This study analyzed changes in the bacterial and fungal microbiome of soil samples treated with biosolarization, which uses solar heating and organic amendments. Sequencing analysis found that biosolarization had a stronger impact on the relative abundance of bacterial phyla than fungi. Network analysis identified microbial clusters correlated with volatile fatty acid accumulation, suggesting genera like Clostridium, Weissella and Acetobacter can tolerate and potentially produce these compounds. The results provide insight into structural changes in the soil microbiome during biosolarization as related to volatile fatty acid levels.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
1. The effect of continuous tubular reactor
technologies on the pretreatment of lignocellulosic
biomass at pilot-scale for bioethanol production
Background
• Effective pretreatment of biomass is a critical unit operation in the
production of biofuels and bioproducts.
• Most pretreatment technologies used today operate in batch
mode.
• Continuous operation is preferred, since productivity is a key
aspect in industrial processes at commercial scale..
Approach
• A pilot-scale continuous tubular reactor (PCTR) was employed for
the isothermal pretreatment of agave bagasse (AG), corn stover
(CS), sugarcane bagasse (SC), and wheat straw (WS) with three
residence times.
Outcomes and Impacts
• Deformation in cellulose and hemicellulose linkages and xylan
removal of up to 60% were achieved after pretreatment.
• The shortest residence time tested (20 min) resulted in the highest
glucan to glucose conversion in the low solid loading (4% w/v)
enzymatic saccharification step for AG (83.3%), WS (82.8%), CS
(76.1%) and SC (51.8%).
• Final ethanol concentrations after SSF from PCTR-pretreated
biomass were in the range of 38 to 42 g/L (11.0–11.3 kg of ethanol
per 100 kg of untreated biomass).
• Approach can be readily adapted to include organisms that can
produce more advanced biofuels and bioproducts from pretreated
biomass.
Perez-Pimienta et al. (2020) RSC Advances, doi: 10.1039/d0ra04031b
Plot of sugar consumption and ethanol production levels
from SSF of four pretreated samples (AG = agave bagasse,
CS = corn stover, SC = sugarcane bagasse, and WS = wheat
straw) loaded at 20% solids.
2. Synthesis and function of complex sphingolipid
glycosylation
Background
• Glycosylinositol phosphorylceramides (GIPCs) constitute up
to 40% of the plasma-membrane lipids in plants.
• The complex glycan headgroups of GIPCs vary between
plant species and tissues and has importance for
development, abiotic stress tolerance, and interactions with
pathogenic and symbiotic microorganisms.
Approach
• The paper reviews recent studies of the biosynthesis and
function of GIPC glycan headgroups.
Outcomes and Impacts
• The headgroup is synthesized in the Golgi lumen by
glycosyltransferases similar to those involved in cell wall
biosynthesis.
• Biosynthesis depends on the same nucleotide sugar
transporters in the Golgi that are required for cell wall
biosynthesis and protein glycosylation.
• All plant GIPCs have glucuronic acid as the first sugar. It is
added by an enzyme related to xylan
glucuronosyltransferases and is essential for plant growth.
• Specific GIPC headgroups are essential for symbiosis with
mycorrhizal fungi nitrogen fixing bacteria in root nodules.
• Modulation of GIPC headgroups may be a way to improve
stress tolerance and biotic interactions in bioenergy crops.
Mortimer J.C., Scheller H.V. (2020) Trends in Plant Science, doi: 10.1016/j.tplants.2020.03.007
Structure of GIPCs. All plant GIPCs have glucuronic acid as the
first sugar. It is added by an enzyme related to xylan
glucuronosyltransferases. Subsequent sugars and chain length
vary between species and tissues.
(B)
IPC
IPC
IPC
IPC
GlcAHex GlcNAcMan
IPC
IPC
GlcN
(A)
Celluose synthase
complex (CSC)
Ion
transporter Receptors
Cytosol
Apoplast
GIPC
Sterol
Glucosylceramide Phospholipid
GPI-anchored protein
Schematic view of the plant plasma membrane. The figure highlights
the asymmetry between the inner and outer leaflet, and how the GIPC
glycan headgroups can be tightly packed on the surface. Examples of
classes of membrane proteins are shown, whose functions have been
proposed to be affected by GIPC headgroup structure.
3. Chemoinformatic-guided engineering of
polyketide synthases
Background
• Polyketide synthase (PKS) engineering is an attractive method to
generate new molecules such as commodity, fine and specialty
chemicals.
• A significant challenge is re-engineering a partially reductive PKS
module to produce a saturated β-carbon through a reductive loop (RL)
exchange.
Approach
• We sought to establish that chemoinformatics, a field traditionally used
in drug discovery, offers a viable strategy for RL exchanges.
• We introduced a set of donor RLs of diverse genetic origin and
chemical substrates into the first extension module of the lipomycin
PKS (LipPKS1).
• We then introduced RLs of divergent chemosimilarity into LipPKS2 and
determined triketide lactone production.
Outcomes and Impacts
• Product titers of these engineered unimodular PKSs correlated with
chemical structure similarity between the substrate of the donor RLs
and recipient LipPKS1, reaching a titer of 165 mg/L of short-chain fatty
acids produced by the host Streptomyces albus J1074.
• Our results determined statistical significance in the correlation between
production and the chemosimilarity of the substrate between the donor
and recipient modules.
• These design principles may accelerate the combinatorial approach
currently used for de novo biosynthesis and help provide a framework
to more rapidly produce valuable biofuels, bioproducts and
biochemicals.
Zargar et al. (2020) J Am Chem Soc, doi: 10.1021/jacs.0c02549
A chemoinformatic approach to reductive loop exchanges.
(A) ClusterCad search revealed the closest substrates to
LipPKS1 containing full RLs. (B) Production levels of
junction B. RL exchanges are ordered from highest KR
substrate similarity with LipPKS1 (MonA2, LaidS2, and
NanA2) to progressively less similarity (IdmO, AurB, and
SpnB) in biological triplicate (error bars denote standard
deviation).
4. Design of orthogonal regulatory systems for
modulating gene expression in plants
Background
• Current plant-based green technologies are limited by a dearth of tools
and components available for synthetic plant biology.
• This study developed a novel method for the assembly and
characterization of synthetic elements for modulating gene expression
strength. Additionally, this system allows for tissue-specific and
environmentally responsive gene expression in plants.
Approach
• We developed an orthogonal system that harnesses transcription
factors (TFs) and their associated binding motifs from yeast along with
minimal promoters from plants. By leveraging the inherent variability of
each element we constructed over 500 unique TF/promoter pairs, each
with a distinct output profile, and tested them in varying plant systems.
Outcomes and Impacts
• Our orthogonal system allows for the integration of synthetic gene
circuits that operate in parallel to the endogenous systems of the host.
• The sequence variability of our promoter elements will help limit the
phenomenon of gene-silencing often observed in transgenic lines.
• We developed a method that can theoretically be applied to any
eukaryotic host by introducing host-specific minimal promoters.
• This system can now be tested in our Sorghum protoplast pipeline with
the introduction of Sorghum spp. specific minimal promoters.
• Our parts can be applied for more targeted and elegant engineering of
any bioenergy crop in future studies and will assist in the development
and refinement of plant-based green technologies.
Belcher et al. (2020) Nature Chemical Biology; https://doi.org/10.1038/s41589-020-0547-4
Synthetic chimeric transcriptional regulators were
developed with elements from both yeast and plants.
These regulators can be leveraged for the modulation of
gene expression in both a tissue-specific and
environmentally responsive manner.
5. Structural changes in bacterial and fungal soil
microbiome components during biosolarization as
related to volatile fatty acid accumulation
Background
• Biosolarization, where moist soil is covered with clear tarp to
induce elevated soil temperatures through passive solar heating,
is an integrated pest management strategy that combines soil
solarization with organic amendment application.
• There is a gap in understanding the changes in the bacterial and
fungal microbiome that occur during biosolarization.
Approach
• Soil samples taken after biosolarization using tomato pomace and
green waste compost (GWC) amendments were analyzed via
sequencing and bioinformatic analysis of 16S rRNA gene and ITS2
amplicons to elucidate changes to the soil microbiome, including
both fungal and bacterial communities.
Outcomes and Impacts
• Structural and network analyses were used to quantify differences
in microbiota between treatments and these results were compared
against measured levels of volatile fatty acids in the soils.
• The results showed that biosolarization had a stronger impact on
the bacterial community relative abundance profile than on the
fungal community at the phyla and order levels.
• Network analysis established microbial clusters and correlation
between the clusters and soil volatile fatty acid (VFA) production
suggested the bacterial Clostridium, Weissella and Acetobacter
genera tolerate, and perhaps drive, VFA accumulation.
Achmon et al. Applied Soil Ecology 153 (2020): 103602., doi.org/10.1016/j.apsoil.2020.103602
0
10
20
30
40
50
60
70
80
90
100
Soil100%str
TomatoStr
Tomato+GWCstr
Soil100%RT
TomatoRT
Tomato+GWCRT
Soil100%0-7.5cm
Soil100%7.5-15cm
Soil100%15-22.5cm
Tomato0-7.5cm
Tomato7.5-15cm
Tomato15-22.5cm
Tomato+GWC0-7.5cm
Tomato+GWC7.5-15cm
Tomato+GWC15-22.5cm
Relativeabandenc(%)
p__Firmicutes p__Proteobacteria p__Acidobacteria
p__Bacteroidetes p__Cyanobacteria p__Actinobacteria
p__Gemmatimonadetes p__Chloroflexi p__Verrucomicrobia
Others
Bacterial community as a function of soil solarization
Trichurus
Fungi
Hydropisphaera
RhodocyclalesOR
Steroidobacter
SphingobacterialesOR
Xanthomonadaceae
Talaromyces
Methylobacterium
Balneimonas
Microbial co-occurrence networks
