Biology subjects

Farrell, A.

Publications and source records attributed to Farrell, A..

13 recordsLinked to original sources

bioRxiv2025

Synthetic Control of Implanted Engineered Liver Tissue Growth

Despite the promise of engineered tissue implants for the treatment of organ failure, scaling of these constructs to sizes of therapeutic relevance remains a barrier to clinical translation. Here, we propose a strategy to circumvent this limitation: to instead implant a small-scale construct and then induce it to grow in situ after its engraftment into a host. Using engineered liver tissue as a proof-of-concept application, we integrated synthetic biology and tissue engineering tools to build liver tissues that can be expanded on-demand after implantation in vivo. To achieve this goal, we first identified the combination of YAP and growth factor signaling as sufficient to drive human hepatocyte proliferation in dense, 3D engineered tissues. We then engineered control of these signaling axes using synthetic biology tools to drive human liver tissue expansion both in vitro and in vivo. As such, this work establishes a genetic strategy for generating large organ implants through bioengineered, on-demand outgrowth using synthetic triggers (BOOST). TeaserPresenting bioengineered on-demand outgrowth via synthetic biology triggering (BOOST) for in situ solid cell therapy scale up.

bioengineering↗

bioRxiv2025

Is there a fly in my soup? To what extent do metabarcoding and individual barcoding tell the same story?

O_LIMetabarcoding has become the method of choice for characterising complex arthropod communities. The extent to which metabarcoded bulk samples will recover the same community composition as individual sequencing of all individuals in the sample remains poorly quantified. Biases such as unequal extraction of DNA from different taxa, primer mismatches and non-random PCR may cause the selective drop-out of species from metabarcoding data. At the same time, DNA metabarcoding may reveal arthropod taxa present not as individuals, but as DNA residues on the surface or in the gut of insects. C_LIO_LITo quantify the consistency in sample contents established by different means, we metabarcoded 45 bulk insect samples, then extracted all arthropods and sequenced them individually. Metabarcoding targeted 418 bp at the 3 end of the Folmer barcoding region, while individual barcodes captured the entire 658 bp Folmer region. The metabarcoding workflow, including PCR amplification, sequencing, and bioinformatics, was performed in three replicates from three separate lysate aliquots per sample. Sequences were assigned to Barcode Index Numbers (BINs) as identical taxonomic categories across data types. C_LIO_LIWe found that metabarcoding is replicable, as different replicates of the same sample recover similar species richness and composition. Individual barcoding and metabarcoding provide the same impression of relative differences in community structure: estimates of relative species richness and relative dissimilarity between sample pairs are congruent among data types. Dissimilarity between data types varies with BIN richness in the sample, but this relationship reflects nestedness rather than turnover: metabarcoding recovers the same set of core species as individual barcoding but adds hundreds of species on top. Any BIN recovered as an individual occurred with high probability in the metabarcoding data, and any BIN found in high read abundances by metabarcoding was likely found as an individual. C_LIO_LIOur analysis suggests that metabarcoding data will closely mimic the sample contents in terms of arthropod species richness and composition. Taxa recovered in low copy numbers in metabarcoding sequence data will typically represent DNA left as residues from past biotic interactions. Barring sequencing errors, both types of data yield biologically relevant insights into the taxa present in the source community. C_LI

ecology↗

bioRxiv2025

Single-cell RNA sequencing in Hirschsprungs disease tissues reveals lack of neuronal differentiation in the aganglionic colon segment

The enteric nervous system (ENS) is a complex network of neurons and glial cells. Hirschsprungs disease (HSCR) is a congenital condition characterized by the absence of ganglion cells in the distal colon, leading to functional bowel obstruction. In this study, we used single-cell RNA sequencing (scRNA-seq) and whole genome sequencing (WGS) to analyze healthy and aganglionic colon segments from HSCR patients. Using scRNA-seq, we identified 13 major cell types in patient samples and observed that neural progenitor cells were present in both healthy and aganglionic colon regions, while mature neurons were absent from aganglionic colon. In these progenitor cells, critical differentiation pathway genes displayed reduced expression in the aganglionic colon, suggesting a disruption in their transition to mature neuronal cell types. Furthermore, transcriptomic analysis revealed significant alterations in gene expression across several stromal cell types. These transcriptomic shifts, particularly in mast cells, support the hypothesis that altered gene expression in the microenvironment of neural progenitor cells contributes to impaired differentiation. Our findings support the hypothesis that neural precursors in HSCR are capable of migration, but they are defective in their differentiation to mature cell types. Our analysis provides insights into potential therapeutic targets to stimulate neurogenesis in the aganglionic colon.

genomics↗

bioRxiv2024

Inhibition of PIM kinase in tumor associated macrophages suppresses inflammasome 1 activation and sensitizes prostate cancer to immunotherapy

Immunotherapy has changed the treatment paradigm for many types of cancer, but immune checkpoint inhibitors (ICIs) have not shown benefit in prostate cancer (PCa). Chronic inflammation contributes to the immunosuppressive prostate tumor microenvironment (TME) and is associated with poor response to ICIs. The primary source of inflammatory cytokine production is the inflammasome. Here, we identify PIM kinases as important regulators of inflammasome activation in tumor associated macrophages (TAMs). Analysis of clinical data from a cohort of treatment naive, hormone responsive PCa patients revealed that tumors from patients with high PIM1/2/3 display an immunosuppressive TME characterized by high inflammation (IL-1{beta} and TNF) and a high density of repressive immune cells, most notably TAMs. Strikingly, macrophage-specific knockout of PIM reduced tumor growth in syngeneic models of prostate cancer. Transcriptional analyses indicate that eliminating PIM from macrophages enhanced the adaptive immune response and increased cytotoxic immune cells. Combined treatment with PIM inhibitors and ICIs synergistically reduced tumor growth. Immune profiling revealed that PIM inhibitors sensitized PCa tumors to ICIs by increasing tumor suppressive TAMs and increasing the activation of cytotoxic T cells. Collectively, our data implicate macrophage PIM as a driver of inflammation that limits the potency of ICIs and provides preclinical evidence that PIM inhibitors are an effective strategy to improve the efficacy of immunotherapy in prostate cancer.

cancer biology↗

bioRxiv2021

Novel temporal and spatial patterns of metastatic colonization from rapid-autopsy tumor biopsies

BackgroundMetastatic breast cancer is a deadly disease with a low 5-year survival rate. Tracking metastatic spread in living patients is difficult, and thus poorly understood. ResultsVia rapid autopsy, we have collected 30 tumor samples over 3 timepoints and across 8 organs from a triple-negative metastatic breast cancer patient. The large number of sites sampled, together with deep whole genome sequencing and advanced computational analysis, allowed us to comprehensively reconstruct the tumors evolution at subclonal resolution. The most unique, previously not reported aspect of the tumors evolution we observed in this patient was the presence of "subclone incubators", i.e. already metastatic sites where substantial tumor evolution occurred before colonization of additional sites and organs by subclones that evolved at the incubator site. Overall, we identified four discrete waves of metastatic expansions, each of which resulted in a number of new, genetically similar metastasis sites that also enriched for particular organs (e.g. abdominal vs bone and brain). The lung played a critical role in facilitating metastatic spread in this patient: the lung was the first site of metastatic escape from the primary breast lesion; subclones at this site were the source of all four subsequent metastatic waves; and multiple sites in the lung acted as subclone incubators. Finally, functional annotation revealed that many known driver or metastasis-promoting tumor mutations in this patient were shared by some, but not all metastatic sites, highlighting the need for more comprehensive surveys of a patients metastases for effective clinical intervention. ConclusionsOur analysis revealed the presence of substantial tumor evolution at metastatic incubator sites, with potentially important clinical implications. Our study demonstrated that sampling of a large number of metastatic sites affords unprecedented detail for studying metastatic evolution.

cancer biology↗

bioRxiv2021

A global dataset for the projected impacts of climate change on four major crops

Reliable estimates of the impacts of climate change on crop production are critical for assessing the sustainability of food systems. Global, regional, and site-specific crop simulation studies have been conducted for nearly four decades, representing valuable sources of information for climate change impact assessments. However, the wealth of data produced by these studies has not been made publicly available. Here, we develop a global dataset by consolidating previously published meta-analyses and data collected through a new literature search covering recent crop simulations. The new global dataset builds on 8314 simulations from 203 studies published between 1984 and 2020. It contains projected yields of four major crops (maize, rice, soybean, and wheat) in 91 countries under major emission scenarios for the 21st century, with and without adaptation measures, along with geographical coordinates, current temperatures, local and global warming levels. This dataset provides a basis for a comprehensive understanding of the impacts of climate change on crop production and will facilitate the rapidly developing data-driven machine learning applications.

plant biology↗

bioRxiv2021

The extracellular milieu of Toxoplasma-s lytic cycle drives lab-adaptation and promotes changes in lipid metabolism primarily driven by transcriptional reprogramming

To map host-independent in vitro virulence traits of Toxoplasma gondii, evolve and resequencing (E&R) during the lab-adaption was applied. Phenotypic assessments of the lytic cycle revealed that only traits needed in the extracellular milieu evolved. Surprisingly, only non-synonymous mutations in a P4 flippase fixed in two populations. However, dramatic changes in the transcriptional signature of extracellular parasites revealed a "pro-tachyzoite" profile as well as upregulation of fatty acid biosynthesis (FASII) pathway genes. More general, a set of 300 genes which expression profile changes during evolution mapped to specific traits. Validation of a select number of genes in this set by knock-outs indeed confirmed their role in lab-adaptation. Finally, assembly of an ApiAP2 and Myb transcription factor network revealed the transcriptional program underlying the adapting extracellular state. Overall, E&R is a new genomic tool successfully applied to map the development of polygenic traits underlying in vitro virulence of T. gondii.

microbiology↗

bioRxiv2020

Deep whole genome sequencing of multiple proband tissues and parental blood reveals the complex genetic etiology of congenital diaphragmatic hernias

The diaphragm is a mammalian muscle critical for respiration and separation of the thoracic and abdominal cavities. Defects in the development of the diaphragm are the cause of congenital diaphragmatic hernia (CDH), a common birth defect. In CDH, weaknesses in the developing diaphragm allow abdominal contents to herniate into the thoracic cavity and impair lung development, leading to a high neonatal mortality. The genetic etiology of CDH is complex. Single nucleotide variants (SNVs), insertion/deletions (indels), and structural/copy number variants in more than 150 genes have been associated with CDH, although few genes are recurrently mutated in multiple patients and recurrently mutated genes can be incompletely penetrant. This suggests that multiple genetic variants in combination, other not yet investigated classes of variants, and/or nongenetic factors contribute to CDH susceptibility. However, to date no studies have comprehensively investigated the contribution of all possible classes of variants throughout the genome to the etiology of CDH. In our study, we used a unique cohort of four patients with isolated CDH with samples from blood, skin, and diaphragm connective tissue and parental blood samples and deep whole genome sequencing to assess germline and somatic de novo and inherited variants of various sizes (SNVs, indels, and structural variants) in exons, introns, UTRs, and intergenic regions. In each patient we found a different mutational landscape that included germline de novo, and inherited SNVs and indels in multiple genes. We also found in two patients an inherited 343 bp deletion interrupting an annotated enhancer of the CDH associated gene, GATA4, and we hypothesize that this common deletion (found in 1-2% of the population) acts as a sensitizing allele for CDH. Overall, our comprehensive reconstruction of the genetic architecture of four CDH individuals demonstrates that the etiology of CDH is heterogeneous and multifactorial. AUTHOR SUMMARYDeep whole genome sequencing of family trios shows that etiology of congenital diaphragmatic hernias is heterogeneous and multifactorial.

genetics↗

bioRxiv2019

Coinfection of semi-infectious particles can contribute substantially to influenza infection dynamics

AbstractInfluenza is an RNA virus with a genome comprised of eight gene segments. Recent experiments show that the vast majority of virions fail to express one or more gene segments and thus cannot cause a productive infection on their own. These particles, called semi-infectious particles (SIPs), can induce virion production through complementation when multiple SIPs are present in an infected cell. Previous within-host influenza models ignore the potential effects of coinfection and SIPs during virus infection. Here, to investigate the extent that SIPs and coinfection impact viral dynamics, we constructed two within-host models that explicitly keep track of SIPs and coinfection, and fitted the models to clinical data published previously. We found that the model making a more realistic assumption that viruses can only reach a limited number of target cells allows for frequent co-infection during early viral exponential growth and predicts that SIPs contribute substantially to viral load. Furthermore, the model provides a new interpretation of the determinants of viral growth and predicts that the virus within-host growth rate (a measure of viral fitness) is relatively insensitive to the fraction of virions being SIPs, consistent with biological observations. Our results highlight the important role that cellular co-infection can play in regulating infection dynamics and provide a potential explanation for why SIP production is not highly deleterious. More broadly, the model can be used as a general framework to understand coinfection/superinfection in other viral infections. Author SummaryInfluenza A viruses (IAVs) represent a large public health burden across the world. Currently, our understanding of their infection dynamics is incomplete, which hinders the development of effective vaccines and treatment strategies. Recently, it was shown that a large fraction of virions, called semi-infectious particles, do not cause productive infection on their own; however, coinfection of these particles leads to productive infection. The extent that semi-infectious particles and, more broadly, coinfection contribute to overall influenza infection dynamics is not clear. To address this question, we constructed mathematical models explicitly keeping track of semi-infectious particles and coinfection. We show that coinfection can be frequent over the course of infection and that SIPs play an important role in regulating infection dynamics. Our results have implications towards developing effective therapeutics.

microbiology↗

bioRxiv2018

Pedigree-based estimation of human mobile element retrotransposition rates

Germline mutation rates in humans have been estimated for a variety of mutation types, including single nucleotide and large structural variants. Here we directly measure the germline retrotransposition rate for the three active retrotransposon elements: L1, Alu, and SVA. We utilized three tools for calling Mobile Element Insertions (MEIs) (MELT, RUFUS, and TranSurVeyor) on blood-derived whole genome sequence (WGS) data from 603 CEPH individuals, comprising 33 three-generation pedigrees. We identified 27 de novo MEIs in 440 births. The retrotransposition rate estimates for Alu elements, one in 40, is roughly half the rate estimated using phylogenetic analyses, a difference in magnitude similar to that observed for single nucleotide variants. The L1 retrotransposition rate is one in 62 births and is within range of previous estimates (1:20-1:200 births). The SVA retrotransposition rate, one in 55 births, is much higher than the previous estimate of one in 900 births. Our large, three-generation pedigrees allowed us to assess parent-of-origin effects and the timing of insertion events in either gametogenesis or early embryonic development. We find a statistically significant paternal bias in Alu retrotransposition. Our study represents the first in-depth analysis of the rate and dynamics of human retrotransposition from WGS data in three-generation human pedigrees.

genetics↗

bioRxiv2018

Within-host infectious disease models accommodating cellular coinfection, with an application to influenza

Within-host models are useful tools for understanding the processes regulating viral load dynamics. While existing models have considered a wide range of within-host processes, at their core these models have shown remarkable structural similarity. Specifically, the structure of these models generally consider target cells to be either uninfected or infected, with the possibility of accommodating further resolution (for example, cells that are refractory to infection and cells that are in an eclipse phase). Recent findings, however, indicate that cellular coinfection is the norm rather than the exception for many viral infectious diseases, and that cells with high multiplicity of infection are present over at least some duration of an infection. The reality of these cellular coinfection dynamics is not accommodated in current within-host models although it may be critical for understanding within-host dynamics. This is particularly the case if multiplicity of infection impacts infected cell phenotypes such as their death rate and their viral production rates. Here, we present a new class of within-host disease models that allow for cellular coinfection in a scalable manner by retaining the low-dimensionality that is a desirable feature of many current within-host models. The models we propose adopt the general structure of epidemiological macroparasite models that allow hosts to be variably infected by parasites such as nematodes and host phenotypes to flexibly depend on parasite burden. Specifically, our within-host models consider target cells as hosts and viral particles as macroparasites, and allow viral output and infected cell lifespans, among other phenotypes, to depend on a cells multiplicity of infection. We show with an application to influenza that these models can be statistically fit to viral load and other within-host data, that they can reproduce notable features of within-host viral dynamics, and that important in vivo quantities such as the mean multiplicity of cellular infection can be easily quantified with these models once parameterized. The within-host model structure we develop here provides an alternative approach for modeling within-host viral load dynamics and allows for a new class of questions to be addressed that consider the effects of cellular coinfection, collective viral interactions, and viral complementation in within-host viral dynamics and evolution.

epidemiology↗

bioRxiv2017

Multi-platform discovery of haplotype-resolved structural variation in human genomes

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, and strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three human parent-child trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (<50 bp) and 27,622 SVs ([≥]50 bp) per human genome. We also discover 156 inversions per genome--most of which previously escaped detection. Fifty-eight of the inversions we discovered intersect with the critical regions of recurrent microdeletion and microduplication syndromes. Taken together, our SV callsets represent a sevenfold increase in SV detection compared to most standard high-throughput sequencing studies, including those from the 1000 Genomes Project. The method and the dataset serve as a gold standard for the scientific community and we make specific recommendations for maximizing structural variation sensitivity for future large-scale genome sequencing studies.

genomics↗

bioRxiv2017

Limited contribution of rare, noncoding variation to autism spectrum disorder from sequencing of 2,076 genomes in quartet families

Genomic studies to date in autism spectrum disorder (ASD) have largely focused on newly arising mutations that disrupt protein coding sequence and strongly influence risk. We evaluate the contribution of noncoding regulatory variation across the size and frequency spectrum through whole genome sequencing of 519 ASD cases, their unaffected sibling controls, and parents. Cases carry a small excess of de novo (1.02-fold) noncoding variants, which is not significant after correcting for paternal age. Assessing 51,801 regulatory classes, no category is significantly associated with ASD after correction for multiple testing. The strongest signals are observed in coding regions, including structural variation not detected by previous technologies and missense variation. While rare noncoding variation likely contributes to risk in neurodevelopmental disorders, no category of variation has impact equivalent to loss-of-function mutations. Average effect sizes are likely to be smaller than that for coding variation, requiring substantially larger samples to quantify this risk.

genomics↗