases, JUDI not only enables reproducing research under published values of parameters but also facilitates exploring newer results under novel parameter settings. AVAILABILITY AND IMPLEMENTATION: https://github.com/ncbi/JUDI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Co-SELECT reveals sequence non-specific contribution of DNA shape to transcription factor binding in vitro.

Understanding the principles of DNA binding by transcription factors (TFs) is of primary importance for studying gene regulation. Recently, several lines of evidence suggested that both DNA sequence and shape contribute to TF binding. However, the following compelling question is yet to be considered: in the absence of any sequence similarity to the binding motif, can DNA shape still increase binding probability? To address this challenge, we developed Co-SELECT, a computational approach to analyze the results of in vitro HT-SELEX experiments for TF-DNA binding. Specifically, Co-SELECT leverages the presence of motif-free sequences in late HT-SELEX rounds and their enrichment in weak binders allows Co-SELECT to detect an evidence for the role of DNA shape features in TF binding. Our approach revealed that, even in the absence of the sequence motif, TFs have propensity to bind to DNA molecules of the shape consistent with the motif specific binding. This provides the first direct evidence that shape features that accompany the preferred sequence motifs also bestow an advantage for weak, sequence non-specific binding.

MSC: a metagenomic sequence classification algorithm.

MOTIVATION: Metagenomics is the study of genetic materials directly sampled from natural habitats. It has the potential to reveal previously hidden diversity of microscopic life largely due to the existence of highly parallel and low-cost next-generation sequencing technology. Conventional approaches align metagenomic reads onto known reference genomes to identify microbes in the sample. Since such a collection of reference genomes is very large, the approach often needs high-end computing machines with large memory which is not often available to researchers. Alternative approaches follow an alignment-free methodology where the presence of a microbe is predicted using the information about the unique k-mers present in the microbial genomes. However, such approaches suffer from high false positives due to trading off the value of k with the computational resources. In this article, we propose a highly efficient metagenomic sequence classification (MSC) algorithm that is a hybrid of both approaches. Instead of aligning reads to the full genomes, MSC aligns reads onto a set of carefully chosen, shorter and highly discriminating model sequences built from the unique k-mers of each of the reference sequences. RESULTS: Microbiome researchers are generally interested in two objectives of a taxonomic classifier: (i) to detect prevalence, i.e. the taxa present in a sample, and (ii) to estimate their relative abundances. MSC is primarily designed to detect prevalence and experimental results show that MSC is indeed a more effective and efficient algorithm compared to the other state-of-the-art algorithms in terms of accuracy, memory and runtime. Moreover, MSC outputs an approximate estimate of the abundances. AVAILABILITY AND IMPLEMENTATION: The implementations are freely available for non-commercial purposes. They can be downloaded from https://drive.google.com/open?id=1XirkAamkQ3ltWvI1W1igYQFusp9DHtVl.

Efficient sequential and parallel algorithms for finding edit distance based motifs.

BACKGROUND: Motif search is an important step in extracting meaningful patterns from biological data. The general problem of motif search is intractable and there is a pressing need to develop efficient, exact and approximation algorithms to solve this problem. In this paper, we present several novel, exact, sequential and parallel algorithms for solving the (l,d) Edit-distance-based Motif Search (EMS) problem: given two integers l,d and n biological strings, find all strings of length l that appear in each input string with atmost d errors of types substitution, insertion and deletion. METHODS: One popular technique to solve the problem is to explore for each input string the set of all possible l-mers that belong to the d-neighborhood of any substring of the input string and output those which are common for all input strings. We introduce a novel and provably efficient neighborhood exploration technique. We show that it is enough to consider the candidates in neighborhood which are at a distance exactly d. We compactly represent these candidate motifs using wildcard characters and efficiently explore them with very few repetitions. Our sequential algorithm uses a trie based data structure to efficiently store and sort the candidate motifs. Our parallel algorithm in a multi-core shared memory setting uses arrays for storing and a novel modification of radix-sort for sorting the candidate motifs. RESULTS: The algorithms for EMS are customarily evaluated on several challenging instances such as (8,1), (12,2), (16,3), (20,4), and so on. The best previously known algorithm, EMS1, is sequential and in estimated 3 days solves up to instance (16,3). Our sequential algorithms are more than 20 times faster on (16,3). On other hard instances such as (9,2), (11,3), (13,4), our algorithms are much faster. Our parallel algorithm has more than 600 % scaling performance while using 16 threads. CONCLUSIONS: Our algorithms have pushed up the state-of-the-art of EMS solvers and we believe that the techniques introduced in this paper are also applicable to other motif search problems such as Planted Motif Search (PMS) and Simple Motif Search (SMS).

KCMBT: a k-mer Counter based on Multiple Burst Trees.

MOTIVATION: A massive number of bioinformatics applications require counting of k-length substrings in genetically important long strings. A k-mer counter generates the frequencies of each k-length substring in genome sequences. Genome assembly, repeat detection, multiple sequence alignment, error detection and many other related applications use a k-mer counter as a building block. Very fast and efficient algorithms are necessary to count k-mers in large data sets to be useful in such applications. RESULTS: We propose a novel trie-based algorithm for this k-mer counting problem. We compare our devised algorithm k-mer Counter based on Multiple Burst Trees (KCMBT) with available all well-known algorithms. Our experimental results show that KCMBT is around 30% faster than the previous best-performing algorithm KMC2 for human genome dataset. As another example, our algorithm is around six times faster than Jellyfish2. Overall, KCMBT is 20-30% faster than KMC2 on five benchmark data sets when both the algorithms were run using multiple threads. AVAILABILITY AND IMPLEMENTATION: KCMBT is freely available on GitHub: (https://github.com/abdullah009/kcmbt_mt). CONTACT: rajasek@engr.uconn.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In search of perfect reads.

BACKGROUND: Continued advances in next generation short-read sequencing technologies are increasing throughput and read lengths, while driving down error rates. Taking advantage of the high coverage sampling used in many applications, several error correction algorithms have been developed to improve data quality further. However, correcting errors in high coverage sequence data requires significant computing resources. METHODS: We propose a different approach to handle erroneous sequence data. Presently, error rates of high-throughput platforms such as the Illumina HiSeq are within 1%. Moreover, the errors are not uniformly distributed in all reads, and a large percentage of reads are indeed error-free. Ability to predict such perfect reads can significantly impact the run-time complexity of applications. We present a simple and fast k-spectrum analysis based method to identify error-free reads. The filtration process to identify and weed out erroneous reads can be customized at several levels of stringency depending upon the downstream application need. RESULTS: Our experiments show that if around 80% of the reads in a dataset are perfect, then our method retains almost 99.9% of them with more than 90% precision rate. Though filtering out reads identified as erroneous by our method reduces the average coverage by about 7%, we found the remaining reads provide as uniform a coverage as the original dataset. We demonstrate the effectiveness of our approach on an example downstream application: we show that an error correction algorithm, Reptile, which rely on collectively analyzing the reads in a dataset to identify and correct erroneous bases, instead use reads predicted to be perfect by our method to correct the other reads, the overall accuracy improves further by up to 10%. CONCLUSIONS: Thanks to the continuous technological improvements, the coverage and accuracy of reads from dominant sequencing platforms have now reached an extent where we can envision just filtering out reads with errors, thus making error correction less important. Our algorithm is a first attempt to propose and demonstrate this new paradigm. Moreover, our demonstration is applicable to any error correction algorithm as a downstream application, this in turn gives a new class of error correcting algorithms as a by product.